Control of Batch Reactors: A Review

Berber, Ridvan

doi:10.1007/978-94-011-0135-6_18

Cited by 38 publications

(21 citation statements)

References 93 publications

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“…Then the filtered value corresponds to the mean value of the resulted truncated sequence. Such filter is named as the trimming-mean operation, where the expression of the filtered data is given by for (21) where is the arranged components of the considered data window in an increasing order.…”

Section: ) Off-line Filteringmentioning

confidence: 99%

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Application of iterative learning control to an exothermic semibatch chemical reactor

Mezghani¹,

Roux

Cabassud

et al. 2002

IEEE Trans. Contr. Syst. Technol.

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This work focuses on the temperature control of a semibatch chemical reactor used for fine chemicals production. Such a reactor is equipped with a heating/cooling system composed of different thermal fluids. Without extensive modeling investigations, a feedback-feedforward control strategy is proposed for ensuring the tracking performance of the desired temperature profile. Such a strategy is derived from a family of the iterative learning control (ILC) algorithms named batch model predictive control (BMPC). Learning is achieved without requiring a detailed knowledge of the system, which may be affected by unknown but repetitive disturbances. The learning control solution is based on the minimization of a linear quadratic cost function. The synthesis of the proposed strategy is studied, and improvements of the algorithm features are proposed. First, guaranteed convergence of the algorithm is illustrated in a few experimental runs. Second, some practical considerations for the removal of high-frequency disturbance effects are outlined to improve the achieved performance. Third, a robust supervisory control procedure is employed to choose the right fluid and to reduce the superfluous fluid changeovers, mainly when different fluids are available. Finally, experimental results are presented to illustrate the practical appeal and effectiveness of the proposed scheme.

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Section: ) Off-line Filteringmentioning

confidence: 99%

“…Controller performance is mainly dependent on the heating/cooling system associated with the reactor, since the selectivity of the reaction performed in the vessel is strongly temperature dependent [21]. In most cases, the reactor is jacketed and a thermal fluid is used to control the mixture temperature.…”

Section: B Heating/cooling Systemmentioning

confidence: 99%

Application of iterative learning control to an exothermic semibatch chemical reactor

Mezghani¹,

Roux

Cabassud

et al. 2002

IEEE Trans. Contr. Syst. Technol.

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“…Part of this can be attributed to the comparatively lower production volume through batch processing. Another reason for this may be that batch processes present a set of challenges uncommon in continuous processes, including nonstationary operating recipes, the consequent exposure to process nonlinearity, and significant variations in the initial charge condition (Berber, 1996). These challenges are not easily met by the standard linear optimal control theories and tools, which are widely adopted for continuous industrial process control today.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control applied to batch processes: An overview

Lee

2007

Control Engineering Practice

256

106

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With the recent emphasis on batch processing by emerging industries like the microelectronics and biotechnology, the interest in batch process control has been renewed. This paper gives an overview of the iterative learning control (ILC) technique, which can be used to improve tracking control performance in batch processes. The fundamental concepts and review of the various ILC algorithms are presented, with a particular focus on a model-based algorithm called Q-ILC and an application involving a rapid thermal processing (RTP) system. The study indicates that one can solve a seemingly very difficult multivariable nonlinear tracking problem with relative ease by intelligently combining the ILC technique with basic process insights and standard system identification techniques. Some related techniques in the literature are brought forth with the hope of unifying them. We aslo suggest some remaining challenges. r

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“…Model based optimal control of batch and semi-batch processes is increasingly becoming important for surviving in the competitive market conditions owing to the shift in the focus of chemical industry from mass production of low value products to small-scale production of high value or specialty chemicals [1][2][3]. Development of a mathematical model is the key step in any model based optimization and control scheme.…”

Section: Introductionmentioning

confidence: 99%