Rejection of the Feed-Flow Disturbances in a Multi-Component Distillation Column Using a Multiple Neural Network Model-Predictive Controller

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This article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (MPC) of a chemical plant. A combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (MIMO) process with time delays.  An optimization procedure for a neural MPC algorithm based on this model is then developed. The proposed scheme has been tested on a model of an 18-plate multi-component distillation column. The algorithm provides excellent disturbance rejection for this process.