Catalysis Data Analysis

README for Experiment Report on Catalyst Performance, Emissions Reduction, and Process Efficiency

Overview

This project focuses on analyzing catalysis data to explore catalyst performance, emissions reduction, and process efficiency. The analysis uses Python for data processing and visualization, incorporating key techniques such as:

• Scatter Plots to analyze relationships between catalyst properties, performance, and operating conditions.
• Line and Bar Charts to visualize trends in emissions reduction and catalyst efficiency.
• Correlation Heatmaps to identify strong relationships between key metrics.

The dataset is processed to extract insights into catalyst behavior, enabling optimization of catalytic processes for improved performance and reduced emissions.

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Features

• Data Processing: Processes raw catalysis data to extract meaningful insights.
• Performance Analysis: Analyzes catalyst performance under varying operating conditions.
• Emissions Reduction Analysis: Evaluates the effectiveness of catalysts in reducing emissions.
• Efficiency Analysis: Assesses process efficiency using key performance indicators.
• Visualization: Generates scatter plots, line charts, bar charts, and correlation heatmaps for clear interpretation of results.

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Usage

1. Prepare the Data:

   • Ensure the dataset (catalysis.json) is placed in the data/ directory.
   • The dataset should include catalyst properties, performance metrics, and operating conditions.

2. Run the Python Analysis:

   • Execute the script to generate visualizations and save plots:

     python analysis.py  

   • The output will include scatter plots, line charts, bar charts, and a correlation heatmap.

3. Interpret Results:

   • Analyze the generated plots to understand catalyst performance, emissions reduction, and process efficiency.
   • Use insights to optimize catalytic processes for improved outcomes.

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Authors

• Adesoye Michael Ademola:, Phone: 07015060745
• Enoch o. Oladipupo: , Phone: 09135619975

For inquiries or collaboration, feel free to contact the authors.

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License

This project is open-source and free for use in research and development. Proper attribution is appreciated.

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Future Improvements

• Incorporate machine learning models to predict catalyst performance under new conditions.
• Expand the dataset to include additional catalysts and operating conditions.
• Automate data collection and processing for real-time monitoring.

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Acknowledgments

We acknowledge the contributions of researchers and institutions involved in catalysis research. Additionally, we leveraged the following resources to gather data and support this research:

• Open Catalysis Project:

  • Provides extensive catalytic reaction datasets and reaction network insights.
  • Website

• Materials Project:

  • Access properties of materials used in catalysts.
  • Website