Semi-supervised Soft Sensor Modeling Method for Crude Furfuryl Alcohol Distillation Process Based on Cosine Similarity-Discount

In view of the dynamic time variation and multirate data problems in crude furfuryl alcohol distillation process, too few samples of modeling data caused poor generalization ability of soft sensor model and reduced the accuracy of soft sensor model prediction. To solve this problem, this paper proposes a semi-supervised soft sensor modeling method based on cosine similarity-discount(SSSMI-COSD). By calculating the cosine similarity between labeled and unlabeled samples in the same time interval, and combining with the proposed constraints, the data clustering between labeled and unlabeled samples is realized. In addition, in order to avoid the ill-conditioned problem caused by high-dimensional input variables, the clustered data are fused by discount factor value (DFV). An actual data of a crude furfuryl alcohol distillation process (CFADP) simulation experiment were carried out. The results show that the proposed SSSMI-COSD method can effectively improve the soft sensor model prediction accuracy for a crude furfuryl alcohol distillation process.