A new Correlation-Similarity Conjoint Algorithm for developing Encoder-Decoder based deep learning multi-step prediction model of chemical process

Abstract

As an important procedure for the multi-step modeling of chemical process, the feature selection approach for unequal-length process variable series is frequently studied based on statistical analysis rather than chemical mechanism. As such, in this contribution, a convolution-based Correlation-Similarity Conjoint Algorithm (CSCA) considering chemical process mechanism including underlying variable interactions is developed to perform correlation analyses between unequal-length variable series, so that the most correlated features with minimum redundancy can be detected for the multi-step prediction modeling of variable series. Aided by CSCA, the chemical encoder-decoder based deep learning models are built for multi-step predicting flowrates, pressures and temperatures in the actual industrial process. The proposed CSCA proves to be effective in series feature selection, promoting the developments of the multi-step prediction encoder-decoder based deep learning model of different process variables, which are of great value for prospective model predictive control and real-time optimization in chemical engineering.