In order to solve the problem of frequent failures of aluminum electrolytic cells in the aluminum electrolytic production process,a health state diagnosis model of aluminum electrolytic cells based on support vector machine(SVM)was proposed.The thickness of the wall,current efficiency and electrolytic temperature were taken as the comprehensive evaluation indexes of the health state of aluminum electrolytic cells,and the health state of aluminum electrolytic cells was divided into four grades:excellent,good,medium and poor.Considering that traditional support vector machine(SVM)can only be applied to binary classification problem,Adaboost algorithm is used to transform SVM binary classification problem into multi-classification problem to solve aluminum electrolytic cell health diagnosis problem,which fully considers the weight of submodels and strengthens the applicability of the model.The hyperparameters of the model were optimized by using PSO algorithm.The classification accuracy of the model was 94.70%and the Macro-F1 score was 0.9453 in the aluminum electrolytic cells.Compared with the Adaboost-SVM model without optimization algorithm and the PSO-SVM model without integrated algorithm,Adaboost-PSO-SVM improves classification accuracy by 8.34%and 4.93%,and Macro-F1 scores by 8.84%and 5.20%,respectively.Compared with the current mainstream machine learning algorithms DT and KNN,the classification accuracy is improved by 13.64%and 11.11%,respectively,and Macro-F1 scores are improved by 13.47%and 11.04%,respectively.The model provides a comprehensive assessment of the optimal maintenance period for aluminum electrolytic cells.This not only reduces the frequency of failures in aluminum electrolytic cells but also enhances the economic benefits of aluminum plants.
维普
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