Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution

This paper considers the application of the adaptive neuro-fuzzy inference system (ANFIS) instead of the highly nonlinear model of a reactive batch distillation column for optimization. The architecture has been developed for fuzzy modeling that learns information from a data set, in order to compute the membership function and rule base in accordance with the given input–output data. In this work, the differential evolution algorithm has been employed for optimization of operation policy of reactive batch distillation for producing ethyl acetate. In optimization, minimal batch time and high purity of product are considered, and reflux ratio and final batch time are taken as decision parameters. The results show that the reduced model (ANFIS) is able to properly create a robust model of the reactive batch distillation, and CPU use is reduced to 1/18,000 of that of a real mathematical model. The highest yield and mole fraction of ethyl acetate were achieved through the use of the obtained optimization policy.