A roadmap for in silico development and evaluation of industrial NMPC applications: A practical case study

Linear Model Predictive Control (MPC) can be considered as the state of the art advanced process control technology in model-based automation of continuous chemical processes. For (semi-)batch processes, that often present a strongly nonlinear (or even unstable) behavior in combination with fast dynamics, Nonlinear Model Predictive Control (NMPC) is a more suited technology. However, online applications of NMPC have a hard time to penetrate in industry despite methodological developments, tools and examples in academia. In this paper, we propose a roadmap to argue against the intrinsic reasons and practical limitations that slow down the practical online applications of NMPC. This roadmap is applied to an existing semi-batch plant as a practical case study. The results have shown that the NMPC algorithm can provide an improved control, namely a better tracking of the main process variables, a reduction in the reaction time, and robustness with respect to model-plant mismatch and disturbances.