A Light Attention-Mixed-Base Deep Learning Architecture toward Process Multivariable Modeling and Knowledge Discovery

Abstract

A Light Attention-Mixed-Base Deep Learning Architecture (LAMBDA) is developed to simultaneously achieve process knowledge discovery and high-accuracy multivariable modeling. By organizing multiple network bases and a novel light attention mechanism in a special way, the proposed LAMBDA is capable to learn different factors affecting the chemical process outputs, i.e. the basic dynamic characteristics, transient disturbances and other unknown factors. Besides, a development procedure embedding a hyperparameter optimization framework—Optuna is performed to optimize the network architecture. Compared with baselines including FNN, CNN, LSTM and Attention-LSTM, the new architecture displays an outstanding fitting capacity on the discharge flowrates modeling of an actual deethanization process. The process knowledges extracted from the LAMBDA model parameters are also illustrated, which are valuable in the development of advanced process tasks. The proposed LAMDBA can fit any number of outputs without degrading the knowledge discovery ability, making itself potential in the modeling of complex chemical processes.