A Temporal Convolutional-Based Kolmogorov-Arnold Network for Industrial Soft Sensor Modeling

Multi-timescale characteristics, nonlinearity and dynamic features exist extensively in modern industrial processes, impairing the predictive performance and accuracy of soft sensor modeling for primary variables. To address this challenge, a multi-timescale neural network-based soft sensor model, consisting of two modules, is designed for complex industrial processes. The designed temporal convolutional module, which integrates two attention mechanism with multiple parallel temporal convolutional layers using dilated causal convolution, efficiently enhances feature extraction at different time scales. Then, bidirectional long short-term memory networks are developed to capture time features series from the designed temporal convolutional module. The extracted feature variables are fed into the Kolmogorov-Arnold network using B-spline functions, effectively addressing nonlinear data characteristics and improving prediction performance. The superiority of the designed soft sensor model is validated through practical applications in wastewater treatment process and flue gas desulfurization, demonstrating promising potential for real-world applications.