Neural network adaptive real-time optimizing control of industrial processes

Abstract

Real-time optimization (RTO) has attracted considerable interest among researchers and industries for being able to optimise the plant economics such as product efficiency, product quality and process safety in the wake of increasing global competitions. The success of RTO depends much on the quality of model being used in the optimisation. The present study was carried out to explore the use of artificial neural network (ANN) to improve the quality of the model being used in the modified two step (MTS) technique. The MTS is a real-time optimising control algorithm of the modifier adaptation scheme which is used to determine the optimum steady-state control set-points. The proposed new version of MTS technique will be using process model based on ANN. A laboratory scale process of a two continuous stirred tank heat exchanger in series (2CSTHEs) is used as a case study. The multilayer feed forward ANN architecture 4-10-6 with linear function was used to model the 2CSTHEs and then integrates into the MTS technique, the resulted algorithm will be known as Iterative Neural Network Modified Two Step (INNMTS) technique. Simulation studies were conducted to test the performance of the INNMTS technique on the 2CSTHEs process. The results show that the overall value for the coefficient of determination(R²)is equal to one, which indicates adequacy of the model proposed for the prediction of the behavior of 2CSTHEs system. When NN model of 2CSTHEs is applied to the INNMTS technique, the model-plant mismatch is greatly reduced to almost zero, which indicates by significant reduction in the number of iterations to 5which requires by INNMTS compared to 16 iterations by the MTS technique to converge to optimal real solution.

Chemical Engineering Research Bulletin 19(2017) 129-138