Optimal grade transition control for polymerization reactors

Wang, Yang; Seki, Hiroya; Ohyama, Satoshi; Akamatsu, K.; Ogawa, Morimasa; Ohshima, Masahiro

doi:10.1016/s0098-1354(00)00550-0

Cited by 45 publications

(33 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MPC benefits have been validated on polymerization reactors [6][7][8][9] by providing good results (i.e., satisfactory tracking performance, robustness, ability to suppress disturbances, and accuracy under real-time constraints) without significantly increasing computational complexity and effort [10][11][12][13][14][15]. Some recent works also deal with stability and faulttolerant design issues [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Advanced Control of a Continuous Solution Copolymerization Process

Lima

Liñan

Manenti

et al. 2011

International Journal of Chemical Engineering

View full text Add to dashboard Cite

A model-based predictive control system is designed for a copolymerization reactor. These processes typically have such a high nonlinear dynamic behavior to make practically ineffective the conventional control techniques, still so widespread in process and polymer industries. A predictive controller is adopted in this work, given the success this family of controllers is having in many chemical processes and oil refineries, especially due to their possibility of including bounds on both manipulated and controlled variables. The solution copolymerization of methyl methacrylate with vinyl acetate in a continuous stirred tank reactor is considered as an industrial case study for the analysis of the predictive control robustness in the field of petrochemical and polymer production. Both regulatory and servo problems scenarios are considered to check tangible benefits deriving from modelbased predictive controller implementation.

show abstract

Section: Introductionmentioning

confidence: 99%

Advanced Control of a Continuous Solution Copolymerization Process

Lima

Liñan

Manenti

et al. 2011

International Journal of Chemical Engineering

View full text Add to dashboard Cite

show abstract

“…This ideal condition, however, does not always exist. There are a number of efforts in the literature to deal with this issue [1][2][3][4][5][6][7][8][9][10][11]. However, all these efforts have dealt only with melt index (or alternatively the molecular weight) and density of polymers and not the molecular weight distribution of the formed polymer.…”

Section: Introductionmentioning

confidence: 99%

Broadening the polyethylene molecular weight distribution by controlling the hydrogen concentration and catalyst feed rates

Emad

Ali

2010

ISA Transactions

View full text Add to dashboard Cite

“…Wang et al (2000) propose the use of a feedforward-feedback control scheme to implement optimal open-loop trajectories computed via dynamic optimization in a polymer grade transition application. Chatzidoukas et al (2003) formulate grade transition as a mixedinteger dynamic optimization problem, where the determination of a multi-loop PI control structure is included within the optimization framework.…”

Section: Introductionmentioning

confidence: 99%

Reference Trajectory Optimization Under Constrained Predictive Control

2007

View full text Add to dashboard Cite

Flores-Tlacuahuac et al., 2006;Asteasuain et al., 2006). A set of process variables is computed that minimizes a measure of the cost of the transition, subject to constraints on the inputs and possibly other specifi cation and/or operational constraints. The model states are related to the inputs through a dynamic model. The decision space in the above-cited studies includes the open-loop trajectories of certain inputs. McAuley and MacGregor (1992) show that plant/model mismatch could result in deviation of product quality variables from their desired values, and advocate the use of feedback control in the implementation of computed optimal transitions. In a subsequent paper (McAuley and MacGregor, 1993), they develop a non-linear model-based controller for a polymerization process, and apply it to track the profi les of output variables Chemical process systems often need to respond to frequently changing product demands. This motivates the determination of optimal transitions, subject to specifi cation and operational constraints. However, direct implementation of optimal input trajectories would, in general, result in offset in the presence of disturbances and plant/model mismatch. This paper considers reference trajectory optimization of processes controlled by constrained model predictive control (MPC). Consideration of the closed-loop dynamics of the MPC-controlled process in the reference trajectory optimization results in a multi-level optimization problem. A solution strategy is applied in which the MPC quadratic programming subproblems are replaced by their Karush-Kuhn-Tucker optimality conditions, resulting in a single-level mathematical program with complementarity constraints (MPCC). The performance of the method is illustrated through application to two case studies, the second of which considers economically optimal grade transitions in a polymerization process.Les systèmes de procédés chimiques doivent souvent répondre à des changements de production fréquents. Ceci motive la détermination de transitions optimales, soumises à des contraintes de spécifi cation et de fonctionnement. Toutefois, l'implantation directe de trajectoires d'entrée optimales entraîne, en général, un décalage en présence de perturbations et d'une incompatibilité installation/modèle. Cet article porte sur l'optimisation des trajectoires pour des procédés contrôlés par le contrôle prédictif par modèle contraint (MPC). Le fait de considérer la dynamique en boucle fermée du procédé contrôlé par MPC dans l'optimisation des trajectoires de référence cause un problème d'optimisation à plusieurs niveaux. Une stratégie de solution est appliquée dans laquelle les sous-problèmes de programmation quadratique du MPC sont remplacés par des conditions d'optimalité de Karush-Kuhn-Tucker. On obtient ainsi un programme mathématique à niveau unique associé à des contraintes de complémentarité (MPCC). La performance de la méthode est illustrée par l'application de deux études de cas, le second considérant les transitions de grade optimales en ...

show abstract

Optimal grade transition control for polymerization reactors

Cited by 45 publications

References 4 publications

Advanced Control of a Continuous Solution Copolymerization Process

Advanced Control of a Continuous Solution Copolymerization Process

Broadening the polyethylene molecular weight distribution by controlling the hydrogen concentration and catalyst feed rates

Reference Trajectory Optimization Under Constrained Predictive Control

Contact Info

Product

Resources

About