Recurrent neural network integrated with process-priori-knowledge and its application

Most of chemical processes with nonlinear characteristics are difficult to model by general linear modeling approaches in practice.Hence,a novel recurrent neural network integrated with priori-knowledge for modeling nonlinear dynamic processes was presented.In the form of non-linear constraints,the priori-knowledge exploited from industrial chemical processes was embedded into the feed-forward neural network with the NARMAX(nonlinear autoregressive moving average with exogenous input)structure.Meanwhile,based on the augmented Lagrange multiplier(ALM)method,a hybrid PSO-IPOPT algorithm was introduced for network weight optimization.The PKRNN model with process-priori-knowledge constraint either ensured good dynamic modeling and prediction(especially extrapolation)ability,or guaranteed safety in the implementation of industrial chemical processes.The effectiveness of the PKRNN model was validated by an actual double-loop liquid propylene polymerization reaction process.