An Improved Bar-Shaped Sliding Window CNN Tailored to Industrial Process Historical Data with Applications in Chemical Operational Optimizations

With developments of deep learning technologies and increasing demands for industrial process feature extractions, researchers pay much attention on deep learning of process historical data. Currently, the convolutional neural network (CNN) has made inroad into extracting potential deep features from industrial process plant data. However, the existing sliding window associated with CNNs is rarely concerned with the characteristics of process historical data such as slow time varying and variable correlations. In response to this problem, an improved bar-shaped CNN (IBS-CNN) tailored to industrial process historical data is developed in this paper. Therein, process historical data are formulated as bars before trial-and-error methods are used to determine the optimum range of data for each calculating iteration. As a result, the width of sliding window is consistent with variable numbers during the algorithmic operations, in which, the historical data involved into the sliding window are governed by sever...