Quality prediction and sensitivity analysis of fine chemical products based on support matrix machine

Abstract

Real-time detection of product quality in the chemical production process is essential for quality control and process adjustment. At present, in the chemical production process, product quality is sometimes difficult to be tested online, which is mainly obtained through laboratory sampling and testing. Due to the low sampling frequency of products and the large proportion of qualified products, there are problems of small sample size and imbalance between positive and negative samples. In addition, the chemical production process is complex, and there is coupling relationship among process variables. The soft quality prediction model based on the multiclass support matrix machine (MSMM) combined with bagging-SMOTE(BAG-SMOTE) and a sensitivity analysis method based on the change of curve shape is proposed in this paper. First, the time series process data is intercepted over a period of time based on the product sampling time to accurately correspond to the quality data from the laboratory. Second, the MSMM is used to build quality prediction model, which using the structural information between the ranks of the matrix samples consisted of time series process data. Besides, the imbalance problem between positive and negative samples is effectively solved by BAG-SMOTE ensemble sampling method. Then, the ensemble classification method is applied to the Tennessee Eastman (TE) and a fine chemical process. The classification evaluation index G-mean are 93% and F-score are 92%, respectively. The superiority of the proposed ensemble classification method is verified. Finally, in order to analyze the key factors of quality fluctuation, a sensitivity analysis method based on curve shape change is proposed via integrated classification metamodel. The method can effectively find out the main factors affecting the quality fluctuation, and the results are consistent with the actual situation.