标题： 推进第四次工业革命中的机器学习：化学过程罕见事件预测的基准框架 显示英文标题

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

摘要： 此前，我们利用前向通量采样（FFS）和机器学习（ML），开发了多变量报警系统以应对罕见的未预测异常事件。 我们的报警系统利用基于ML的预测模型，将触发概率量化为关键工艺变量（例如温度、浓度等）的函数，这些数据来自FFS模拟。 在此，我们引入了一个新颖且全面的基准框架，用于罕见事件预测，比较了不同复杂度的ML算法，包括线性支持向量回归器和k近邻算法，以及更复杂的算法，如随机森林、XGBoost、LightGBM、CatBoost、密集神经网络和TabNet。 此评估使用综合性能指标，如：$\textit{RMSE}$、模型训练、测试、超参数调优和部署时间，以及报警的数量和效率。 这些指标平衡了模型准确性、计算效率和报警系统效率，识别出用于预测异常罕见事件的最佳ML策略，使操作员能够实现更安全和可靠的工厂运行。 显示英文摘要

摘要： 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.