Robust model-based predictive control of exothermic chemical reactor

Oravec, Juraj; Bakošová, Monika

doi:10.1515/chempap-2015-0146

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…Generally, it can be stated that chemical reactors controllers use various control methods, such as PI controllers, adaptive control methods, robust approaches, predictive control and the like [6][7][8][9][10][11][12][13][14][15]. The model predictive control [16][17][18] belongs to the one of the most popular and successful approaches for semi-batch reactors control. However, this methodology brings some difficulties in finding optimal control sequence especially when complex nonlinear model is utilized.…”

Section: Control Of the Systemmentioning

confidence: 99%

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

Macků

Samek

2018

MATEC Web Conf.

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The aim of this paper is to evaluate the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area. The model predictive control is based on an objective function minimization. Two approaches to model predictive control applied on a nonlinear system are studied here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The nonlinear system simulated here is an exothermic semi-batch reactor mathematical model based on a real chemical exothermic process. Also the input data used here to simulate the process were obtained from the same real process. Results obtained by the simulation means were than evaluated using suitable criterion which was defined for that purpose and discussed.

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Section: Control Of the Systemmentioning

confidence: 99%

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

Macků

Samek

2018

MATEC Web Conf.

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“…Generally, it can be stated that chemical reactors controllers uses various control methods, such as PI controllers, adaptive control methods, robust approaches, predictive control and the like [16][17][18][19][20][21][22][23][24][25]. The model predictive control [26][27][28] belongs to the one of the most popular and successful approaches for semi-batch reactors control. However, this methodology brings some difficulties in finding optimal control sequence especially when complex nonlinear model is utilized.…”

Section: Control Of the Reactormentioning

confidence: 99%

Self - Organizing Migrating Algorithm in Model Predictive Control: Case study on Semi-Batch Chemical Reactor

Macků¹,

Samek²

2016

DAAAM Proceedings

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Control of complex nonlinear systems brings challenges in the controller design. One of methods how to cope with this challenge is the usage of advanced optimization methods. This work presents application of self-organizing migrating algorithm (SOMA) in control of the semi-batch reactor. The reactor is used in chromium recycling process in leather industry. Because of the complexity of this semi-batch reactor control, the model predictive control (MPC) approach is used. The MPC controller includes self-organizing migrating algorithm (SOMA) for the optimization of the control sequence.

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“…A rigorous robust design methodology with H 2 and H ∞ performance for linear and nonlinear CSTR models has been presented in Guay et al (2005), Komari Alaei and Yazdizadeh (2014) and Vasičkaninová et al (2015). Model predictive control (Mayne, 2014; Oravec and Bakošová, 2015) has also received considerable attention by the process control research community. Other approaches include design methods based on multiple models (Krishnan et al, 2017; Kvasnica et al, 2010) and techniques based on a combination of neural networks and MPC or adaptive control (Li, 2013; Vasičkaninová and Bakošová, 2009).…”

Section: Introductionmentioning

confidence: 99%

Temperature and concentration control of exothermic chemical processes in continuous stirred tank reactors

Vlahakis

Halikias

2019

Transactions of the Institute of Measurement and Control

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Exothermic chemical reaction taking place in continuous stirred tank reactor is considered. Heat release from the chemical reaction, non-linear dynamic behavior of the process and uncertainty in parameters are the main factors motivating the use of robust control design. Viewing temperature and molar concentration as variables both accessible in real time, PI and optimal state-feedback controllers driven by temperature and concentration error signals are proposed to regulate the system over reactor’s steady-state working points by counteracting undesired disturbances. Since access to concentration value has proved beneficial for the reactor’s performance, estimation techniques are examined to compensate for the problematic nature of the concentration’s measurement. A linear reduced-order observer is first proposed to estimate the concentration value using temperature measurements. In addition, assuming concentration measurement is available with a relatively short delay via sample analysis, a linear and non-linear discrete-time predictor is constructed to estimate the concentration’s real-time value. A linear combination of the two estimation schemes (observer, predictor) is proposed resulting in a combined estimator, in which the emphasis between the two individual schemes can be controlled via a scalar parameter. The work presented in this paper was supported by the GLOW project – New weather-stable low gloss powder coatings based on bifunctional acrylic solid resins and nanoadditives – as part of the development of novel and efficient processing technologies regarding the production of new families of powder coatings, responding to industrial requirements for quality improvement at lower cost and shorter development cycles.

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Robust model-based predictive control of exothermic chemical reactor

Cited by 9 publications

References 12 publications

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

Self - Organizing Migrating Algorithm in Model Predictive Control: Case study on Semi-Batch Chemical Reactor

Temperature and concentration control of exothermic chemical processes in continuous stirred tank reactors

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