Nonlinear model predictive control of an industrial process with steady-state gain inversion

Abstract Nonlinear model predictive controller (NMPC) is formulated for an industrial process with steady-state gain inversion so that the output variable can be maximized at the peak in presence of disturbances. Appropriate disturbance model formulation is used along with a novel output measurement to allow for robust control in presence of both measured and unmeasured disturbances at the peak where steady-state gain inversion occurs. The constrained nonlinear controller for a process with steady-state gain inversion has been applied successfully with results from plant data that show robustness in maximizing the selectivity of effluent ethylene oxide in the industrial ethylene epoxidation reactor.

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