Random Attention Mechanism for Deep Quality Modeling and Online Prediction in Chemical Processes

Many deep learning methods have recently been introduced for quality prediction in industrial processes, among which the deep latent variable model should be one of the most promising methods due to its simple and explainable model structure. However, how to efficiently extract features from the latent space for quality prediction remains unclear. This paper develops a random attention mechanism upon the basic deep latent variable regression modeling framework. After the latent variables have been extracted from different layers of the deep model, various random attentions are made from the pool of latent variables, based on which different submodels are constructed for the same quality prediction purpose. Then, an information fusion strategy is designed to combine the quality prediction results of different submodels. Results on a debutanizer column and a CO2 absorption column show that the proposed random attention mechanism can reduce the average prediction index (RMSE) by 18% and 31%, respectively, compared to the regular methods.