Control and Monitoring of Chemical Batch Reactors

Caccavale, Fabrizio; Iamarino, Mario; Pierri, Francesco; Tufano, Vincenzo

doi:10.1007/978-0-85729-195-0

Cited by 22 publications

(19 citation statements)

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“…Furthermore, it is emphasized that the chosen example has specially interest in the chemical industry and, although there is many examples in the literature with non-linear systems using this methodology ( [13, 8, among others]), but there is few references in FDI applied to CSTR [20,3].…”

Section: Discussionmentioning

confidence: 99%

Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor

Pico¹,

Adam²

2017

Adv. sci. technol. eng. syst. j.

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Section: Discussionmentioning

confidence: 99%

Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor

Pico¹,

Adam²

2017

Adv. sci. technol. eng. syst. j.

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“…The reasoning developed here for batch reactors 4,5 extends similarly to other batch units such as batch distillation columns 6,7 and batch crystallizers 8,9 .…”

Section: What To Model?mentioning

confidence: 94%

Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches Keywords Batch control, predictive control, iterative learning control, run-to-run control, batch process optimization, dynamic optimization, optimizing control, run-to-run optimization.

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“…where a(k) is normally distributed at kth time interval; and Ti' is temperature after addition of measurement noise. As stated in [15], it is vital to have a control strategy which is sensitive to the reaction variation. Therefore, R 1 is increased to 1.3 times from the original rate.…”

Section: Simulationmentioning

confidence: 99%

Genetic Algorithm Based Multivariable Control for Exothermic Batch Process

Tan

Chuo

Tham

et al. 2012

2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks

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Exothermic process is highly nonlinear and complex process. Large amount of heat will be released during the chemical reaction. As a result of the exothermic behaviour, the reaction may become unstable and consequently poses safety concern to the plant if the reactor temperature exceeds the cooling capacity. In the industrial point of view, heating is needed in order to speed up the reaction rate so that it can reduce the overall process reaction time. Hence, this paper proposes genetic algorithm (GA) to control the reaction temperature as well as to balance the production needs with the safety specification. GA exploits probabilistic search method to optimise the specific objective function based on evolutionary principle. Simulation assessment of the GA has been carried out using a benchmark exothermic batch process model. The results show that GA is a good candidate in controlling the reactor temperature.

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Control and Monitoring of Chemical Batch Reactors

Cited by 22 publications

References 0 publications

Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor

Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor

Control and Optimization of Batch Chemical Processes

Genetic Algorithm Based Multivariable Control for Exothermic Batch Process

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