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Interpretable Machine Learning-Based Model to Classify and Optimize Isoperibolic Batch Reactors
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AI
DOI：10.1021/acs.iecr.3c02462.png)
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Abstract
En 中文
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.
Keywords:
SAFE OPERATING-CONDITIONS
PARAMETRIC SENSITIVITY
RUNAWAY CRITERIA
CLASSIFICATION
TEMPERATURE
STABILITY
SELECTION
Journal
I
IF:
3.9
Papers:
4.0W
Citations:
9.6W