Interpretable Machine Learning-Based Model to Classify and Optimize Isoperibolic Batch Reactors

It is crucial to classify and optimize the thermal behaviors of isoperibolic batch reactors. This article aims to construct an improved supervised machine learning-based (ML-based) model to achieve the task. First, using the minimum redundancy maximum relevance algorithm, the k-nearest neighbor (k-NN) algorithm with a feature subset consisting of 18 dimensions is selected due to the highest total recognition accuracy in 27 different ML algorithms. Additionally, a cost-sensitive learning approach and Bayesian optimization algorithm are employed to further optimize the hyper-parameters of the k-NN model. The accuracies of all data sets using the optimal k-NN model are all 99.8%, indicating that the optimal k-NN model has a superior performance and a good generalization ability. Then, two cases coupled with interpretability techniques are used to interpret the optimal k-NN model. Finally, based on the optimal k-NN model, two novel optimization frameworks (single-objective and multiobjective) are proposed to optimize the mentioned pilot-scale case, and the results prove that the optimization frameworks are reasonable and reliable.