Use of neural nets for dynamic modeling and control of chemical process systems

Bhat, Naveen; McAvoy, Thomas J.

doi:10.1016/0098-1354(90)87028-n

Cited by 634 publications

(190 citation statements)

References 13 publications

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“…(8). Introducing the vector EMBED Equation as the input to the network and defining d +n u context neurones, the approximate inverse control law adopts the following form:…”

Section: In6erse Controller Structurementioning

confidence: 99%

“…Furthermore, the majority of published experimental results concerning process control and, specifically, batch reactors control have used NN for nonlinear process modelling in a control scheme [8][9][10]. Recently, Dirion et al [11] have designed a neural-network controller, and used it for real-time control of an experimental pilot-plant reactor.…”

Section: Introductionmentioning

confidence: 99%

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Application of recurrent neural networks in batch reactors

Galván

Zaldı́var

1998

Chemical Engineering and Processing: Process Intensification

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Although nonlinear inverse and predictive control techniques based on artificial neural networks have been extensively applied to nonlinear systems, their use in real time applications is generally limited. In this paper neural inverse and predictive control systems have been applied to the real-time control of the heat transfer fluid temperature in a pilot chemical reactor. The training of the inverse control system is carried out using both generalised and specialised learning. This allows the preparation of weights of the controller acting in real-time and appropriate performances of inverse neural controller can be achieved. The predictive control system makes use of a neural network to calculate the control action. Thus, the problems related to the high computational effort involved in nonlinear model-predictive control systems are reduced. The performance of the neural controllers is compared against the self-tuning PID controller currently installed in the plant. The results show that neural-based controllers improve the performance of the real plant.

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“…(8). Introducing the vector EMBED Equation as the input to the network and defining d +n u context neurones, the approximate inverse control law adopts the following form:…”

Section: In6erse Controller Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of recurrent neural networks in batch reactors

Galván

Zaldı́var

1998

Chemical Engineering and Processing: Process Intensification

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“…Thus, they can be used to generate NARMA models which are referred in this work as series-parallel models. This approach has been used in numerous applications (Narendra and Parthasarathy, 1990;Bhat and McAvoy, 1990;Chen and Billings, 1992;Levin and Narendra, 1995;Choi et al, 1996;Suykens and Bersini, 1996;Levin and Narendra, 1996;Narendra and Mukhopadhyay, 1997;Lu and Basan, 1998;Noriega and Wang, 1998). However, these models cannot act as process simulators because a discrete sequence of delayed measured output of the process is required.…”

Section: Introductionmentioning

confidence: 99%

PNNARMA model: an alternative to phenomenological models in chemical reactors

Galván¹,

Isasi²,

Zaldı́var³

2001

Engineering Applications of Artificial Intelligence

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This paper is focused on the development of non linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose. However, in this work it is shown that since the parameters of parallel identification models are estimated using multilayer feed forward networks, the approximation of dynamic systems could be not suitable. The solution proposed in this work consists of building up parallel models using a particular recurrent neural network. This network allows to identify the parameter sets of the parallel model in order to generate process simulators. Hence, it is possible to guarantee better dynamic predictions. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The results suggest that parallel models based on the recurrent neural network proposed in this work can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits. #

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“…The approach consists in forming tapped delay line representations of applied ínputs and measured outputs. Thus, they can be used to generate NARMA models which are normaIly referred to as series-parallel models [16][17][18][19]. However, these models cannot act as process simulators because a discrete sequen ce of delayed measured output of the process is required.…”

Section: Introductionmentioning

confidence: 99%

Application of recurrent neural networks in batch reactors

Galván

Zaldı́var

1997

Chemical Engineering and Processing: Process Intensification

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bstractThis paper is focused on the development of nonlinear models predictions when acting as process simulators. The dynamic behaviour of the heat transf chemical reactor has been selected as a case study. Different structures of NARMA (Non studied. The experimental results have allowed to carry out a comparison between the first-principles model. The best neural results are obtained using a parallel model structure b architecture, which guarantees better dynamic approximations than currently employed neur parallel models built up with recurrent networks can be seen as an altemative to phenomen dynamic behaviour of the heating/cooling circuits which change from batch installation to i S.A.

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Use of neural nets for dynamic modeling and control of chemical process systems

Cited by 634 publications

References 13 publications

Application of recurrent neural networks in batch reactors

Application of recurrent neural networks in batch reactors

PNNARMA model: an alternative to phenomenological models in chemical reactors

Application of recurrent neural networks in batch reactors

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