UPDATE: fresh new README for the project

Author: MarekWadinger

README.md

@@ -1,32 +1,85 @@
-# Dynamic Optimization of Growth Model Parameters
+# 🌱 EcoMPC4Greenhouse: Education Platform
 
-<!-- markdownlint-disable MD013 -->
+[![Platform Access](https://img.shields.io/badge/Platform-Access%20Here-brightgreen)](http://ecompc4greenhouse.streamlit.app)
 [![Python application](https://github.com/MarekWadinger/dynamic_opt_growth_model/actions/workflows/python-app.yml/badge.svg)](https://github.com/MarekWadinger/dynamic_opt_growth_model/actions/workflows/python-app.yml)
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 [![codecov](https://codecov.io/gh/MarekWadinger/dynamic_opt_growth_model/graph/badge.svg?token=UWBJ585H4J)](https://codecov.io/gh/MarekWadinger/dynamic_opt_growth_model)
 
-<!-- markdownlint-enable MD013 -->
+![Webpage Overview](publications/figures/webpage.png)
 
-## ⚡️ Quickstart
+## 🌍 Overview
 
-Play around with the model in a [Jupyter notebook](https://github.com/MarekWadinger/dynamic_opt_growth_model/blob/main/main.ipynb).
+**EcoMPC4Greenhouse** is an interactive, web-based platform aimed at enhancing education in Nonlinear Economic Model Predictive Control (NEMPC) applied to greenhouse climate management. By integrating real-time data and dynamic simulations, the platform enables students and researchers to explore how to optimize greenhouse systems for sustainability, balancing plant growth, energy use, and CO₂ emissions.
 
-## 🛠 Installation
+Developed to support the publication *"Carbon Neutral Greenhouse: Economic Model Predictive Control Framework for Education,"* the platform provides hands-on learning with advanced control techniques, bridging theory and practical agricultural applications.
 
-```sh
-python -m venv --upgrade-deps .venv
-source .venv/bin/activate
-pip install -r requirements.txt
+## 🚀 Features
+
+- **Greenhouse Climate Model**: Simulates lettuce growth dynamics influenced by external weather data, temperature, light, and CO₂ concentration.
+- **Economic MPC Framework**: Optimizes climate conditions to balance crop yield, energy consumption, and CO₂ emissions.
+- **Real-Time Data Integration**: Fetches current weather data, carbon intensity forecasts and electricity price to adjust the greenhouse control strategy.
+- **User-Friendly Interface**: Intuitive design for students to visualize simulations, analyze results, and experiment with control parameters.
+- **Educational Focus**: Aimed at bridging the gap between control theory and real-world applications, enhancing problem-solving skills through interactive learning.
+
+## 📜 Citation
+
+If you use this platform for academic purposes, please cite our publication:
+
+```bibtex
+@misc{Wadinger2024,
+  author = {Wadinger, Marek and F'aber, Rastislav and Pavlovi\v cov'a, Erika and Paulen, Radoslav},
+  note   = {Submitted to European Control Conference (ECC)},
+  title  = {Carbon Neutral Greenhouse: Economic Model Predictive Control Framework for Education},
+  url    = {},
+  year   = {2025},
+}
 ```
 
 ## 👐 Contributing
 
 Feel free to contribute in any way you like, we're always open to new ideas and
 approaches.
 
-* Feel welcome to
-[open an issue](https://github.com/MarekWadinger/dynamic_opt_growth_model/issues/new/choose)
+- Feel welcome to
+[open an issue](https://github.com/MarekWadinger/ecompc-greenhouse-platform/issues/new/choose)
 if you think you've spotted a bug or a performance issue.
 
 Please check out the [contribution guidelines](CONTRIBUTING.md)
 if you want to bring modifications to the code base.
+
+## 🛠 For Developers
+
+### Installation (for Local Use)
+
+If you wish to run the platform locally, follow the steps below:
+
+1. Clone the repository:
+
+    ```sh
+    git clone https://github.com/MarekWadinger/ecompc-greenhouse-platform.git
+    ```
+
+2. Navigate to the project folder:
+
+    ```sh
+    cd ecompc-greenhouse-platform
+    ```
+
+3. Create a virtual environment:
+
+    ```sh
+    python -m venv --upgrade-deps .venv
+    source .venv/bin/activate
+    ```
+
+4. Install the required dependencies:
+
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+5. Run the platform locally:
+
+    ```sh
+    streamlit run app.py
+    ```