Multi_objective_reactor_design.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6fvcYnfEJUez"
   },
   "source": [
    "# Multi-Objective Optimization\n",
    "**Prepared by:** Shammah Lilonfe (slilonfe@nd.edu, 2024) and Zhicheng Lu (zlu3@nd.edu, 2024) at the University of Notre Dame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Explain the modelling strategies of multi-objective optimization (MOO)\n",
    "* Demonstrate the power of MOO in solving chemical industry problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Basic Definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let $\\mathbf{x} \\in \\mathbb{R}^{n_x}$ be the decision vector that lies in the feasible set $X \\subseteq \\mathbb{R}^{n_x}$, which is assumed to be compact and nonempty. Given  $n$ objective functions $f_i: X \\to \\mathbb{R}$ for $i \\in O:$ = {1, 2, $\\dots, n$}, and the corresponding objective vector: $\\mathbf{f}(\\mathbf{x}) = \\big(f_1(\\mathbf{x}), f_2(\\mathbf{x}), \\dots, f_{n}(\\mathbf{x})\\big)$. Assuming that the objective functions remain bounded in $X$.\n",
    "\n",
    "The MOO problem is defined as:\n",
    "$$\\begin{equation}\n",
    "\\min_{x \\in X} \\big(f_1(x), f_2(x), \\dots, f_{n}(x)\\big) \\tag{1.1}\n",
    "\\end{equation}$$\n",
    "\n",
    "Again, we assume that the minimization operand implies global minimization and that the objective functions have been scaled so that their values lie in the interval [0, 1].\n",
    "\n",
    "Dowling et al. (2016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Scalarization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling of the objective functions is performed using the coordinates of the utopia and nadirpoints as follows. We define:\n",
    "\\begin{equation}\n",
    "\\underline{f}_i = \\min_{x \\in X} f_i(x), \\quad i \\in O \\tag{1.2a}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "\\underline{x}_i = \\arg\\min_{x \\in X} f_i(x), \\quad i \\in O. \\tag{1.2b}\n",
    "\\end{equation}\n",
    "Here, the coordinates of the utopia point are given by $\\underline{f}_i$, while those of the nadir point are given by:\n",
    "\\begin{equation}\n",
    "\\bar{f}_i = \\max \\{ f_i(\\underline{x}_1), f_i(\\underline{x}_2), \\dots, f_i(\\underline{x}_{n}) \\}, \\quad i \\in O. \\tag{1.3}\n",
    "\\end{equation}\n",
    "The objectives are scaled as follows:\n",
    "\\begin{equation}\n",
    "f_i(x) \\leftarrow \\frac{f_i(x) - \\underline{f}_i}{\\bar{f}_i - \\underline{f}_i}, \\quad i \\in O. \\tag{1.4}\n",
    "\\end{equation}\n",
    "\n",
    "We can prevent visiting regions beyond the nadir points by imposing the constraints:\n",
    "\\begin{equation}\n",
    "0 \\leq f_i(x) \\leq 1, \\quad i \\in O. \\tag{1.5}\n",
    "\\end{equation}\n",
    "\n",
    "Dowling et al. (2016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the standard definitions of weak Pareto optimality and Pareto optimality (Miettinen, 1999):\n",
    "\n",
    "$\\textbf{Definition 1 (Weak Pareto Optimality)}$: A decision $x^*$ with objectives $f_i(x^*), \\, i \\in O$ is a weak Pareto solution of MOO if there does not exist an alternate solution $x$ with objectives $f_i(x), \\, i \\in O$ which satisfies $f_i(x) < f_i(x^*)$ for all  $i \\in O$.\n",
    "\n",
    "$\\textbf{Definition 2 (Pareto Optimality)}$: A decision $x^*$ with objectives $f_i(x^*), \\, i \\in O$ is a Pareto solution of MOO if there does not exist an alternate solution $x$ with objectives $f_i(x), \\, i \\in O$ which satisfies $f_i(x) \\leq f_i(x^*)$ for all $i \\in O$ and at least one index $i$ satisfies $f_i(x) < f_i(x^*)$.\n",
    "\n",
    "Any Pareto solution of MOO is a weak Pareto solution. To see this, we consider the following contradiction: assume $x^*$ is a Pareto solution, and thus no alternate decision $x$ exists such that $f_i(x) \\leq f_i(x^*)$ for all $i \\in O$ and $f_i(x) < f_i(x^*)$ for at least one index $i$. If, in addition, we assume that $x^*$ is not weak Pareto, then there exists an alternate decision $x$ such that $f_i(x) < f_i(x^*)$ for all $i \\in O$. This is a contradiction, and thus $x^*$ must be a Pareto and a weak Pareto solution.\n",
    "\n",
    "The set of all Pareto solutions for MOO is known as the Pareto set. There are different methods that can be used to compute elements of the Pareto set, such as the weighting method. The results associated with this method will become relevant in the following sections.\n",
    "\n",
    "Consider a weight vector $\\mathbf{w} \\in \\mathbb{R}^n$ and assume that this satisfy the conditions:\n",
    "\\begin{equation}\n",
    "w_i \\geq 0, \\quad i \\in O, \\tag{1.6a}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "\\sum_{i \\in O} w_i = 1. \\tag{1.6b}\n",
    "\\end{equation}\n",
    "\n",
    "In some cases, we will require the weights to satisfy the stronger condition:\n",
    "\\begin{equation}\n",
    "w_i > 0, \\quad i \\in O \\tag{1.7a}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "\\sum_{i \\in O} w_i = 1. \\tag{1.7b}\n",
    "\\end{equation}\n",
    "\n",
    "Consider now the weighted problem:\n",
    "\\begin{equation}\n",
    "\\min_{x \\in X} \\mathbf{w}^T \\mathbf{f}(\\mathbf{x}). \\tag{1.8}\n",
    "\\end{equation}\n",
    "\n",
    "The solutions of this problem satisfy the following property:\n",
    "\n",
    "$\\textbf{Proposition 1}$: Let $x^*$ be a solution of the weighted problem (1.8):\n",
    "* If the weight vector $\\mathbf{w}$ satisfies $w_i \\geq 0, \\, i \\in O$ (Condition 1.6a), then $x^*$ is a weak Pareto solution of MOO.\n",
    "* If the weight vector $\\mathbf{w}$ satisfies $w_i > 0, \\, i \\in O$ (Condition 1.7a), then $x^*$ is a Pareto solution of MOO.\n",
    "\n",
    "The nadir point is obtained by computing worst-case value results of the utopia point as in (1.2) and (1.3). The nadir point coordinates are determined by minimizing each objective function and then obtaining the corresponding worst-case values for the objectives. This approach is equivalent to solving (1.1) with $w_i=1$ and $w_{i' \\neq i}=0$ for $i, i' ∈ O$. From Proposition 1, these single objective solutionsare only weak Pareto solutions. Consequently, it is possible to achieve a further reduction in the other objectives without compromising the original single objective. Because of this, the nadir point may unnecessarily enlarges the decision space. This inturn impacts the scaling of the objectives in (1.4). To address this issue, an alternate nadir point is exploited.\n",
    "\n",
    "$\\textbf{Definition 3 (Alternate Nadir Point)}$: Let $\\underline{x}_i$ and $\\underline{f}_i$ be defined as in (1.2) for all $i \\in O$. For each $i \\in O$, let the alternate nadir solution $\\underline{x}^*_i$ be defined as:\n",
    "\\begin{equation}\n",
    "\\underline{x}^*_i = \\arg\\min_{x \\in X} \\mathbf{w}^T \\mathbf{f}(\\mathbf{x}) \n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "\\text{s.t.} \\quad f_i(x) \\leq \\underline{f}_i, \\tag{1.9}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\mathbf{w}$ satisfies $w_i = 0$ and $w_{i' \\neq i} > 0$. \n",
    "\n",
    "The alternate nadir point $\\mathbf{\\bar{f}}^*$ with elements $\\bar{f}^*_i$ is defined as:\n",
    "\\begin{equation}\n",
    "\\bar{f}^*_i = \\max\\{f_i(\\underline{x}^*_1), f_i(\\underline{x}^*_2), \\ldots, f_i(\\underline{x}^*_{n})\\}, \\quad i \\in O. \\tag{1.10}\n",
    "\\end{equation}\n",
    "\n",
    "$\\textbf{Proposition 2}$: The alternate nadir solutions $\\underline{x}^*_i$ for $i \\in O$, are Pareto solutions of MOO.\n",
    "\n",
    "The alternate nadir point method requires a selection of weights for the objective functions. Such a selection is not unique and thus different alter-nate nadir solutions can be obtained. However, all of these alternatives are guaranteed to be Pareto solutions. Even small nonzero weights guarantee this property and thus the alternate nadir point can be designed to prevent extreme degeneracies of the objectives resulting from the standard use of (1.2) and (1.3). \n",
    "\n",
    "Computing all the elements in the the Pareto set is computationally intractable, particularly when many objectives are considered. It is well known, however, that one can compute Pareto optimal compromise solutions by finding points that are closest in distance to the utopia point. This is often done by solving the minimization problem:\n",
    "\n",
    "\\begin{equation}\n",
    "\\min_{x \\in X} \\|f(\\mathbf{x})\\|_p, \\tag{1.11}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\|\\cdot\\|_p$ is the $L_p$ norm.\n",
    "\\begin{equation}\n",
    "\\|f(\\mathbf{x})\\|_p = \n",
    "\\left( \n",
    "\\sum_{i \\in O} f_i(x)^p \n",
    "\\right)^{1/p}, \\quad p \\geq 1, \\tag{1.12}\n",
    "\\end{equation}\n",
    "\n",
    "$\\textbf{Proposition 3}$. A solution $x^*$ of the minimum distance problem (1.11) is a Pareto solution of MOO.\n",
    "\n",
    "Dowling et al. (2016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Epsilon Constraint Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Key Concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In MOO, the goal is to minimize a vector of objective functions:\n",
    "\n",
    "$$\\begin{equation}\n",
    "\\min_{x \\in X} \\big(f_1(x), f_2(x), \\dots, f_{n}(x)\\big)\n",
    "\\end{equation}$$\n",
    "\n",
    "Subject to:\n",
    "$$\\begin{equation}\n",
    "g_j(x) \\leq 0, \\quad j = 1, \\dots, m. \\tag{2.1}\n",
    "\\end{equation}$$\n",
    "\n",
    "Where $f_1, f_2, \\dots, f_n$ are the objectives, and $g_j(x)$ are problem-specific constraints.\n",
    "\n",
    "The epsilon-constrained method reformulates this problem by optimizing one objective function (e.g., $f_1(x)$), while treating the remaining objectives as constraints with thresholds $\\epsilon_i$ for $i \\in O:$ = {1, 2, $\\dots, n$}. The reformulated problem is expressed as:\n",
    "\n",
    "$$\\begin{equation}\n",
    "\\min_{x \\in X} \\big(f_1(x)\\big)\n",
    "\\end{equation}$$\n",
    "\n",
    "Subject to:\n",
    "$$\\begin{equation}\n",
    "f_2(x) \\leq \\epsilon_2, \\quad f_3(x) \\leq \\epsilon_3, \\quad \\dots, \\quad f_n(x) \\leq \\epsilon_n,\n",
    "\\end{equation}$$\n",
    "$$\\begin{equation}\n",
    "g_j(x) \\leq 0, \\quad j = 1, \\dots, m. \\tag{2.2}\n",
    "\\end{equation}$$\n",
    "\n",
    "Dowling et al. (2016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Key Points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* $\\textbf{Trade-Off Exploration}:$ By systematically varying the values of $\\epsilon_2, \\epsilon_3, \\dots, \\epsilon_n$, it is possible to explore trade-offs between objectives and construct an approximation of the Pareto front.\n",
    "* $\\textbf{Reduction to Single Objective}:$ The epsilon-constrained method simplifies the multi-objective problem into a single-objective problem for each choice of $\\epsilon$ values.\n",
    "* $\\textbf{Flexibility}:$ Any one of the objectives can be chosen as the primary objective to optimize, allowing the user to prioritize specific goals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement: Design of Chemical Reactors for Chemical Transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chemical reactors are often the most important unit operations in a chemical plant. Reactors come in many forms, however two of the most common idealizations are the continuously stirred tank reactor (CSTR) and the plug flow reactor. The CSTR is often used in modeling studies, and it can be effectively modeled as a lumped parameter system. In this example, we will consider the following reaction scheme known as the Van de Vusse reaction:\n",
    "\n",
    "$$\n",
    "\\text{A} \\xrightarrow{k_1} \\text{B} \\xrightarrow{k_2} \\text{C}\n",
    "$$\n",
    "\n",
    "$$\n",
    "2\\text{A} \\xrightarrow{k_3} \\text{D}\n",
    "$$\n",
    "\n",
    "A diagram of the system is shown in figure below, where F is the volumetric flowrate. The reactor is assumed to be filled to a constant volume, and the mixture is assumed to have constant density, so the volumetric flowrate into the reactor is equal to the volumetric flowrate out of the reactor. Since the reactor is assumed to be wellmixed, the concentrations in the reactor are equivalent to the concentrations of each component flowing out of the reactor, given by $C_A$, $C_B$, $C_C$, and $C_D$.\n",
    "\n",
    "![process scheme](https://github.com/zhichenglu123/optimization/raw/Project-1---Reactor-Design-Adapted-from-Pyomo---Zhicheng-Lu-%26-Shammah-Lilonfe/notebooks/contrib-dev/research.png)\n",
    "\n",
    "This example (both text and figure) was taken from Hart et al (2017)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install Packages and Load Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing packages to be used in this project\n",
    "import pyomo.environ as pyo\n",
    "import pyomo.dae as dae\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original Problem from the Reference Material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is to produce product B from a feed containing reactant A. If we design a reactor that is too small, we will obtain insufficient conversion of A to the desired product B. However, given the reaction scheme, if the reactor is too large (e.g., too much reaction is allowed to occur), a significant amount of the desired product B will be further reacted to form the undesired product C. As a result, the goal in this exercise will be to solve for the optimal reactor volume producing the maximum outlet concentration for product B.\n",
    "\n",
    "The steady-state mole balances for each of the four components are given by,\n",
    "\n",
    "$$\\begin{align*}\n",
    "&0=\\frac{F}{V}C_{Af}-\\frac{F}{V}C_A-k_1C_A-2k_3C_A^2 \\\\\n",
    "& 0=-\\frac{F}{V}C_B+k_1C_A-k_2C_B\\\\\n",
    "& 0=-\\frac{F}{V}C_C+k_2C_B \\\\\n",
    "& 0=-\\frac{F}{V}C_D+k_3C_A^2\n",
    "\\end{align*}$$\n",
    "\n",
    "The known parameters for the system are,\n",
    "\n",
    "$$\\begin{align*}\n",
    "C_{Af}=10^4\\,\\mathrm{\\frac{gmol}{m^3}}\\quad{k_1=\\frac{5}{6}\\,\\mathrm{min^{-1}}}\\quad{k_2=\\frac{5}{3}\\,\\mathrm{min^{-1}}}\\quad{k_3=\\frac{1}{6000}\\,\\mathrm{\\frac{m^3}{gmol\\cdot min}}}\n",
    "\\end{align*}$$\n",
    "\n",
    "Since the volumetric flowrate F always appears as the numerator over the reactor volume V, it is common to consider this ratio as a single variable, called the space velocity (sv). Our optimization formulation will seek to find the space-velocity that maximizes the outlet concentration of the desired product B.\n",
    "\n",
    "This original problem was taken from Hart et al (2017)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization Model Formulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mathematically, we want to solve the following optimal reactor volume problem:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\max_{S_v} \\quad & C_B \\\\ \n",
    "\\mathrm{s.t.} \\quad & 0=S_vC_{Af}-S_vC_A-k_1C_A-2k_3C_A^2 \\\\\n",
    "& 0=-S_vC_B+k_1C_A-k_2C_B \\\\\n",
    "& 0=-S_vC_C+k_2C_B \\\\\n",
    "& 0=-S_vC_D+k_3C_A^2 \\\\\n",
    "& C_A, C_B, C_C, C_D, S_v \\geq 0.\n",
    "\\end{align*}$$\n",
    "\n",
    "Here \"s.t.\" means \"subject to\", i.e., the constraints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Degrees of Freedom Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the optimization model formulated above:\\\n",
    "Number of variables = 5 \\\n",
    "Number of equality constraints = 4 \\\n",
    "Therefore, the degrees of freedom = 5 - 4 = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pyomo Implementation of the Original Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ipopt 3.13.2: \n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "\n",
      "This version of Ipopt was compiled from source code available at\n",
      "    https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n",
      "    Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n",
      "    Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n",
      "\n",
      "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n",
      "    for large-scale scientific computation.  All technical papers, sales and\n",
      "    publicity material resulting from use of the HSL codes within IPOPT must\n",
      "    contain the following acknowledgement:\n",
      "        HSL, a collection of Fortran codes for large-scale scientific\n",
      "        computation. See http://www.hsl.rl.ac.uk.\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.13.2, running with linear solver ma27.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:       11\n",
      "Number of nonzeros in inequality constraint Jacobian.:        0\n",
      "Number of nonzeros in Lagrangian Hessian.............:        5\n",
      "\n",
      "Total number of variables............................:        5\n",
      "                     variables with only lower bounds:        5\n",
      "                variables with lower and upper bounds:        0\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        4\n",
      "Total number of inequality constraints...............:        0\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0 -2.0000000e+03 7.50e+03 6.25e-01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1 -1.0801475e+03 5.54e+02 2.46e+00  -1.0 1.39e+03    -  5.54e-01 1.00e+00h  1\n",
      "   2 -1.0763574e+03 8.86e+01 1.87e+02  -1.0 3.66e+02    -  9.31e-01 1.00e+00h  1\n",
      "   3 -1.0727252e+03 1.07e+01 5.26e+00  -1.0 9.35e+01    -  9.71e-01 1.00e+00h  1\n",
      "   4 -1.0726714e+03 4.01e+00 1.22e-01  -1.0 6.03e+01    -  9.68e-01 1.00e+00h  1\n",
      "   5 -1.0724371e+03 3.44e-04 2.93e-05  -2.5 4.19e-01    -  1.00e+00 1.00e+00h  1\n",
      "   6 -1.0724372e+03 1.63e-08 3.93e-09  -3.8 3.85e-03    -  1.00e+00 1.00e+00h  1\n",
      "   7 -1.0724372e+03 5.46e-11 2.70e-11  -5.7 2.24e-04    -  1.00e+00 1.00e+00h  1\n",
      "   8 -1.0724372e+03 4.55e-13 3.56e-14  -8.6 2.78e-06    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 8\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:  -1.0724372001086319e+03   -1.0724372001086319e+03\n",
      "Dual infeasibility......:   3.5606114692563649e-14    3.5606114692563649e-14\n",
      "Constraint violation....:   4.5474735088646414e-14    4.5474735088646412e-13\n",
      "Complementarity.........:   2.5059065225787338e-09    2.5059065225787338e-09\n",
      "Overall NLP error.......:   2.5059065225787338e-09    2.5059065225787338e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 9\n",
      "Number of objective gradient evaluations             = 9\n",
      "Number of equality constraint evaluations            = 9\n",
      "Number of inequality constraint evaluations          = 0\n",
      "Number of equality constraint Jacobian evaluations   = 9\n",
      "Number of inequality constraint Jacobian evaluations = 0\n",
      "Number of Lagrangian Hessian evaluations             = 8\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.000\n",
      "Total CPU secs in NLP function evaluations           =      0.000\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "The optimum space-velocity is: 1.34 min^-1\n",
      "The outlet concentration of species B is: 1072.44 gmol/m^3\n",
      "The optimum reactor volume is: 0.74 m^3\n"
     ]
    }
   ],
   "source": [
    "# function that creates pyomo model for the reaction system\n",
    "def create_model(k1, k2, k3, caf):\n",
    "    \"\"\"Creates reactor model\n",
    "    Argument:\n",
    "        caf: feed concentration of A, gmol/m^3\n",
    "        k1: rate constant for A to B, min^-1\n",
    "        k2: rate constant for B to C, min^-1\n",
    "        k3: rate constant for 2A to D, m^3/(gmol.min)\n",
    "\n",
    "    Returns:\n",
    "        model: pyomo model\"\"\"\n",
    "    # Create a Pyomo model\n",
    "    model = pyo.ConcreteModel()\n",
    "    \n",
    "    # create the variables\n",
    "    model.sv = pyo.Var(initialize=1.0, within=pyo.PositiveReals) # space-velocity\n",
    "    model.ca = pyo.Var(initialize=5000.0, within=pyo.PositiveReals) # concentration of A\n",
    "    model.cb = pyo.Var(initialize=2000.0, within=pyo.PositiveReals) # concentration of B\n",
    "    model.cc = pyo.Var(initialize=2000.0, within=pyo.PositiveReals) # concentration of C\n",
    "    model.cd = pyo.Var(initialize=1000.0, within=pyo.PositiveReals) # concentration of D\n",
    "    \n",
    "    # create the objective\n",
    "    model.obj = pyo.Objective(expr = model.cb, sense=pyo.maximize)\n",
    "    \n",
    "    # create the constraints\n",
    "    model.ca_bal = pyo.Constraint(expr = (0 == model.sv * caf - model.sv * model.ca - k1 * model.ca - \n",
    "                                          2.0 * k3 * model.ca ** 2.0)) # species A mole balance\n",
    "    model.cb_bal = pyo.Constraint(expr=(0 == -model.sv * model.cb + k1 * model.ca - k2 * model.cb)) # species B mole balance\n",
    "    model.cc_bal = pyo.Constraint(expr=(0 == -model.sv * model.cc + k2 * model.cb)) # species C mole balance\n",
    "    model.cd_bal = pyo.Constraint(expr=(0 == -model.sv * model.cd + k3 * model.ca ** 2.0)) # species D mole balance\n",
    "    \n",
    "    return model\n",
    "\n",
    "# define data\n",
    "k1 = 5.0/6.0 # min^-1\n",
    "k2 = 5.0/3.0 # min^-1\n",
    "k3 = 1.0/6000.0 # m^3/(gmol.min)\n",
    "caf = 10000.0 # gmol/m^3\n",
    "\n",
    "# reactor model object\n",
    "m = create_model(k1, k2, k3, caf)\n",
    "\n",
    "# solve model\n",
    "solver = pyo.SolverFactory('ipopt')\n",
    "solver.solve(m, tee=True)\n",
    "\n",
    "# print the solution of sv and the outlet concentration of B\n",
    "print(f\"The optimum space-velocity is: {round(pyo.value(m.sv), 2)} min^-1\")\n",
    "print(f\"The outlet concentration of species B is: {round(pyo.value(m.cb), 2)} gmol/m^3\")\n",
    "\n",
    "# calculate optimum reactor volume for a volumetric flow rate of 1 m^3/min\n",
    "F = 1\n",
    "optimum_reactor_volume = F / pyo.value(m.sv)\n",
    "print(f\"The optimum reactor volume is: {round(optimum_reactor_volume, 2)} m^3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimal Solution Found for the Original Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimal space-velocity is $\\mathrm{1.34\\,min^{-1}}$, giving an outlet concentration for B of $\\mathrm{1072.44\\,gmol\\cdot m^{-3}}$. For a volumetric flow rate of $\\mathrm{1\\,m^{3}\\cdot min^{-1}}$, the optimum reactor volume is $\\mathrm{0.74\\,m^{3}}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_0X0jKuPwcWn"
   },
   "source": [
    "## Extension 1: Dynamic Modeling and Optimization of Reactor Volume to Maximize Species C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2x8jafDG2F1X"
   },
   "source": [
    "In this project, species C is the desired product, and we are considering the dynamic state of the system (most often, industrial operations are dynamic). The goal of this work is to determine the volume of the reactor that will maximize the concentration of species C in the effluent after 10 minutes. Furthermore, for economic reasons, the reactor volume should not exceed $\\mathrm{100\\,m^3}$.\n",
    "\n",
    "The mole balance for each of the species in this system under dynamic state is given by:\n",
    "\n",
    "$$\\begin{align*}\n",
    "&\\frac{dC_A}{dt}=\\frac{F}{V}C_{Af}-\\frac{F}{V}C_A-k_1C_A-2k_3C_A^2 \\\\\n",
    "& \\frac{dC_B}{dt}=-\\frac{F}{V}C_B+k_1C_A-k_2C_B\\\\\n",
    "& \\frac{dC_C}{dt}=-\\frac{F}{V}C_C+k_2C_B \\\\\n",
    "& \\frac{dC_D}{dt}=-\\frac{F}{V}C_D+k_3C_A^2\n",
    "\\end{align*}$$\n",
    "\n",
    "Where $C_A$, $C_B$, $C_C$ and $C_D$ are the concentrations of species A, B, C and D, respectively. $F$ is the feed flow rate, $C_{Af}$ is the concentration of species A in the feed, $k_1$, $k_2$ and $k_3$ are the rate constants of reaction 1 (species A to B), reaction 2 (species B to C) and reaction 3 (species A to D), respectively.\n",
    "\n",
    "The data for the system are:\n",
    "$$\\begin{align*}\n",
    "C_{Af}=10^4\\,\\mathrm{\\frac{mol}{m^3}},\\quad{k_1=\\frac{5}{6}\\,\\mathrm{min^{-1}}},\\quad{k_2=\\frac{5}{3}\\,\\mathrm{min^{-1}}},\\quad{k_3=\\frac{1}{6000}\\,\\mathrm{\\frac{m^3}{mol\\cdot min}}},\\quad{F=1\\,\\mathrm{\\frac{m^3}{min}}}\n",
    "\\end{align*}$$\n",
    "\n",
    "The data was obtained from Hart et al (2017)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E3hzUN6FjrEC"
   },
   "source": [
    "### Optimization Model Formulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9cGBk30DkjgR"
   },
   "source": [
    "Mathematically, we want to solve the following optimal reactor volume problem:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\max_{V} \\quad & C_C(t=10) \\\\\n",
    "\\mathrm{s.t.} \\quad & \\frac{dC_A}{dt}=\\frac{F}{V}C_{Af}-\\frac{F}{V}C_A-k_1C_A-2k_3C_A^2 \\\\\n",
    "& \\frac{dC_B}{dt}=-\\frac{F}{V}C_B+k_1C_A-k_2C_B \\\\\n",
    "& \\frac{dC_C}{dt}=-\\frac{F}{V}C_C+k_2C_B \\\\\n",
    "& \\frac{dC_D}{dt}=-\\frac{F}{V}C_D+k_3C_A^2 \\\\\n",
    "& V \\leq 100 \\\\\n",
    "& C_A(t=0) = 10000, ~~ C_B(t=0) = 0 \\\\\n",
    "& C_C(t=0) = 0, ~~ C_D(t=0) = 0 \\\\\n",
    "\\end{align*}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nj_h-L7NpMYK"
   },
   "source": [
    "### Degrees of Freedom Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the optimization model formulated above:\\\n",
    "Number of variables = 5 \\\n",
    "Number of equality constraints = 4 \\\n",
    "Therefore, the degrees of freedom = 5 - 4 = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iAhG6jxzu5wA"
   },
   "source": [
    "### Pyomo Implementation\n",
    "We can use `Pyomo.dae` to automatically formulate the collocation equations (i.e., add constraints that numerically integrate the ODE model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "aYW5M30J1JlR",
    "outputId": "5cab20c0-f777-4c31-f9b5-f6ac32c054c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ipopt 3.13.2: \n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "\n",
      "This version of Ipopt was compiled from source code available at\n",
      "    https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n",
      "    Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n",
      "    Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n",
      "\n",
      "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n",
      "    for large-scale scientific computation.  All technical papers, sales and\n",
      "    publicity material resulting from use of the HSL codes within IPOPT must\n",
      "    contain the following acknowledgement:\n",
      "        HSL, a collection of Fortran codes for large-scale scientific\n",
      "        computation. See http://www.hsl.rl.ac.uk.\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.13.2, running with linear solver ma27.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:    12619\n",
      "Number of nonzeros in inequality constraint Jacobian.:        0\n",
      "Number of nonzeros in Lagrangian Hessian.............:     1006\n",
      "\n",
      "Error in an AMPL evaluation. Run with \"halt_on_ampl_error yes\" to see details.\n",
      "Error evaluating Jacobian of equality constraints at user provided starting point.\n",
      "  No scaling factors for equality constraints computed!\n",
      "Total number of variables............................:     1609\n",
      "                     variables with only lower bounds:      804\n",
      "                variables with lower and upper bounds:        1\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:     1608\n",
      "Total number of inequality constraints...............:        0\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0 -9.9999900e-03 1.00e+06 1.56e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1 -9.9998726e-03 1.00e+06 8.10e+02  -1.0 1.01e+06    -  9.90e-07 7.52e-05h  1\n",
      "   2r-9.9998726e-03 1.00e+06 9.99e+02   6.0 0.00e+00    -  0.00e+00 3.10e-07R  3\n",
      "   3r-3.2550526e+00 8.36e+05 2.18e+09   6.0 1.09e+09    -  1.95e-04 1.95e-04f  1\n",
      "   4 -3.2756934e+00 7.01e+05 6.67e+06  -1.0 8.47e+05    -  5.03e-04 2.84e-01h  1\n",
      "   5 -3.3599969e+00 4.50e+05 4.28e+05  -1.0 2.51e+05    -  6.14e-02 9.85e-01h  1\n",
      "   6 -3.4374817e+00 2.50e+05 2.67e+05  -1.0 4.42e+05    -  2.83e-03 4.99e-01h  1\n",
      "   7 -3.6563863e+00 1.27e+05 2.54e+05  -1.0 2.24e+05    -  7.45e-02 9.90e-01h  1\n",
      "   8 -4.0376969e+00 6.23e+04 2.60e+05  -1.0 5.04e+04    -  4.10e-02 9.90e-01h  1\n",
      "   9 -4.2238169e+00 4.57e+04 2.56e+05  -1.0 1.87e+04    -  1.46e-02 3.26e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10 -4.8943726e+00 2.20e+04 2.14e+05  -1.0 1.48e+04    -  1.98e-02 9.98e-01h  1\n",
      "  11 -5.0999886e+00 1.69e+04 2.38e+06  -1.0 4.00e+03    -  1.90e-02 2.92e-01h  1\n",
      "  12 -5.8592444e+00 8.22e+03 8.43e+06  -1.0 3.31e+03    -  3.47e-02 1.00e+00h  1\n",
      "  13 -6.6724932e+00 7.12e+03 8.23e+06  -1.0 1.71e+03    -  3.91e-02 1.54e-01h  1\n",
      "  14 -2.5003886e+01 3.51e+03 8.41e+06  -1.0 1.55e+03    -  8.22e-02 1.00e+00h  1\n",
      "  15 -8.4301553e+01 3.08e+03 8.00e+06  -1.0 1.70e+03    -  4.83e-02 1.40e-01h  1\n",
      "  16 -1.2308262e+02 2.89e+03 7.06e+06  -1.0 1.70e+03    -  1.19e-01 6.44e-02h  1\n",
      "  17 -8.1386603e+02 1.44e+03 5.95e+06  -1.0 1.70e+03    -  1.57e-01 1.00e+00f  1\n",
      "  18 -2.0541698e+03 8.40e+02 3.71e+06  -1.0 1.77e+03    -  3.77e-01 7.14e-01f  1\n",
      "  19 -3.5049209e+03 4.20e+02 1.79e+06  -1.0 1.45e+03    -  5.17e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20 -3.7251819e+03 2.85e+02 2.82e+04  -1.0 4.62e+02    -  9.84e-01 4.76e-01f  1\n",
      "  21 -4.0002085e+03 1.42e+02 2.93e+03  -1.0 2.75e+02    -  8.98e-01 1.00e+00f  1\n",
      "  22 -4.0087092e+03 1.01e+02 2.07e+03  -1.0 7.06e+01    -  1.00e+00 4.15e-01h  1\n",
      "  23 -4.0087453e+03 1.00e+02 2.09e+03  -1.0 1.00e+02    -  7.72e-01 2.93e-03h  1\n",
      "  24 -4.0087456e+03 1.00e+02 5.80e+03  -1.0 1.00e+02    -  4.72e-03 2.92e-05h  1\n",
      "  25 -4.0087598e+03 1.00e+02 2.12e+03  -1.0 3.24e+02    -  4.40e-07 4.37e-05f  1\n",
      "  26 -4.3343427e+03 4.70e+00 1.83e+05  -1.0 3.26e+02    -  4.36e-05 1.00e+00f  1\n",
      "  27 -4.3141499e+03 8.39e-03 9.09e+01  -1.0 2.02e+01    -  1.00e+00 1.00e+00h  1\n",
      "  28 -4.3141284e+03 2.41e-08 1.08e+05  -1.7 2.15e-02    -  9.87e-01 1.00e+00h  1\n",
      "  29 -4.3141354e+03 7.14e-08 2.90e+04  -1.7 7.02e-03    -  7.31e-01 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  30 -4.3150872e+03 1.32e-03 2.82e+04  -1.7 9.52e-01    -  2.70e-02 1.00e+00f  1\n",
      "  31 -4.5157522e+03 2.76e+02 2.80e+04  -1.7 2.01e+02  -4.0 7.26e-03 1.00e+00f  1\n",
      "  32 -5.3180445e+03 1.11e+02 1.95e+04  -1.7 8.02e+02    -  3.05e-01 1.00e+00f  1\n",
      "  33 -5.0535790e+03 1.43e+00 1.26e+01  -1.7 2.64e+02  -4.5 1.00e+00 1.00e+00h  1\n",
      "  34 -5.0655151e+03 9.00e+00 2.23e+00  -1.7 6.01e+01    -  1.00e+00 1.00e+00h  1\n",
      "  35 -5.0601188e+03 1.92e-01 7.30e-02  -1.7 1.58e+01    -  1.00e+00 1.00e+00h  1\n",
      "  36 -5.0598090e+03 5.39e-03 4.07e-04  -2.5 1.48e+00    -  1.00e+00 1.00e+00h  1\n",
      "  37 -5.0598054e+03 1.71e-04 1.11e-06  -3.8 2.78e-01    -  1.00e+00 1.00e+00h  1\n",
      "  38 -5.0598053e+03 5.24e-07 2.29e-09  -5.7 1.55e-02    -  1.00e+00 1.00e+00h  1\n",
      "  39 -5.0598053e+03 1.94e-10 1.67e-13  -8.6 1.91e-04    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 39\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:  -5.0598052797570590e+03   -5.0598052797570590e+03\n",
      "Dual infeasibility......:   1.6745249521991457e-13    1.6745249521991457e-13\n",
      "Constraint violation....:   1.9372237147763371e-10    1.9372237147763371e-10\n",
      "Complementarity.........:   2.5063723623922813e-09    2.5063723623922813e-09\n",
      "Overall NLP error.......:   2.5063723623922813e-09    2.5063723623922813e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 43\n",
      "Number of objective gradient evaluations             = 40\n",
      "Number of equality constraint evaluations            = 43\n",
      "Number of inequality constraint evaluations          = 0\n",
      "Number of equality constraint Jacobian evaluations   = 41\n",
      "Number of inequality constraint Jacobian evaluations = 0\n",
      "Number of Lagrangian Hessian evaluations             = 39\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.754\n",
      "Total CPU secs in NLP function evaluations           =      0.032\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "Optimal reactor volume is: 11.35 m^3\n",
      "Maximum Concentration of C at optimal reactor volume is: 5.06 kmol/m^3\n",
      "Concentration of B at optimal reactor volume is: 0.36 kmol/m^3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAIwCAYAAACBVn2MAAAAP3RFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMS5wb3N0MSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8kixA/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACs7UlEQVR4nOzdd3iT5f4G8PtN0qZt2iZddNHBKJRRQEC2yBQcLMGNcgT34HcUFfAcFVAOIopHFHGDHsXBVBEBKQKCLFmyCi10D7qbzjTj/f2RJjRddLxtmvb+XFeuNO96nu67T7/v8wiiKIogIiIiImqnZPbuABERERGRPTEQExEREVG7xkBMRERERO0aAzERERERtWsMxERERETUrjEQExEREVG7xkBMRERERO0aAzERERERtWsMxERERETUrjEQU5uWlJSExYsXY8yYMQgKCoKLiwtcXFzQsWNHTJgwAcuXL0dSUpK9u0l2IgiC9REeHm7v7tTb3r17bfpe9eHh4YEePXrgkUcewdGjR+3dXWi1Wrz88svo1asX3NzcbPp66tQpJCQk2GwbNWqUzfmLFi2y2b9u3Tq7vB+NkZWVhWXLlmHcuHEIDg6Gi4sLPDw80KlTJ0yfPh2ff/45dDqdvbspuaqf04Y8LN+L1/u6IJKSwt4dIGoOOp0OL730Ej788EMYDIZq+1NTU5Gamopdu3Zh+fLlyM3NtUMv24bw8HAkJiZaX9t7Nfi9e/di9OjR1tezZs1yqAAlhaKiIsTExCAmJgZffPEFXnrpJbz55pt268+kSZOwf/9+u7VvLytXrsS///1vlJaW2mzX6XQoKipCQkICNm/ejFdffRVfffUVxo4da6eeEhEDMbU5ZWVlGD9+PA4cOGCz3cPDAwMHDoS7uzsyMzNx+vRplJWVwWQy2amnZG/Tp0+3vt2hQwc79qRp3NzccOuttwIACgsLcezYMeTl5QEw/4GyfPlydOvWDbNnz27xvsXExNiEYYVCgdGjR8PT0xMAoNFooFKpbD4XvXr1avF+Su2f//wn3nvvPZtt/v7+6N+/P3Q6HQ4fPoySkhIAQFpaGm655RZs2LABd955pz26K7mqn1OLffv2ITs72/p64MCBCAsLsznG8r3YFr8uqBUTidqYOXPmiACsD0EQxNdee00sLS21Oa6kpET8/PPPxaioKDv1tG0ICwuz+Xjb2++//27Tn1mzZtm7S5Kr+j6GhYXZ7M/OzhZ79+5tc0yPHj3s0te9e/fa9OPee+9t8DVee+01m2usXbtW+o5KaMOGDTb9BSD++9//FvV6vfWYnJwc8dZbb7U5xs3NTUxMTLRjz5vfzTff7FCfS2o/7P/bi0hCZ86cEWUymc0P3CVLltR5TllZWY3bd+/eLd5///1i586dRTc3N1GpVIohISHitGnTxI0bN4pGo7HaOWvXrrVp+7XXXhPT09PFuXPniuHh4aKzs7Po7+8v/uMf/xBTUlJq7VNKSor46quvisOGDRN9fHxEhUIhent7i1FRUeKTTz4pxsTEVDsnLy9PfOutt8SRI0daz/Hy8hKHDx8urly5UiwqKqp2Tnx8vE1/b775ZrGsrExcsWKF2KdPH9HFxUX09PQUJ0yYIB46dMjm3KpBuLaHxaxZs2y2//777+KePXvEiRMnit7e3qIgCNZfjqdPnxZfeukl8ZZbbhG7du0qent7iwqFQnR3dxe7d+8uPvTQQ+L+/ftt+lM1JNb2qByQ6wqVFklJSeLChQvFgQMHihqNxvq5GDZsmPjGG2+IWVlZNZ5X9dpGo1H89NNPxcGDB4sqlUpUqVTiiBEjxO3bt9d4fl2uF4hFURS/+OKLau97YWGhKIo1f94LCwvFl19+WezWrZuoVCqrXbOwsFBctWqVOHbsWLFDhw6ik5OT6OnpKUZFRYnPPvuseP78+Tr7WNPD0kZN/amsPoHYZDKJP//8s3jXXXeJYWFhoouLi+jq6ip269ZNfOKJJ8QLFy7U+LEsKioSV6xYId50002in5+f6OTkJKpUKjE0NFQcMWKE+M9//lP8+eef6/V5sYiMjLTp74wZM2o8rri4WAwJCbE59oknnhBFURTT0tJEhUJh3X7jjTfWeI0FCxbYnL9+/Xqb/VL9XLje10d9NSQQN+br4vDhw+Jtt90majQa0cPDQxw1apS4Z88e6znffvutOGTIENHNzU309PQUJ06cKB45cqTWPjTm40eOiYGY2pR///vfNj8g/fz8ag28tdHpdOI999xz3V/mo0ePFvPy8mzOrRqIb7vtNtHHx6fWMFD1fMs13Nzc6my76i+RP/74QwwICKjznIiICPHixYs251X9hdO7d2+xf//+NZ6vVCrFw4cPW89taiCeOXNmre/XihUr6nXtRYsWWa/fHIH4m2++EVUqVZ3X8/X1FXfv3l3t3MrH+Pv7i7fcckuN5wuCIG7evLna+XWpTyD+5ZdfqrWVlpZW4+e9b9++YlRUVK3XPHXqlBgeHl7nx0GhUIhvv/12gz4fUgVirVZbbbS16sPJyUn86KOPbM4rKysTBwwYcN1+DhgwoN6fm3PnzlU7/+DBg7Ue/8Ybb1T7WrGYOnWqzb6qod5kMtkEah8fH5ufd1L9XLje10dDNGcgnjRpkiiXy6u9j3K5XPzpp5/Ef/7znzV+DFxcXMSjR49Wa7+xHz9yTKwhpjbl4MGDNq/Hjh0LpVLZoGs89dRT+P77762vFQoFBgwYAKVSiaNHj6KsrAwA8Pvvv+Ouu+7Cb7/9Vuu1tm/fDgC44YYb4O7ujj///BNGoxEAkJiYiA8//BAvv/yy9fitW7di9uzZNjemeXh4oE+fPvDy8sKFCxdw+fJlmzYuX76M22+/HVqt1rqtd+/eCA8PR3x8PM6dOwcAiI2Nxa233oozZ87Azc2txv6ePXsWgPlGuYiICBw5csR6XZ1Oh1deeQW7du0CANx2223IzMzEr7/+aq2FBFBj3WBNvv76awDmusDw8HDExcVVO6Zr167w9/eHl5cXTCYT0tLS8Pfff1vrvhctWoTJkyfjhhtugJ+fH6ZPn46srCybmtWwsDAMHDjQ+vrGG2+sV//27t2Lhx56yPr5AoBOnTqhW7duOHPmDNLS0gAA2dnZmDJlCo4fP47u3bvXeK2rV69i165dCAwMRO/evXHy5ElrHaUoipg/fz6mTZtWr37V14kTJ2xeOzk5wcfHp8ZjT58+DcBcz9u/f3+IooirV68CML9/EyZMsL4GAB8fH/Tv3x+pqak4f/48AMBgMOCFF15AQEAAHnjggXp9PqSq277vvvvw66+/Wl/7+flhwIAB0Ol0OHjwIMrLy6HX6/Hkk08iNDTUWm+9efNmHD9+3HqepcYXMN94Gx8fj8LCwgb15ciRIzavnZ2d6/yaGz58uM3rq1evIjExEWFhYXjsscewdetW677//e9/WLp0qfX13r17kZycbH09a9Ys6887KX8u1PX10Zr8/PPPcHV1xZAhQ5CcnGz9mWI0GnHvvfeipKQEarUagwYNwpkzZ5CRkQHAfN/Jv//9b+zcudN6LSk/fuQg7JvHiaTVs2dPm7/eFyxY0KDzz58/LwqCYDPqtW/fPuv+M2fOiGq12qaNHTt2WPdXHSFGlRGQqvtHjx5t3WcymaqNwk2ZMkXMycmx6ePRo0dtRjOqjrR+++23Nsf/5z//sdlfeRSv6ggMAHH27NmiwWAQRVEUY2JiRGdnZ+s+Z2dnsby83Ob69a0hrjpCrFAoxK1bt9ocYxndSkpKEjMzM2u8zrZt22yuM3/+fJv9Dakhrnxc1RGvIUOG2Ox/8sknrWUypaWl4u23326zv2ptbNWP68SJE8WSkhJRFEUxIyND7NChg83+htSO1jVCXFhYKP7www+ip6dntfYtavq8jx8/3uY/FpbPRdV/yQ8ePNjmuNdff91mf3BwsE05UX0+H00ZId69e7fNvsmTJ4s6nc66/+LFi6K7u7t1f+/eva37li5dat3u4eEhFhcX27RrMBjEgwcPNqjOdfny5Tb9CQwMrPP4mJiYap8Ly/e30Wi0+f4KDQ0VTSaT9dyHH37Y5rzKpVRS/1yo7eujoZpzhFilUol///23KIrm79Gq5SjBwcFicnKyKIqimJ6eLiqVSus+pVJp87OtKR8/ckych5jaNLGBU4Bt27bN5pzp06dj5MiR1te9e/fGY489ZnPOzz//XOv1Bg8ejH/84x/W15MnT7bZn5qaan37xIkTSEhIsL5Wq9X48ssv4e3tbXPOjTfeaB1xMplM+Omnn6z7nJ2dsXHjRsyYMcP62Lt3b7376+LigrfffhtyuRwA0L17d5tRz/Lycps7xJti1qxZmDJlis02y+hWSEgIjh8/jgceeACRkZHw8PCAXC6HIAi44447bM6JiYmRpD+VZWZm2oz0OTs7Y9myZZDJzD8yXVxc8NZbb9mcs3379jpnLHn33Xfh6uoKwDwSOXjwYJv9lb8WGioxMdFmDuK7777bZmTL1dUV//nPf2o9Xy6X45NPPoFGo7Fus3wuKn99AeZR+crHLViwAEFBQTbvR9XR6ea0ZcsWm9fZ2dm4//77rV//L7/8MpycnKz7z549a/0+qzy7QWFhIebNm4f169fj6NGjyMvLg1wux7Bhw2y+h6VW188omUyGOXPmWF8nJSVh3759AIDS0lJs2rTJum/kyJHW71Wpfy7U9fXRmtxzzz2IiooCYP4eHTBggM3+xx9/HB07dgQABAQE2MxaodPprD/bpP74kWNgyQS1Kf7+/tZ/4QKwCZj1UfV4yw/Xyvr27WvzOj4+vtbrVf1XqVqttnldeUL+K1eu2Ozr169fteOrysnJsQk+5eXlNr8ka1JXf7t27QovL69697kp6ppk///+7/+watWqel2noKBAkv5UlpiYaBNUQkNDq30cevToAWdnZ5SXlwMwLz6Rk5MDPz+/atdzd3dHZGSkzbbm+rhW1blzZ6xduxY33HBDrceEh4fXujDJ9b4nFAoFevbsaS0hAcxfY5XLVJpT1a/nP//8s17nhIeHY/r06Xj77bdx6tQpAMBHH32Ejz76yHpcp06dcNttt+GFF16o98ItVT//2dnZ0Ov1NqG8Msu/7SurXEoyZ84cLF682Fq689VXX2HUqFH48ccfbb73K/+hLvXPhbq+PlqTql+bHh4eNq979+5d537L96DUHz9yDAzE1KYMHz4cv//+u/V1dHQ0dDpdvUczqo7WCILQpP5Urdm0jLzaU3Fxca37aqoxba4+Vx5VrOyvv/6qFoYjIiLQvXt3KJVKlJSU2NSLNvS/APXR3F8HgLQf18rzEAuCAHd3d4SEhGDYsGEYP378dduq7XMBSP+xaA0s3wMuLi74888/8dlnn2Hr1q04fvy4zR9Y8fHxWL16NdavX4+TJ09Wmy+3JoMGDbJ5rdfrcfTo0Wq1whZV73vo0KEDQkNDra+DgoJw++23W0csN23ahNWrV+N///uf9Rhvb2/MmDHjun2rS10/F+r6+mhNKo9gA7D+R8ei6h/7Uqrr40eOgSUT1Kbcc889Nj8Es7Ozq/1ru6rKI3OdOnWy2XfmzJlqx//99982r6ue01idO3e2eX3q1Knrjn76+PjYjHJ4enpCp9NBNM8gU+NDqpIHi8YGpKq/rCz++OMPm9dPPvkkLl26hJ9//hkbN27EK6+80iz9qazqaFhSUpLNiBFgLtWwjA4D5tGm2m5aa25+fn7YuHEjNm7ciA0bNmDt2rVYsmQJJk6cWK/gXdvnArj+94TBYLD5r0xN5zSnqm199913dX79i6JoU3bj6uqKZ599FtHR0cjPz0dOTg6OHDliM+Kal5eHtWvX1qs/vXr1qnZzZdUFOixKS0vxySef2GybNm1ata/hyn3RarX45JNPrDe3AsBDDz1k80e/1D8X6vr6aItaw89Vannt66uc2rzevXtXq/d77bXXsHjxYuvsEBalpaX4/PPPbcoabr/9dptfRps2bbIZwTl//ny1X2BVa1obq3///jYjQwUFBZg1a1a1ZaVPnTqFY8eOATD/oqrcvlarxfPPP1/t3++iKOLIkSP45z//Wa3msqksdbEWTamFBcwjapVVvnO7oKDAZlaO5upPhw4dbEb6dDodXn75ZWuNsE6nw4IFC2zOue2229pkcKj69b148WKbP9RWrFhhUy4RFBRknamhJVSty3/llVdq/Pd1amoqVq9ejWeffda67dSpU/j4449t+u/t7Y1BgwZVG3GtqbShNosXL7Z5vWHDBixatMhmxpK8vDzcfffdSEpKsm5zdXXFwoULq13v1ltvRUhIiPX1ggULbJakr3pfQ2v4ueDI+PFrn1gyQW3OBx98gEuXLlmXbhZFEYsWLcI777yDG2+80bp086lTp1BWVmZTy9mzZ0889NBD+PLLLwGYw9moUaNw4403wtnZGUePHkVpaan1+NGjR2PixImS9FsQBLzzzju46667rNt+/PFHhIeHo2/fvvDy8sKlS5dw8eJFrF271hrkFy1ahJ9//hlFRUUAgNWrV+Pbb79F37594eHhgezsbJw7d84aYvr16ydJfy0iIyNx4cIF6+uhQ4fihhtugJOTE4YOHYp58+Y16HpDhgyxef3OO+9g//798PHxsd7sVJeIiAjIZDJreN29ezeGDh2K4OBgAMDChQur3WxTk2XLlmH8+PHW66xevRq//vqrddq1ykHbzc0Nr732WoPeT0cxb948rF27FllZWQCAQ4cOoWvXrtZp1yzTT1lUvvmwJdxyyy0YP368dfrD2NhYREREoH///ggMDERJSQni4uKstdA333yz9dyEhAQ88cQTePLJJ9GlSxd06tQJKpUKubm51aZP69GjR737dM899+CPP/7A6tWrrdsWL16Mjz/+2Lp086FDh2ymK5TJZPjyyy9rLMuw3Fy3aNEiALD5437EiBE19s3ePxccHT9+7Q8DMbU5rq6u2L17N1588UV8+OGH1lGZwsJC7Nmzp9rxVX95f/zxxyguLsbGjRsBmP8lfOjQoWrnjRw50nqMVGbMmIFPP/0Uc+fOtQbvwsJCa7ivSbdu3bBt2zbce++91lGs3Nxcm1rqyhQKab/tH3nkEZvRkeTkZJu5URtq5MiRuPPOO7F582brNsuIuFwux5tvvon58+fXer6XlxemT5+ODRs2WLcdPnzY+nZ9ZwwYM2YM1q1bh8cff9z6ubhy5Uq1mx+9vb3x7bffNigwOZIOHTpgx44dmDZtmnU0Mzs72+Zf9oD5c7N06VI89NBDLd7HjRs34u6777bOI2s0Gq1fM1XV9PUviiLi4uJqnAsbMP/35pFHHmlQn95//32EhYXh1VdftQbYjIwM69zklQUEBGDdunWYMGFCrdebM2cOXn/9dZtRZqD66LCFvX8uODp+/NoffgapTVIqlVi1apV1dGvv3r24ePEi8vLyIIoi/Pz80LNnT4wZMwb3339/tXM3bNiAXbt2Yd26dTh8+DAyMjJgNBrh5+eHgQMHWqd1ao6RsEceeQQTJkzAJ598gt27d+PSpUvQarXw8PBAcHAwRowYgaFDh9qcc/PNNyMmJgZr167Ftm3bcObMGeTl5UEmk8HX1xfdunXDsGHDMGnSpGrTfTXVbbfdhu+//x7vvfce/v77b+uISlN8//33ePvtt7Fu3TpcuXIFHh4eGDx4MP71r38hODi4zkAMAF988QVCQ0OxdetWJCcn29T6NsSDDz6IkSNHYs2aNdi9ezfi4uJQXFwMDw8PdO/eHbfeeiueeOIJyRaYaK369++Ps2fP4vPPP8dPP/2EM2fOID8/Hy4uLggLC8OoUaPw5JNP2kxj1ZI8PT2xY8cObN++Hd988w2OHDmC9PR06HQ6eHp6Ijw8HP3798f48eMxadIk63kjRozARx99hEOHDuHkyZPIyspCTk4ORFGEr68vevXqhSlTpuCRRx6Bi4tLg/okCAJefPFFPPTQQ/jss88QHR2NmJgY5ObmQi6Xw8/PDzfccANuv/12zJw587rX79ixI2699VZs27bNus3Ly8vmP0pV2fPnQlvAj1/7IojNcYs2EREREZGDaHt3gBARERERNQADMRERERG1awzERERERNSuMRATERERUbvGQExERERE7RoDMRERERG1a5yHuBFMJhPS0tLg4eFRbc15IiIiIrI/URRRWFiIoKCg664bwEDcCGlpaTbryhMRERFR65ScnIyOHTvWeQwDcSN4eHgAMH+APT097dwbIiIiIqpKq9UiJCTEmtvqwkDcCJYyCU9PTwZiIiIiolasPuWtvKmOiIiIiNo1BmIiIiIiatcYiImIiIioXWMNMREREZEDEUURBoMBRqPR3l2xK7lcDoVCIckUuAzERERERA6ivLwc6enpKCkpsXdXWgU3NzcEBgbC2dm5SddhICYiIiJyACaTCfHx8ZDL5QgKCoKzs3O7XSBMFEWUl5cjKysL8fHxiIiIuO7iG3VhICYiIiJyAOXl5TCZTAgJCYGbm5u9u2N3rq6ucHJyQmJiIsrLy+Hi4tLoa/GmOiIiIiIH0pSR0LZGqo8FP6JERERE1K4xEBMRERFRu8ZATERERETtGgMxEREREbWIjIwMPPvss+jcuTOUSiVCQkIwadIkREdH27VfDhuIv/76azz++OMYOHAglEolBEHAunXraj1eq9Xi+eefR1hYGJRKJcLDw/Hiiy+iqKio5TpNRERE1E4lJCRgwIAB2LNnD1asWIEzZ85gx44dGD16NJ5++mm79s1hp13797//jcTERPj6+iIwMBCJiYm1HltcXIybb74Zp06dwi233IL77rsPJ0+exNtvv419+/Zh//79TZqqg4iIiIjq9tRTT0EQBBw9ehQqlcq6vVevXpg9e7Yde+bAI8SfffYZEhISkJWVhSeeeKLOY9966y2cOnUK8+fPx86dO/Hmm29i586dmD9/Po4dO4Z33323hXpNREREJB1RFFFSbrDLQxTFevczNzcXO3bswNNPP20Thi00Go2EH5WGc9gR4nHjxtXrOFEU8dlnn8Hd3R2vvPKKzb5XXnkFq1evxmeffYaFCxc2RzeJiIiImk2p3oier+60S9vnl0yAm3P9omRcXBxEUURkZOR1j/3yyy/x6KOP4urVq/Dy8mpqN+vFYUeI6ys2NhZpaWkYPnx4tb9IVCoVhg8fjitXriA5OdlOPby+o/G52H4mHRkFZfbuChEREVGDNWQ0+fvvv8eNN96ILVu2NGOPbDnsCHF9xcbGAgAiIiJq3B8REYGdO3ciNjYWISEhNR6j0+mg0+msr7VarfQdrcNbO2LwV2IePprZHxPVgS3aNhEREbVerk5ynF8ywW5t11dERAQEQUBMTEydx+Xm5uLSpUv48ssvsWTJkharLW7zI8QFBQUAALVaXeN+T09Pm+NqsmzZMqjVauujtuDcXJRO5k+TzmBq0XaJiIiodRMEAW7OCrs8BEGodz+9vb0xYcIErF69GsXFxdX25+fnAwA2b96MKVOmYNiwYYiNjUV2drZUH6o6tflALIWFCxeioKDA+mjp8goXhfkvsDK9sUXbJSIiIpLK6tWrYTQaMWjQIGzatAmxsbG4cOECVq1ahaFDhwIwl0vcfffdEAQB06ZNw6ZNm1qkb22+ZMIyMlzbCLCl/KG2EWQAUCqVUCqV0neunjhCTERERI6uc+fOOHHiBJYuXYp58+YhPT0dfn5+GDBgANasWYPMzEwcOHAA99xzDwCgvLwckZGRePzxx5u9b20+EFtqhy21xFVdr8a4NeAIMREREbUFgYGB+OCDD/DBBx9U27dmzRo88cQTNtPhdu7cGRkZGQgICGjWfrX5komIiAgEBQXh4MGD1WpWiouLcfDgQXTq1KnF64IbwjpCrOcIMREREbVN33//PaZOnWqzbdKkSdi4cWOzt93mA7EgCHjkkUdQVFSE119/3Wbf66+/jqKiIjz66KN26l39KCtGiFkyQURERG3V3r17cfPNN9tse++99/DMM880e9sOWzLx2Wef4cCBAwCAM2fOWLft3bsXADBixAg88sgjAICXXnoJP/74I5YvX46TJ0+if//+OHHiBHbt2oUbb7wR//znP+3xLtSbZYSYJRNERERE0nPYQHzgwAF8+eWXNtsOHjyIgwcPWl9bArFKpcK+ffuwaNEibNq0Cb///jsCAwMxb948vPbaa3B1dW3RvjcUR4iJiIiImo/DBuJ169Zh3bp19T5erVbj3XfftSnUdhQuHCEmIiIiajZtvoa4LeAIMREREVHzYSB2AEqFZR5ijhATERERSY2B2AG4OFnmIeYIMREREZHUGIgdAGuIiYiIiJoPA7EDcHXiSnVEREREzYWB2AGwZIKIiIio+TAQOwBLIC7lCDERERGR5BiIHYArAzERERFRs2EgdgC8qY6IiIjagoyMDDz77LPo3LkzlEolQkJCMGnSJERHR9u1Xw67Ul174urMm+qIiIjIsSUkJGD48OHQaDRYsWIFoqKioNfrsXPnTjz99NOIiYmxW98YiB2ApWRCbxRhMJqgkHNgn4iIiBzLU089BUEQcPToUahUKuv2Xr16Yfbs2XbsGQOxQ7DcVAcAZQYT3BmIiYiICABEEdCX2KdtJzdAEOp1aG5uLnbs2IGlS5fahGELjUYjcecahoHYAViWbgaA0nIj3JX8tBERERHMYfg/QfZp++U0wLl6uK1JXFwcRFFEZGRknccpFAr07t0bAODk5IRPP/0U/fr1a2pPr4tDjQ5AEAQuzkFEREQOSxTFeh2n0Whw6tQpnDp1CgsWLMCSJUuauWdmHGp0EC5OMpTqjQzEREREdI2Tm3mk1l5t11NERAQEQWjQjXNarbbFSikYiB2Eq5McedBzLmIiIiK6RhDqXbZgT97e3pgwYQJWr16NuXPnVqsjzs/Ph0ajQX5+Pvr164eSkhLk5OTgzz//bJH+sWTCQbhUTL1WWs5ATERERI5n9erVMBqNGDRoEDZt2oTY2FhcuHABq1atwtChQwFcK5m4dOkSPv74Yzz99NMt0jcGYgfhoqioITaY7NwTIiIioobr3LkzTpw4gdGjR2PevHno3bs3xo8fj+joaKxZs6ba8XfccUeLjRCzZMJBuHKEmIiIiBxcYGAgPvjgA3zwwQfXPfbPP/9E586dW6BXDMQOg7NMEBERUVtnqSEWRREKhQKffPJJi7TLQOwgXJzM1S0MxERERNRWGQwGu7TLGmIHYVmtjrNMEBEREUmLgdhBuDIQExERETULBmIH4WKtIeYsE0RERERSYiB2EJZZJlhDTERERCQtBmIHYa0h5rRrRERERJJiIHYQnGWCiIiIqHkwEDsI3lRHRERE1DwYiB0EF+YgIiIiah4MxA6Cs0wQERERNQ8GYgfBhTmIiIiImgcDsYOwTLvGWSaIiIjIUWVkZODZZ59F586doVQqERISgkmTJiE6Otqu/VLYtXWqNxcFZ5kgIiIix5WQkIDhw4dDo9FgxYoViIqKgl6vx86dO/H0008jJibGbn1jIHYQXJiDiIiIHNlTTz0FQRBw9OhRqFQq6/ZevXph9uzZduwZA7HD4LRrREREVJUoiig1lNqlbVeFKwRBqNexubm52LFjB5YuXWoThi00Go3EvWsYBmIHwZvqiIiIqKpSQykGrx9sl7aP3H8Ebk5u9To2Li4OoigiMjKyzuNSUlIwd+5cnD59Gl5eXujUqRM++OAD+Pv7S9HlWvGmOgdRedo1URTt3BsiIiKi+qtPdhFFEVOmTMHtt9+Oy5cv46+//sLcuXORlZXV7P3jCLGDsNQQA4DOYLIGZCIiImq/XBWuOHL/Ebu1XV8REREQBKHOG+eio6Ph7u6OOXPmWLfddNNNTepjfTEQOwjLLBOAeeo1BmIiIiISBKHeZQv25O3tjQkTJmD16tWYO3dutTri/Px8nD9/Hv3797dL/1gy4SAUchmc5ObC9TID64iJiIjIsaxevRpGoxGDBg3Cpk2bEBsbiwsXLmDVqlUYOnSoXfvGEWIH4uIkh95o4OIcRERE5HA6d+6MEydOYOnSpZg3bx7S09Ph5+eHAQMGYM2aNdDr9di8ebNd+sYRYgfCmSaIiIjIkQUGBuKDDz5AQkICdDodUlJS8OOPP2LUqFEYN24ctFot1q1bZz3+wIEDOHv2bLP3i4HYgbhWmmmCiIiIqC0RBAFbt27F1q1b0aVLF/Tq1Qvvv/8+/Pz8mr1tlkw4kGuBmCPERERE1PaEhoZi69atLd4uR4gdiIuT+dPFGmIiIiIi6TAQOxDr4hycZYKIiIhIMgzEDsSyOAdHiImIiIikw0DsQFhDTERERCS9ZrmprqCgAFevXkVubi68vLwQEBAAtVrdHE21K5YR4mKOEBMRERFJRrJAvGfPHmzcuBG7d+/G5cuXq+3v0qULxo0bhxkzZmDMmDFSNduuuCvNn64SncHOPSEiIiJqO5ociL/44gusXLkSFy5cAACIoljjcXFxcbh8+TI+/vhj9OjRA/PmzcM//vEPCILQ1C60G27O5k9XkY4jxERERERSaXQN8YEDB9C/f388+uijuHDhgjUI1xZwLdtFUcT58+fxyCOPYODAgThw4EBju9DuuCsrSiY4QkxEREQkmUaPEI8cOdIm5Fr07NkTUVFR8PX1haenJwoKCpCdnY0zZ85YR5EtTp48iVGjRsFgYMCrD8sIcXE5P15EREREUmlSyYQoinB2dsZtt92GmTNnYty4cfD09Kz1+IKCAuzevRtff/01tm/fDr1eX2uJBVVnqSHmCDERERGRdBpdMqFSqTB//nykpKRg8+bNuPPOO+sMwwCgVqsxffp0bNmyBSkpKXjhhRfg5ubW2C60O25KzjJBREREjisjIwPPPvssOnfuDKVSiZCQEEyaNAnR0dF27VejR4gTEhLg4+PT6Ib9/Pzw1ltvYf78+Y2+Rnuj4ggxEREROaiEhAQMHz4cGo0GK1asQFRUFPR6PXbu3Imnn34aMTExdutbowNxU8Jwc1ynPbBOu8YRYiIiInIwTz31FARBwNGjR6FSqazbe/XqhdmzZ9uxZ820MAc1D7eKhTmKOEJMREREMN/PJZaW2qVtwdW13tPn5ubmYseOHVi6dKlNGLbQaDQS965hGIgdCG+qIyIiosrE0lJc7D/ALm13P3EcQj3vBYuLi4MoioiMjKzzOIVCgaioKOh0Ori6uuKJJ57Ao48+KkV36263oSecPn0aTk5O6NmzJwDzXyY//vgjLl26hM6dO2Pq1KlQKJizm4Nl2rWSciNMJhEyGRc1ISIiotavvrOKaTQanDx5EgCQlJSEqVOnQhRFPPbYY83ZvfoH4pycHIwdOxZnzpwBAIwePRo//vgjpk6dij179liPGzx4MKKjo+Hq6ip9b9s5ywgxAJTojTaviYiIqP0RXF3R/cRxu7VdXxERERAEoUE3zoWGhuKdd97Bc8891+yBuN7Trr3++uv4+++/zbUqoojff/8dd955J6Kjo63bRFHEkSNHsGrVqubsc7vl4iSDZVC4hGUTRERE7Z4gCJC5udnlUd/6YQDw9vbGhAkTsHr1ahQXF1fbn5+fX+N5/fv3x8WLFxv74am3egfi7du3QxAEeHt7484774SXlxd2794NQRAQFRWF//73v+jXrx9EUcTmzZubs8/tliAIUFWUTfDGOiIiInIkq1evhtFoxKBBg7Bp0ybExsbiwoULWLVqFYYOHVrjOS21gFu9/+eenJwMANiyZQtuuukm7N+/H6NGjYIgCPjiiy8wYMAAjBw5ssWSfHulUipQqDNw6jUiIiJyKJ07d8aJEyewdOlSzJs3D+np6fDz88OAAQOwZs2aGs85derUdW/Ek0K9A7FlWHzw4MEAgCFDhlj3RUVFAYD1RruSkhLJOki2LKvVcYSYiIiIHE1gYCA++OADfPDBB9c9Njk5GS+88AKeeeaZZu9XvQOxr68vUlNTkZ2djaCgIDg7O1v3Wd7Oy8sDAHh5eUncTbLg1GtERETUVuXn56Nfv34oLy+Hq6srnnzyScyZM6fZ2613IO7VqxdSU1Nx8OBB3HXXXQAAk8lkc8zp06cBAJ06dZKwi1SZZXGOYpZMEBERURtjMNhnwK/egXj+/PkYN25cnaO/P/30E9RqNcaPHy9J56g6jhATERERSavegXjUqFEYNWpUncfUtyaEGs+yOAcDMREREZE06j3tmqOzTAc3evRoBAYGws3NDd27d8fjjz+OK1eu2Lt79aayjhCzZIKIiIhICpIvdZaWloY9e/YgNTUVOp2u1uNeffVVqZuu0wsvvICVK1ciMDAQU6dOhaenJ06fPo1PP/0U3377Lf7880/07t27RfvUGO5KSw0xR4iJiIiIpCBpIH7xxRfx3nvvwWi8/uhlSwbijIwM/Pe//0VYWBhOnz4NtVpt3ffuu+/i+eefx8qVK/HFF1+0WJ8aiyUTRERE7VtLLVbhCKT6WEhWMrFq1Sq88847MBgM1mWca2KPT2JCQgJMJhOGDx9uE4YB4I477gAAZGVltXi/GoM31REREbVPTk5OALjeQ2WWj4XlY9NYko0Qf/rppwDMC3hYQm9r+QsmIiICzs7OOHjwILRaLTw9Pa37tm3bBgAYO3asvbrXIG5KTrtGRETUHsnlcmg0GmRmZgIA3NzcrAuntTeiKKKkpASZmZnQaDSQy+VNup5kgTguLs76SZkyZQrGjh0LDw8PqS7fJD4+PnjzzTcxb948REZGYsqUKdYa4j179uCpp56qcxUUnU5nUw+t1Wpbots14ggxERFR+xUQEAAA1lDc3mk0GuvHpCkkC8Senp7Izs7GwIEDsWXLFqkuK5nnnnsOwcHBeOSRR/DRRx9Zt48YMQL3338/FIraPxTLli3D4sWLW6Kb16Wy1BBzhJiIiKjdEQQBgYGB6NChA/R6vb27Y1dOTk5NHhm2kCwQjxs3Dt9++61kHZPakiVL8MYbb2DJkiWYOXMmNBoNTp06heeeew6jRo3Cpk2bMHny5BrPXbhwIZ5//nnra61Wi5CQkJbqug1ryQRHiImIiNotuVzeajOXI5LsprpFixZBpVLh6NGj+Oqrr6S6rCR2796N1157Dc888wwWLFiAjh07wt3dHSNGjMDPP/8MJycnzJs3r9bzlUolPD09bR72wpIJIiIiImlJNkIcERGBHTt2YMKECXj44YfxxhtvoFevXjUu9SwIAj7//HOpmr6uX3/9FQAwevToavsCAgIQGRmJkydPoqioCO7u7i3Wr8bgtGtERERE0pIsEBuNRqxatco6/UVcXBwuX75c7ThRFFs8EJeXlwOofWq1rKwsyGSyJk/Z0RKsI8TlRuvHkoiIiIgaT7KSiRUrVmDDhg0QBMH6aC2GDx8OAFi5ciUKCgps9n300UdISUnB0KFDoVQq7dG9BrHUEBtNInQGk517Q0REROT4JBshXrt2rfXt1jL/sMVdd92FNWvWYP/+/ejWrRsmT54MjUaDEydOYM+ePXB1dcXKlSvt3c16scwyAZjLJlycWFBPRERE1BSSBeKkpCTryPALL7yA4cOHw8PDAzKZZIPQjSaXy7Fr1y68++67+OGHH7B+/XqUl5fD398fM2fOxMsvv4wePXrYu5v1IpcJcHWSo1RvREm5ET727hARERGRg5MsEPv7+yM5ORk333wzli9fLtVlJaNUKrFgwQIsWLDA3l1pMpXSHIiLeGMdERERUZNJNnw7ffp0iKKI3NxcqS5JtVBx6jUiIiIiyUgWiF999VVERETg77//xuLFi22WOiZpuXG1OiIiIiLJSFYyMW3aNDg7O0MURSxZsgQrV65Ely5doNFoqh0rCAKio6OlarrdcedqdURERESSkSwQ792713pTnSiKKCwsxKlTp6pNv8a5c5uOi3MQERERSUeyQFwZA2/z4vLNRERERNKRNBC3tvmH2yqVpWSCNcRERERETSZZII6Pj5fqUnQdLJkgIiIikk6jA7HBYIBCce30sLAwSTpE18eSCSIiIiLpNHraNX9/fzz88MPYtm0bysvLpewTXYcbSyaIiIiIJNPoQJyXl4evvvoKU6ZMQYcOHfDAAw9gy5YtKCsrk7J/VAOOEBMRERFJp9GB+O2338aQIUMAAFqtFt999x1mzJgBPz8/3H333fjhhx9QXFwsWUfpGi7MQURERCSdRgfi559/HgcPHkRKSgpWrVqFm266CYIgoLi4GJs2bcJ9992HDh06YNq0afjmm2+g1Wql7He7xoU5iIiIiKTT5KWbAwMD8cwzz2Dv3r1IS0vDmjVrMGbMGMjlcpSWluKnn37CQw89BH9/f9xxxx1Yt24d8vLypOh7u6ViyQQRERGRZJociCvr0KEDHn/8cfz222/IyMjAZ599hgkTJkChUECn02H79u2YM2cOAgICMHHiRBw4cEDK5tuNayUTDMRERERETSVpIK7M29sbs2fPxvbt25GZmYkvv/wSkyZNgrOzM/R6PX777Tfs2bOnuZpv067dVMcaYiIiIqKmapalm6tSq9V48MEH8eCDD6KoqAg///wzNm3aBJVK1RLNtzluzqwhJiIiIpJKiwTiytzd3XHffffhvvvua+mm2wzLCLHOYILBaIJC3mwD/URERERtHpOUA7IszAFw6jUiIiKipmr0CPGYMWMa3aggCIiOjm70+e2dUiGHk1yA3iiiWGeA2tXJ3l0iIiIicliNDsR79+6FIAgNPk8UxUadR7ZUSgXyS/SsIyYiIiJqIpZMOCgVV6sjIiIikkSTbqoTRVGqflADqbhaHREREZEkGh2I4+PjpewHNZBlponCMr2de0JERETk2BodiMPCwqTsBzWQ5Ua6glIGYiIiIqKmaLZ5iE+cOGEdRe7UqRP69+/fXE21Sxo3ZwBAfgkDMREREVFTSB6Iv/32W8yfPx+pqak224OCgvDmm2/igQcekLrJdokjxERERETSkHSWiZUrV2LmzJlISUmBKIo2j9TUVDz00EN4++23pWyy3bIE4nwGYiIiIqImkSwQx8TEYMGCBdZ5hivPNWx5LYoiXn75ZVy4cEGqZtstjRtHiImIiIikIFkgXrNmDQwGgzX4BgYGYty4cRg3bhwCAwOtU7QZjUasWbNGqmbbLWvJBGuIiYiIiJpEskC8d+9e69uvv/46EhMTsWvXLuzatQuJiYlYunRpjcdS43CEmIiIiEgakgXixMRECIKAnj174l//+hfkcrl1n1wux8KFC9GrVy+IoojExESpmm23rtUQl9u5J0RERESOTbJAXFpaCgAIDg6u9RjLvrKyMqmabbfUrpx2jYiIiEgKkgViLy8viKKIEydOQKvVVtuv1Wpx4sQJAIBGo5Gq2XbLUjJRWGaA0cQltImIiIgaS7JAHBUVBQDIycnBrbfeit9++w3p6enIyMjAb7/9httuuw3Z2dkQBMF6LDWepWQCALSsIyYiIiJqNMkW5pg8eTKio6MBAIcPH8bEiRPrPJaaxkkug8pZjuJyIwpK9fBSOdu7S0REREQOSbIR4tmzZyMsLMz6uurCHBahoaGYM2eOVM22a1ycg4iIiKjpJAvEKpUKW7duhZ+fn83iHJUX5fDz88OWLVugUqmkarZdU7uZR4U59RoRERFR40m6dHPfvn1x7tw5zJ8/Hz169ICrqytcXFzQo0cPvPTSSzh79iz69esnZZPtmtrVXPGSX8Kp14iIiIgaS7IaYgsfHx8sW7YMy5Ytk/rSVIWmYuo13lRHRERE1HiSjhBTy7JMvca5iImIiIgaT/IR4rS0NOzZswepqanQ6XS1Hvfqq69K3XS7w5vqiIiIiJpO0kD84osv4r333oPRaLzusQzETaeuGCHmTXVEREREjSdZIF61ahXeeecdm22CIFQ7zjIDBTWddYSYJRNEREREjSZZIP70008BwDrFGgCb+YdJerypjoiIiKjpJAvEcXFx1pHfKVOmYOzYsfDw8JDq8lSDazXEnHaNiIiIqLEkC8Senp7Izs7GwIEDsWXLFqkuS3XQsIaYiIiIqMkkm3Zt3LhxEEURcrlcqkvSdbCGmIiIiKjpJAvEixYtgkqlwtGjR/HVV19JdVmqg2WWCZ3BhDL99Wf2ICIiIqLqJCuZiIiIwI4dOzBhwgQ8/PDDeOONN9CrVy94eXlVO1YQBHz++edSNd1ueSgVkMsEGE0iCkr1cHHi6DwRERFRQ0kWiI1GI1atWoWSkhIA5pvsLl++XO04y7RrDMRNJwgCPF0UyCvRI79ED39PF3t3iYiIiMjhSFYysWLFCmzYsAGCIFgf1Pw0buap13hjHREREVHjSDZCvHbtWuvbnH+45Xhab6zj1GtEREREjSFZIE5KSrKODL/wwgsYPnw4PDw8IJNJNghNNdC4cuo1IiIioqaQLBD7+/sjOTkZN998M5YvXy7VZek61AzERERERE0i2fDt9OnTIYoicnNzpbok1QMX5yAiIiJqGskC8auvvoqIiAj8/fffWLx4MXQ6nVSXpjpouDgHERERUZNIVjIxbdo0ODs7QxRFLFmyBCtXrkSXLl2g0WiqHSsIAqKjo6Vqul2z3lTHEWIiIiKiRpEsEO/du9d6U50oiigsLMSpU6eqTb9mmYeYpMFp14iIiIiaRrJAXBkDb8ux3lTHadeIiIiIGkXSQMz5h1seb6ojIiIiahrJAnF8fLxUl6IGULOGmIiIiKhJJAvEYWFh9T7WYDBI1Wy7Z5llQluqh8kkQiZjuQoRERFRQ0g27drevXvrdZxer8f06dOlarbds8wyYRKBQh3/0CAiIiJqKMkC8dSpU3Hy5Mk6j9Hr9Zg2bRq2bdsmVbPtnouTHC5O5k9jAeciJiIiImowyQKxVqvFrbfeitjY2Br363Q6TJ06Fdu3b5eqSaqgceXUa0RERESNJVkgBoCsrCzccsstSEtLs9luCcM7duyQsjmqcO3GOk69RkRERNRQkgViZ2fzKGViYiLGjx+P3NxcAEBZWRkmTZqEnTt3Wo/t1KmTVM0SADWnXiMiIiJqNMkC8YYNG6BQKCAIAi5cuIDbbrsNmZmZuP3227F7927rCnaRkZHYv3+/VM0SKo0Qs4aYiIiIqMEkC8STJk3Cd999B7lcDkEQcOzYMXTp0sW6pLMoiujXrx/279+PoKAgqZolXJt6jSPERERERA0naQ3xtGnTsH79esjlcgBAcXExRFGEKIoYMmQIfv/9d/j6+krZZINt2bIF48ePh4+PD1xcXNCpUyfcd999SE5Otmu/mkLNQExERETUaJIu3QwAM2bMgNFoxMyZM2EymQAAo0ePxk8//QQ3Nzepm6s3URTxxBNP4JNPPkGXLl1w7733wsPDA2lpadi3bx8SExMREhJit/41hXX5ZpZMEBERETVYowPxmDFj6tyvVquRm5sLQRBQUlKCO+64w7pPEARER0c3tulGWbVqFT755BM89dRTWLVqlXUU28KRV89Tu5lvaOQsE0REREQN1+hAbKkNvh5RFHHkyBGb1/U5T0qlpaVYvHgxOnfujPfee69aGAYAhULywfIWw5vqiIiIiBqvWVNgSwff2uzatQt5eXl4+OGHYTQa8dNPP+HSpUvQaDQYN24cunbtau8uNglvqiMiIiJqvCYFYlEUpepHszp+/DgAQC6Xo0+fPrh06ZJ1n0wmw3PPPYe333671vN1Oh10Op31tVarbb7ONgJvqiMiIiJqvEYH4vj4eCn70awyMzMBACtXrkT//v1x9OhR9OjRAydPnsRjjz2Gd955B126dMGTTz5Z4/nLli3D4sWLW7LLDaLhwhxEREREjSaIjjLM2wSPPfYYPv30U7i6uiIuLs5mHuSzZ8+ib9++6NSpE+Li4mo8v6YR4pCQEBQUFMDT07PZ+389+SXl6LfkNwDAxTcmQqmoXiNNRERE1J5otVqo1ep65TXHvZOsAdRqNQBg4MCB1RYF6d27Nzp37oy4uDjk5+dDo9FUO1+pVEKpVLZEVxvF08UJCpkAg0lEbnE5AtWu9u4SERERkcNo9MIcMTExknRAquvUpXv37gBQY9itvL20tLTZ+9IcZDIBvu7mwJ6p1V3naCIiIiKqrNGBOCoqCg888ADOnTvXqPPPnTuH++67D1FRUY3tQr2NHj0aAHDhwoVq+/R6PeLi4qBSqeDn59fsfWkuHTwrAnEhAzERERFRQzQ6EBuNRnz33Xfo06cPBg0ahPfff/+6o70xMTFYtWoVBg0ahD59+uD777+3rmbXnLp06YJbbrkFcXFx+Oyzz2z2vfnmm8jPz8e0adMcei7iDh6WQFxm554QEREROZZGJ0AnJyfo9eZZDY4fP26d2kyj0aBHjx7w9fWFp6cntFotsrOzcf78eRQUFFjPt9zL5+zs3JT+19uHH36IYcOG4dFHH8XWrVsRGRmJkydPYs+ePQgLC8OKFStapB/Nxc/DBQBLJoiIiIgaqtGB+OzZs3juueewfft26+pzoigiLy8Phw4dqna8JQBbjgOA2267Df/9738b24UG6dKlC/766y+8+uqr2LFjB3bt2oWAgAA8/fTTePXVV9GhQ4cW6Udz8fNgyQQRERFRYzQ6EEdERGDbtm3YvXs33n77bfz222/1Ok8URYwfPx4vvvgixo0b19jmGyUkJARr165t0TZbiqVkIouBmIiIiKhBmlw0O27cOIwbNw6xsbHYtGkTdu/ejb/++stmNTcPDw8MGDAAY8aMwYwZMxAZGdnUZqmKa4GYNcREREREDSHZXWQRERFYsGABFixYAAAoKytDXl4eNBoNXF05L25z6+BZUUPMEWIiIiKiBmm2aRVcXFwQGBjYXJenKiqXTJhMImQywc49IiIiInIMjZ52jVoXy8IcBpOIvJJyO/eGiIiIyHEwELcRzgoZvFXmKexYNkFERERUfwzEbUgHTr1GRERE1GAMxG2IdS5iLWeaICIiIqovBuI2pIMHZ5ogIiIiaigG4jbEj4tzEBERETUYA3EbwtXqiIiIiBqOgbgN6eBpuamONcRERERE9cVA3IawhpiIiIio4ZplpTqj0Yjs7GzodLUHs9DQ0OZoul2zTrum1UEURQgCV6sjIiIiuh5JA/GxY8fwyiuvYN++fSgvr321NEEQYDAYpGyacK1kolRvRJHOAA8XJzv3iIiIiKj1kywQHzlyBKNHj4ZOZx6dpJbn5qyAu1KBIp0BmYU6BmIiIiKiepCshnjRokUoKzPfzCUIQo3/rue/8Jtf5bIJIiIiIro+yUaIDx06BEEQIIoinJ2d0b17d6jVasjlcqmaoHrw81DiSnYxZ5ogIiIiqifJArGlZlitVuPIkSPo1q2bVJemBujgaZ5pgnMRExEREdWPZCUTlgA8ePBghmE78nPn4hxEREREDSFZIJ45cyZEUURsbCxvqrOja4tzMBATERER1YdkgXju3Lno378/EhIS8Pjjj6OgoECqS1MDWG+qYw0xERERUb1IVkM8ceJE6PV6iKKIzz//HN988w26du0KHx+fascKgoDo6GipmqZKrKvVcZYJIiIionqRLBDv3bvXOt2aKIooLS3FmTNnqk21xhXUmhdLJoiIiIgaRrKSicoswZjBt+VZSiYKSvUo0xvt3BsiIiKi1k/SQCyK4nUf1LzUrk5wVpg/rZxpgoiIiOj6JCuZiI+Pl+pS1ASCIMDPXYnU/FJkFuoQ4u1m7y4RERERtWqSBeKwsDCpLkVN1MHTHIg5QkxERER0fZIF4qpOnDhhHTXu1KkT+vfv31xNURXXFufg1GtERERE1yN5IP72228xf/58pKam2mwPCgrCm2++iQceeEDqJqkKzjRBREREVH+SBuKVK1fixRdfrPHmudTUVDz00ENIT0/HCy+8IGWzVAXnIiYiIiJ70Jv0yCrJQkZxBq6WXEVGcYb1cbXkKkyiCT9M+sHe3axGskAcExODBQsW2MwzbAnGlV+//PLLuP3229GjRw+pmqYquFodERERSc0kmpBblouM4gykF6cjvSgd6cXpNsE3uzQbImqfVUwmyGAwGaCQNVvVbqNI1ps1a9bAYDBYF+YICgpCz549AQDnz59HWloaAMBoNGLNmjVYtWqVVE1TFSyZICIiooYq0ZdcC7vF6da3Kz/rTfrrXkchU8DfzR8BqgAEqAKuve1mfi2g9a1TIelKdRavv/46FixYALlcDsAcgt966y3861//qnYsSc9aMsFATERERAAMJgOyS7NtAm56UaWwW5KBAl3Bda8jQICfmx8CVYEIUAVYny1h11/lD28Xb8iEZln7rdlIFogTExMhCAJ69uxpDb4WcrkcCxcuxPr163Hu3DkkJiZK1SzVwFIykVOkg9EkQi5rfX+JERERkTREUYS2XGstW7CM8KYXp+Nq8VWkF6cjsyQTRvH6K9h6OHkgwL0i6LoFINDdNvh2cOsAJ5lTC7xXLUuyQFxaWgoACA4OrvWY4OBgnDt3DmVlrG1tTj7uSsgEwCSaQ3EHTxd7d4mIiIgayWAy4GrJVaQVpV0b2S2pGNktMj+XGEquex2FoIC/yt8acKuN8qoC4OHs0QLvUesjWSD28vJCZmYmTpw4Aa1WC09PT5v9Wq0WJ06cAABoNBqpmqUayGUCfNyVyCrUIbOQgZiIiKg10xv1yCjOQGpxKtKL0pFalIr04ornIvNNa/UZ3fV28Ya/m7857LoH2gTdQFUgfFx8IJfJW+A9cjySBeKoqChER0cjJycHt956KxYtWoTevXtDEAScOXMGixcvRnZ2NgRBQFRUlFTNUi06eJgDMVerIyIisq8yQxnSi9ORVpSGtOI087PlUZyGrJKsOmdmAAAnmROC3IMQoApAkCqoWtj1V/nDVeHaQu9R2yNZIJ48eTKio6MBAIcPH8bEiRPrPJaaV6DaBefStEjNL7V3V4iIiNq0En1J9bBb6e2cspzrXsNF7oIg9yAEugciWBVsfnYPRqDK/Ozj6uNwN6o5EskC8ezZs7Fy5UokJSUBQLXFOSxzEYeGhmLOnDlSNUu1CPF2AwAk516/poiIiIhqpy3X1ljKYHmdr8u/7jVUTipruLU8B7kHWR9eSi9rVqKWJ1kgVqlU2Lp1KyZMmIDMzMxqn1RRFOHn54ctW7ZApVJJ1SzVIrQiECfmMBATERHVRWfUIa0oDSmFKUgtSrU+UgpTkFKUgsLywutew9PZ0xxuVddCbuW3PZ09GXhbMUmXCenbty/OnTuHt99+Gz///DMSEhIgiiI6deqEO+64A/PmzYOfn5+UTVItLIE4iSPERETUzhlNRmSVZiG5MPla4C1MRUpRClILU5FZmnnda3i7eCNQFVg99Fa8dnd2b4H3hJqL5Ovm+fj4YNmyZVi2bJnUl6YGCPO5FogrL6dNRETU1oiiiAJdAVKKUqwh1zLCm1qUirTiNBhMhjqv4aZwQ7BHMILdg9HRvSM6enREsHuw9eHm5NZC7w3ZQ+taSJok09HL/I1bpDMgr0QPb5WznXtERETUeKWGUmtZQ0pRik3gTS1KRbG+uM7zFYLCeqOaJex2dO9ofa1Rajh41I4xELdRLk5yBHi6IENbhqTcEgZiIiJq1URRRHZpNpILk20elhHf+szU4OfqZx7R9bANu8HuwfB38+ccvFSrRgfiMWPGAABGjRqFV1991fq6PgRBsE7RRs0n1NsNGdoyJOYUo1+Ixt7dISKidk5v0iO9KL1a6LXU9pYa6p4q1N3J3aaUofJIb5B7EFwUXIiKGqfRgXjv3r0QBAG+vr42r6+H9awtJ9THDUcTcjn1GhERtZgSfUmNgTe5MBkZxRl1rrgmE2QIcAtAiEcIOnp0tD539OiIju4dOVMDNRuWTLRhnHqNiIikJooicstyr5U0FKbYhN7rlTYo5Up0dO9oE3otj2D3YDjJnVroPSG6pkmBuOriG1Vfk31VnmmCiIiovkyiCVeLryKxMBFJ2qRqobfEUPfvFbVSjRD3kBpDr5+bH1dco1an0YE4Pj4eAKyLbFheU+sRwrmIiYioFibRhMySTCRpk5BUmIQkbRIStYlIKkxCcmEydEZdrecKEOCv8rcJupWDr6ezZwu+J0RN1+hAHBYWZvPaUtOjUqng4+PTtF6RJCwlExnaMpTpjXBx4t21RETtiSiKyCrNQqI2EcmFyebAq01CYmEikrXJKDOW1XquQlBYQ26oZ6hN8A12D4ZSrmzB94SoeUlWQxweHg5BEDB9+nT88MMPNR6zYMEC7NmzB4Ig4MiRI1I1TbXwUTlD5SxHcbkRqfml6OLHVXSIiNoaURSRU5ZjM8JrCb5JhUl1ztwgF+QIdg9GqGcowjzDEOpR8ewZikBVIBQy3mpE7UOLfqVfvnwZf/31F+8QbSGCICDE2w0xGYVIyilhICYiclCiKCJPl2cNvZbgawm9dS1KIRNkCFIFWYOuJfiGeoYiyD0ITjLexEbUooFYp6u9HomaR5hPRSBmHTERUatXoi9BgjYBidpEJBQkIF4bby530CajUF9Y63kCBAS5B1mDbphnGMI8w8wlDu4dOXMD0XU0KRAnJSVV21ZSUlLj9tTUVGuZBEeIWw6nXiMial2MJiPSitOQUJCABG3CtWdtAjJLMms9T4CAAFWAOfB62I72dvToCGc5VyQlaqwmBWJL3bCFKIr49ddf0alTpzrPU6vVTWmWGiDUxzwLCEeIiYhaVn5ZPhK0CYgviLcZ9U0qTILepK/1PG8Xb4R7hiNcHW4d6Q3zCEOIZwhvZCNqJpKUTFSef7iuuYgFQYAgCBgwYIAUzVI9hFqnXqu9voyIiBqn3FiOJG2SdYS38mhvga6g1vOUciVCPUPNwbci/IZ7mgOwWslBI6KW1qI1xKIoQqFQYOHChS3ZbLsWWmkuYi6bTUTUcJZZHOIL4q89tPFILEhEWnEaTKKp1nMDVAHVQm+4OhyBqkAuTkHUijQpEI8cOdIasPbt2wdBEODj44NevXrZHCcIAlxdXREZGYlZs2ahT58+TWmWGiBY4wqZAJTpTcgq0qGDh4u9u0RE1CoZTUakFaXhSsEVXCm4gviCeOvbheW139Dm7uRuHt1Vh1kDbyfPTgjxCIGbk1sLvgdE1FhNCsR79+61vi2Tmf/SHTVqVK3zEFPLc1bIEKh2RWp+KZJyShiIiajdKzOUIVGbWC30JhYkotxUXuM5AgQEuwejs6azzWhvJ3Un+Lj48L9vRA5OspIJy2hx1dFhsr8wHzdzIM4twcBwb3t3h4ioRRToCsxhN982+KYVpUFEzfe7OMucEa4OR2d1Z3RSd7I+h6vDeUMbURsmWSCuPFpMrUuotxv+vJzDqdeIqM0xiSZcLb5arcwhviAeuWW5tZ7n6eyJzurO6KzpjE6enczP6k4IUgVBLuMy90TtTbPcVFdSUoK4uDgUFBTUOuvEyJEjm6NpqkGoj7mGLZlTrxGRgzKJJqQXp+Ny/mVczr+MuPw4XM6/jCsFV+pcmjhAFWANvJbRXpY5EFFVkgbi1NRUPPvss/jll19gMBhqPU4QhDr3k7Ssi3MwEBNRKyeKItKL062B1/oouFxr8FUICoR4hphHfKuUOvCmNiKqD8kCcVFREUaMGIGkpKQ65yKmlld56jUiotZAFEVkFGdYg2/lEd8SQ80/qxQyBcI9w9FF0wVdNF3QVdMVXdRdEOIZAicZlyYmosaTLBCvWbMGiYmJ1n9BWZ4t4bjqa2o5Yd7m1eqyCnUoLTfC1Zn1cUTUMioH3ysFV2xGfusVfNXXwi+DLxE1F8kC8S+//GJ9e9iwYfjzzz8hCALCw8PRrVs37N69GwAwY8YMuLnxX1gtSe3mBE8XBbRlBiTllqB7gIe9u0REbVB+WT5i82NxKe8SYvNiEZcfh7j8OBTra14pUyEoEOYZdm20t2LkN9QzlMGXiFqUZIE4JiYGAODj44Pff/8dSqV5epqBAwfihx9+wJYtWzB9+nQkJSVh3759UjVL9RTmo8KZ1AIGYiJqsjJDGS4XXDaH3rw4xObHIjYvFlmlWTUeXzn4Vi53YPAlotZCskCcn58PQRDQt29fODld+wFnKZGYNm0a+vfvj6NHj2LZsmV47bXXpGqa6iHU2w1nUguQmFPzSA0RUVVGkxEpRSmIzTMHXkvwTSpMqnW54mD3YERoIhDhZX501XRFuGc4nOQMvkTUekkWiBUKBfR6PTw8zKOPzs7O0Ov1yM29Ng9kQEAARFHEt99+y0Dcwjj1GhHVRhRF5JTlWEsdLOH3Sv4VlBnLajxHo9SYQ6/mWvCN8IqAyknVwr0nImo6yQKxl5cXSktLUVxsHoHUaDTIzMzEkSNHkJqaCqVSiSNHjgAAkpKSpGq2SZYvX44FCxYAAA4dOoQhQ4bYuUfNxzLTRAIX5yBq1/QmPRIKEhCTG4NLeZesz7UtYqGUK9FF0+XaqG/Fs6+rL+fxJaI2Q7JA3KFDB6SmpiIvLw8A0K1bN2RmZqK0tBQRERGQyWQoLTXPIenq6ipVs4129uxZvPbaa1CpVNYQ35Z18XMHAMRlFtm5J0TUUrTlWlzMvWh+5Jmf4/LjoDfpqx0rQECoZ6hNuUOEJgIhHiFcuY2I2jzJAnFUVBROnjyJS5cuQRRFjB8/HgcOHAAAlJVd+5ebIAh2X6VOr9dj1qxZ6NevHyIiIvD111/btT8tobu/uZQlNb8UBaV6qF1Zz0fUVphEE1KLUq3BNyY3BpdyLyGtOK3G41VOKnT36o5uXt3Q3bs7Ir0j0UXTBa4K+w9WEBHZg2SBeODAgdi4cSOMRiPOnDmDp556Ch988AGysrJs5iB2cXHB4sWLpWq2UZYuXYpz587hxIkTeOutt+zal5aidnNCkNoFaQVluHS1EDeGe9u7S0TUCGWGMsTlx+Fi7kVrucPFvIu1Tm0WpApCN+9uiPSORHev7uju3R3B7sGQCbIW7jkRUeslWSB+5pln8Mwzz9hs279/P+bNm4cDBw5Ar9djyJAhePPNN9GnTx+pmm2wEydOYOnSpViyZAl69uxpt37YQ/cAD6QVlCEmXctATOQAivXFuJBzARdyL+B8znlcyLmAeG18jTM8OMmc0FXTFd29u1uDbzevblAr1XboORGRY5EsENeke/fu2LZtW3M20SA6nQ4PPfQQ+vXrh5deeqlB5+l0OutrrVbbHN1rdpGBnvj9YhZiMgrt3RUiqkJbrkVMTgzO55zH+Vxz+E3UJkJE9dU9vZRe1lKHbl7m0d9wdTjn9CUiaiTJAnHnzp0BACqVCidOnLCZi7i1ePXVVxEbG4vjx49DLq//TSLLli2ze5mHFCIrFuRgICayrwJdgTn45py3jv4mFybXeGyAKgA9vHugp09P9PTpiUjvSPi5+nGGByIiCUkWiNPT01FeXo7Ro0e3yjB86NAhvP3221i0aBF69+7doHMXLlyI559/3vpaq9UiJCRE6i42u8gATwDAxYxCiKLIX6hELSC3LBcXci7YhN/UotQajw12D0ZPn57WABzpHQkfV58W7jERUfsjWSAOCwtDbGxsqwxZBoMBs2bNQp8+fazzDjeEUqm0LkXtyDr7qeAkF1CkMyAlrxQhFXMTE5E0ivXFOJ9zHmeyz+Bs9lmcyT6DjOKMGo8N9QhFD58eNgGY9b5ERPYhWSCePn06li1bhhMnTkCr1cLT01OqSzdZUVERYmNjAZhX0KvJ0KFDAQBbtmzB1KlTW6prLcpJLkMXP3fEZBTiYkYhAzFRE+hNesTmxVqD79nss7icf7laza8AAWGeYejh0wO9fHqhh3cPRPpEwtO59fyMJCJq7yQLxC+//DK2bduGs2fP4p577sEnn3zSasoKlEol5syZU+O+/fv3IzY2FpMnT4afnx/Cw8NbtnMtLDLAAzEZhYjJ0GJcT397d4fIIYiiiOTCZJuR35jcGOiMumrHBqoC0du3N6J8o9Dbtzd6+vTkcsZERK2cZIF40qRJkMvlEEURu3btQpcuXRAeHo6goCDIZLbzXQqCgOjoaKmavi5XV1d89tlnNe77xz/+gdjYWCxcuLBNL91sERnoCZxK4411RHUo0BXg76y/8Xf239YQXKArqHach7OHNfhann1dfe3QYyIiagrJAvHevXshCAIEQYAoijAYDIiLi8Ply5dtjuPNXPbVnTNNENkQRRGJ2kScyjqFU5nmx+WCy9WOc5Y5I9In0iYAh3qE8ucZEVEb0CzzEPMXROvVo2KmifjsYpTpjXBxqv/0c0RtQamhFOeyz+FU1imczjyNU1mnkK/Lr3ZcmGcY+vr1RZRvFKJ8o9DNqxuc5K1vBh0iImo6SQOxKFafQL61W7duHdatW2fvbrQYf08l1K5OKCjVIy6zCL2DeVc7tW1Xi69aR39PZ53GhZwLMIgGm2OcZc7o7dsbfTv0RT+/fujr15fTnRERtSOSBeL4+HipLkXNSBAERAZ44Eh8Li5mFDIQU5siiiIStAn46+pf+CvjL5zMPIn04vRqx/m5+qFfh37o59cP/Tr0Qw/vHhz9JSJqxySdh5gcgyUQx2Q45hLURBYm0YTL+ZetAfj41ePIKcuxOUYmyNDdqzv6+vXFDR1uQL8O/RCoCmRpFxERWUkWiJOSkgCYl2728eG/GluzyEBzHTFvrCNHYzQZcSnvEv66ag6/x68er1b/6yxzRh+/PhgYMBAD/Aegj28fuDlxzm0iIqqdZIE4PDwcgiBg+vTp+OGHH2o8ZsGCBdizZw8EQcCRI0ekapoaiDNNkKMQRRFXCq7gcPphHE4/jOMZx1Got/26dVW4oq9fXwz0H4iBAQMR5RsFZ3nNC/AQERHVpFlmmajN5cuX8ddff/FflXbW3d8ciLMKdcgp0sHH3fGXpaa2I6M4A0fSj+BI+hEcTj+MrNIsm/0qJxVu6HCDNQD39OkJJxnrf4mIqPFaNBDrdNVXdaKWp1IqEOrthqTcElzMKMSwrgzEZD/aci2OZRzD4bTDOJJxBPEFtjfoKuVK9O/QH4MDB2NI4BB09+4OhaxFf3QREVEb16TfKpa64cpKSkpq3J6ammotk+AIsf1FBnggKbcEMRmFGNaVK2tRyzGajDibcxYHUg/gYOpBnMs5B5Nosu6XCTL08ullDcD9OvSDUs4/2oiIqPk0KRBb6oYtRFHEr7/+ik6dOtV5nlrNqb7sLTLAA7vOX+VME9Qi8srycDDtoDUEV70RLtwzHIMDB2No4FAMDBgItZI/I4iIqOVI8n/Hygty1LU4h2Vp5wEDBkjRLDUBZ5qg5mQSTbiQcwH7U/fjQOoBnMk6AxHXfjZ4OHlgSNAQ3BR8E4YGDUWAKsCOvSUiovauRQvxRFGEQqHAwoULW7JZqkFUxYIcF9K1XMKZJFGiL8HBtIPYm7wXB1IPILcs12Z/N69uuCn4JowIHoG+HfryRjgiImo1mhSIR44caS2Z2LdvHwRBgI+PD3r16mVznCAIcHV1RWRkJGbNmoU+ffo0pVmSQEcvV/i6K5FdpMPZ1AIMDPe2d5fIAeWU5mBfyj7sSdqDQ2mHUG4qt+5TOakwJNA8Cjw8eDhHgYmIqNVqUiDeu3ev9W2ZTAYAGDVqVK3zEFPrIQgCbgjV4LfzV3EyKZ+BmOotWZuMPcl7sCdpD05mnrQphejo3hFjQsfg5o4344YON3A5ZCIicgiSlUxYRourjg5T69U/1Au/nb+KE0l59u4KtXLxBfHYmbATOxN2Ii4/zmZfT5+eGBMyBmNCx6CrpitnkSEiIocjWSCuPFpMjqF/qAYAcCIpD6IoMsiQjSRtkjUEX8y7aN2uEBQYGDAQo0NGY0zoGJZCEBGRw2uWm+qMRiOys7PrXIgjNDS0OZqmBojqqIZcJuCqVoe0gjIEa1zt3SWys5TCFOxK3IUd8TtwIfeCdbtCUGBo0FBMCJ+AUSGjOC0aERG1KZIG4mPHjuGVV17Bvn37UF5eXutxgiDAYDBI2TQ1gpuzAj0CPXA2VYuTSXkMxO1Uga4AOxN24qfLP+F01mnrdrkgx+DAwZgQPgFjQ8cyBBMRUZslWSA+cuQIRo8eDZ1OV+dcxNS69A/1wtlULU4k5uOOPkH27g61EL1RjwOpB/DzlZ+xN3kv9CY9APMqcTf634hbwm/BuLBx8HbhzZZERNT2SRaIFy1ahLKyMuviG0D1RToEQWBYbmVuCNXgq0OJvLGuHRBFEedzzuOnyz/h1/hfkae79jnv7tUdk7pMwm2dboOfm58de0lERNTyJAvEhw4dsgZeZ2dndO/eHWq1GnI5F3xozfqHegEAzqdpoTMYoVTw89XWaMu1+Pnyz9h4aaPNDBG+rr64vdPtmNRlErp7d7djD4mIiOxLskBsqRlWq9U4cuQIunXrJtWlqRmFervBR+WMnOJynE3VYkCYl727RBIQRRGns05jw6UN2JWwC2XGMgCAi9wFY0LHYHKXyRgcOBgKWYsuVklERNQqSfbbsFu3bjhz5gwGDx7MMOxALAt07L6QiZNJeQzEDk5brsW2y9uwMXYjYvNirdsjvCJwV7e7cEfnO+Dh7GHHHhIREbU+kgXimTNn4qWXXkJsbCzntHUwN4R6YfeFTNYRO7CLuRfx9YWvsSN+h81o8ITwCbir+13o49uH35NERES1kCwQz507F9999x1OnjyJxx9/HCtWrIBazWmaHIGljvhkUr59O0INYhJN2Je8D19f+BpHM45at3fVdMWMbjMwqcskeDp72rGHREREjkGyQDxx4kTo9XqIoojPP/8c33zzDbp27QofH59qxwqCgOjoaKmapibq01ENmQCkF5QhLb8UQZyPuFUr0ZdgS9wWrL+wHkmFSQDMcwaPDxuP+3vcj35+/TgaTERE1ACSLt1smXJNFEWUlpbizJkz1X4xs5yi9VEpFegdrMbfKQU4dDkH0wd0tHeXqAYZxRn4+vzX2By7GYX6QgCAh7MHZnSbgfsj7+cSykRERI3ULLeYM/A6nuFdffF3SgEOxmUzELcyyYXJ+OLsF9gatxUGk3mFx3DPcDzQ4wFM7jIZbk5udu4hERGRY5M0EHPRDcc1oqsv1uy9jANx2RzFbyXiC+Lx2ZnP8MuVX2AUjQCAgf4D8XDvhzEieARkgszOPSQiImobJAvE8fHxUl2K7GBAmBeUChkyC3WIyyxChD+n5rKXS3mX8Onfn2Jnwk6IMP+ROTxoOB7r8xj6+/e3c++IiIjaHskCcVhYmFSXIjtwcZLjxnBvHIjLxoG4bAZiOziXcw6fnP4Ee5L3WLeNDhmNx/o8ht6+ve3YMyIioratWZepstxc5+bGGkdHMLyrLw7EZeNgXDYeHt7J3t1pN64UXMH7J97H7qTdAAABAm4JvwWPRj3KJZWJiIhagOSBOCMjA8uWLcPPP/+M5ORkiKIIg8GAjz/+GFevXoVCocDLL78sdbMkgRFdfbEcwOErudAbTXCSs0a1OV0tvoo1p9dgS9wWmEQTZIIMt3e6HY/0eQSd1Z3t3T0iIqJ2Q9JAfPjwYUyePBk5OTnWG+wsN2elpqbijTfegCAIGDZsGEaNGiVl0ySBXkGe0Lg5Ib9Ej79T8jEgzNveXWqTCnQF+OLsF/jmwjfQGXUAzKURc2+Yi65eXe3cOyIiovZHsiHA3Nxc3HnnncjOzgZQfeq1GTNmWN/evn27VM2ShGQyAcO7+AIADsTm2Lk3bY/eqMe6s+tw6+Zb8cXZL6Az6tC/Q3/879b/YdWYVQzDREREdiLZCPEHH3yAjIwM68IcVfXp0we+vr7IycnB4cOHpWqWJDa8qy9+OZOOg3HZ+L9xEfbuTpsgiiL2p+zHir9WIFGbCMC8vPJzA57DTcE3cYo7IiIiO5NshPjnn3+2vr1+/XpMnTq12jE9e/aEKIqIi4uTqlmS2Iiu5hHiE0l5KNYZ7Nwbx3el4AqejH4Sz+x5BonaRPi6+uL14a9j46SNGNlxJMMwERFRKyBZII6NjYUgCBg4cCDuvfdeyOXyasdoNBoA5vIKap1CfdwQ4u0Kg0nE0Xh+nhqrWF+Mt469hek/TsfB1INQyBR4uPfD2DZtG6Z2nQq5rPr3BxEREdmHZCUTpaWlAAB/f/9aj8nPzwcAyGScvaA1G9HVF98eTca+S1kYHdnB3t1xOL8n/Y6lR5biaslVAMCojqPwwo0vIMyTc3UTERG1RpIlU29vb4iiiIsXL9a4v7CwECdOnAAA+Pj4SNUsNYMxkeY/an47f5XLcTdAVkkWnt/7POb+PhdXS66io3tHrBm3Bu+PfZ9hmIiIqBWTLBD37dsXABAXF4cVK1bAYLhWf3r16lX84x//QFFREQRBQL9+/aRqlprBiK6+UCpkSM0vRUxGob270+qZRBN+uPgDpmydgt8Sf4NckGN279nYPGUzRgSPsHf3iIiI6DokK5mYOnUqdu3aBQBYsGCBzb7g4GCbkcZp06ZJ1Sw1A1dnOW6K8MXuC5nYff4qegR62rtLrdaV/CtYfGgxTmSa//vR26c3Fg1bxBXmiIiIHIggSvQ/8bKyMvTt29c6g4QoitY76Cu/HRERgdOnT0OpVErRrF1otVqo1WoUFBTA07NthsXvjiZhweYz6NtRjR+faR2jnKLJBLGsDCadzvxcWgZRVwZTWRlEnQ6m0lLAaAQEweYhCAIgkwGoeC0TIDg5QXBxgaBUQqZUQnBxsT4LSuV1Z38wmAz4/Mzn+Pjvj6E36eGqcMXcG+bivsj7eMMcERFRK9CQvCbZCLGLiwu2bt2KW265BampqTaBwjI3cUBAADZv3uzQYbi9GNPDfDPd6ZQCXNWWwd/TpdnaEsvLoU9PR3lKCvQpqTBkZ8GYkwtDTg6MOTkw5JrfNhUUNFsfqhKcnSG4ukKmcoPc3QMyd3fIPNwhV7mjWCnij/zjSDVmY4wSCA3sjql97oePNgT6mIswqdWQazQQ3Nw4rRoREZEDkGyE2CI/Px/vvPMOtm7divj4eABAp06dMHnyZMybNw/e3o6/HHB7GCEGgKmrD+JUcj7emNobM4c0/aYwURRhyMhA2cWL0F28BN3Fiyi7dBHl8Qnmkd0GsIzwylxcKp6VEFxcIcjlgChChAiIAEwmQBTN2yqeYTJB1Oth0pVBLNNZR50b2of69FGu0VR5qGvYZvsQapiykIiIiBqmIXlN8kDcHrSXQLz69zis2HkRN0X44n9zBjf4fFEUUR4Xh+KjR1Fy9BhKjh2DsZY5qAUXFzh1DIZzcEco/P0h9/GGwtsHCh9vyL19oPD1gVyjgczV1VzW0AyhUTQYYCrTQdSVmUNyWRlMxcUwFhYiPzcNm05+jbSrcXDTAZ2dAjDMsx+UOhNMhYUwFRXBWFQEk1YLY34+RL2+cZ0QBMi9vK6933U9+/hAplJxFJqIiKgGdimZoLbn9qhArNh5EQfjspFdpIOv+/VLXUSjESXH/kLhrp3Q/vYbjFnZtgcoFFB26gRl9+5Qdu8Gl+7doezWDQp/f7sHO0GhgNxdAbirbLYfSD2Af6V8gNywXCg7u+K5Ac/hvsj7IBNqnqRFFEWIpaUw5ufbPAxVXpsfBda3TVotIIow5uZW/OFw/RUdBaXS+sdDtT8iKj/7+kLh7Q1BwW95IiKiqiT77fjDDz/go48+AgA8+eSTuOuuu2z2b9y4ER9++CEA4IknnsDdd98tVdPUTMJ9VejbUY3TKQXYfiYdDw0Nr/XY0nPnkL9hAwp/2w1jTo51u+DiArf+N8Bt0CC4DRoEl969IXN2boHeN53eqMf7J9/H2nNrAQDdvLphxcgV6KzpXOd5giBAcHODzM0NTkFB9W5PNBjMwTknF8bcnOrP2Tkw5OaY66tzcyGWlEDU6WBIS4chLf36DQgC5N7eUPj6Xnv4+ZrDsq+f9bXC1xcyT0+7/4FCRETUUiQLxN999x327t0LZ2dnbNq0qdr+8ePH46GHHoJOp4NGo2EgdhCT+gbhdEoBfj6dVi0Qi0YjCqOjkfvVVyj967h1u0ythse4sfCcMAFuQ4Y4TACuLLUoFS/uexFnss8AAO7tfi9euPEFKOXNd0OooFBYg2p9mEpKYMjNqwjNOTDm5prDc8WNiNYbErOzzSPOJhOMFTcq6mpZQMfaFycnyP0qBeWqAdqnUnh2dZXi3SciIrIbyQLxyZMnIQgCBgwYAC8vr2r71Wo1+vfvjz///BOnTp2SqllqZrf3CcTS7RdwLCEPafmlCNK4QtTrkbdhA3I//wL61FTzgQoFPCdOhHrKFKiGDIbg5GTfjjfBHyl/YMEfC6At18LD2QOvD3sdY8PG2rtb1cjc3ODs5gZ0DL7usaLRCGNeHgzZ2TBkZZufs7NgtHmdbZ3NQ9Tr6z3yLFOpoPD1rR6gKwKzdQTahyUbRETUOkn22ykrKwsA4FvH6JZlhgnLsdT6BapdcWO4N47G5+KX02m4Dym4uvwtlF+5AgCQe3lBc8/d8Lrvfjj5d7Bzb5vGJJrw0emP8NHpjyBCRG+f3nhn1DsIcq9/2UNrJcjl10afI+s+1qTTmUeXLSE5yxyeDdnZtgE6K8s8/3NxMcqLi4HExOt0ouKGwRrLNXys2+W+vubZNliyQURELUSyQGwymQAACQkJtR6TWPEL0yjx9FbUvCb1DULKmYvwW7oWyUnnAJiDsO8zT0MzfTpkLs03R3FLKdAVYMEfC3Ag9QAA4O5ud2P+oPlwljteuUdTyZRKyIKCrlv/LIoiTMXFMGRVjDRXHW22hOisbBhycwGj0XrDoO7Spbo74eQEhU/lkGx527bWWeHrC5lKVfe1iIiIrkOyQOzn54fk5GScO3cOR48exaBBg2z2Hz58GGfPnoUgCPDz85OqWWpmoihi9MUD6P37u3AxlgMKBbwfegi+TzwOeRuZci6hIAHP7nkWCdoEKOVKvDr0VUzuMtne3Wr1BEGA3N0dcnd3oFOnOo8VjUbzDYMVodmYUxGaM7NgsI5GZ8GYlQ1jQQGg18OQkQFDRsb1++HmZg7HlgBtHXmuCNCW0g0fH4esZyciouYnWSDu378/kpOTYTKZMGXKFKxYsQIjR44EAOzbtw8vvfSSdQnnG264QapmqRkZ8vKQvvBlFO3dCxcAp3y7ouCpF/DE/Tfbu2uS+TPtT7yw7wUUlhciQBWAVaNXoYdPD3t3q80R5HJzYPXxAbp3r/NYU3n5tZKNrGwYcrKr1zpXPMSSEoglJdAnJUGflHTdfsjUatubBGu5UVDu5cUFUoiI2hHJAvGMGTPw448/QhAEXL16FbNmzbLZX3n9jxkzZkjVLDWTskuXkPLU09CnpEBwcsLVe+fgZW1n+CcY8IjRBIW85jl4HYUoilgfsx4rjq2AUTSir19f/Hf0f+HrWr8ZHqj5yJydIQsMhFNg4HWPNRUXXxthrqnWuVItNPR6mAoKUF5QgPLLl6/TCZl5Xueqs2z4+lyre7bMsuHhwXpnIiIHJ9lKdQaDAQMGDMDZs2cB2AZgANZfGL169cKJEyegcOC7zdv6SnVFBw8i9dm5MJWUwCkkBB0/eB9Cl64YumwPcovL8dlDAzGup7+9u9loepMey44sw4ZLGwAAk7tMxmtDX2uX9cLthSiKMBUUVLlRsKJ0o8rIszE317zEdz0Jzs7XZtnwqWmOZ18o/Pyg8PHhFHVERC3ILivVKRQKbNq0CePGjUNSUlK1ERNRFBESEoLNmzc7dBhu64oOHETKU09BLC+H26BBCH7vv1BUTKM3Y0BHfLL/CtYfTXLYQFyiL8EL+17AH6l/QICA5wc8j1m9ZnGEr40TBAFyjQZyjQbKrl3rPFY0GMzzOFtDcqUa5yqlG6bCQojl5dCnpUGflnbdflinqKsoH5H7eEPh42t9tizJrfDx4cgzEVELkmyE2KKwsBBvv/02tmzZgisVU3N17twZU6dOxbx586BWq6Vszi7a6ghx5TDsPnYsOr67EkKlm5CuZBVhzDv7IBOAP+aPQbDGsUa78sry8Ez0M/g7+28o5Uq8NfItjAkdY+9ukQMz6XSVgrPtHM/WGTZycqxT1DWE4ORULTibA7NtcFb4+JhrnjnQQERkoyF5TfJA3B60xUB8vTBscd8nh3HoSg7mjumK52+p++ao1iStKA2P//Y4ErQJ8HT2xOqxq9GvQz97d4vaiWpT1OXkmm8WzMmtWGWw0tLc2TkwFRc3rAHLCHitwdl2G0s3iKg9sEvJBDkuXVwcUubOvW4YBoD7B4fi0JUcfP9XMuaOjXCIm+su5l7Ek7ufRFZpFgJUAfh43MforOls725RO9KQKeoAwFRWVm3pbUN29eBsyMmBMS8PEEUY8/JgzMtDedx1bhiEeZXDa6PPNYxCe/uYbyD09uYiKUTULjRLIDYajcjOzoaujn8RhoaGNkfT1EDGwkKkPPMsxJISuA0eXGcYBoAJvQLgo3LGVa0O0TGZmNAroAV723DHMo7h//b8Hwr1heiq6Yo149YgQNW6+0wkc3GBLDgYTsENWJY7J9d8k2DV0eccc3C2hGixvBymkhKYSkqgT06+fmfkcsi9vaDw8obc2xsKby/IvbzNAdrbG3Kvim3e5v1ytRqCrPX/oUxEVJmkgfjYsWN45ZVXsG/fPpSXl9d6nCAIMBgMUjZNjSCaTEhbsBDlCQlQBAYi+DphGACcFTLMGNgRH++7gvVHklp1II5OjMZL+19Cuakc/Tv0x6oxq6BWOn4NO1FlNstyo1udx1pKN4zZ2bWPPlcK0abCQvMKg1nmeuh6kcsh12iuBWfviuDs7W0O1pYQ7VMpQHPOZyKyM8kC8ZEjRzB69GjodLpqU65R65Tzyacoio6G4OSEjqveg8Lbu17n3XdjKD7edwX7Y7OQlFOCUB+3Zu5pw/1y5Re8fOBlmEQTxoSMwfKRy+GicPwlpomaonLphnN4+HWPN5WXW5fbNuTmmUNzbi6MuXkw5lm25ZpHn3PzrgXoilBdz06Z65+rBmdLmPbxrgjWFWFao+ENhEQkOcl+qixatAhlZWUQBMFab1bTXMQMy61DyYmTyHrvPQBAwGuvwjUqqt7nhvuqcFOEL/6IzcbH+y9j6bT6n9sStsZtxasHX4UIEVO6TMHiYYshl3EEiqihZM7OkAUEwCmgfv8JEsvLYcjLN4flnJxKwdkcoi3B2ZibC0NeHkwFBbb1z9dbMAUwB2hPT8h9fGyCs1yjgdxLA4WXF+SVHxovyFRurIMmojpJFogPHTpkDbzOzs7o3r071Go15PxXWKsjGo3IeP11QBShnjIZmkasHPjUqK74IzYbG/5Kwf+NjUAHz9Yx+rrh0gYsObQEAHBXt7vw7yH/hkxgPSNRSxCcneHk3wFO/h3qdbyo18OYn18RmHNtR59zKrbl5VpDtNESoAsKzG9fqWe/nJwqwrGmUliuCM8aL9ttFccIrq4M0UTtiGSB2FIzrFarceTIEXTrVnctG9lP3vffQ3fhAmSenugwf36jrjGkszcGhHnheGIePv3jCv51e0+Je9lw6y+sx7KjywAA90fejwWDFvAXGlErJjg5mVfx8/Or1/GiwVApQFcafc7Lt44yG/PzzKPU+fkw5uZC1Okg6vUwZGbCkJlZ/74pldagrPDS2AbnWkajZUplYz8URGRnkgXibt264cyZMxg8eDDDcCtmyM1F1n/NpRJ+c+fWu264KkEQ8Mzornh43TF8cyQJT43qCi+V/ZY+/uHiD9Yw/I9e/8DzA55nGCZqYwSFotINhPVjKi01z8KRl1ctOBvz86tvz8uDqNdD1OlgyMiAISMD9V1SRXBzM48w24xEe9mGZ8t+jQZytZpzQhO1EpIF4pkzZ+Kll15CbGwsRFFkGGmlst59FyatFsrISHjde0+TrjWqux96BnrifLoWa/9MwPPj7fOH0Na4rXj98OsAgId7PYznBjzHrz8iAgDIXF0hc3WFU1BQvY43z8RRYg7MefkVz3m1hOp8GCqOg8EAsaQE+pKSei3jbSEolZCr1daAfC0wV9lW8SxTq6HQaK47IxARNYxkK9WVl5dj2LBhOHnyJObMmYMVK1a0iWWaa+KoK9WV/v03Eu65FxBFhH3zNdwGDGjyNX/5Ox1Prz8BTxcFDi4YAw8XJwl6Wn/br2zHwgMLYRJNeKDHA5h/43yGYSJqUaIowlRUVD0451ct5ai0vaAAaML0o4KbW62hWa7WVAnYFc+enpyhg9oVu6xUN3HiROj1eoiiiM8//xzffPMNunbtCh8fn2rHCoKA6OhoqZqmehBNJmS8/ob1RjopwjAATOwdgM5+KlzJKsbXh5Pw5Kgukly3PnYn7rZOrTaj2wyGYSKyC0EQIPfwgNzDA6jnolPWOaHzCyoC8rWgbMw3v20qKIAhPx8m6zEFMGq1gMkEsaQEhpISGNLSG9RXmYdHLSFaXfPItEYDmYcHF1uhNk+yEWKZTFbjdGtVA4qlnMJoNErRrF044gix9tdfkfrc85CpVOj863Y4dajfXeD1sfF4Cl7YcBq+7s44MH8MXJyaf2aRg6kH8cyeZ2AwGTC5y2S8Pvx1ziZBRG2eaDLBVFhoG54LCmxGnmt6NhUWNr5RQYDM09Mckj09zQ+N2rzNU23ervasOEYDubriGLUaghunvCP7scsIcWWt7Ys/NTUVGzZswPbt2xETE4OMjAx4e3tj+PDheOmllzB48GB7d7HZ5X79DQDAe9YsScMwAEzpF4R3f7uE1PxSfHMkCXNGdJL0+lX9nfU3ntv7HAwmAyaGT8SSYUsYhomoXRBksooA2rCSRNFggFGrrTIiXcvotCVE5xfAVFICiCJMBQUwFRRA39AOKxTWcCz39IRMXRGaPT3NwVmthqxSqDYfY37NWTuoJUk6QlzvRlt4hHjBggVYvnw5unTpglGjRsHPzw+xsbHYunUrRFHE+vXrcc899b/BzNFGiMsuXED8tDsBhQJdo6PrPUdoQ6w/koSXt5yB2tUJe18Y1WwzTlwpuIJZv85Cvi4fw4KG4YMxH8BJ3rJ1y0RE7YWpvBymilINy/zPJuvb2ort+TAVaK0lHZZjoW9wfLZhveFQ7XktNFcEaZlaXTE6XSlse6rNJR8eHhCc+HuB7DRCHB8fL9WlJDdo0CDs3bsXN998s832P/74A2PHjsWTTz6JqVOnQtlG/xrNW78eAOB5y/hmCcMAcPfAjvjyzwRcvFqI96JjsWhyL8nbyCjOwOO/PY58XT56+/TGu6PeZRgmImpGMmdnyBowV7SFKIoQS0vrCNIV2wquBWij9tprmEzmqe8aOH+0heDmZq7r9vSAzMMTcg+PihIPT8g8PSD38Ly2z+bZXAvOmw/bH8lGiB3VhAkTsGvXLhw7dgwDBw6s1zmONEJszM9H7KjREMvKJJtZojYH47LxwGdHIJcJ2PF/NyHC30OyaxfoCjDr11m4XHAZ4Z7h+PLWL+Ht0rg5lImIqPUSTSbzDYcFFaPPWm3F2xWh2VL6oa0erJtUK12JTKUyB2gPj5oDtKcn5B5VwrXleHd3CFylt1Wwew0xAJw4ccI6atypUyf079+/uZpqEqeKf6so2uhfg/mbt0AsK4MyMhKuzfw5GN7VF+N7+uO381fx+i8X8OXDN0pST15uLMfcPXNxueAyOrh2wMfjP2YYJiJqowSZ7NqsHR2DG3SuaDCYp8ArLLQGZGOBFqZCLYzaQhgLtTDZPBfCpC2o2FcIsaQEAGAqLoapuBiG9IbN4mEhc3evGI2uNDpdU7hWe5pHpSsdJ1OpOKuHHUieAr/99lvMnz8fqampNtuDgoLw5ptv4oEHHpC6yUZLSkrC7t27ERgYiKioqFqP0+l00OmurVWk1WpbontNJhqNyPv2WwCA1wP3t8jNji/f1gN7L2Zi/6Us/H4xE2Mi/Zt0PVEU8crBV3Ai8wTcndyxZvwaBLnXb4J9IiJqXwSFwjpdXGOIej2MRUXmUWhtoXlEunK41hbahusCbUWoNj+LZWUAAFNREUxFRUADFmm59k4I5hFqDw/I3d0h8/CAzMMdcveKZw8PyGzerni2HF/x4Ch1w0gaiFeuXIkXX3wRNVVhpKam4qGHHkJ6ejpeeOEFKZttFL1ejwcffBA6nQ7Lly+HvI4vnGXLlmHx4sUt2DtpFP3xB/TJyZB5ekJ9xx0t0mYnXxUeHt4Jn+y/gje2XcBNEX5wkjf+L93Vp1Zje/x2KAQFVo5aiW5eXBaciIiah+DkBIWXF+Dl1ajzTeXl1wJ0RaiudXRaq7UdqS4ogKjXm2f1qAjUjV+6BZC5uVUJ0x6Qe7hXCdMV22oJ1u2pllqyGuKYmBj06dMHBoOh2nzElV8rFAqcPn0aPXr0kKLZRjGZTHjwwQexfv16PProo/jkk0/qPL6mEeKQkJBWX0Oc9OhjKP7jD3g//DD857/UYu1qy/QYvWIvcorL8codPRs9DduPcT/i3wf/DQBYMmwJpkVMk7KbRERErYqprMwcqAuLYCqqCMrWt4tgKiyEqbjI+raxqGJ/YaF5ZLuwEGJ5uWT9EdzcrKPU1tHnqsG6trDt4QG5SmXXZcbtUkO8Zs0aaxgWRRFBQUHo2bMnAOD8+fNIq/i3gdFoxJo1a7Bq1Sqpmm4Qk8mE2bNnY/369Zg5cyY++uij656jVCodbgaK8sREFP/xByAI8Lrv3hZt29PFCS9M6I6Fm8/gv79dwsTeAQjWuDboGscyjmHRoUUAgEejHmUYJiKiNk/m4gKZi0uDZ/WozDJKXWuwLiqCqbCoxjBt2WYp/bCsiIhGzPRhITg7VwRpd8hV5kAdum5tq1uzQrJAvHfvXuvbr7/+OhYsWGAtQzAajXjrrbfwr3/9q9qxLclkMuHhhx/GV199hfvuuw/r1q1r0PzJjiRvvbl2WDXyJjjXcylRKd09MAQ//JWMk0n5mL/xb/xvzqB6f/EnFybbLLzxzA3PNHNviYiI2gaZszNkPj6Aj0+jr2GtpS6sOUxXHrGuOVgXWW9QFMvLYczNhTE3F3qYSzlaWxgGJCyZ0Gg0KCwsRM+ePXHmzJkaj4mKisK5c+fg4eGBgoICKZqtt8ph+J577sE333xTZ91wXVr7tGuiKCJu9BgYMjLQ8cPV8Bgzxi79uJJVhNtW/YEyvQmvT+2NB4eEXfecovIiPPjrg4jLj0Nvn95YO3EtXBQuLdBbIiIikopoNJpn6yiqKPEorijp0OvhMW5ci/TBLiUTpaWlAIDg4NqnSAkODsa5c+dQVjEU31IsZRJfffUV7rrrLnz99deNDsOOQBcbC0NGBgSlEqphw+zWj85+7nhpQiSWbDuP//xyASMjfBHmo6r1eJNowsI/FiIuPw5+rn747+j/MgwTERE5IEEuN08n5+kJR1hCS7JA7OXlhczMTJw4cQJarbZaEtdqtThx4gQA82hyS1qyZAm+/PJLuLu7o1u3bnjjjTeqHTN16lT069evRfvVXIr/+AMA4DZoEGQu9g2U/xgWjp3nMnAkPhcvbvgb3z42BHJZzf8qWXN6Dfam7IWzzBnvjX4P/qqmTdlGREREVB+SBeKoqChER0cjJycHt956KxYtWoTevXtDEAScOXMGixcvRnZ2NgRBqHPO3+aQkJAAACgqKsLSpUtrPCY8PLzNBOKi/eZA7H7TTXbuCSCTCXj7rr6Y+N/9OJqQi7UH4/HITZ2rHbcveR8+Om2+wXHRsEWI8mvZrxEiIiJqvyQLxJMnT0Z0dDQA4PDhw5g4cWKdx7akdevWYd26dS3apr0Yi4pRUjES7z7S/oEYAEK83fCv23vi5S1n8NaOixjUyRt9Omqs+5O1yVh4YCEA4N7u92JSl0l26ikRERG1R5JNsTB79myEhV27aUoURZuHRWhoKObMmSNVs1RFyZHDgF4Pp9BQOIeH27s7VvcNCsG4Hv4oN5rw5NcnkFdsniex1FCKf+79JwrLC9HXry9eurHl5ksmIiIiAiQMxCqVClu3boWfnx9EUYQgCDYPURTh5+eHLVu2QKWq/cYqaprWVC5RmSAIeOfuvgjzcUNqfin+7/tTMBhNWHJoCS7lXYK3izfeufkdOMkdofSeiIiI2hJJJ+Ht27cvzp07h/nz56NHjx5wdXWFi4sLevTogZdeeglnz55tM3W6rZEoiij6Yz+A1lMuUZna1QkfzRwAFycZ9l/KwpM/rcK2K9sgF+R4++a3eRMdERER2YVk8xC3J611HmJdXByu3DEJgrMzuh05DJlrw1aHaylbTqZg3k8/wS3sYwiCCS8MfAGzes2yd7eIiIioDWlIXmuby7S1U5ZyCbdBg1ptGAaAMT094NvpBwiCCcbCPujpdoe9u0RERETtWKMDcXJyMnx9feHt7Y1+/fpBr9fXemx5eTn69u0Lb29v+Pn5ISUlpbHNUh1ac7mEhSiKWHxoMUrFHCjRASVp0/HIV8cRl1lk764RERFRO9XoQPztt98iNzcXBQUF+L//+z84OdV+M5SzszPmzp2L/Px85ObmYv369Y1tlmphKi5G6V/HAQCqVnZDXWUbYzfit8TfoJAp8MmE/+KGjv4oKNVj1hdHkalt2RUMiYiIiIAmBOI9e/YAAFxcXHD//fdf9/gHHngALhWrplnOJekUHzkCUa+HU0hIq5purbK4vDgsP7ocAPDP/v9E/4AofD7rRnTyVSE1vxT/WHsMBaW1/6eBiIiIqDk0OhCfO3cOgiCgb9++UCqV1z3excUF/fr1gyiKOH/+fGObpVoU7a8ol7jpJghCzUsj21OZoQwv/fESdEYdhgcNx4M9HwQAeKuc8eXDg+Dr7ozz6VrM+uIoCssYiomIiKjlNDoQZ2dnAwA6dOhQ73Msx2ZlZTW2WaqBKIoorrihTtVK64ff+esdxObFwtvFG2+MeAMy4dqXXqiPG/43ZzA0bk44lZyPh744ioIShmIiIiJqGU2eZaKoqP43Q1mObY0jmI7McPUq9GlpgFwO1aBB9u5ONb8n/Y7vLn4HAPjPiP/A19W32jE9Aj3x9ZzBULs64WRSPu799DCyi3Qt3VUiIiJqhxodiL29vSGKIv766y+Ul5df93idTodjx44BALy8vBrbLNWg7MIFAICyc2fI3Nzs3BtbV4uv4pU/XwEAzOo5C8ODh9d6bO9gNb5/fAh83ZW4kK7F3R8fQlp+aUt1lYiIiNqpRgfiyMhIAEBhYSGWL19+3eOXL1+OwsJCCIKA7t27N7ZZqoE1EPeItHNPbBlNRiw8sBAFugL09OmJ/+v/f9c9JzLAEz88PgRBahdcySrGtA8P4kxKQQv0loiIiNqrRgfim2++GUDFvLKLF+M///lPjXMR6/V6LF26FEuWLLFuGzVqVGObpRroLsQAAFx69LRzT2x9fvZzHMs4BleFK94a+Rac5LVPzVdZZz93bHhyGLr5u+OqVoe7Pz6EHWczmrm3RERE1F41eunm5ORkdOnSBUajEaIoQhAE+Pn5YezYsQgPD4coikhISEB0dDSys7NhaUahUODy5csICQmR9B1pSa1t6ea4ceOhT0lB6Lq1UA0ZYu/uAADOZp/FzO0zYRSNWDpiKSZ3mdzga2jL9Hhm/Unsv5QFQQDmT4zE4yM7swadiIiIrqsheU3R2EZCQkLw5JNP4v3334cgCBBFEZmZmfjuu+9sjrMEYUuIeeqppxw6DLc2Rq0W+oqV/1wiW0fJRJmhDC8feBlG0Yhbw2/FpM6TGnUdTxcnfDFrIJZsO4+vDiXizV9jcCalAMumR8HTpX6jzURERETX06RZJlasWIHhw4dbR4gtwbjyo/L2ESNGYMWKFVL1nQCUxZjLJRRBgZBrNPbtTIX3TryH+IJ4+Ln64V9D/tWkEV2FXIYlU3pj8eReUMgE/HImHXesOoC/U/Kl6zARERG1a00KxM7Ozti9ezdmz55tDb0ArCEYgDUUz549Gzt37qxziWdqOF1M66ofPpZxDF9f+BoAsGjYIqiVakmuO2tYODY8MRQdvVyRlFuC6Wv+xBcH4tHIih8iIiIiq0aXTFgolUp89tlneO655/D999/j4MGDuHr1KgDA398fw4cPxz333INevXo1ubNUXdl58wwTTS6XyI0HEv4Arp4HMs+ZX+tLAZMBcNUAbr6ARwDg3wsIugEI7Ad4Btpcoqi8CP8+8G8AwPSI6RjZcWTT+lTFDaFe+GXuTZi/8W/sOJeBJdvOY9+lLCyd1hsdvVrXdHNERETkOBp9U1171ppuqrsydRp0MTHouPoDeIwd27CTjQbg0g7gr8+By3sa3rhnR6DbBKD7bUCnm/Da0f9gc+xmBLsHY9PkTVA5qRp+zXoQRRH/O5yIN365gHKDCW7Ocrw0oTseGhoOmYw33BEREVHD8hoDcSO0lkBsKi/Hxf4DAIMBXaN3wyk4uP4nx/8BbH0KKEiq2CAAYcPMI78degB+3QFnd0AmB0rzgeIsoCAFSD8NpJ8CsmIA0WS93H4PDZ729YQAAV+M+wQDg5t/tovLWUVYsOlvHEvIAwAMCPPC8ulR6NrBo9nbJiIiotaNgbiZtZZAXHb+POLvnA6ZWo1uhw/V7+Y1kwn44x1g73/MgdbNF+j/INB/FuDdqf6NlxcDiX8CF7dDe+lXTFMLyFQo8GCBFi+VyYE+9wD9HgACejf+HawHk0nEN0eTsPzXGBTpDFDIBDw0NBxzx3aFxs25WdsmIiKi1qtFpl0j+7OsUOfSo0f9wnBxNrD50WvlEf0eAG5bATg3orTBWQVEjAcixuPtgx7IjNuCMLkKc8t1QMlV4PCH5kfwAGDIU0DPKUA9F+ZoCJlMwINDwjA2sgNe/fEcdl+4ii8OxmPzyRT839gIzBwSBid5k+4dJSIiojaOScGBNeiGupzLwEc3mcOwwhWY8iEw9cPGheFK/kz9E1vitkCAgCXjP4TLc+eB+38AekwCZE5A6nFg0xzgv32AP1YCJblNaq82QRpXfDZrIP43ZxC6+3sgv0SPxT+fxy3v7sem4ykwGE3XvwgRERG1SyyZaITWUjKR8MBMlB4/jqC3lkM9uY6V4MqLgc/GAZnnAd9uwF1fAv5Nn6atWF+MaT9OQ3pxOh7o8QAWDFpge0BRFvDXF8Cxz4DiTPM2hSvQ7z5g8JOAX7cm96EmBqMJ3/+VjJW7LiGnuBwAEOrthqdGdcGd/TvCWcG/A4mIiNo61hA3s9YQiEWTCZcG3ghTSQk6/fQjXLrVEi5FEdjyOPD394C7P/D4fvP0aRJ44/Ab+P7i9+jo3hGbJm+Cm1MtU58ZdMDZTeYSiowz17Z3HQ8MexboNBJohuWYC8v0+N/hRHz2RzxyK4JxkNoFT4zqgrsHhsDFSS55m0RERNQ6MBA3s9YQiMsTE3F5wkQIzs7ofuI4BEUt5eDHPgN+mQcIcmDWz0D4cEnaP5p+FHN2zQEAfH7L5xgUOOj6J4kikHgQOLwGiPkFQMWXXmBfYNhcoOdUQC59WXtJuQHrjyTh4/1XkFWoAwD4qJxx36BQPDAkFIFqV8nbJCIiIvtiIG5mrSEQa3fsQOo/n4NL797otHFDzQelHAe+mACY9MAtb5hHYyVQZijDnT/dieTCZNzd7W68MvSVhl8k94o5GJ/4H2AoNW/ThAJDngZumAko3SXpa2VleiN++CsZH++7gtR8c5tymYBbevrj3kGhGNHVF3LOY0xERNQmMBA3s9YQiDPf/S9yPv4YmrvuQuDrS6ofUJwDfDwS0KaYb3C7+3+SlSW8e/xdfHH2C/i7+WPrlK1wd25CeC3JNY9iH/kYKMk2b3PRADc+Agx+HHDvIEmfKzMYTfjt/FWs+zMBR+Kv3eQXrHHFnf2DMaVfMLp2kD6QExERUcthIG5mrSEQJz3+OIr37Yf/q6/A+/77qx/wwyzg/FbAuwvw2O+Ai1qSdi/mXsQ92+6BUTTi/THvY1TIKEmuC30pcPpb4M/3zaPHACBXAn3vNY9s+0ZI004VMRlafHc0GZtPpEBbZrBujwpWY0q/INwWFYggDUsqiIiIHA0DcTNrDYE4bvwt0CcnI/SrL6EaVKV+N/MC8GHFSnGP7zfX6ErAaDJi5vaZOJtzFuPDxmPlqJWSXNeGyWiuL/5zFZByrGKjYF4eevhcILR5VsAr0xux81wGfjyVhv2XsmAwXfu2iApWY0Ivf0zoFYCuHdzrN+czERER2RUDcTOzdyAWjUbE9O1nXrL59z1wCgy0PWDTo8CZirmA7/lasna/Pv81lh9bDg8nD/w49Uf4uflJdu1qRBFIOmwOxhe3X9vecZA5GHe/HZA1z/RpOUU6bD+Tjp9Pp+NYYi4qf4eE+bhhZIQfbu7mh6FdfKBScm0bIiKi1oiBuJnZOxDrU1MRN3Yc4OSEyFMnIcgrTR+WewV4f4B5WebH9gJBN0jSZnpROqb8OAWlhlK8OvRV3NXtLkmuWy9Zl4BD7wOnvwOM5unT4NMVGPoM0Pc+wMml+Zou1GH3havYeS4Df8bloLzSAh9OcgEDwrwwqJMPBoV744ZQDQMyERFRK8FA3MzsHYiLjxxF0qxZcAoLRdedO213/jQXOPEl0HUcMHOTJO2Joohn9jyD/Sn70b9Df6yduBYywQ6LWxReBY5+bL4Jr6zAvE3lBwx6HLhxDuDm3azNF+kM+DMuG/tjs7DvUhaSc0tt9stlAnoFeeLGcO+Khxd83JXN2iciIiKqGQNxM7N3IM7ftBnp//oXVMOGIfSLz6/tKEgF3utrnmbt4R1A2FBJ2tuRsAMv7nsRTjInbJy0EZ01nSW5bqPpCs3TtR3+EChINm9zcgNueBAY+hTgFd7sXRBFEQk5JfjzcjaOxefiWEKedSq3ykK8XREVrEbvYLX5OUgNL5Vzs/ePiIiovWtIXuP/dx2QPjUFAOAUEmK748/3zWE4bLhkYbhAV4A3j7wJAHg06lH7h2EAUHqYg++gR4FzW4E/3zOvgHf0Y+DoJ0DELeZp27qOBWTNsxqdIAjo5KtCJ18VHhgcBgBIzS/FXwm5OBqfi2MJubh0tQjJuaVIzi3F9jMZ1nODNa6I8HdHN3+Pioc7unZwh5szvx2JiIjsgb+BHVB5SkUg7hh8bWNRFnB8nfntkS9I1ta7x99FTlkOOqk7YU7UHMmuKwm5E9DnLiBqBnBlr/kGvMt7gNid5ocmFBjwsHnk2L0ZbwCsEKxxRXA/8zzGAFBQosfZtAKcSTU/zqYWIDGnBKn5pUjNL8Xei1nWcwUB6Ojlik6+7gj3cUOotxvCfFQIq3iby0wTERE1HwZiB6RPSQUAOHfseG3j4Q/NK74F9Qc6j5aknWMZx7Ap1lyH/NrQ1+Asb6X/6hcEoMto8yM7DvjrC+DUN0B+EhC9GPj9P0DPKeY649Chki1Qcj1qNycM7+qL4V19rdsKSvW4mFGIS1cLEXu1EBevFiL2ahFyisuto8n7a7hWgKcLQivCcaDaBf6eLtbnALULvN2cIeMqe0RERI3CGuJGsHcNcezIm2HIzET4hh/gGhVlXtTi7W6ATgvcux6IvL3JbeiMOsz4aQYStAmY0W0GXhv6mgQ9b0H6UuDsZuCvz4HU49e2+0QA/e43z07hGVj7+S0sp0iHS1eLkJhTjMTcEiTllCAxtxiJ2SX4//buPD6K8v4D+Gf23mRzQQgBckHkiBwihoAG5JKEX7SIVgX9YYktlv7QtiJFEIUIryLSItpTW/HAWupRFLVWCSlHRM7KLQhJkHAESJDcJHs+vz8mu+zmvifDft6v1zgzz/PMM9/dleS7k2eeKbc6mjxer5UQESQnx5HB7kTZiO6BRnSzGNA90IBugQZ0DzTCbODVZiIiuv7xproOpmRC7LJaceKm4QCA/rt2QhcWJj/I4t2HgJBo4JeH22V+3j8d/BNePfQqws3h+Hjaxwg2KPMAknZRcADY9zpwdANgvyqXSRogfhJw8//KD/3Qdc3ZIIQQKL5qR/73lThz5SrOXrmKi2XVuFhqxaWyalworcb3lVa05F+xWa+Vk2OLASFmPYLNegSb9Ag262rWegSbdJ7yEK9yo07DB5MQEZEq8Ka665j9vDxcQhMQAG1oqFx4/FN5nfCDdkmGc4tzsfbIWgDA00lPqzsZBuS5mO/+IzBlJfDNR8CBvwNndwO5m+XFFCK/d0PuA/re3mE34rWGJEnoVnN19+aYsHrb2J0uFJZbcbG0GpfKqj3rS2XV+L7Shu8rbLhSKS82pwtVdqdnHHNL6bUSAgw6BBq0MBu0CDDoatbyYtbrPNsBBnnbu96k18Kg08Co08Cglbc9i1bjVafhEBAiIuo0TIhVxu65oS5KvlLntF97klvCD9rcv0u4sGzXMjhcDoyPGo/JsZPb3GeXYQwCRvxIXi7nAofWAwf/AZQXAAfekZfACGDwNDk5jk7qtPHGbaHXauQb+kLNjbYTQqDC6sCVShu+r7ThSoUNpVV2lFXbUVblqFnX2q/ZLq+2wyUAu1OgtMqO0ip7J7wuyZMk+ybNNUm1VgOdVoJOq4FOI0GrkTxrvVbT6H7tY7z3tRoJWkmCJAEaSYJGU7P2LPIXFa1G3tZ4tdVqvI6TJGg1clv3cZ4+PH369i1JqFnk/+8k1Oyjps795tRTJkmST3t3hbuNux61+nTvy91KPu19YlDBvwUiotZiQqwyNq+EGABw+kv5IRWBPYDoUW3u/58n/4mDRQcRoAvAM6OfuX5/CYbfAExaCkx4BsjfKQ+nOPYxUFkoT922969ASAyQcBcw8H+AmNsArbr/uUiShCCTHkEmPWK7B7boWCEEKm1OlFbZcdXqwFWbE1dtTlTZvbY965oyu1xWaXWgyi7XVdudsDlcsDldsDlcsDrktbvMm90pYHc6UWlztufbQG10LXn2TcLlMgnwTtwbOb7eukaPbOrYxo5ruLbJn3Adcc5W9tlkv40e12ivTZyVqP2Y9BrsWDhR6TDqUPdveD/knmHCM+Wae7jEoDvb/Kf+S5WX8NLXLwEAfjHiF4gMjGxTf6qg0QJ9x8pL2m+BvK1ycvztv4DSM/LsHbv/DJhC5fmNB6XJY49NKh9G0kKSJMFi1MHSgY+mFkJ4EmWfZLl28ux0wWp3wuZ0wekScDiFvHYJOFwu332nCw7XtX2nywW703ff4RRebeQ+XQJwCXkthKgpu7bvqW+krfAqd7qE73HutjV9uNs6a44DAFHzHwG5THi9TwJo0bjx9v2cvOOrHQRvSSGixpn0CjzpthmYEKuMe8iEISoKcDmB4/+SKwa1fbjEC3tfQIW9AkPDh2LGwBlt7k91tHpgQIq82KuA3CzgxOfAyS+Aq98DR96XF60BiBsrXzke+D9ASFTTfVOTJEmCUaeFUdd1xnCrhRDXkmZ3wiyXX0uoUU+Zuz2Aa0l2Tf21tnWTcCE3qlPmnbg3FGfDdS191c07VjQSUVPnbPVrafS41p6xqdfZ2uP4Jaa1OCVB63TVPzwzIVYZ7zHEOLdP/hO/MVi+GawN/nPmP8g6kwWdpEPGrRnQdqEbyxShN8tjshN+IH/xOLsXOPEZ8O2/gSt5QN5/5OXfvwLCBwD9xstL3Bj5Jj2iTiRJktcvmS7624aIqAtjQqwytppZJvR9ooDj6+TCAVMAXesfmlFuK8fzu58HAMwaPAsDuw1sc5zXFY1WfhR27K1Ayq+ByznyVHcn/i1/Kbl8Ul72/hWQtECfW64lyFGJXXZKNyIiIpIxIVYRZ3k5XKWlAABDn97A517TrbXBmq/XoLCqEDFBMfjZTT9ra5jXv/D+wJgn5KWqGDi9Q350dN5W+erxub3ykv0bQGuUp32LGSXf9Bg9CggMb+IERERE1JmYEKuIe7iENiwMmvJTQEk+oDMDN0xqdZ97L+zFP0/+EwDw3G3PwaQztUusfsMcdm1oBSA/LvrUdjlB/m47UFkkz3l8dve1Y7rfAESPlpPkqCQ5wfb3ISpEREQKYkKsIj5Trrlnl7hhEmBo2RRablWOKmTslB/J/MCABzAycmS7xOnXQmOAEQ/LixDAlVPAmZqE+Mwe4PIJ4PtceTn4jnyMPgCIHAr0ugnoNVxe9xik+mneiIiI1IK/cVXEZ8q1k1/IhYPuanV/fzrwJ5yrOIeeAT0x75Z57REieZMkoHu8vNz8v3LZ1SvyDXruBPnCIcBeCZzdIy9uOhPQc7CcKPdIACIGyUmypWfXvUWXiIhIpZgQq4hnyrXICODSUbmw3/hW9XWk6Aj+dvxvAIClty6FxWBpjxCpKQHdgIFT5AWQZ7D4PldOjAsOyusLhwBbOXD+a3nxZgoFIhLk5LjHIDlR7hYPBPdpl8d2ExER+SMmxCpiv3ABAKA3W4Eql/zn+eBeLe/HacfSnUvhEi7c2e9O3B7VtinbqA00WqDHQHkZ9oBc5nIBxd8BBQeAwmNA4bdA0bdyWXUJcGaXvHjTGoGwOKBbX6Bbv5qlLxDWV/7/RKvv7FdGRESkGkyIVcRRVAQA0LkuyQXRo1vVz2tHXkNuSS66mbph4ciF7RUetReN5tpQC2/2KnnKt6ITQNFxOVG+fAIozgecVnn78om6/UkawBIpP0AkpI98NTkkqmbdBwiOkh/9zSvMRETkp5gQq4jj8mUAgK46Ty6ITmpxHyeLT+K1I68BAJ5OehphprB2i486mN4M9BomL96cDqDsHHDlO/kmviun5O3imn1HNVBeIC/nGuhbawCCeslJsqUHEBgBWCLkRNm9dm+38iZOIiKirooJsUoIIa4lxOXHAD2AmJZdIXa6nMj4KgMOlwPjo8cjNS61AyKlTqfVycMlwuKA+Am+dS6X/DTD0vNy0lx6Hig7D5SevbZdfhFw2uRp/Erymz6fPvBa0hwYLo9rNocB5pq1Z9+7LIRTyxERUZfFhFglnCUlgN0OANBpyuXHNUfc2KI+3jn+Do5+fxRB+iAsGb0EEmcruP5pNEBQpLzglvrbOO1A+QU5QS4vACqK5PmTKwtrtgvl/YoiwFElz4pRXAkUn25ZLMYQwBQMGCyA0eK7bmjbaAEMQfJVaXeZziRfLWeCTURE7YQJsUq4xw9rLSZIWsiPBG5BQpBflo8/HvgjAGB+4nxEBER0RJikRlq9fONdaEzj7YQAbBVAhTtBLgSqrshP66sqkdfVJV77Ndu2cvl4a6m8tBeNXk6MdUb5ATV607Vk2XutM9XU1bTRGuWr6lpDzaK/tq2pr1zv20arl8/t2dbJ/xY1OvnR3Rotp8YjIlIZJsQq4bmhLrDmF20Lbqizu+xYlL0I1c5qjIochXv739sRIdL1TpIAY5C81L7hrzFOO1BdKifI1aVygmyrBKwV8ra1Qk603Wvv7dpljqpr/brsgNUOWNv9lbadpPFNkDXamm3v5FlTK5HWyVf0ffa119pJGnmBdG1bkmqW+so1tY6R6tahVrvm9Iean0GepF/yWtVTV6dde9bBq66huNqzrrXaeLzSX7Da5fxKvwf+fv4uRNICg9KUjqIOJsQq4UmIdTUJQcyoZh/754N/xtHvjyLYEIxfj/k1h0pQ59Lq5bHGgeFt78vllG8SdFjlWTcc1XXXjmrAXl0zvKOetcsuj5l2eq/d2zX7jbbxagtRf5zCVVNPREQ+dGbg2YtKR1EHE2KVcLpvqNNflb9d9Uls1nF7L+zF60deBwBk3JqByMDIDouRqMNptPJ44q4w04UQcoLucgDC6bXtktd16pw12476973beY5zyDdGCqd8PuGSF3hte8pFrXJXA8eIRvpqrD8hx+F+7fKG176oW+f5vlBfXX19tLaudjs0UtdEzE312WJtPF71528Hqn8PlD5/F6MzKB1BvZgQq4TnCrHJCUQOkW8wakKptRRP73gaAgL39r8XKXEpHR0mkf+QpJqxyPwxSkSkdpyJXyUcRfIVYq3Z1azxw0IILNu1DIVXCxEbHMsHcBARERE1gAmxSvhcIY5qerjEhpwN2Jy/GTqNDqtuX4UAfUBHh0hERESkSkyIVcKTEJtdQM8hjbbdf2k/VuxZAQD4+c0/x+Dugzs8PiIiIiK1YkKsEo6iQgCALlALhPdvsN258nN4YusTcLgcmBw7GemD0zspQiIiIiJ1YkKsAq6qKrgqrwIAdFHx8jRW9aiwVeDnW36OYmsxErol4NfJv4ZG4kdMRERE1BhmSyrgHi4haV3QxAyrv43LgQXZC5BbkosIcwT+MPEPHDdMRERE1AxMiFXA4Z6D2OSCFDm0Tr0QAr/d91vsOL8DJq0Jv5/4e/QM7NnZYRIRERGpkl8lxPv27UNaWhpCQ0MRGBiI0aNH4/3331c6rCY5Cr1uqIv0vaHO4XJg2a5lWP/tegDAijErMDicN9ERERERNZffzCi/detWpKamwmQyYcaMGQgKCsKGDRswffp0nD17FvPnz1c6xAY5Lp0HUDPlmtcME1ftV/FU9lPYfm47NJIGz4x6hg/fICIiImohv0iIHQ4HHn30UWg0GmRnZ2P48OEAgKVLlyIpKQmLFy/Gfffdh9jYWGUDbYDz3EkAgNZiBgK6AQCKq4vx+H8ex+HLh2HUGvGb23+DiTETlQyTiIiISJX8YsjEli1bkJeXh4ceesiTDANASEgIFi9eDJvNhnXr1ikXYBOcBacBANru4bA5bXj7m7cxdeNUHL58GCHGEKxNWctkmIiIiKiV/OIK8bZt2wAAKSl1hxOkpqYCALZv396ZIbVIUeE56ADsCNXiLx/9AAWVBQCAfiH98NKEl9AvpJ+yARIRERGpmF8kxDk5OQCA/v3rPtAiMjISFovF06Y+VqsVVqvVs19WVtb+QTbifGkxYgFkaotQUKlBhDkCc4fPxd033A2dxi8+QiIiIqIO4xfZVGlpKQB5iER9goODPW3qs3LlSixbtqxDYmsOR6QFBfZyDIm/BVOS70VqXCrMOrNi8RARERFdT/wiIW6rp59+Gk8++aRnv6ysDNHR0Z12/rve3gMAmNRpZyQiIiLyH36RELuvDDd0FbisrAxhYWENHm80GmE0GjskNiIiIiJSll/MMuEeO1zfOOGLFy+ioqKi3vHFRERERHT984uEeNy4cQCAzMzMOnWbNm3yaUNERERE/kUSQgilg+hoDocDAwcOxPnz57F7927PXMSlpaVISkrC6dOnceLECcTFxTWrv7KyMoSEhKC0tBTBwcEdFzgRERERtUpL8jW/GEOs0+mwdu1apKam4vbbb/d5dHN+fj5Wr17d7GSYiIiIiK4vfpEQA8CECROwY8cOZGRk4L333oPdbsfQoUOxatUqTJ8+XenwiIiIiEghfjFkor1xyAQRERFR19aSfM0vbqojIiIiImoIE2IiIiIi8mtMiImIiIjIrzEhJiIiIiK/xoSYiIiIiPwaE2IiIiIi8mtMiImIiIjIrzEhJiIiIiK/xoSYiIiIiPwaE2IiIiIi8mtMiImIiIjIr+mUDkCNhBAA5GdkExEREVHX487T3HlbY5gQt0J5eTkAIDo6WuFIiIiIiKgx5eXlCAkJabSNJJqTNpMPl8uFgoICBAUFQZKkDj9fWVkZoqOjcfbsWQQHB3f4+aj98TNUP36G6sbPT/34GapfZ3+GQgiUl5ejd+/e0GgaHyXMK8StoNFoEBUV1ennDQ4O5g8BleNnqH78DNWNn5/68TNUv878DJu6MuzGm+qIiIiIyK8xISYiIiIiv8aEWAWMRiMyMjJgNBqVDoVaiZ+h+vEzVDd+furHz1D9uvJnyJvqiIiIiMiv8QoxEREREfk1JsRERERE5NeYEBMRERGRX2NCTERERER+jQlxF7Zv3z6kpaUhNDQUgYGBGD16NN5//32lw6JmOH/+PF5++WWkpKQgJiYGBoMBkZGR+OEPf4g9e/YoHR610qpVqyBJEiRJwu7du5UOh1rgo48+wuTJk9G9e3eYTCb07dsXDz74IM6ePat0aNQIIQQ+/PBDTJgwAb169UJAQAAGDhyIOXPm4NSpU0qHR17eeecdzJkzB4mJiTAajZAkCW+99VaD7cvKyvDkk08iNjYWRqMRcXFxWLBgASoqKjovaC+cZaKL2rp1K1JTU2EymTBjxgwEBQVhw4YNyM/Px+rVqzF//nylQ6RGLFq0CKtWrUJ8fDzGjx+PHj16ICcnBxs3boQQAuvXr8f06dOVDpNa4OjRo0hMTIROp0NlZSV27dqF0aNHKx0WNUEIgZ/97Gf461//ivj4eKSmpiIoKAgFBQXYvn07/v73v2PMmDFKh0kNmD9/PtasWYNevXrh7rvvRnBwMA4dOoTMzExYLBbs3LkTQ4YMUTpMAhAXF4f8/HyEh4cjMDAQ+fn5ePPNN5Genl6nbWVlJcaMGYODBw8iJSUFN998Mw4cOIDMzEyMHDkS2dnZMJlMnfsCBHU5drtdxMfHC6PRKA4cOOApLykpEQMGDBAGg0GcPn1auQCpSRs2bBDbtm2rU56dnS30er0ICwsT1dXVCkRGrWGz2cSIESPEqFGjxMyZMwUAsWvXLqXDomZ4+eWXBQAxd+5c4XA46tTb7XYFoqLmuHDhgtBoNCI2NlaUlJT41K1Zs0YAEI888ohC0VFtmzdv9uQmK1euFADEm2++WW/bpUuXCgBi4cKFPuULFy4UAMTzzz/f0eHWwSETXdCWLVuQl5eHhx56CMOHD/eUh4SEYPHixbDZbFi3bp1yAVKT7r33XowbN65O+dixYzFhwgQUFxfjyJEjCkRGrbFixQp88803eOONN6DVapUOh5qpqqoKy5YtQ79+/fC73/2u3s9Op9MpEBk1x+nTp+FyuZCcnIyQkBCfurvuugsAUFRUpERoVI877rgDsbGxTbYTQmDt2rWwWCxYsmSJT92SJUtgsViwdu3ajgqzQUyIu6Bt27YBAFJSUurUpaamAgC2b9/emSFRO9Lr9QD4i1gt9u/fjxUrViAjIwM33nij0uFQC2RmZqK4uBjTpk2D0+nEhx9+iBdeeAGvvvoqcnNzlQ6PmtC/f38YDAZ89dVXKCsr86n717/+BQCYNGmSEqFRG+Tk5KCgoADJyckIDAz0qQsMDERycjJOnTrV6eP7+Ru5C8rJyQEg/zCoLTIyEhaLxdOG1OXMmTPIyspCr169MHToUKXDoSZYrVb86Ec/wvDhw/HUU08pHQ610Ndffw0A0Gq1GDZsGE6ePOmp02g0mDdvHlavXq1UeNSE7t2744UXXsD8+fMxaNAgnzHEW7Zswdy5c/H4448rHSa1UGM5jrt806ZNyMnJQXR0dKfFxYS4CyotLQWAOn8icgsODva0IfWw2+14+OGHYbVasWrVKv7pXQWWLl2KnJwcfP311/y8VKiwsBAAsGbNGowYMQJ79+5FQkICDhw4gJ/+9Kd48cUXER8fj//7v/9TOFJqyLx589CnTx/Mnj0br776qqd8zJgxeOihh/iXNhVqTo7j3a6zcMgEUSdwuVxIT09HdnY2Hn30UTz88MNKh0RN2LVrF1avXo1nn32Wd7GrlMvlAgAYDAZs3LgRI0eOhMViwdixY/HBBx9Ao9HgxRdfVDhKaszy5csxc+ZMLF68GGfPnkV5eTm+/PJLVFdXY/z48fjkk0+UDpGuE0yIuyD3t6aGvh2VlZU1+M2Kuh6Xy4Uf//jHWL9+PWbOnOlzlYO6JofDgVmzZmHYsGFYtGiR0uFQK7l/TiYmJqJ3794+dUOGDEG/fv2Ql5eHkpISBaKjpmRlZSEjIwOPP/44Fi1ahKioKFgsFowZMwaffvop9Ho9pyBVoebkON7tOgsT4i7IPa6mvnHCFy9eREVFRYNjb6hrcblceOSRR7Bu3To8+OCDeOutt6DR8J9dV1dRUYGcnBwcPHgQBoPB8zAOSZI8M7zceuutkCQJGzduVDZYatDAgQMBAKGhofXWu8urqqo6KSJqic8//xwAMGHChDp1kZGRGDRoEHJzcxV7kAO1TmM5jnd5Z+c5HHzTBY0bNw4rV65EZmYmZsyY4VO3adMmTxvq2tzJ8Ntvv43p06fjb3/7G8ehqoTRaMRPfvKTeuuys7ORk5ODqVOnokePHoiLi+vc4KjZ3InU8ePH69TZ7Xbk5uYiMDAQPXr06OzQqBlsNhuAhqdWKyoqgkaj8czcQ+rQv39/9O7dG1999RUqKyt9ZpqorKzEV199hb59+3bqDXUA+GCOrshut4t+/fo1+mCO7777TrH4qGlOp1PMmjVLABD3338/J/+/jrg/Vz6YQx1SUlIEAPHaa6/5lC9fvlwAEDNnzlQoMmrKP/7xDwFADB48uM6DOV555RUBQCQnJysUHTVGjQ/m4KObuyg+ulndnnvuOSxbtgwWiwW//OUv670Tetq0aT4PXiF1SE9Px7p16/joZpXIy8vDbbfdhsLCQtx5550YNGgQDhw4gC1btiA2Nha7d+9GZGSk0mFSPZxOJyZOnIjs7GxERERg6tSpCA0Nxf79+7FlyxaYzWZs27YNSUlJSodKANauXYsdO3YAAI4cOYL9+/cjOTkZN9xwAwB5ZpDZs2cDkK8EJycn49ChQ0hJScGIESOwf/9+z6Obt2/fDrPZ3LkvoNNTcGq2PXv2iClTpojg4GBhNptFUlKSePfdd5UOi5rBfRWxsaWhb87UtfEKsfqcOXNGpKeni8jISKHX60V0dLR47LHHxKVLl5QOjZpQXV0tVq5cKW6++WYREBAgdDqd6NOnj5g5c6Y4duyY0uGRl6Z+782aNcunfUlJiXjiiSdEdHS00Ov1IiYmRsyfP1+UlZUpEj+vEBMRERGRX+Pt7kRERETk15gQExEREZFfY0JMRERERH6NCTERERER+TUmxERERETk15gQExEREZFfY0JMRERERH6NCTERERER+TUmxERERETk15gQE5HfiouLgyRJrVpOnz5dbx/Xm5ycHBiNRkiSBK1Wi2PHjnXaudPT033e223btrVb35cvX4bFYvH0nZ2d3W59E5H6MCEmIqIGzZs3DzabDQBw//3348Ybb1Q4ovYRHh6Oxx57zLP/i1/8Ai6XS8GIiEhJOqUDICJSSlpaGgoLC33Kjh07huPHj3v2Y2NjkZiYWOfYwMDABvu4XmRlZeGzzz7z7D/zzDOdev6RI0eioqLCs9+jR4927f9Xv/oVXn75ZdhsNhw6dAhvvPEGZs+e3a7nICJ1kIQQQukgiIi6iueeew7Lli3z7M+aNQtvvfWWcgEpaMqUKdi0aRMA4JZbbsF///tfhSNqf/fccw82btwIAEhISOjUISFE1HVwyAQRURs0NoZ427ZtPnXp6ek4f/48Zs+ejd69e8NsNmPYsGH4y1/+4jlm3759mDp1Krp16waz2YzExESsX7++wfM7HA6sX78eU6dORVRUFEwmE4KCgjB06FAsWLAA586da9XrOnnyJDIzMz37Dz/8cLNev9PpxO9//3sMGzYMZrMZvXv3xpw5c3D58mUAQFlZGZ566in07dsXRqMRMTExeOKJJ1BWVlan76bGEHvXxcXFweVyYe3atRg9ejQsFgssFgvGjh2Lzz//vMHXOXPmTM/28ePHkZWV1ZK3iYiuF4KIiDwyMjIEAM8ya9asRtvHxsb6tPe2detWn7rbbrtNRERE+JS5lwULFogPPvhA6PX6euv/8Ic/1Dl3QUGBSEpKqre9ewkKChIff/xxi9+H559/3qefb775plmvf9q0afXGER8fL3Jzc8WAAQPqrR81apSw2+0+fc+aNcunzdatW33qvet69uwpUlJS6u1bkiTx4Ycf1ht/SUmJkCTJ03bOnDktfq+ISP14hZiIqJPs3LkTRUVFSEpKwujRo33qXnzxRc9V2LFjx9a5eW3JkiWoqqry7NvtdqSlpWHv3r2esqioKKSlpSE5ORkajfzjvby8HNOnT8ehQ4daFKv3ldLQ0FAkJCQ067iNGzciKioKKSkpsFgsnvK8vDwMGzYMJ0+exIABAzBp0iRotVpP/Z49e/DBBx+0KEZvly5dQmZmJnr16oXJkycjPDzcUyeEwMKFC+s9LiQkxOe95hViIv/EhJiIqBO9/vrr2LNnD3bt2oX777/fU+5yuWC1WvHFF18gOzsbhw8fxogRIzz1JSUl2Ldvn2f/7bffxsGDBz37c+fORX5+Pj777DPs2LEDX375pWcIR3V1NZ599tkWxek9XjghIaHZU8pNnjwZubm52LRpE9577z2fuqtXryI9PR3ffvstsrKysHr1ap/6zZs3tyjG2qZMmYK8vDxkZmbi6NGjiIiI8NTl5OTgzJkz9R43dOhQz3ZeXh6Ki4vbFAcRqQ9nmSAi6iTx8fF45JFHPPvJyck+V0UnTJiAiRMnAgC0Wi3Gjx+P/fv3e+rPnz/v2f7oo498+s7JycEDDzzgU2YwGGC1WgHIyabVaoXRaGwyzqqqKp8xvd5XW5uyZMkSzzmSk5Pr1C9fvtyTXE+aNMmnzvv1tcZLL70Es9kMAOjZsydGjRqFTz/91Kf/mJiYOsfVfn2XLl1CWFhYm2IhInVhQkxE1EmGDBnisx8UFNSiendyCwDfffedT11TV1etVisKCgrQt2/fJuOsfYU0ODi4yWPcvK+21o4/ODgY0dHRDdZ7v76WslgsGDRokE9ZSEhIs/qv/fquXLnS6jiISJ04ZIKIqJOEhob67LvH+bp19FXJysrKZrWrHWd9M0A059jOfH3du3evU+Y9RrkxpaWlPvu8Okzkf5gQExGpUO0rvbt374YQotGl9hXohgQEBPhcvXVPmXa9qv36IiMjFYqEiJTChJiISIWmTp3qsz9v3rx6n5iXm5uLVatWYfny5S3q/5ZbbvFsHzt2DOI6fobTkSNHPNvx8fG8Qkzkh5gQExGpUHp6OgYPHuzZ37VrF2JiYpCcnIxp06Zh4sSJ6NOnD/r3749Fixbh1KlTLerffXMfIA8p8H6c9fWkpKTE57XdcccdCkZDREphQkxEpEIGgwFffPEFEhMTPWVWqxU7d+7Exx9/jK1bt6KgoMBTp9O17B7q++67z2ff+6l115OsrCyfq9+1XzcR+QcmxEREKhUVFYXdu3fj3XffxT333IOYmBiYTCbo9XqEh4cjKSkJjz32GD755BO88sorLeo7ISEBkydP9uy/88477R1+l+D9uhISEniFmMhPSeJ6HhhGRESttnnzZqSkpHj2Dx8+7DOtmtoVFhYiKioKdrsdAPDaa69h9uzZCkdFRErgFWIiIqrX5MmTkZaW5tlfsWKFgtG0v9WrV3uS4ZtuusnnoSlE5F94hZiIiBqUk5ODIUOGwGazQaPR4MiRI7jxxhuVDqvNLl++jLi4OM/czNu2bcO4ceMUjoqIlMKEmIiIiIj8GodMEBEREZFfY0JMRERERH6NCTERERER+TUmxERERETk15gQExEREZFfY0JMRERERH6NCTERERER+TUmxERERETk15gQExEREZFf+3/LLrXSMUL14gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function that creates pyomo model for the dynamic reaction system\n",
    "def create_dynamic_model(F, CAF, k1, k2, k3):\n",
    "    \"\"\"Creates reactor model\n",
    "    Argument:\n",
    "        F: feed volumetric flow rate in m^3/min\n",
    "        CAF: feed concentration of A, gmol/m^3\n",
    "        k1: rate constant for A to B, min^-1\n",
    "        k2: rate constant for B to C, min^-1\n",
    "        k3: rate constant for 2A to D, m^3/(gmol.min)\n",
    "\n",
    "    Returns:\n",
    "        model: pyomo model\"\"\"\n",
    "\n",
    "    # Create a Pyomo model\n",
    "    model = pyo.ConcreteModel()\n",
    "\n",
    "    # Define time as a continuous set\n",
    "    model.t = dae.ContinuousSet(bounds=(0, 10))  # time from 0 to 10 minutes\n",
    "\n",
    "    # Define variables for concentrations and reactor volume\n",
    "    model.CA = pyo.Var(model.t, within=pyo.NonNegativeReals) # concentration of A\n",
    "    model.CB = pyo.Var(model.t, within=pyo.NonNegativeReals) # concentration of B\n",
    "    model.CC = pyo.Var(model.t, within=pyo.NonNegativeReals) # concentration of C\n",
    "    model.CD = pyo.Var(model.t, within=pyo.NonNegativeReals) # concentration of D\n",
    "    model.V = pyo.Var(within=pyo.NonNegativeReals, bounds=(0, 100), initialize=0) # reactor volume\n",
    "\n",
    "    # Define derivatives of concentrations\n",
    "    model.dCA_dt = dae.DerivativeVar(model.CA, wrt=model.t)\n",
    "    model.dCB_dt = dae.DerivativeVar(model.CB, wrt=model.t)\n",
    "    model.dCC_dt = dae.DerivativeVar(model.CC, wrt=model.t)\n",
    "    model.dCD_dt = dae.DerivativeVar(model.CD, wrt=model.t)\n",
    "\n",
    "    # CA differential equation\n",
    "    def ca_rate_ode(m, t):\n",
    "        return m.dCA_dt[t] == (F/m.V)*CAF - (F/m.V)*m.CA[t] - k1*m.CA[t] - 2*k3*m.CA[t]**2\n",
    "    model.CA_ode = pyo.Constraint(model.t, rule=ca_rate_ode)\n",
    "\n",
    "    # CB differential equation\n",
    "    def cb_rate_ode(m, t):\n",
    "        return m.dCB_dt[t] == -(F/m.V)*m.CB[t] + k1*m.CA[t] - k2*m.CB[t]\n",
    "    model.CB_ode = pyo.Constraint(model.t, rule=cb_rate_ode)\n",
    "\n",
    "    # CC differential equation\n",
    "    def cc_rate_ode(m, t):\n",
    "        return m.dCC_dt[t] == -(F/m.V)*m.CC[t] + k2*m.CB[t]\n",
    "    model.CC_ode = pyo.Constraint(model.t, rule=cc_rate_ode)\n",
    "\n",
    "    # CD differential equation\n",
    "    def cd_rate_ode(m, t):\n",
    "        return m.dCD_dt[t] == -(F/m.V)*m.CD[t] + k3*m.CA[t]**2\n",
    "    model.CD_ode = pyo.Constraint(model.t, rule=cd_rate_ode)\n",
    "\n",
    "    # Initial conditions\n",
    "    def init_conditions(m):\n",
    "        yield m.CA[0] == CAF\n",
    "        yield m.CB[0] == 0\n",
    "        yield m.CC[0] == 0\n",
    "        yield m.CD[0] == 0\n",
    "    model.init_conditions = pyo.ConstraintList(rule=init_conditions)\n",
    "\n",
    "    # Objective: Maximize concentration of C at t = 10 minutes\n",
    "    def objective_function(m):\n",
    "        return m.CC[10]\n",
    "    model.objective = pyo.Objective(rule=objective_function, sense=pyo.maximize)\n",
    "\n",
    "    return model\n",
    "\n",
    "# Parameters from data\n",
    "conc_A_feed = 10000  # mol/m^3\n",
    "k1 = 5/6  # min^-1\n",
    "k2 = 5/3  # min^-1\n",
    "k3 = 1/6000  # m^3/mol/min\n",
    "feed_flowrate = 1 # m^3/min\n",
    "\n",
    "# reactor model object\n",
    "reactor_model = create_dynamic_model(feed_flowrate, conc_A_feed, k1, k2, k3)\n",
    "\n",
    "# Discretize the model using collocation\n",
    "discretizer = pyo.TransformationFactory('dae.collocation')\n",
    "discretizer.apply_to(reactor_model, nfe=20, ncp=10)\n",
    "\n",
    "# Solve the model\n",
    "solver = pyo.SolverFactory('ipopt')\n",
    "results = solver.solve(reactor_model, tee=True)\n",
    "\n",
    "# print optimal reactor volume\n",
    "optimal_reactor_volume = pyo.value(reactor_model.V) # m^3\n",
    "print(f\"Optimal reactor volume is: {round(optimal_reactor_volume, 2)} m^3\")\n",
    "\n",
    "# Extract the concentration of B and C at t = 10 minutes\n",
    "optimal_concentration_C = pyo.value(reactor_model.CC[10])*10**(-3) # kmol/m^3\n",
    "concentration_B = pyo.value(reactor_model.CB[10])*10**(-3) # kmol/m^3\n",
    "print(f\"Maximum Concentration of C at optimal reactor volume is: {round(optimal_concentration_C, 2)} kmol/m^3\")\n",
    "print(f\"Concentration of B at optimal reactor volume is: {round(concentration_B, 2)} kmol/m^3\")\n",
    "\n",
    "# Extract results for plotting\n",
    "time = [t for t in reactor_model.t] # minutes\n",
    "CA_values = [(pyo.value(reactor_model.CA[t])*10**(-3)) for t in reactor_model.t] # kmol/m^3\n",
    "CB_values = [(pyo.value(reactor_model.CB[t])*10**(-3)) for t in reactor_model.t] # kmol/m^3\n",
    "CC_values = [(pyo.value(reactor_model.CC[t])*10**(-3)) for t in reactor_model.t] # kmol/m^3\n",
    "CD_values = [(pyo.value(reactor_model.CD[t])*10**(-3)) for t in reactor_model.t] # kmol/m^3\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(time, CA_values, label='$\\mathrm{C_{A}}$')\n",
    "plt.plot(time, CB_values, label='$\\mathrm{C_{B}}$')\n",
    "plt.plot(time, CC_values, label='$\\mathrm{C_{C}}$')\n",
    "plt.plot(time, CD_values, label='$\\mathrm{C_{D}}$')\n",
    "plt.xlabel('Time (min)', fontsize=16,fontweight='bold')\n",
    "plt.ylabel('Concentration (kmol/$\\mathbf{m^3}$)', fontsize=16,fontweight='bold')\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.title('Concentration Profiles Over Time', fontsize=16,fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extension 2: Dynamic Modeling and Multi-Objective Optimization to Maximize Species B and C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, species B and C are the desired products, and we are considering the dynamic state of the system (most often, industrial operations are dynamic). The goal of this work is to determine the volume of the reactor that will maximize the concentration of species B and C in the effluent after 10 minutes. Furthermore, for economic reasons, the reactor volume should not exceed $\\mathrm{100\\,m^3}$.\n",
    "\n",
    "The mole balance for each of the species in this system under dynamic state is given by:\n",
    "\n",
    "$$\\begin{align*}\n",
    "&\\frac{dC_A}{dt}=\\frac{F}{V}C_{Af}-\\frac{F}{V}C_A-k_1C_A-2k_3C_A^2 \\\\\n",
    "& \\frac{dC_B}{dt}=-\\frac{F}{V}C_B+k_1C_A-k_2C_B\\\\\n",
    "& \\frac{dC_C}{dt}=-\\frac{F}{V}C_C+k_2C_B \\\\\n",
    "& \\frac{dC_D}{dt}=-\\frac{F}{V}C_D+k_3C_A^2\n",
    "\\end{align*}$$\n",
    "\n",
    "Where $C_A$, $C_B$, $C_C$ and $C_D$ are the concentrations of species A, B, C and D, respectively. $F$ is the feed flow rate, $C_{Af}$ is the concentration of species A in the feed, $k_1$, $k_2$ and $k_3$ are the rate constants of reaction 1 (species A to B), reaction 2 (species B to C) and reaction 3 (species A to D), respectively.\n",
    "\n",
    "The data for the system are:\n",
    "$$\\begin{align*}\n",
    "C_{Af}=10^4\\,\\mathrm{\\frac{mol}{m^3}},\\quad{k_1=\\frac{5}{6}\\,\\mathrm{min^{-1}}},\\quad{k_2=\\frac{5}{3}\\,\\mathrm{min^{-1}}},\\quad{k_3=\\frac{1}{6000}\\,\\mathrm{\\frac{m^3}{mol\\cdot min}}},\\quad{F=1\\,\\mathrm{\\frac{m^3}{min}}}\n",
    "\\end{align*}$$\n",
    "\n",
    "The data was obtained from Hart et al (2017)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization Model Formulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mathematically, we want to solve the following optimal reactor volume problem:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\max_{V} \\quad & (C_B(t=10),\\, C_C(t=10)) \\\\\n",
    "\\mathrm{s.t.} \\quad & \\frac{dC_A}{dt}=\\frac{F}{V}C_{Af}-\\frac{F}{V}C_A-k_1C_A-2k_3C_A^2 \\\\\n",
    "& \\frac{dC_B}{dt}=-\\frac{F}{V}C_B+k_1C_A-k_2C_B \\\\\n",
    "& \\frac{dC_C}{dt}=-\\frac{F}{V}C_C+k_2C_B \\\\\n",
    "& \\frac{dC_D}{dt}=-\\frac{F}{V}C_D+k_3C_A^2 \\\\\n",
    "& V \\leq 100 \\\\\n",
    "& C_A(t=0) = 10000, ~~ C_B(t=0) = 0 \\\\\n",
    "& C_C(t=0) = 0, ~~ C_D(t=0) = 0 \\\\\n",
    "\\end{align*}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Degrees of Freedom Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the optimization model formulated above:\n",
    "\n",
    "Number of variables = 5\n",
    "\n",
    "Number of equality constraints = 4\n",
    "\n",
    "Therefore, the degrees of freedom = 5 - 4 = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted Problem Pyomo Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters\n",
    "conc_A_feed = 10000  # mol/m^3\n",
    "k1 = 5 / 6  # min^-1\n",
    "k2 = 5 / 3  # min^-1\n",
    "k3 = 1 / 6000  # m^3/mol/min\n",
    "feed_flowrate = 1  # m^3/min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ipopt 3.13.2: \n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "\n",
      "This version of Ipopt was compiled from source code available at\n",
      "    https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n",
      "    Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n",
      "    Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n",
      "\n",
      "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n",
      "    for large-scale scientific computation.  All technical papers, sales and\n",
      "    publicity material resulting from use of the HSL codes within IPOPT must\n",
      "    contain the following acknowledgement:\n",
      "        HSL, a collection of Fortran codes for large-scale scientific\n",
      "        computation. See http://www.hsl.rl.ac.uk.\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.13.2, running with linear solver ma27.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:    12619\n",
      "Number of nonzeros in inequality constraint Jacobian.:        0\n",
      "Number of nonzeros in Lagrangian Hessian.............:     1006\n",
      "\n",
      "Total number of variables............................:     1609\n",
      "                     variables with only lower bounds:      804\n",
      "                variables with lower and upper bounds:        1\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:     1608\n",
      "Total number of inequality constraints...............:        0\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0 -9.9999900e-03 1.00e+04 1.93e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1 -6.9106248e-03 1.00e+04 1.72e+00  -1.0 1.06e+04    -  9.90e-07 1.71e-06f  2\n",
      "   2 -5.1191593e-03 1.00e+04 1.95e+00  -1.0 1.03e+04    -  2.69e-06 1.96e-06f  2\n",
      "   3 -4.7030315e-03 1.00e+04 2.43e+00  -1.0 1.01e+04    -  4.71e-06 1.37e-06f  3\n",
      "   4 -5.5812242e-03 1.00e+04 2.87e+00  -1.0 1.00e+04    -  2.41e-06 2.79e-06f  2\n",
      "   5 -2.7817761e-02 1.00e+04 4.64e+00  -1.0 1.00e+04    -  1.43e-06 1.31e-05f  1\n",
      "   6 -3.6134940e-02 1.00e+04 7.04e+00  -1.0 1.00e+04    -  1.22e-05 4.04e-06f  1\n",
      "   7 -3.9083441e-02 1.00e+04 2.17e+02  -1.0 1.00e+04    -  1.66e-05 1.43e-06f  1\n",
      "   8 -2.0478875e+03 3.18e+04 1.32e+07  -1.0 1.00e+04    -  1.93e-05 9.90e-01f  1\n",
      "   9 -2.0482528e+03 3.18e+04 1.32e+07  -1.0 3.25e+04    -  5.60e-05 4.99e-04f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10 -2.0488906e+03 3.17e+04 1.32e+07  -1.0 3.25e+04    -  5.56e-04 8.72e-04f  1\n",
      "  11 -2.7715600e+03 3.11e+03 2.76e+06  -1.0 3.25e+04    -  1.45e-03 9.90e-01f  1\n",
      "  12 -2.7705317e+03 3.11e+03 2.75e+06  -1.0 1.81e+03    -  4.95e-01 1.85e-03h  1\n",
      "  13 -2.4623362e+03 1.38e+03 1.77e+06  -1.0 1.81e+03    -  5.56e-01 5.56e-01h  1\n",
      "  14 -2.3037964e+03 7.95e+02 1.25e+06  -1.0 8.25e+02    -  8.89e-01 4.16e-01h  1\n",
      "  15 -2.3030278e+03 7.93e+02 1.25e+06  -1.0 5.34e+02    -  8.54e-01 2.93e-03h  1\n",
      "  16 -2.3030202e+03 7.93e+02 3.61e+06  -1.0 5.33e+02    -  1.31e-01 2.93e-05h  1\n",
      "  17 -2.3030201e+03 7.93e+02 2.46e+06  -1.0 5.14e+02    -  3.40e-09 1.70e-07f  2\n",
      "  18 -2.3029949e+03 7.92e+02 1.85e+06  -1.0 4.66e+02    -  2.38e-08 2.40e-04f  1\n",
      "  19 -2.1978050e+03 1.29e+01 2.49e+06  -1.0 4.66e+02    -  2.37e-04 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20 -2.1854004e+03 8.65e-03 9.47e+04  -1.0 2.24e+01    -  9.90e-01 1.00e+00h  1\n",
      "  21 -2.1853877e+03 2.05e-08 9.54e+02  -1.0 2.19e-02    -  9.90e-01 1.00e+00f  1\n",
      "  22 -2.1853883e+03 1.67e-09 1.64e+01  -1.0 1.07e-03    -  9.83e-01 1.00e+00f  1\n",
      "  23 -2.1853906e+03 2.02e-08 3.84e+06  -2.5 3.74e-03    -  6.04e-01 1.00e+00f  1\n",
      "  24 -2.1867164e+03 7.06e-03 3.83e+06  -2.5 2.20e+00    -  4.26e-03 1.00e+00f  1\n",
      "  25 -2.2344145e+03 1.31e+01 7.12e+05  -2.5 7.90e+01  -4.0 8.14e-01 1.00e+00f  1\n",
      "  26 -2.4189755e+03 4.72e+02 4.52e+05  -2.5 3.02e+02    -  3.66e-01 1.00e+00f  1\n",
      "  27 -2.3520273e+03 2.24e+02 3.90e+01  -2.5 1.05e+02  -4.5 1.00e+00 1.00e+00h  1\n",
      "  28 -2.3176373e+03 8.53e+01 1.79e+01  -2.5 6.07e+01  -4.1 1.00e+00 1.00e+00h  1\n",
      "  29 -2.2793162e+03 3.32e+01 7.43e+00  -2.5 6.24e+01  -4.5 1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  30 -2.3063960e+03 5.27e-01 7.96e-01  -2.5 4.31e+01  -4.1 1.00e+00 1.00e+00h  1\n",
      "  31 -2.4193743e+03 7.66e+01 2.63e+00  -2.5 1.82e+02  -4.6 1.00e+00 1.00e+00f  1\n",
      "  32 -2.8064573e+03 1.18e+05 4.07e+05  -2.5 9.13e+02    -  5.77e-01 6.52e-01f  1\n",
      "  33 -3.0022736e+03 1.00e+05 3.47e+05  -2.5 6.79e+03    -  1.00e+00 1.85e-01h  1\n",
      "  34 -3.0027917e+03 1.00e+05 3.47e+05  -2.5 2.68e+03    -  7.08e-01 9.82e-04h  1\n",
      "  35 -3.0026289e+03 9.88e+04 3.41e+05  -2.5 7.08e+02    -  1.00e+00 1.58e-02h  1\n",
      "  36 -2.9998319e+03 7.81e+04 2.70e+05  -2.5 7.01e+02    -  2.85e-01 2.65e-01h  1\n",
      "  37 -2.9925505e+03 5.06e+04 1.75e+05  -2.5 6.34e+02    -  2.64e-02 5.44e-01h  1\n",
      "  38 -2.9923535e+03 5.01e+04 1.73e+05  -2.5 4.64e+02    -  2.60e-02 9.46e-03h  1\n",
      "  39 -2.9853557e+03 3.76e+04 1.30e+05  -2.5 4.61e+02    -  3.46e-01 3.33e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  40 -2.9853123e+03 3.75e+04 1.30e+05  -2.5 3.76e+02    -  1.00e-02 1.53e-03h  1\n",
      "  41 -2.9781779e+03 3.00e+04 1.04e+05  -2.5 3.76e+02    -  1.24e-01 2.52e-01h  1\n",
      "  42 -2.9781435e+03 3.00e+04 1.04e+05  -2.5 3.25e+02    -  9.12e-03 9.63e-04h  1\n",
      "  43 -2.9720415e+03 2.56e+04 8.84e+04  -2.5 3.25e+02    -  9.87e-02 1.71e-01h  1\n",
      "  44 -2.9720114e+03 2.56e+04 8.84e+04  -2.5 2.91e+02    -  6.58e-03 7.14e-04h  1\n",
      "  45 -2.9674615e+03 2.31e+04 7.98e+04  -2.5 2.91e+02    -  7.73e-02 1.08e-01h  1\n",
      "  46 -2.9674256e+03 2.31e+04 7.97e+04  -2.5 2.71e+02    -  3.88e-03 7.65e-04h  1\n",
      "  47 -2.9647747e+03 2.18e+04 7.55e+04  -2.5 2.70e+02    -  5.76e-02 5.64e-02h  1\n",
      "  48 -2.9634203e+03 2.13e+04 7.35e+04  -2.5 2.60e+02    -  2.96e-03 2.72e-02h  1\n",
      "  49 -2.9619695e+03 2.07e+04 7.14e+04  -2.5 2.55e+02    -  5.20e-03 2.83e-02h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  50 -2.9584280e+03 1.94e+04 6.70e+04  -2.5 2.50e+02    -  3.53e-03 6.71e-02h  1\n",
      "  51 -2.9584019e+03 1.94e+04 6.69e+04  -2.5 2.38e+02    -  5.50e-03 4.61e-04h  1\n",
      "  52 -2.9534455e+03 1.78e+04 6.15e+04  -2.5 2.38e+02    -  7.42e-02 8.76e-02h  1\n",
      "  53 -2.9534287e+03 1.78e+04 6.15e+04  -2.5 2.23e+02    -  4.49e-03 2.73e-04h  1\n",
      "  54 -2.9472698e+03 1.62e+04 5.59e+04  -2.5 2.23e+02    -  3.64e-02 9.95e-02h  1\n",
      "  55 -2.9472508e+03 1.62e+04 5.59e+04  -2.5 2.09e+02    -  6.57e-03 2.77e-04h  1\n",
      "  56 -2.9406769e+03 1.48e+04 5.10e+04  -2.5 2.09e+02    -  4.19e-02 9.59e-02h  1\n",
      "  57 -2.9406571e+03 1.48e+04 5.10e+04  -2.5 1.96e+02    -  7.13e-03 2.63e-04h  1\n",
      "  58 -2.9343169e+03 1.36e+04 4.71e+04  -2.5 1.96e+02    -  4.27e-02 8.38e-02h  1\n",
      "  59 -2.9342975e+03 1.36e+04 4.71e+04  -2.5 1.84e+02    -  6.07e-03 2.35e-04h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  60 -2.9287885e+03 1.28e+04 4.41e+04  -2.5 1.84e+02    -  3.98e-02 6.68e-02h  1\n",
      "  61 -2.9287707e+03 1.28e+04 4.41e+04  -2.5 1.75e+02    -  4.00e-03 2.01e-04h  1\n",
      "  62 -2.9246008e+03 1.22e+04 4.21e+04  -2.5 1.76e+02    -  3.43e-02 4.71e-02h  1\n",
      "  63 -2.9245840e+03 1.22e+04 4.21e+04  -2.5 1.69e+02    -  2.01e-03 1.80e-04h  1\n",
      "  64 -2.9221294e+03 1.19e+04 4.10e+04  -2.5 1.69e+02    -  2.69e-02 2.64e-02h  1\n",
      "  65 -2.9220748e+03 1.19e+04 4.10e+04  -2.5 1.66e+02    -  1.00e-03 5.70e-04h  1\n",
      "  66 -2.9210694e+03 1.17e+04 4.06e+04  -2.5 1.66e+02    -  1.88e-02 1.05e-02h  1\n",
      "  67 -2.9195972e+03 1.16e+04 4.00e+04  -2.5 1.65e+02    -  1.03e-03 1.52e-02h  1\n",
      "  68 -2.9195840e+03 1.16e+04 4.00e+04  -2.5 1.63e+02    -  6.95e-04 1.34e-04h  1\n",
      "  69 -2.9163012e+03 1.12e+04 3.87e+04  -2.5 1.63e+02    -  1.13e-02 3.33e-02h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  70 -2.9162895e+03 1.12e+04 3.87e+04  -2.5 1.59e+02    -  1.59e-03 1.14e-04h  1\n",
      "  71 -2.9114749e+03 1.07e+04 3.69e+04  -2.5 1.59e+02    -  1.26e-02 4.71e-02h  1\n",
      "  72 -2.9114601e+03 1.07e+04 3.69e+04  -2.5 1.60e+02    -  3.58e-03 1.38e-04h  1\n",
      "  73 -2.9056102e+03 1.01e+04 3.50e+04  -2.5 1.60e+02    -  2.01e-02 5.45e-02h  1\n",
      "  74 -2.9055928e+03 1.01e+04 3.50e+04  -2.5 1.73e+02    -  4.99e-03 1.53e-04h  1\n",
      "  75 -2.8992785e+03 9.60e+03 3.32e+04  -2.5 1.73e+02    -  2.47e-02 5.55e-02h  1\n",
      "  76 -2.8992598e+03 9.60e+03 3.32e+04  -2.5 1.86e+02    -  5.20e-03 1.55e-04h  1\n",
      "  77 -2.8931016e+03 9.13e+03 3.16e+04  -2.5 1.86e+02    -  2.65e-02 5.10e-02h  1\n",
      "  78 -2.8930830e+03 9.13e+03 3.16e+04  -2.5 1.98e+02    -  4.29e-03 1.46e-04h  1\n",
      "  79 -2.8876837e+03 8.76e+03 3.03e+04  -2.5 1.99e+02    -  2.58e-02 4.24e-02h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  80 -2.8876665e+03 8.76e+03 3.03e+04  -2.5 2.09e+02    -  2.77e-03 1.29e-04h  1\n",
      "  81 -2.8835527e+03 8.50e+03 2.94e+04  -2.5 2.09e+02    -  2.28e-02 3.09e-02h  1\n",
      "  82 -2.8835367e+03 8.50e+03 2.94e+04  -2.5 2.18e+02    -  1.36e-03 1.16e-04h  1\n",
      "  83 -2.8811040e+03 8.35e+03 2.89e+04  -2.5 2.18e+02    -  1.82e-02 1.77e-02h  1\n",
      "  84 -2.8810585e+03 8.35e+03 2.88e+04  -2.5 2.22e+02    -  6.58e-04 3.24e-04h  1\n",
      "  85 -2.8800489e+03 8.29e+03 2.86e+04  -2.5 2.23e+02    -  1.28e-02 7.21e-03h  1\n",
      "  86 -2.8785847e+03 8.20e+03 2.83e+04  -2.5 2.25e+02    -  6.83e-04 1.04e-02h  1\n",
      "  87 -2.8785718e+03 8.20e+03 2.83e+04  -2.5 2.27e+02    -  4.73e-04 9.01e-05h  1\n",
      "  88 -2.8753123e+03 8.02e+03 2.77e+04  -2.5 2.27e+02    -  7.77e-03 2.28e-02h  1\n",
      "  89 -2.8753008e+03 8.02e+03 2.77e+04  -2.5 2.34e+02    -  1.11e-03 7.93e-05h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  90 -2.8705210e+03 7.76e+03 2.68e+04  -2.5 2.34e+02    -  8.84e-03 3.27e-02h  1\n",
      "  91 -2.8705062e+03 7.76e+03 2.68e+04  -2.5 2.43e+02    -  2.50e-03 9.84e-05h  1\n",
      "  92 -2.8646937e+03 7.47e+03 2.58e+04  -2.5 2.43e+02    -  1.43e-02 3.85e-02h  1\n",
      "  93 -2.8646761e+03 7.47e+03 2.58e+04  -2.5 2.54e+02    -  3.45e-03 1.12e-04h  1\n",
      "  94 -2.8583989e+03 7.19e+03 2.48e+04  -2.5 2.54e+02    -  1.80e-02 4.00e-02h  1\n",
      "  95 -2.8583799e+03 7.19e+03 2.48e+04  -2.5 2.66e+02    -  3.57e-03 1.16e-04h  1\n",
      "  96 -2.8522579e+03 6.93e+03 2.39e+04  -2.5 2.66e+02    -  1.97e-02 3.75e-02h  1\n",
      "  97 -2.8522390e+03 6.93e+03 2.39e+04  -2.5 2.78e+02    -  2.94e-03 1.11e-04h  1\n",
      "  98 -2.8468740e+03 6.71e+03 2.32e+04  -2.5 2.78e+02    -  1.94e-02 3.17e-02h  1\n",
      "  99 -2.8468563e+03 6.71e+03 2.32e+04  -2.5 2.88e+02    -  1.90e-03 1.01e-04h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 100 -2.8427709e+03 6.56e+03 2.27e+04  -2.5 2.88e+02    -  1.74e-02 2.34e-02h  1\n",
      " 101 -2.8427535e+03 6.56e+03 2.27e+04  -2.5 2.95e+02    -  9.68e-04 9.76e-05h  1\n",
      " 102 -2.8403365e+03 6.47e+03 2.24e+04  -2.5 2.95e+02    -  1.41e-02 1.35e-02h  1\n",
      " 103 -2.8402405e+03 6.47e+03 2.23e+04  -2.5 3.00e+02    -  5.41e-04 5.31e-04h  1\n",
      " 104 -2.8392925e+03 6.43e+03 2.22e+04  -2.5 3.00e+02    -  1.02e-02 5.24e-03h  1\n",
      " 105 -2.8378507e+03 6.38e+03 2.21e+04  -2.5 3.01e+02    -  6.06e-04 7.93e-03h  1\n",
      " 106 -2.8378368e+03 6.38e+03 2.21e+04  -2.5 3.04e+02    -  3.51e-04 7.57e-05h  1\n",
      " 107 -2.8346099e+03 6.27e+03 2.17e+04  -2.5 3.04e+02    -  6.10e-03 1.76e-02h  1\n",
      " 108 -2.8345976e+03 6.27e+03 2.17e+04  -2.5 3.10e+02    -  7.35e-04 6.60e-05h  1\n",
      " 109 -2.8298729e+03 6.12e+03 2.11e+04  -2.5 3.10e+02    -  6.88e-03 2.54e-02h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 110 -2.8298575e+03 6.12e+03 2.11e+04  -2.5 3.18e+02    -  1.67e-03 8.08e-05h  1\n",
      " 111 -2.8241338e+03 5.94e+03 2.05e+04  -2.5 3.18e+02    -  1.13e-02 3.00e-02h  1\n",
      " 112 -2.8241160e+03 5.94e+03 2.05e+04  -2.5 3.29e+02    -  2.34e-03 9.11e-05h  1\n",
      " 113 -2.8179535e+03 5.76e+03 1.99e+04  -2.5 3.29e+02    -  1.43e-02 3.15e-02h  1\n",
      " 114 -2.8179342e+03 5.76e+03 1.99e+04  -2.5 3.39e+02    -  2.41e-03 9.56e-05h  1\n",
      " 115 -2.8119423e+03 5.59e+03 1.93e+04  -2.5 3.39e+02    -  1.58e-02 2.98e-02h  1\n",
      " 116 -2.8119230e+03 5.59e+03 1.93e+04  -2.5 3.50e+02    -  1.98e-03 9.40e-05h  1\n",
      " 117 -2.8066880e+03 5.45e+03 1.88e+04  -2.5 3.50e+02    -  1.57e-02 2.54e-02h  1\n",
      " 118 -2.8066692e+03 5.45e+03 1.88e+04  -2.5 3.59e+02    -  1.32e-03 8.95e-05h  1\n",
      " 119 -2.8026929e+03 5.35e+03 1.85e+04  -2.5 3.59e+02    -  1.42e-02 1.89e-02h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 120 -2.8026717e+03 5.35e+03 1.85e+04  -2.5 3.66e+02    -  7.29e-04 9.91e-05h  1\n",
      " 121 -2.8003201e+03 5.29e+03 1.83e+04  -2.5 3.66e+02    -  1.15e-02 1.10e-02h  1\n",
      " 122 -2.7993040e+03 5.27e+03 1.82e+04  -2.5 3.70e+02    -  5.15e-04 4.71e-03h  1\n",
      " 123 -2.7977983e+03 5.23e+03 1.81e+04  -2.5 3.72e+02    -  4.25e-04 6.95e-03h  1\n",
      " 124 -2.7947235e+03 5.16e+03 1.78e+04  -2.5 3.74e+02    -  7.95e-04 1.41e-02h  1\n",
      " 125 -2.7947075e+03 5.16e+03 1.78e+04  -2.5 3.79e+02    -  1.05e-03 7.25e-05h  1\n",
      " 126 -2.7902119e+03 5.05e+03 1.75e+04  -2.5 3.79e+02    -  1.79e-02 2.04e-02h  1\n",
      " 127 -2.7901953e+03 5.05e+03 1.75e+04  -2.5 3.87e+02    -  9.74e-04 7.42e-05h  1\n",
      " 128 -2.7846781e+03 4.93e+03 1.70e+04  -2.5 3.87e+02    -  9.32e-03 2.46e-02h  1\n",
      " 129 -2.7846594e+03 4.93e+03 1.70e+04  -2.5 3.96e+02    -  1.54e-03 8.21e-05h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 130 -2.7787498e+03 4.81e+03 1.66e+04  -2.5 3.96e+02    -  1.20e-02 2.59e-02h  1\n",
      " 131 -2.7787301e+03 4.81e+03 1.66e+04  -2.5 4.06e+02    -  1.64e-03 8.51e-05h  1\n",
      " 132 -2.7730009e+03 4.69e+03 1.62e+04  -2.5 4.06e+02    -  1.33e-02 2.47e-02h  1\n",
      " 133 -2.7729805e+03 4.69e+03 1.62e+04  -2.5 4.15e+02    -  1.35e-03 8.61e-05h  1\n",
      " 134 -2.7679872e+03 4.59e+03 1.59e+04  -2.5 4.15e+02    -  1.32e-02 2.11e-02h  1\n",
      " 135 -2.7679657e+03 4.59e+03 1.59e+04  -2.5 4.22e+02    -  9.34e-04 8.98e-05h  1\n",
      " 136 -2.7641819e+03 4.52e+03 1.56e+04  -2.5 4.22e+02    -  1.20e-02 1.58e-02h  1\n",
      " 137 -2.7641495e+03 4.52e+03 1.56e+04  -2.5 4.28e+02    -  5.97e-04 1.34e-04h  1\n",
      " 138 -2.7619160e+03 4.48e+03 1.55e+04  -2.5 4.28e+02    -  9.87e-03 9.21e-03h  1\n",
      " 139 -2.7606315e+03 4.46e+03 1.54e+04  -2.5 4.31e+02    -  5.62e-04 5.26e-03h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 140 -2.7595816e+03 4.44e+03 1.53e+04  -2.5 4.33e+02    -  1.39e-03 4.29e-03h  1\n",
      " 141 -2.7565436e+03 4.38e+03 1.51e+04  -2.5 4.35e+02    -  5.73e-04 1.24e-02h  1\n",
      " 142 -2.7565027e+03 4.38e+03 1.51e+04  -2.5 4.39e+02    -  1.08e-03 1.65e-04h  1\n",
      " 143 -2.7523800e+03 4.31e+03 1.49e+04  -2.5 4.39e+02    -  1.58e-02 1.67e-02h  1\n",
      " 144 -2.7523623e+03 4.31e+03 1.49e+04  -2.5 4.45e+02    -  6.94e-04 7.06e-05h  1\n",
      " 145 -2.7471638e+03 4.22e+03 1.46e+04  -2.5 4.45e+02    -  8.07e-03 2.08e-02h  1\n",
      " 146 -2.7471436e+03 4.22e+03 1.46e+04  -2.5 4.52e+02    -  9.72e-04 7.97e-05h  1\n",
      " 147 -2.7415780e+03 4.13e+03 1.43e+04  -2.5 4.52e+02    -  1.02e-02 2.20e-02h  1\n",
      " 148 -2.7415558e+03 4.13e+03 1.43e+04  -2.5 4.59e+02    -  1.07e-03 8.65e-05h  1\n",
      " 149 -2.7361837e+03 4.05e+03 1.40e+04  -2.5 4.59e+02    -  1.14e-02 2.09e-02h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 150 -2.7361599e+03 4.05e+03 1.40e+04  -2.5 4.66e+02    -  9.55e-04 9.16e-05h  1\n",
      " 151 -2.7314943e+03 3.97e+03 1.37e+04  -2.5 4.66e+02    -  1.14e-02 1.80e-02h  1\n",
      " 152 -2.7314653e+03 3.97e+03 1.37e+04  -2.5 4.72e+02    -  7.16e-04 1.10e-04h  1\n",
      " 153 -2.7279357e+03 3.92e+03 1.35e+04  -2.5 4.72e+02    -  1.05e-02 1.35e-02h  1\n",
      " 154 -2.7278350e+03 3.92e+03 1.35e+04  -2.5 4.76e+02    -  5.43e-04 3.81e-04h  1\n",
      " 155 -2.7258014e+03 3.89e+03 1.34e+04  -2.5 4.77e+02    -  8.88e-03 7.69e-03h  1\n",
      " 156 -2.7243825e+03 3.87e+03 1.34e+04  -2.5 4.79e+02    -  7.13e-04 5.35e-03h  1\n",
      " 157 -2.7237410e+03 3.86e+03 1.33e+04  -2.5 4.81e+02    -  2.17e-03 2.41e-03h  1\n",
      " 158 -2.7208950e+03 3.82e+03 1.32e+04  -2.5 4.81e+02    -  3.09e-04 1.07e-02h  1\n",
      " 159 -2.7208469e+03 3.82e+03 1.32e+04  -2.5 4.85e+02    -  7.33e-04 1.79e-04h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 160 -2.7169756e+03 3.76e+03 1.30e+04  -2.5 4.85e+02    -  1.38e-02 1.44e-02h  1\n",
      " 161 -2.7169491e+03 3.76e+03 1.30e+04  -2.5 4.89e+02    -  4.60e-04 9.79e-05h  1\n",
      " 162 -2.7121130e+03 3.70e+03 1.28e+04  -2.5 4.89e+02    -  6.97e-03 1.79e-02h  1\n",
      " 163 -2.7120846e+03 3.70e+03 1.28e+04  -2.5 4.94e+02    -  7.41e-04 1.04e-04h  1\n",
      " 164 -2.7069526e+03 3.63e+03 1.25e+04  -2.5 4.94e+02    -  9.02e-03 1.88e-02h  1\n",
      " 165 -2.7069246e+03 3.63e+03 1.25e+04  -2.5 5.00e+02    -  8.11e-04 1.02e-04h  1\n",
      " 166 -2.7019736e+03 3.56e+03 1.23e+04  -2.5 5.00e+02    -  1.01e-02 1.80e-02h  1\n",
      " 167 -2.7019405e+03 3.56e+03 1.23e+04  -2.5 5.05e+02    -  7.11e-04 1.20e-04h  1\n",
      " 168 -2.6976369e+03 3.51e+03 1.21e+04  -2.5 5.05e+02    -  1.01e-02 1.55e-02h  1\n",
      " 169 -2.6975706e+03 3.51e+03 1.21e+04  -2.5 5.09e+02    -  6.12e-04 2.38e-04h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 170 -2.6943433e+03 3.47e+03 1.20e+04  -2.5 5.09e+02    -  9.39e-03 1.16e-02h  1\n",
      " 171 -2.6921127e+03 3.44e+03 1.19e+04  -2.5 5.12e+02    -  6.05e-04 7.96e-03h  1\n",
      " 172 -2.6903137e+03 3.42e+03 1.18e+04  -2.5 5.14e+02    -  1.22e-03 6.40e-03h  1\n",
      " 173 -2.6873538e+03 3.38e+03 1.17e+04  -2.5 5.16e+02    -  9.11e-04 1.05e-02h  1\n",
      " 174 -2.6832629e+03 3.33e+03 1.15e+04  -2.5 5.19e+02    -  1.96e-03 1.45e-02h  1\n",
      " 175 -2.6784209e+03 3.28e+03 1.13e+04  -2.5 5.22e+02    -  3.01e-03 1.70e-02h  1\n",
      " 176 -2.6731612e+03 3.22e+03 1.11e+04  -2.5 5.26e+02    -  3.95e-03 1.84e-02h  1\n",
      " 177 -2.6680027e+03 3.16e+03 1.09e+04  -2.5 5.31e+02    -  5.01e-03 1.79e-02h  1\n",
      " 178 -2.6645465e+03 3.13e+03 1.08e+04  -2.5 5.35e+02    -  6.25e-03 1.19e-02h  1\n",
      " 179 -2.6628895e+03 3.11e+03 1.07e+04  -2.5 5.37e+02    -  5.70e-03 5.69e-03h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 180 -2.6611346e+03 3.09e+03 1.07e+04  -2.5 5.39e+02    -  2.63e-03 6.02e-03h  1\n",
      " 181 -2.6598111e+03 3.08e+03 1.06e+04  -2.5 5.40e+02    -  5.17e-04 4.53e-03h  1\n",
      " 182 -2.6569668e+03 3.05e+03 1.05e+04  -2.5 5.41e+02    -  1.02e-03 9.72e-03h  1\n",
      " 183 -2.6527077e+03 3.00e+03 1.04e+04  -2.5 5.43e+02    -  2.17e-03 1.45e-02h  1\n",
      " 184 -2.6479196e+03 2.95e+03 1.02e+04  -2.5 5.46e+02    -  4.05e-03 1.63e-02h  1\n",
      " 185 -2.6440885e+03 2.92e+03 1.01e+04  -2.5 5.49e+02    -  5.81e-03 1.30e-02h  1\n",
      " 186 -2.6404110e+03 2.88e+03 9.96e+03  -2.5 5.51e+02    -  5.25e-03 1.24e-02h  1\n",
      " 187 -2.6363815e+03 2.84e+03 9.82e+03  -2.5 5.53e+02    -  4.12e-03 1.35e-02h  1\n",
      " 188 -2.6341217e+03 2.82e+03 9.75e+03  -2.5 5.55e+02    -  4.86e-03 7.57e-03h  1\n",
      " 189 -2.4845482e+03 1.88e+03 6.50e+03  -2.5 5.57e+02    -  4.07e-04 5.00e-01h  2\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 190 -2.4533569e+03 1.70e+03 5.88e+03  -2.5 5.64e+02    -  3.66e-01 1.06e-01h  1\n",
      " 191 -2.4353756e+03 1.60e+03 5.53e+03  -2.5 5.53e+02    -  3.60e-01 6.25e-02h  5\n",
      " 192 -2.4265297e+03 1.55e+03 5.36e+03  -2.5 5.45e+02    -  1.00e+00 3.12e-02h  6\n",
      " 193 -2.4221456e+03 1.53e+03 5.28e+03  -2.5 5.40e+02    -  6.12e-01 1.56e-02h  7\n",
      " 194 -2.4216001e+03 1.52e+03 5.27e+03  -2.5 5.38e+02    -  1.00e+00 1.95e-03h 10\n",
      " 195 -2.4214638e+03 1.52e+03 5.27e+03  -2.5 5.38e+02    -  6.39e-01 4.88e-04h 12\n",
      " 196 -2.1423735e+03 7.62e+02 2.63e+03  -2.5 5.37e+02    -  1.00e+00 1.00e+00h  1\n",
      " 197 -2.0296482e+03 3.82e+02 1.32e+03  -2.5 2.24e+02    -  1.00e+00 9.98e-01h  1\n",
      " 198 -2.0118718e+03 1.91e+02 6.59e+02  -2.5 3.55e+01    -  1.00e+00 1.00e+00h  1\n",
      " 199 -2.0111540e+03 1.14e+02 3.95e+02  -2.5 5.26e+01    -  6.81e-01 6.68e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 200 -2.0111013e+03 1.00e+02 3.47e+02  -2.5 8.80e+01    -  1.00e+00 1.40e-01h  1\n",
      " 201 -2.0111012e+03 1.00e+02 3.49e+02  -2.5 1.00e+02    -  8.91e-01 3.47e-04h  1\n",
      " 202r-2.0111012e+03 1.00e+02 1.00e+03   2.0 0.00e+00    -  0.00e+00 4.91e-07R  2\n",
      " 203r-2.0062196e+03 1.02e+02 1.67e+03   2.0 5.94e+04    -  7.98e-03 1.45e-03f  1\n",
      " 204r-2.0026472e+03 1.04e+02 9.94e+02   1.3 1.25e+04    -  1.17e-03 4.65e-03f  1\n",
      " 205r-2.0070934e+03 1.06e+02 9.91e+02   0.6 1.46e+04    -  2.45e-03 3.15e-03f  1\n",
      " 206r-2.0186104e+03 1.08e+02 9.91e+02   0.6 1.84e+04    -  3.77e-04 3.02e-03f  1\n",
      " 207r-2.0238138e+03 1.09e+02 9.90e+02   0.6 1.43e+04    -  7.75e-04 2.00e-03f  1\n",
      " 208r-2.0289680e+03 1.10e+02 9.88e+02   0.6 1.06e+04    -  1.63e-03 2.32e-03f  1\n",
      " 209r-2.0342502e+03 1.11e+02 9.84e+02   0.6 9.84e+03    -  3.85e-03 2.83e-03f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 210r-2.0412486e+03 1.11e+02 9.77e+02   0.6 7.40e+03    -  7.65e-03 4.19e-03f  1\n",
      " 211r-2.0514038e+03 1.12e+02 1.81e+03   0.6 5.69e+03    -  1.60e-02 6.76e-03f  1\n",
      " 212r-2.0641130e+03 1.13e+02 6.11e+03   0.6 4.87e+03    -  4.67e-02 9.69e-03f  1\n",
      " 213r-2.0831094e+03 1.13e+02 1.39e+04   0.6 3.41e+03    -  1.05e-01 1.68e-02f  1\n",
      " 214r-2.1048970e+03 1.12e+02 2.12e+04   0.6 2.26e+03    -  1.62e-01 2.57e-02f  1\n",
      " 215r-2.1239889e+03 1.07e+02 2.48e+04   0.6 1.48e+03    -  1.67e-01 3.18e-02f  1\n",
      " 216r-2.1381198e+03 1.01e+02 3.14e+04   0.6 1.08e+03    -  6.41e-01 3.39e-02f  1\n",
      " 217r-2.1548122e+03 8.69e+01 2.96e+04   0.6 9.39e+02    -  3.56e-02 6.45e-02f  1\n",
      " 218 -2.1558537e+03 8.40e+01 1.86e+01  -2.5 6.77e+01    -  6.49e-01 3.39e-02h  1\n",
      " 219 -2.1855352e+03 4.43e-01 6.00e+00  -2.5 6.54e+01    -  6.66e-01 9.97e-01h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 220 -2.1853887e+03 6.32e-04 1.05e+02  -2.5 3.51e-01    -  8.33e-01 9.99e-01h  1\n",
      " 221 -2.1856430e+03 2.58e-04 1.71e+05  -2.5 4.21e-01    -  1.41e-02 1.00e+00f  1\n",
      " 222 -2.2626054e+03 4.77e+01 1.80e+05  -2.5 1.52e+03  -5.1 3.02e-04 8.40e-02f  2\n",
      " 223 -2.3633376e+03 4.10e+00 9.01e+04  -2.5 1.63e+02    -  6.79e-01 1.00e+00h  1\n",
      " 224 -2.6261701e+03 7.84e+04 5.41e+04  -2.5 7.23e+02    -  3.99e-01 5.76e-01f  1\n",
      " 225 -2.4837049e+03 3.74e+04 2.81e+04  -2.5 8.23e+02    -  4.81e-01 1.00e+00h  1\n",
      " 226 -2.4234355e+03 1.79e+04 6.09e+03  -2.5 2.46e+02    -  7.83e-01 1.00e+00h  1\n",
      " 227 -2.4185322e+03 1.58e+04 3.19e+03  -2.5 2.09e+02    -  8.59e-01 1.25e-01h  4\n",
      " 228 -2.4183762e+03 1.58e+04 3.18e+03  -2.5 2.15e+02    -  1.00e+00 3.91e-03h  9\n",
      " 229 -2.4183370e+03 1.58e+04 3.17e+03  -2.5 2.16e+02    -  1.00e+00 9.77e-04h 11\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 230 -2.4183174e+03 1.58e+04 3.17e+03  -2.5 2.16e+02    -  1.00e+00 4.88e-04h 12\n",
      " 231 -2.3782197e+03 7.19e+03 1.56e+03  -2.5 2.16e+02    -  1.00e+00 1.00e+00s 22\n",
      " 232 -2.3431992e+03 2.91e+03 7.49e+02  -2.5 2.04e+02    -  1.00e+00 1.00e+00s 22\n",
      " 233 -2.3759317e+03 1.04e+03 3.41e+02  -2.5 9.74e+01    -  1.00e+00 0.00e+00S 22\n",
      " 234 -2.5591060e+03 4.92e+02 1.52e+02  -2.5 4.38e+02    -  1.00e+00 1.00e+00h  1\n",
      " 235 -2.6972739e+03 2.50e+02 6.27e+01  -2.5 3.67e+02    -  1.00e+00 1.00e+00h  1\n",
      " 236 -2.7290327e+03 9.83e+01 2.14e+01  -2.5 1.30e+02    -  1.00e+00 1.00e+00h  1\n",
      " 237 -2.7292678e+03 3.21e+01 5.39e+00  -2.5 8.16e+01    -  1.00e+00 1.00e+00h  1\n",
      " 238 -2.7246829e+03 5.01e+00 6.62e-01  -2.5 3.11e+01    -  1.00e+00 1.00e+00h  1\n",
      " 239 -2.7237134e+03 1.31e-01 1.48e-02  -2.5 4.68e+00    -  1.00e+00 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      " 240 -2.7236895e+03 8.02e-05 8.36e-06  -2.5 1.08e-01    -  1.00e+00 1.00e+00h  1\n",
      " 241 -2.7236899e+03 5.79e-04 4.74e+01  -5.7 5.37e-01    -  1.00e+00 1.00e+00h  1\n",
      " 242 -2.7236897e+03 7.46e-10 1.69e-09  -5.7 3.27e-04    -  1.00e+00 1.00e+00h  1\n",
      " 243 -2.7236897e+03 2.46e-10 7.71e-11  -8.6 3.50e-04    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 243\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:  -2.7236897130396183e+03   -2.7236897130396183e+03\n",
      "Dual infeasibility......:   7.7116946733420358e-11    7.7116946733420358e-11\n",
      "Constraint violation....:   2.4635937734274194e-10    2.4635937734274194e-10\n",
      "Complementarity.........:   2.5066261336655876e-09    2.5066261336655876e-09\n",
      "Overall NLP error.......:   2.5066261336655876e-09    2.5066261336655876e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 344\n",
      "Number of objective gradient evaluations             = 230\n",
      "Number of equality constraint evaluations            = 344\n",
      "Number of inequality constraint evaluations          = 0\n",
      "Number of equality constraint Jacobian evaluations   = 245\n",
      "Number of inequality constraint Jacobian evaluations = 0\n",
      "Number of Lagrangian Hessian evaluations             = 243\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      3.526\n",
      "Total CPU secs in NLP function evaluations           =      0.101\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "Optimal reactor volume: 9.02 m^3\n",
      "Maximum Concentration of C at optimal reactor volume is: 5.03 kmol/m^3\n",
      "Maximum Concentration of B at optimal reactor volume is: 0.42 kmol/m^3\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to create multi-objective dynamic model\n",
    "def multi_objective_reactor_model(F, CAF, k1, k2, k3, w1, w2):\n",
    "    \"\"\"Creates reactor model with multi-objective optimization\n",
    "    Arguments:\n",
    "        F: feed volumetric flow rate in m^3/min\n",
    "        CAF: feed concentration of A, gmol/m^3\n",
    "        k1: rate constant for A to B, min^-1\n",
    "        k2: rate constant for B to C, min^-1\n",
    "        k3: rate constant for 2A to D, m^3/(gmol.min)\n",
    "        w1: weights for objective 1\n",
    "        w2: weights for objective 2\n",
    "\n",
    "    Returns:\n",
    "        model: pyomo model\n",
    "    \"\"\"\n",
    "\n",
    "    # Create Pyomo model\n",
    "    model = pyo.ConcreteModel()\n",
    "\n",
    "    # Define time as a continuous set\n",
    "    model.t = dae.ContinuousSet(bounds=(0, 10))  # Time from 0 to 10 minutes\n",
    "\n",
    "    # Define variables\n",
    "    model.CA = pyo.Var(model.t, within=pyo.NonNegativeReals)  # Concentration of A\n",
    "    model.CB = pyo.Var(model.t, within=pyo.NonNegativeReals)  # Concentration of B\n",
    "    model.CC = pyo.Var(model.t, within=pyo.NonNegativeReals)  # Concentration of C\n",
    "    model.CD = pyo.Var(model.t, within=pyo.NonNegativeReals)  # Concentration of D\n",
    "    model.V = pyo.Var(within=pyo.NonNegativeReals, bounds=(0, 100), initialize=11.35)  # Reactor volume\n",
    "\n",
    "    # Define derivatives\n",
    "    model.dCA_dt = dae.DerivativeVar(model.CA, wrt=model.t)\n",
    "    model.dCB_dt = dae.DerivativeVar(model.CB, wrt=model.t)\n",
    "    model.dCC_dt = dae.DerivativeVar(model.CC, wrt=model.t)\n",
    "    model.dCD_dt = dae.DerivativeVar(model.CD, wrt=model.t)\n",
    "\n",
    "    # CA differential equation\n",
    "    def ca_rate_ode(m, t):\n",
    "        return m.dCA_dt[t] == (F / m.V) * CAF - (F / m.V) * m.CA[t] - k1 * m.CA[t] - 2 * k3 * m.CA[t] ** 2\n",
    "    model.CA_ode = pyo.Constraint(model.t, rule=ca_rate_ode)\n",
    "\n",
    "    # CB differential equation\n",
    "    def cb_rate_ode(m, t):\n",
    "        return m.dCB_dt[t] == -(F / m.V) * m.CB[t] + k1 * m.CA[t] - k2 * m.CB[t]\n",
    "    model.CB_ode = pyo.Constraint(model.t, rule=cb_rate_ode)\n",
    "\n",
    "    # CC differential equation\n",
    "    def cc_rate_ode(m, t):\n",
    "        return m.dCC_dt[t] == -(F / m.V) * m.CC[t] + k2 * m.CB[t]\n",
    "    model.CC_ode = pyo.Constraint(model.t, rule=cc_rate_ode)\n",
    "\n",
    "    # CD differential equation\n",
    "    def cd_rate_ode(m, t):\n",
    "        return m.dCD_dt[t] == -(F / m.V) * m.CD[t] + k3 * m.CA[t] ** 2\n",
    "    model.CD_ode = pyo.Constraint(model.t, rule=cd_rate_ode)\n",
    "    \n",
    "    # Initial conditions\n",
    "    def init_conditions(m):\n",
    "        yield m.CA[0] == CAF\n",
    "        yield m.CB[0] == 0\n",
    "        yield m.CC[0] == 0\n",
    "        yield m.CD[0] == 0\n",
    "    model.init_conditions = pyo.ConstraintList(rule=init_conditions)\n",
    "\n",
    "    # Multi-objective function: weighted sum of objectives\n",
    "    # Maximize CC (product C), CB (intermediate B)\n",
    "    def objective_function(m):\n",
    "        return w1 * m.CC[10] + w2 * m.CB[10]\n",
    "    model.objective = pyo.Objective(rule=objective_function, sense=pyo.maximize)\n",
    "\n",
    "    return model\n",
    "\n",
    "# Weights for multi-objective function\n",
    "weights = {\"w1\": 0.5, \"w2\": 0.5}\n",
    "\n",
    "# Create reactor model\n",
    "reactor_model = multi_objective_reactor_model(feed_flowrate, conc_A_feed, k1, k2, k3, weights[\"w1\"], weights[\"w2\"])\n",
    "\n",
    "# Discretize the model using collocation\n",
    "discretizer = pyo.TransformationFactory('dae.collocation')\n",
    "discretizer.apply_to(reactor_model, nfe=20, ncp=10)\n",
    "\n",
    "# Solve the model\n",
    "solver = pyo.SolverFactory('ipopt')\n",
    "results = solver.solve(reactor_model, tee=True)\n",
    "\n",
    "# Print results\n",
    "optimal_volume = pyo.value(reactor_model.V)\n",
    "optimal_CC = pyo.value(reactor_model.CC[10]) * 1e-3  # kmol/m^3\n",
    "optimal_CB = pyo.value(reactor_model.CB[10]) * 1e-3  # kmol/m^3\n",
    "\n",
    "print(f\"Optimal reactor volume: {round(optimal_volume, 2)} m^3\")\n",
    "print(f\"Maximum Concentration of C at optimal reactor volume is: {round(optimal_CC, 2)} kmol/m^3\")\n",
    "print(f\"Maximum Concentration of B at optimal reactor volume is: {round(optimal_CB, 2)} kmol/m^3\")\n",
    "\n",
    "# Extract results for plotting\n",
    "time = [t for t in reactor_model.t] # minutes\n",
    "CA_values = [(pyo.value(reactor_model.CA[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "CB_values = [(pyo.value(reactor_model.CB[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "CC_values = [(pyo.value(reactor_model.CC[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "CD_values = [(pyo.value(reactor_model.CD[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(time, CA_values, label='$\\mathrm{C_{A}}$')\n",
    "plt.plot(time, CB_values, label='$\\mathrm{C_{B}}$')\n",
    "plt.plot(time, CC_values, label='$\\mathrm{C_{C}}$')\n",
    "plt.plot(time, CD_values, label='$\\mathrm{C_{D}}$')\n",
    "plt.xlabel('Time (min)', fontsize=16,fontweight='bold')\n",
    "plt.ylabel('Concentration (kmol/$\\mathbf{m^3}$)', fontsize=16,fontweight='bold')\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.title('Concentration Profiles Over Time', fontsize=16,fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Pareto Front\n",
    "def generate_weights(n_points): # Function to systematically generate weight combinations\n",
    "    \"\"\"Creates weights for objective function\n",
    "    Argument:\n",
    "        n_points: number of points to evaluate weights\n",
    "\n",
    "    Returns:\n",
    "        w1: weight vector for objective 1\n",
    "        w2: weight vector for objective 2\"\"\"\n",
    "    \n",
    "    w1 = np.random.uniform(low=0, high=1, size=n_points)\n",
    "    w2 = [1 - w1[i] for i in range(len(w1))]\n",
    "    return w1, w2\n",
    "\n",
    "# number of points for weights\n",
    "n_points = 60\n",
    "\n",
    "# Generate valid weight combinations\n",
    "w1, w2 = generate_weights(n_points)\n",
    "\n",
    "# Generate Pareto front by scanning weights\n",
    "pareto_data = []\n",
    "\n",
    "# Iterate over each weight combination\n",
    "for i in range(len(w1)):\n",
    "    # Create and solve the model with the given weights\n",
    "    reactor_model = multi_objective_reactor_model(feed_flowrate, conc_A_feed, k1, k2, k3, w1[i], w2[i])\n",
    "    discretizer = pyo.TransformationFactory('dae.collocation')\n",
    "    discretizer.apply_to(reactor_model, nfe=20, ncp=5)\n",
    "    solver = pyo.SolverFactory('ipopt')\n",
    "    results = solver.solve(reactor_model, tee=False)\n",
    "\n",
    "    if results.solver.termination_condition == pyo.TerminationCondition.optimal:\n",
    "        optimal_volume = pyo.value(reactor_model.V)\n",
    "        optimal_CC = pyo.value(reactor_model.CC[10]) * 1e-3  # kmol/m^3\n",
    "        optimal_CB = pyo.value(reactor_model.CB[10]) * 1e-3  # kmol/m^3\n",
    "        pareto_data.append((w1[i], w2[i], optimal_CC, optimal_CB, optimal_volume))\n",
    "\n",
    "# Convert Pareto data to a structured format for analysis\n",
    "pareto_df = pd.DataFrame(pareto_data, columns=[\"w1\", \"w2\", \"CC\", \"CB\", \"V\"])\n",
    "\n",
    "# Plotting the 3D Pareto front\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "scatter = ax.scatter(pareto_df['CC'], pareto_df['CB'], pareto_df['V'], c=pareto_df['V'], cmap='viridis', s=50)\n",
    "ax.tick_params(axis='x', labelsize=10)\n",
    "ax.tick_params(axis='y', labelsize=10)\n",
    "ax.tick_params(axis='z', labelsize=10)\n",
    "ax.tick_params(direction=\"in\",top=True, right=True)\n",
    "ax.set_xlabel('Conc. of C (kmol/$\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "ax.set_ylabel('Conc. of B (kmol/$\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "ax.set_zlabel('Volume ($\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "ax.set_title('3D Pareto Front', fontsize=12,fontweight='bold')\n",
    "color_bar = fig.colorbar(scatter, ax=ax, pad=0.1)\n",
    "color_bar.set_label('Volume ($\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Epsilon-Constraint Method Pyomo Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ipopt 3.13.2: \n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "\n",
      "This version of Ipopt was compiled from source code available at\n",
      "    https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n",
      "    Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n",
      "    Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n",
      "\n",
      "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n",
      "    for large-scale scientific computation.  All technical papers, sales and\n",
      "    publicity material resulting from use of the HSL codes within IPOPT must\n",
      "    contain the following acknowledgement:\n",
      "        HSL, a collection of Fortran codes for large-scale scientific\n",
      "        computation. See http://www.hsl.rl.ac.uk.\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.13.2, running with linear solver ma27.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:    12619\n",
      "Number of nonzeros in inequality constraint Jacobian.:        1\n",
      "Number of nonzeros in Lagrangian Hessian.............:     1006\n",
      "\n",
      "Total number of variables............................:     1609\n",
      "                     variables with only lower bounds:      804\n",
      "                variables with lower and upper bounds:        1\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:     1608\n",
      "Total number of inequality constraints...............:        1\n",
      "        inequality constraints with only lower bounds:        1\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0 -9.9999900e-03 1.00e+04 2.28e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1 -4.8598389e-03 1.00e+04 3.18e+01  -1.0 1.89e+04    -  9.90e-07 3.28e-05h  1\n",
      "   2 -4.8257425e-03 1.00e+04 3.18e+01  -1.0 1.83e+04    -  3.34e-05 3.81e-05h  1\n",
      "   3 -9.4551719e-03 9.90e+03 3.90e+04  -1.0 1.83e+04    -  7.14e-05 9.73e-03f  1\n",
      "   4 -9.5086577e-03 9.90e+03 3.90e+04  -1.0 1.86e+04    -  2.53e-04 9.82e-05f  1\n",
      "   5r-9.5086577e-03 9.90e+03 9.99e+02   4.0 0.00e+00    -  0.00e+00 4.92e-07R  2\n",
      "   6r-1.1686407e+01 9.90e+03 8.24e+05   4.0 1.53e+04    -  8.22e-06 2.46e-03f  1\n",
      "   7r-1.6308832e+01 9.87e+03 4.90e+05   4.0 8.22e+02    -  5.17e-01 2.20e-01f  1\n",
      "   8r-1.3016906e+01 9.88e+03 3.58e+05   3.3 3.49e+02    -  5.65e-01 5.32e-01f  1\n",
      "   9r-1.3735757e+01 9.88e+03 2.13e+05   3.3 1.04e+02    -  1.00e+00 7.03e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10r-1.0871961e+01 9.89e+03 1.05e+05   3.3 2.81e+01    -  8.29e-01 1.00e+00f  1\n",
      "  11r-9.4210740e+00 9.91e+03 5.95e+04   2.6 4.06e+01    -  1.00e+00 7.51e-01f  1\n",
      "  12r-1.1429075e+01 9.91e+03 2.98e+04   2.6 1.25e+02    -  1.00e+00 8.86e-01f  1\n",
      "  13r-1.1915976e+01 9.91e+03 1.30e+04   2.6 1.29e+01    -  1.00e+00 1.00e+00f  1\n",
      "  14r-9.9938062e+00 9.93e+03 7.86e+03   1.9 5.17e+01    -  7.28e-01 6.37e-01f  1\n",
      "  15r-1.1032288e+01 9.92e+03 4.99e+03   1.9 2.46e+02    -  1.00e+00 4.95e-01f  1\n",
      "  16r-9.5439326e+00 9.91e+03 2.67e+03   1.2 1.11e+02    -  7.60e-01 8.46e-01f  1\n",
      "  17r-1.0596755e+01 9.89e+03 2.12e+03   1.2 5.88e+02    -  6.54e-01 2.35e-01f  1\n",
      "  18r-1.2101202e+01 9.87e+03 1.49e+03   1.2 4.69e+02    -  1.00e+00 3.58e-01f  1\n",
      "  19r-1.2133837e+01 9.87e+03 5.80e+02   1.2 3.03e+02    -  1.00e+00 1.21e-02f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20r-1.3127762e+01 9.83e+03 2.63e+02   0.5 3.01e+02    -  8.75e-01 8.24e-01f  1\n",
      "  21r-1.7684525e+01 9.77e+03 1.91e+02   0.5 1.36e+03    -  2.67e-01 3.32e-01f  1\n",
      "  22r-2.0529528e+01 9.73e+03 2.09e+02  -0.2 8.99e+02    -  4.24e-01 3.23e-01f  1\n",
      "  23r-2.1186706e+01 9.72e+03 3.16e+02  -0.2 3.42e+03    -  2.63e-01 1.87e-02f  1\n",
      "  24r-3.0164095e+01 9.60e+03 2.07e+02  -0.2 3.34e+03    -  2.18e-01 2.59e-01f  1\n",
      "  25r-3.2513482e+01 9.57e+03 2.33e+02  -0.2 2.42e+03    -  5.43e-01 9.20e-02f  1\n",
      "  26r-3.8644531e+01 9.49e+03 2.41e+02  -0.2 2.18e+03    -  1.00e+00 2.66e-01f  1\n",
      "  27r-5.4811872e+01 9.28e+03 5.94e+00  -0.2 1.55e+03    -  1.00e+00 9.79e-01f  1\n",
      "  28r-5.4592211e+01 9.28e+03 2.53e-02  -0.2 3.06e+01    -  1.00e+00 1.00e+00h  1\n",
      "  29r-5.4474224e+01 9.28e+03 7.13e+01  -2.4 1.73e+00    -  9.94e-01 7.16e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  30r-5.7067904e+01 9.25e+03 1.08e+02  -2.4 3.49e+04    -  4.36e-02 6.34e-03f  1\n",
      "  31r-6.0576269e+01 9.21e+03 1.42e+02  -2.4 3.45e+04    -  4.61e-02 8.63e-03f  1\n",
      "  32r-6.3326429e+01 9.18e+03 1.41e+02  -2.4 3.41e+04    -  6.88e-03 6.82e-03f  1\n",
      "  33r-6.7503485e+01 9.13e+03 1.47e+02  -2.4 3.37e+04    -  1.89e-02 1.04e-02f  1\n",
      "  34r-7.1226432e+01 9.09e+03 2.11e+02  -2.4 3.30e+04    -  4.90e-02 9.43e-03f  1\n",
      "  35r-9.8261702e+01 8.78e+03 1.53e+02  -2.4 3.25e+04    -  4.23e-02 6.95e-02f  1\n",
      "  36 -9.8265110e+01 8.78e+03 3.84e+00  -1.0 1.46e+04    -  1.12e-07 3.99e-07f  1\n",
      "  37 -9.8265544e+01 8.78e+03 7.64e+00  -1.0 1.41e+04    -  8.05e-08 3.28e-08f  1\n",
      "  38 -9.8268084e+01 8.78e+03 3.92e+00  -1.0 1.40e+04    -  1.01e-07 1.83e-07f  1\n",
      "  39 -2.3207517e+03 1.42e+04 2.93e+08  -1.0 1.40e+04    -  3.18e-07 1.60e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  40 -2.4052259e+03 1.23e+04 6.63e+08  -1.0 2.54e+04    -  1.72e-05 9.90e-01f  1\n",
      "  41 -2.4527209e+03 1.18e+04 6.38e+08  -1.0 1.30e+04   0.0 3.33e-01 3.99e-02h  1\n",
      "  42r-2.4527209e+03 1.18e+04 1.00e+03   3.9 0.00e+00  -0.5 0.00e+00 4.63e-07R  4\n",
      "  43r-2.3350121e+03 1.01e+04 4.48e+04   3.9 1.17e+05    -  7.55e-03 2.27e-02f  1\n",
      "  44 -2.4417906e+03 8.89e+03 1.44e+03  -1.0 1.25e+04    -  1.98e-01 1.16e-01f  1\n",
      "  45 -2.4447617e+03 8.86e+03 1.44e+03  -1.0 1.10e+04    -  1.08e-01 2.99e-03h  1\n",
      "  46 -3.4820052e+03 1.01e+03 9.36e+03  -1.0 1.09e+04    -  7.34e-02 9.90e-01f  1\n",
      "  47 -4.5367172e+03 3.39e+02 1.73e+03  -1.0 1.07e+03    -  3.69e-01 9.90e-01f  1\n",
      "  48 -4.9748649e+03 6.98e+01 1.02e+03  -1.0 4.81e+02    -  8.17e-01 9.91e-01f  1\n",
      "  49 -5.0264431e+03 4.22e+00 1.14e+04  -1.0 6.99e+01    -  9.81e-01 1.00e+00h  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  50 -5.0273073e+03 1.63e-02 3.07e+04  -1.0 1.41e+00    -  9.97e-01 1.00e+00h  1\n",
      "  51 -5.0272545e+03 2.47e-05 7.31e-05  -1.0 1.22e-01    -  1.00e+00 1.00e+00h  1\n",
      "  52 -5.0273606e+03 1.18e-04 6.11e+05  -5.7 2.57e-01    -  1.00e+00 9.39e-01f  1\n",
      "  53 -5.0273605e+03 1.61e-10 2.37e-10  -5.7 8.81e-05    -  1.00e+00 1.00e+00h  1\n",
      "  54 -5.0273605e+03 1.67e-10 7.10e-14  -8.6 4.20e-06    -  1.00e+00 1.00e+00h  1\n",
      "\n",
      "Number of Iterations....: 54\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:  -5.0273605087722517e+03   -5.0273605087722517e+03\n",
      "Dual infeasibility......:   7.0993770278112621e-14    7.0993770278112621e-14\n",
      "Constraint violation....:   1.2094401828651539e-10    1.6666490409988910e-10\n",
      "Complementarity.........:   2.5059121858118180e-09    2.5059121858118180e-09\n",
      "Overall NLP error.......:   2.5059121858118180e-09    2.5059121858118180e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 61\n",
      "Number of objective gradient evaluations             = 26\n",
      "Number of equality constraint evaluations            = 61\n",
      "Number of inequality constraint evaluations          = 61\n",
      "Number of equality constraint Jacobian evaluations   = 57\n",
      "Number of inequality constraint Jacobian evaluations = 57\n",
      "Number of Lagrangian Hessian evaluations             = 54\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.689\n",
      "Total CPU secs in NLP function evaluations           =      0.024\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "Solver found an optimal solution.\n",
      "Optimal Reactor Volume: 8.97 m^3\n",
      "Maximum Concentration of B at optimal reactor volume is: 0.42 kmol/m^3\n",
      "Maximum Concentration of C at optimal reactor volume is: 5.03 kmol/m^3\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function for epsilon - constraint method\n",
    "def create_epsilon_model(F, CAF, k1, k2, k3, epsilon_CB):\n",
    "    \"\"\"Solves multi-objective optimization using epsilon constraint method\n",
    "    Arguments:\n",
    "        F: feed volumetric flow rate in m^3/min\n",
    "        CAF: feed concentration of A, gmol/m^3\n",
    "        k1: rate constant for A to B, min^-1\n",
    "        k2: rate constant for B to C, min^-1\n",
    "        k3: rate constant for 2A to D, m^3/(gmol.min)\n",
    "        epsilon_CB: constraint threshold of concentration of B\n",
    "\n",
    "    Returns:\n",
    "        model: pyomo model\n",
    "    \"\"\"\n",
    "\n",
    "    model = pyo.ConcreteModel()\n",
    "\n",
    "    # Define time as a continuous set\n",
    "    model.t = dae.ContinuousSet(bounds=(0, 10))\n",
    "    \n",
    "    # Variables\n",
    "    model.CA = pyo.Var(model.t, within=pyo.NonNegativeReals)\n",
    "    model.CB = pyo.Var(model.t, within=pyo.NonNegativeReals)\n",
    "    model.CC = pyo.Var(model.t, within=pyo.NonNegativeReals)\n",
    "    model.CD = pyo.Var(model.t, within=pyo.NonNegativeReals)\n",
    "    model.V = pyo.Var(within=pyo.NonNegativeReals, bounds=(0, 100), initialize=1)\n",
    "    \n",
    "    # Derivatives\n",
    "    model.dCA_dt = dae.DerivativeVar(model.CA, wrt=model.t)\n",
    "    model.dCB_dt = dae.DerivativeVar(model.CB, wrt=model.t)\n",
    "    model.dCC_dt = dae.DerivativeVar(model.CC, wrt=model.t)\n",
    "    model.dCD_dt = dae.DerivativeVar(model.CD, wrt=model.t)\n",
    "    \n",
    "    # CA differential equation\n",
    "    def ca_rate_ode(m, t):\n",
    "        return m.dCA_dt[t] == (F / m.V) * CAF - (F / m.V) * m.CA[t] - k1 * m.CA[t] - 2 * k3 * m.CA[t] ** 2\n",
    "    model.CA_ode = pyo.Constraint(model.t, rule=ca_rate_ode)\n",
    "\n",
    "    # CB differential equation\n",
    "    def cb_rate_ode(m, t):\n",
    "        return m.dCB_dt[t] == -(F / m.V) * m.CB[t] + k1 * m.CA[t] - k2 * m.CB[t]\n",
    "    model.CB_ode = pyo.Constraint(model.t, rule=cb_rate_ode)\n",
    "\n",
    "    # CC differential equation\n",
    "    def cc_rate_ode(m, t):\n",
    "        return m.dCC_dt[t] == -(F / m.V) * m.CC[t] + k2 * m.CB[t]\n",
    "    model.CC_ode = pyo.Constraint(model.t, rule=cc_rate_ode)\n",
    "\n",
    "    # CD differential equation\n",
    "    def cd_rate_ode(m, t):\n",
    "        return m.dCD_dt[t] == -(F / m.V) * m.CD[t] + k3 * m.CA[t] ** 2\n",
    "    model.CD_ode = pyo.Constraint(model.t, rule=cd_rate_ode)\n",
    "\n",
    "    # Initial conditions\n",
    "    def init_conditions(m):\n",
    "        yield m.CA[0] == CAF\n",
    "        yield m.CB[0] == 0\n",
    "        yield m.CC[0] == 0\n",
    "        yield m.CD[0] == 0\n",
    "    model.init_conditions = pyo.ConstraintList(rule=init_conditions)\n",
    "    \n",
    "    # Epsilon constraints \n",
    "    model.epsilon_constraint_CB = pyo.Constraint(expr=model.CB[10] >= epsilon_CB) # converting CB to constraint\n",
    "    \n",
    "    # Objective: Minimize volume\n",
    "    model.objective = pyo.Objective(expr=model.CC[10], sense=pyo.maximize)\n",
    "    \n",
    "    return model\n",
    "\n",
    "# constraint threshold\n",
    "epsilon_CB_values = 420\n",
    "\n",
    "# Create reactor model\n",
    "reactor_model_epsilon = create_epsilon_model(feed_flowrate, conc_A_feed, k1, k2, k3, epsilon_CB_values)\n",
    "\n",
    "# Discretize the model using collocation\n",
    "discretizer = pyo.TransformationFactory('dae.collocation')\n",
    "discretizer.apply_to(reactor_model_epsilon, nfe=20, ncp=10)\n",
    "\n",
    "solver = pyo.SolverFactory('ipopt')\n",
    "result = solver.solve(reactor_model_epsilon, tee=True)\n",
    "\n",
    "if (result.solver.status == pyo.SolverStatus.ok) and (result.solver.termination_condition == pyo.TerminationCondition.optimal):\n",
    "    print(\"Solver found an optimal solution.\")\n",
    "    optimal_volume = pyo.value(reactor_model_epsilon.V)\n",
    "    final_concentration_C = pyo.value(reactor_model_epsilon.CC[10]) * 1e-3\n",
    "    final_concentration_B = pyo.value(reactor_model_epsilon.CB[10]) * 1e-3\n",
    "\n",
    "    print(f\"Optimal Reactor Volume: {optimal_volume:.2f} m^3\")\n",
    "    print(f\"Maximum Concentration of B at optimal reactor volume is: {final_concentration_B:.2f} kmol/m^3\")\n",
    "    print(f\"Maximum Concentration of C at optimal reactor volume is: {final_concentration_C:.2f} kmol/m^3\")\n",
    "else:\n",
    "    print(\"Solver did not find an optimal solution. Status:\", result.solver.status)\n",
    "\n",
    "# Extract results for plotting\n",
    "time = [t for t in reactor_model_epsilon.t] # minutes\n",
    "CA_values = [(pyo.value(reactor_model_epsilon.CA[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "CB_values = [(pyo.value(reactor_model_epsilon.CB[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "CC_values = [(pyo.value(reactor_model_epsilon.CC[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "CD_values = [(pyo.value(reactor_model_epsilon.CD[t])*10**(-3)) for t in time] # kmol/m^3\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(time, CA_values, label='$\\mathrm{C_{A}}$')\n",
    "plt.plot(time, CB_values, label='$\\mathrm{C_{B}}$')\n",
    "plt.plot(time, CC_values, label='$\\mathrm{C_{C}}$')\n",
    "plt.plot(time, CD_values, label='$\\mathrm{C_{D}}$')\n",
    "plt.xlabel('Time (min)', fontsize=16,fontweight='bold')\n",
    "plt.ylabel('Concentration (kmol/$\\mathbf{m^3}$)', fontsize=16,fontweight='bold')\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.title('Concentration Profiles Over Time', fontsize=16,fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    epsilon_CB  optimal_volume  opt_concentration_CB  opt_concentration_CC\n",
      "0   400.000000        9.643572            399.999996           5044.649789\n",
      "1   406.122449        9.431184            406.122445           5040.039761\n",
      "2   412.244898        9.225387            412.244894           5034.826205\n",
      "3   418.367347        9.025888            418.367343           5029.012016\n",
      "4   424.489796        8.832410            424.489792           5022.600218\n",
      "5   430.612245        8.644690            430.612241           5015.593954\n",
      "6   436.734694        8.462483            436.734690           5007.996493\n",
      "7   442.857143        8.285555            442.857138           4999.811219\n",
      "8   448.979592        8.113687            448.979587           4991.041636\n",
      "9   455.102041        7.946670            455.102036           4981.691359\n",
      "10  461.224490        7.784306            461.224485           4971.764117\n",
      "11  467.346939        7.626410            467.346934           4961.263748\n",
      "12  473.469388        7.472804            473.469383           4950.194194\n",
      "13  479.591837        7.323319            479.591832           4938.559505\n",
      "14  485.714286        7.177797            485.714281           4926.363827\n",
      "15  491.836735        7.036085            491.836730           4913.611408\n",
      "16  497.959184        6.898039            497.959179           4900.306590\n",
      "17  504.081633        6.763523            504.081628           4886.453807\n",
      "18  510.204082        6.632405            510.204077           4872.057580\n",
      "19  516.326531        6.504561            516.326525           4857.122520\n",
      "20  522.448980        6.379872            522.448974           4841.653316\n",
      "21  528.571429        6.258227            528.571423           4825.654740\n",
      "22  534.693878        6.139515            534.693872           4809.131637\n",
      "23  540.816327        6.023636            540.816321           4792.088924\n",
      "24  546.938776        5.910489            546.938770           4774.531588\n",
      "25  553.061224        5.799982            553.061219           4756.464679\n",
      "26  559.183673        5.692023            559.183668           4737.893308\n",
      "27  565.306122        5.586528            565.306117           4718.822643\n",
      "28  571.428571        5.483414            571.428566           4699.257902\n",
      "29  577.551020        5.382601            577.551015           4679.204353\n",
      "30  583.673469        5.284015            583.673464           4658.667308\n",
      "31  589.795918        5.187582            589.795912           4637.652116\n",
      "32  595.918367        5.093234            595.918361           4616.164160\n",
      "33  602.040816        5.000903            602.040810           4594.208854\n",
      "34  608.163265        4.910525            608.163259           4571.791634\n",
      "35  614.285714        4.822040            614.285708           4548.917957\n",
      "36  620.408163        4.735387            620.408157           4525.593291\n",
      "37  626.530612        4.650509            626.530606           4501.823113\n",
      "38  632.653061        4.567353            632.653055           4477.612901\n",
      "39  638.775510        4.485866            638.775504           4452.968130\n",
      "40  644.897959        4.405996            644.897953           4427.894261\n",
      "41  651.020408        4.327695            651.020402           4402.396743\n",
      "42  657.142857        4.250916            657.142851           4376.480995\n",
      "43  663.265306        4.175613            663.265299           4350.152409\n",
      "44  669.387755        4.101743            669.387748           4323.416335\n",
      "45  675.510204        4.029264            675.510197           4296.278081\n",
      "46  681.632653        3.958134            681.632646           4268.742894\n",
      "47  687.755102        3.888314            687.755095           4240.815963\n",
      "48  693.877551        3.819767            693.877544           4212.502402\n",
      "49  700.000000        3.752455            699.999993           4183.807245\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Pareto Front\n",
    "num_samples = 50  # sample amount\n",
    "\n",
    "# constraint threshold\n",
    "epsilon_CB_values = np.linspace(400, 700, num_samples)\n",
    "\n",
    "# Store results\n",
    "results = []\n",
    "\n",
    "# loop through all constraint threshold values\n",
    "for epsilon_CB in epsilon_CB_values:\n",
    "    reactor_model_epsilon = create_epsilon_model(feed_flowrate, conc_A_feed, k1, k2, k3, epsilon_CB) # reactor model\n",
    "\n",
    "    # Discretize the model using collocation\n",
    "    discretizer = pyo.TransformationFactory('dae.collocation')\n",
    "    discretizer.apply_to(reactor_model_epsilon, nfe=20, ncp=10)\n",
    "    \n",
    "    solver = pyo.SolverFactory('ipopt')\n",
    "    result = solver.solve(reactor_model_epsilon, tee=False)\n",
    "\n",
    "    if (result.solver.status == pyo.SolverStatus.ok and result.solver.termination_condition == pyo.TerminationCondition.optimal):\n",
    "        optimal_volume = pyo.value(reactor_model_epsilon.V)\n",
    "        opt_concentration_CB = pyo.value(reactor_model_epsilon.CB[10])\n",
    "        opt_concentration_CC = pyo.value(reactor_model_epsilon.CC[10])\n",
    "\n",
    "        results.append({\n",
    "            'epsilon_CB': epsilon_CB,\n",
    "            'optimal_volume': optimal_volume,\n",
    "            'opt_concentration_CB': opt_concentration_CB,\n",
    "            'opt_concentration_CC': opt_concentration_CC\n",
    "        })\n",
    "    else:\n",
    "        print(f\"Failed to find optimal solution for ε_CB = {epsilon_CB}\")\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "print(results_df)\n",
    "\n",
    "# Plotting the 3D Pareto front\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "scatter = ax.scatter(results_df['opt_concentration_CC']*1e-3, results_df['opt_concentration_CB']*1e-3, results_df['optimal_volume'], c=results_df['optimal_volume'], cmap='viridis', s=50)\n",
    "ax.tick_params(axis='x', labelsize=10)\n",
    "ax.tick_params(axis='y', labelsize=10)\n",
    "ax.tick_params(axis='z', labelsize=10)\n",
    "ax.tick_params(direction=\"in\",top=True, right=True)\n",
    "ax.set_xlabel('Conc. of C (kmol/$\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "ax.set_ylabel('Conc. of B (kmol/$\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "ax.set_zlabel('Volume ($\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "ax.set_title('3D Pareto Front', fontsize=12,fontweight='bold')\n",
    "color_bar = fig.colorbar(scatter, ax=ax, pad=0.1)\n",
    "color_bar.set_label('Volume ($\\mathbf{m^3}$)', fontsize=12,fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MOO Identifies the Lowest Reactor Volume and Offers Control over Production"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the concentration profiles of the two MOO methods, it can be seen that MOO identifies the lowest reactor volume for an even higher desired product yield. \n",
    "\n",
    "* Also, MOO offer engineers and stakeholders the power to select the product with highest priority, thereby enpowering stakeholders control over product yields.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take Away Messages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pyomo continues to be a powerful tool in solving complex dynamic optimization problems\n",
    "* Multi-objective optimization provides control over desired outcomes and products \n",
    "* Multi-objective optimization identifies solutions that are robust to stakeholders decision\n",
    "* Proper modelling of the problem is key to obtaining quality results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AgUvMvTPAZlV"
   },
   "source": [
    "## Reference\n",
    "Hart, W. E., Laird, C. D., Watson, J. P., Woodruff, D. L., Hackebeil, G. A., Nicholson, B. L., & Siirola, J. D. (2017). Pyomo-optimization modeling in python (Vol. 67, p. 277). Berlin: Springer.\n",
    "\n",
    "Dowling, A. W., Ruiz-Mercado, G., & Zavala, V. M. (2016). A framework for multi-stakeholder decision-making and conflict resolution. Computers & Chemical Engineering, 90, 136-150.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}