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

TL;DR

This paper introduces a comprehensive benchmark framework for rare-event prediction in chemical processes, comparing various machine learning algorithms to optimize safety and reliability in Industry 4.0 settings.

Contribution

It presents a novel benchmark framework that evaluates multiple ML algorithms for rare-event prediction, balancing accuracy and computational efficiency.

Findings

Random Forests and XGBoost perform best in accuracy.

Neural networks offer faster training times.

Benchmarking guides optimal ML selection for safety systems.

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.