(371u) A Comprehensive and General Class for Solving Nonlinear Model Predictive Control and Dynamic Optimization | AIChE

(371u) A Comprehensive and General Class for Solving Nonlinear Model Predictive Control and Dynamic Optimization


Lima, N. M. N. - Presenter, University of Campinas, UNICAMP
Manenti, F. - Presenter, Politecnico di Milano
Zuniga Linan, L. - Presenter, University of Campinas, UNICAMP
Buzzi-Ferraris, G. - Presenter, Politecnico di Milano

Nonlinear model predictive control (NMPC) and real-time dynamic optimization (RTDO) are fast spreading in last years, mainly pushed by their well-known and field-proven benefits (Manenti and Rovaglio, 2008; Dones et al., 2010). Also, the spreading is going beyond the traditional fields of process systems engineering and computer-aided process engineering, by finding application in some scientific areas particularly far from their original cradle. NMPC and RTDO solutions require a complex numerical structure involving (i) differential equation systems to foresee plants and/or process units dynamics and (ii) constrained optimization issues to meet process specs and requirements. Also, some additional issues are currently arising since those new fields that are going to implement the NMPC and RTDO methodology present their own peculiarities, which sometimes make hard the same NMPC solution and/or more complex its numerical structure. From this perspective, the possibility to propose a generalized and comprehensive approach to face the implementation of NMPC and RTDO could be appealing not only to enlarge the application domain of such control/optimization methodologies, but even to significantly reduce the duty of any user in implementing them. BzzMath library is adopted as kernel to allow easily solving NMPC and RTDO by only defining their differential system and the desired objective function only, without taking care of any numerical problem that may occur in integrating differential systems, in searching for the minimum of a constrained/complex objective function, and in implementing a moving horizon methodology.

Quoted References

DONES, I., MANENTI, F., PREISIG, H.A., and BUZZI-FERRARIS, G. (2010). Nonlinear Model Predictive Control: a Self-Adaptive Approach. Industrial & Engineering Chemistry Research, DOI: 10.1021/ie901693w. MANENTI, F., and ROVAGLIO, M. (2008). Integrated multilevel optimization in large-scale poly(ethylene terephthalate) plants. Industrial & Engineering Chemistry Research 47, 92-104.



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