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Efficient SRU Optimization via Deep Learning-driven LSTM Dynamical Models

Project Overview:

This project utilizes Long Short-Term Memory (LSTM) networks to estimate key variables in a sulfur recovery unit (SRU) of a refinery plant. The goal is to achieve accurate predictions for process optimization, even in offline analyzer scenarios.

Files Included:

  • IN_Table.csv: Input dataset containing essential measurement series for predicting SO2 and H2S concentrations.
  • OUT_Table.csv: Output dataset containing target variables Out1 and Out2.
  • H2S_Model.ipynb: Jupyter Notebook containing the code for the H2S concentration prediction model.
  • SO2_Model.ipynb: Jupyter Notebook containing the code for the SO2 concentration prediction model.
  • H2S_GUI.ipynb: Jupyter Notebook containing the Streamlit code for the H2S concentration prediction GUI.

Instructions for Running:

  1. Ensure you have Python and Jupyter Notebook installed on your system.
  2. Clone this repository to your local machine using git clone <repository-url>.
  3. Navigate to the project directory.
  4. Place the IN_Table.csv and OUT_Table.csv datasets in the same directory.
  5. Open the desired Jupyter Notebook (H2S_Model.ipynb or SO2_Model.ipynb) in Jupyter Notebook.
  6. Follow the instructions provided in the notebook to run the code and train the LSTM model.
  7. For the GUI application, open H2S_GUI.ipynb and follow the instructions to run the Streamlit app.

Dependencies:

  • Python 3.x
  • Jupyter Notebook
  • Pandas
  • NumPy
  • Matplotlib
  • TensorFlow
  • Streamlit (for GUI)