An Artificial-Knowledge-based Decision Approach for Process Parameter Optimization

In process industries, dynamic uncertainties necessitate that experienced operators adjust process parameters. This paper tries to mine the decision knowledge of operators and proposes an artificial knowledge-based (AKB) decision approach for process parameter optimization. The methodology comprises three functionally interdependent stages: Data preprocessing, quality prediction, and AKB decision modeling. Data preprocessing includes outlier processing which adopts a sliding-window-based iForest method to detect the outlier caused by batch changeover and data alignment which aligns delayed quality indicators with process variables based on a dynamic-window-based distance correlation. Quality prediction uses temporal convolutional networks with feature processing and temporal attention mechanisms (FP-TCN-TA) to reconstruct the operators' realtime quality assessment references. AKB decision modeling combines one-dimensional convolutional neural network (1D-CNN) for local parameter feature extraction within finite time steps and multilayer perceptron (MLP) for nonlinear adjustment mapping. It emulates operators' decision logic. A case study using real-world operating data collected from the tire tread extrusion line demonstrates the approach's capability to replicate operator decisions for process parameter optimization. The effectiveness of each methodological component is also confirmed by experiments.