A multimode mechanism-guided product quality estimation approach for multi-rate industrial processes

Abstract

Discrete and delayed laboratory analyses of product quality restrict the operational optimization of industrial processes. However, it is challenging to build an accurate online estimation model for product quality because of complex process dynamics, multiple working conditions, and multi-rate characteristics. Therefore, a multimode mechanism-guided product quality variable estimation model is proposed in this study. First, representative features are extracted from high-dimensional and redundant process variables via both feature engineering and deep learning to describe the internal reaction state. Then, the representative features are used to partition the data samples which are used to train the multi-mode long short-term memory (LSTM) network to increase the adaptability of the estimation model. Finally, the LSTM units are cascaded to learn the variation in the quality variable against time to handle the multi-rate problem. The mechanism models are placed in parallel with the LSTM units to guide the learning process. The estimation model utilizes production data, mechanism knowledge and working condition information, which increases model interpretability and adaptability. A zinc fluidized bed roaster is used to illustrate the proposed estimation approach. The simulation results demonstrate the feasibility and effectiveness of the proposed multi-rate estimation approach.