A stacking-based ensemble learning model for kneading paste quality intelligent prediction: A real case study

Abstract

Paste kneading is a vital process of prebaked carbon anode production, and the quality of the paste has a great impact on the quality of the final product. However, it is difficult to inspect the paste quality in line. Because the inspection of the paste quality in the laboratory is not real-time, the paste has already entered the next process after the results are obtained. And the manual quality inspection is labor-intensive and unsafe. Therefore, a stacking-based ensemble learning model for kneading paste quality prediction is proposed in this paper. The gradient boosting decision tree, random forest, k-nearest neighbors, and support vector machine are used as base learners, and logistic regression is used as meta-learner. Kneading production data of 572 paste pots are collected for quality prediction, where each pot of paste data contains 44 signals. The correlation coefficient-based feature engineering method was applied, and 10 features with the greatest correlation with paste quality were identified to construct the dataset. The up-sampling and under-sampling methods are used to solve the problem of sample imbalance. Parallel comparison is applied to verify the advantage of the stacking-based ensemble learning model, and the results indicate that the model performs better than every single classifier and has higher accuracy and generalization ability, especially for imbalanced samples.