Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes

• URL: http://arxiv.org/abs/2409.00485v1
• Date: Sat, 31 Aug 2024 15:41:10 GMT
• Title: Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes
• Authors: Vikram Sudarshan, Warren D. Seider,
• Abstract summary: We introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity. We identify optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtained in FFS simulations. Herein, we introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity, including Linear Support-Vector Regressor and k-Nearest Neighbors, to more sophisticated algorithms, such as Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet. This evaluation uses comprehensive performance metrics, such as: $\textit{RMSE}$, model training, testing, hyperparameter tuning and deployment times, and number and efficiency of alarms. These balance model accuracy, computational efficiency, and alarm-system efficiency, identifying optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.

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