Neural network and support vector machine predictive control of tert-amyl methyl ether reactive distillation column

Abstract

An algorithm of model predictive control based on artificial neural network and least-square support vector machine method is presented for a class of industrial process with strong nonlinearity such as tert-amyl methyl ether (TAME). Integral constant is added to improve the performance of the controller. In the present work, two different control methodologies neural network predictive control (NNPC) and support vector machine-based predictive control (SVMPC) are implemented and compared with a conventional proportional-integral-derivative (PID) control methodology to a TAME reactive distillation column. The simulation result shows that both NNPC and SVMPC gives better control performance than PID for set-point change as well as for load change of ±10% in methanol feed flow rate and molar ratio of methanol to isoamylene in reactor effluent feed.