Temperature Prediction in Discrete-Continuous Hybrid Tin Chemical Process Using Trapezoidal Rule and Improved Stochastic Configuration Networks

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.