AI-Augmented Process Operations at Western Sugar Scotts Bluff Manufacturing Facility

Project Overview

This project aims to prototype AI and machine learning models to augment process operations at the Western Sugar Scotts Bluff manufacturing facility. The primary objectives are to investigate and develop predictive models for fault detection, optimize sensor placement, and minimize plant disruption.

Key Features

• Prototyping AI and machine learning models for process optimization
• Developing predictive models for fault detection
• Optimizing sensor placement within the manufacturing facility
• Minimizing plant disruption through AI-driven strategies

Prerequisites

• Python 3.x
• Required libraries: NumPy, Pandas, TensorFlow, Keras, Scikit-learn, Matplotlib, Seaborn
• Access to the Western Sugar Scotts Bluff manufacturing facility's process data and sensor information

Installation

1. Clone the repository:

   git clone https://github.com/saikiranpennam/sugarbeet_analysis.git
   

2. Install the required libraries:

   pip install -r requirements.txt
   

3. Prepare the data:

   • Obtain the necessary process data and sensor information from the Western Sugar Scotts Bluff manufacturing facility.
   • Place the data files in the appropriate directories within the project.

Usage

1. Fault/Anamoly Detection:

   • Run the anamoly_detection.py script to train and evaluate the anamoly detection.

2. Beet Handling Data:

   • Extracted, Transformed, and Loaded the pipeline for the Initial Stage of Beet Handling data.

3. Filter Data:

   • Extracted, Transformed, and Loaded the pipeline for the Filter(second) Stage.

4. Carbonization Data:

   • Extracted, Transformed, and Loaded the pipeline for the Carbonization(third) Stage.

5. Visualization and Analysis:

   • Use the provided Jupyter notebooks in the python-scripts/ directory to visualize and analyze the results of the AI and machine learning models.
   • Customized the notebooks to generate specific insights and reports relevant to the Western Sugar Scotts Bluff manufacturing facility.

Contributing

Contributions to this project are welcome. If you have any ideas, suggestions, or bug fixes, please submit a pull request or open an issue on the GitHub repository.

License

This project is licensed under the MIT License.

Contact

For any questions or inquiries, please contact:

• Project Lead: Steven Conrad @ CSU-Systems Engineering
• Email: [email protected]
• Project Analyst: Sai Pennam @ CSU-Computer Science
• Email: [email protected]

Feel free to reach out if you have any further questions or require assistance with the project.

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This README file provides an overview of the project, its objectives, and the necessary steps to set up and run the AI and machine learning models for augmenting process operations at the Western Sugar Scotts Bluff manufacturing facility. It serves as a starting point for understanding the project structure and provides instructions for installation, usage, and contribution.