A robustly model predictive control strategy applied in the control of a simulated industrial polyethylene polymerization process

Nogueira, Idelfonso B. R.; Fontes, Raony M.; Ribeiro, Ana M.; Pontes, Karen Valverde; Embiruçu, Marcelo; Martins, Márcio A. F.

doi:10.1016/j.compchemeng.2019.106664

Cited by 10 publications

(3 citation statements)

References 41 publications

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“…The offset-free control was accomplished by the delta-input formulation. Nogueira et al (Nogueira et al 2020) suggested the Infinite Horizon MPC (IHMPC) for controlling the polyethylene polymerization process control in the chemical industry. The CSTR was connected to the plug flow reactor (PFR) in that model.…”

Section: Mpc Controllers For Non-linear Control Of Chemical Plantsmentioning

confidence: 99%

Review on PID, fuzzy and hybrid fuzzy PID controllers for controlling non-linear dynamic behaviour of chemical plants

Mohindru

2024

Artif Intell Rev

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The chemical production process is tedious due to the integration of different types of equipment and variables. Designing the controller is crucial in the chemical industry due to the interactive and non-linear system behaviour. An intelligent autonomous controller can improve the operating efficiency of the industry. Although several controllers have been developed, different system failures are frequently reported. Hence, controllers such as proportional integral derivative (PID), fuzzy logic controller (FLC), and hybrid fuzzy PID (F-PID) applied in the chemical industries are critically reviewed in the paper. Initially, the PID controller-based approaches are reviewed for different purposes in the chemical industry. After that, the FLC-based controllers-based papers are reviewed. In order to satisfy the issues in both controllers, the H-PID controllers have been reviewed. This review paper will provide an effective solution for operation control in the chemical industry under different operating conditions.

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Section: Mpc Controllers For Non-linear Control Of Chemical Plantsmentioning

confidence: 99%

Review on PID, fuzzy and hybrid fuzzy PID controllers for controlling non-linear dynamic behaviour of chemical plants

Mohindru

2024

Artif Intell Rev

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“…[1][2][3] However, the modeling and control of the polystyrene polymerization reaction process remains a difficult research area, primarily due to the processes' complex reaction kinetics, highly nonlinear nature, and interactivity between process variables. 4 One of the important objectives of the polystyrene polymerization reaction process is to ensure that the enduse polystyrene products have the desired properties for a specific application, such as the molecular weight distribution, chain lengths, and monomer conversion. For this purpose, various advanced control techniques, including proportional-integral-derivative (PID) control, 5 fuzzy control (FC), 6 iterative learning control (ILC), [7][8][9] and model predictive control (MPC), 10,11 have been implemented to improve the product quality of polystyrene polymerization reaction process.…”

Section: Introductionmentioning

confidence: 99%

Tube‐based batch model predictive control for polystyrene polymerization reaction process

Zhou

Jia

Zhou

2023

Asia-Pacific J Chem Eng

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This paper focuses on product quality control issue of polystyrene polymerization reaction process. A novel tube‐based batch model predictive control (BMPC) strategy based on a data‐driven model is presented, which is inspired by the tube‐based robust model predictive control (MPC) strategy. First, the dynamic behavior of the polystyrene polymerization reaction process is captured with high accuracy by establishing a just‐in‐time learning (JITL) model. Then, the built JITL model is regarded as the nominal system, and a BMPC is designed on the basis of JITL model to obtain the nominal trajectory, which is an integrated control system framework composed of iterative learning control (ILC) and MPC. Meanwhile, another auxiliary MPC (AMPC) is designed to minimize the deviation between the actual trajectory and the nominal trajectory, and the actual tracking errors are limited to the tube invariant set centered on the nominal errors to restrain the performance deterioration of the control system caused by the modeling errors. Finally, the effectiveness of the proposed tube‐based BMPC method is verified by simulating an industrial batch polystyrene polymerization reaction process. The results indicate that the presented control algorithm not only enhances the tracking performance of the system but also provides a more robust system stability than existing methods. This paper provides a new solution to improve the product quality of polystyrene polymerization reaction process.

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“…), multivariable process and optimal criterion can be easily managed. NMPC strategy has been widely used to control polymerization processes Nogueira et al, 2020). Some examples of NMPC implementation for styrene polymerization processes with traditional jacket cooling mechanism can also be found in Hidalgo and Brosilow (1990), Prasad et al (2002), Hosen et al (2011), Novak and Chalupa (2013), and Hall (2018.…”

Section: Introductionmentioning

confidence: 99%

Model development and control of an auto-refrigerated polystyrene polymerization reactor

Mokhtarname

Safavi

Urbas

et al. 2021

Transactions of the Institute of Measurement and Control

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Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.

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A robustly model predictive control strategy applied in the control of a simulated industrial polyethylene polymerization process

Cited by 10 publications

References 41 publications

Review on PID, fuzzy and hybrid fuzzy PID controllers for controlling non-linear dynamic behaviour of chemical plants

Review on PID, fuzzy and hybrid fuzzy PID controllers for controlling non-linear dynamic behaviour of chemical plants

Tube‐based batch model predictive control for polystyrene polymerization reaction process

Model development and control of an auto-refrigerated polystyrene polymerization reactor

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