Temperature Prediction in Discrete-Continuous Hybrid Tin Chemical Process Using Trapezoidal Rule and Improved Stochastic Configuration Networks
Chuangyan Yang, Peng Li, Mingxi Ai, Jiande Wu · IEEE Transactions on Industrial Electronics · 2025
Temperature prediction in the tin chemical process (TCP) is crucial for implementing production process monitoring and predictive control. However, TCP’s discrete-continuous hybrid structure, incorporating both binary and continuous variables, poses challenges for traditional prediction models. To overcome these limitations, this study introduces a TCP temperature prediction approach using a trapezoidal rule (TR) and improved stochastic configuration networks (TR-ISCNs). First, the TR transforms discrete variables into continuous forms. Next, the continuous data are used to train improved stochastic configuration networks (ISCNs) to develop a temperature prediction model. Finally, a TR-ISCNs-based model update framework is implemented, enabling the prediction model to leverage recent window data for real-time parameter updates and temperature forecasts as new data becomes available. The proposed method’s effectiveness is demonstrated through application in a real TCP.