Model predictive control of an intensified continuous reactor using a neural network Wiener model

In this work a model predictive control approach based on a neural network Wiener model is developed and applied for an intensified continuous reactor. The Wiener model is constituted by two parts: a linear state space identified model based on nonlinear first-principle model, a nonlinear neural network model developed to predict the nonlinear controlled output. Next, a local linearization of neural network model at every sample instant is developed to guarantee an efficient online optimization. The performance of nonlinear controller is illustrated by simulations. NMPC based on neural network Wiener model is proposed.NMPC is applied on an three-phase catalytic intensified continuous reactor.Nonlinear model is linearized locally at every sampling instant.

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