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Matrix Control (matrix + control)
Selected AbstractsDynamic Simulation and Control of an MTBE Catalytic Distillation ColumnASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 3-4 2000H. Lin A dynamic model of an MTBE (methyl tert butyl ether) catalytic distillation column was developed using the SpeedUp simulation package. Experimental kinetic rate data, rigorous thermodynamics, vapour-liquid nonidealities and tray hydraulics were incorporated in the simulation. The steady state results from the dynamic model were compared with the steady state results from a steady state model developed using the AspenPlus simulation package. The steady state results from the two simulations were in perfect agreement with each other. Open hop step tests were performed on the dynamic model of this process and the process model indicated a non-linear, self-regulating behaviour and did not exhibit any multiplicities. Multiloop linear control systems were designed and PI, PID and Dynamic Matrix Control (DMC) controllers were tested on the simulation for load disturbances and setpoint changes. All controllers performed adequately; the DMC controller consistently resulted in better dynamic control performance than the other two controllers. [source] Modeling and predictive control using fuzzy logic: Application for a polymerization systemAICHE JOURNAL, Issue 4 2010Nádson M. N. Lima Abstract In this study, a predictive control system based on type Takagi-Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] Nonlinear model predictive control for the polymorphic transformation of L -glutamic acid crystalsAICHE JOURNAL, Issue 10 2009Martin Wijaya Hermanto Abstract Polymorphism, a phenomenon where a substance can have more than one crystal forms, has recently become a major interest to the food, speciality chemical, and pharmaceutical industries. The different physical properties for polymorphs such as solubility, morphology, and dissolution rate may jeopardize operability or product quality, resulting in significant effort in controlling crystallization processes to ensure consistent production of the desired polymorph. Here, a nonlinear model predictive control (NMPC) strategy is developed for the polymorphic transformation of L -glutamic acid from the metastable ,-form to the stable ,-form crystals. The robustness of the proposed NMPC strategy to parameter perturbations is compared with temperature control (T-control), concentration control (C-control), and quadratic matrix control with successive linearization (SL-QDMC). Simulation studies show that T-control is the least robust, whereas C-control performs very robustly but long batch times may be required. SL-QDMC performs rather poorly even when there is no plant-model mismatch due to the high process nonlinearity, rendering successive linearization inaccurate. The NMPC strategy shows good overall robustness for two different control objectives, which were both within 7% of their optimal values, while satisfying all constraints on manipulated and state variables within the specified batch time. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Non-Linear Model Predictive Control: A Personal Retrospective,THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2007B. Wayne Bequette Abstract An overview of non-linear model predictive control (NMPC) is presented, with an extreme bias towards the author's experiences and published results. Challenges include multiple solutions (from non-convex optimization problems), and divergence of the model and plant outputs when the constant additive output disturbance (the approach of dynamic matrix control, DMC) is used. Experiences with the use of fundamental models, multiple linear models (MMPC), and neural networks are reviewed. Ongoing work in unmeasured disturbance estimation, prediction and rejection is also discussed. On présente un aperçu général du contrôle prédictif par modèles non linéaires (NMPC), en mettant l'accent en particulier sur les expériences des auteurs et les résultats publiés. Les défis incluent des solutions multiples (à partir des problèmes d'optimisation non convexes), ainsi que la divergence entre les sorties de modèle et d'installation lorsque la perturbation de sortie additive constante (la méthode du contrôle de matrice dynamique, DMC) est utilisée. Les expériences avec les modèles fondamentaux, les modèles linéaires multiples (MMPC) et les réseaux neuronaux sont examinées. Le travail actuellement mené sur l'estimation, la prédiction et le rejet des perturbations non mesurées est également examiné. [source] | ||||||||||