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Opportunities and Challenges of Model Predictive Control in Food Technologies

Modern food industry has gone transformation from classical production concepts based on intensive manual work and off-line monitoring to a highly automated computer on-line controlled processes. The main focus in process automation is on application of modern process analytical technologies (PAT) and computer models for analysis and synthesis of information from on-line sensor signals with basic engineering principles of heat, mass and momentum, chemical and biochemical reactions, and industrial microbiology. New trends in process information synthesis and analysis of complex multidimensional data are based on: chemometric methods, such as principal component analysis (PCA) and partial least squares (PLS) ; and artificial intelligence (AI) algorithms, such as artificial neural networks (ANN), and fuzzy logic inference. For incorporation of computer algorithms in model predictive control (MPC) needed are developments of mathematical and statistical models for prediction of future outputs of multivariate nonlinear systems over a finite time horizon based on a set on multivariate inputs. Development of accurate and robust multivariate models for food technologies represents the main challenge and is crucial for MPC applicability. Methodology of MPC requires de-termination of manipulative inputs by optimization of a control objective function with constraints on manipulative and state output variables. From a practical point of view, main advantage of MPC (and the reason for its industrial success) is its true multivariate structure and ability to handle systems with constraints in a systematic and transparent manner. From process management point of view, MPC control can support process operation in a flexible and dynamic way to meet changing market re-quirements. The MPC technology is used to steer processes closer to their physical limits to obtain a better economical result. Main opportunities of MPC are in process control for production of food with improved nutritional and organoleptic properties, product quality assurance, environment protection, and increased product market value.

model predictive control MPC, principal component analysis PCA, partial least squares PLS, neural networks ANN, ARMAX models

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