Multi-model adaptive control of a simulated pH neutralization process

Böling, Jari M.; Seborg, Dale E.; Hespanha, João P.

doi:10.1016/j.conengprac.2006.11.008

Cited by 92 publications

(46 citation statements)

References 14 publications

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“…This approach is not new [7], [8] (see a tutorial on [9]), but continues to attract attention, both for theoretical extensions [10], [11], [12], [13] and applications [14], [15]. In what concerns neuromuscular blockade control, successful clinical results of the application of variants of SMMAC are reported in [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Supervised multi-model adaptive control of neuromuscular blockade with off-set compensation

Oliveira

Hespanha

Lemos

et al. 2009

2009 European Control Conference (ECC)

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Abstract-The paper considers the control of neuromuscular blockade (NMB) induced by atracurium on patients subject to general surgery. The high levels of uncertainty in the process model are tackled with supervised multi-model adaptive control, for which an innovative supervisor based on least squares estimation is presented to deal with constant unknown offsets in output measurement. In order to avoid excessive switching among the controllers, a model clustering is made, with a common controller in each cluster. Furthermore, an antiwindup algorithm that may be applied to an integrator that is common to a bank of controllers is proposed. The control structure is tested on simulation using realistic nonlinear models for NMB with parameters estimated from actual patient data.

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Section: Introductionmentioning

confidence: 99%

Supervised multi-model adaptive control of neuromuscular blockade with off-set compensation

Oliveira

Hespanha

Lemos

et al. 2009

2009 European Control Conference (ECC)

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“…[1][2][3]. It was introduced as an efficient and powerful method to cope with modelling and control difficulties when complex non linear and/or uncertain processes are concerned.…”

Section: Introductionmentioning

confidence: 99%

A Multimodel Approach for Complex Systems Modeling based on Classification Algorithms

Elfelly

Dieulot

Benrejeb

et al. 2014

INT J COMPUT COMMUN

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In this paper, a new multimodel approach for complex systems modeling based on classification algorithms is presented. It requires firstly the determination of the model-base. For this, the number of models is selected via a neural network and a rival penalized competitive learning (RPCL), and the operating clusters are identified by using the fuzzy K-means algorithm. The obtained results are then exploited for the parametric identification of the models. The second step consists in validating the proposed model-base by using the adequate method of validity computation. Two examples are presented in this paper which show the efficiency of the proposed approach.

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“…Artificial intelligence techniques and mathematical models have demonstrated their efficiency and optimal results, particularly when applied in a multivariable and nonlinear system, such as water treatment plants (Böling, Seborg, & Hespanha, 2007). For example, the use of mathematical modeling in water treatment process foregrounds the study of variations in costs and inputs at various stages, including clotting, according to operating conditions (Malzer & Strugholtz, 2008;Mostafa, Bahareh, Elahe, & Pegah, 2013;Ogwueleka & Ogwueleka, 2009 Intelligent methods, such as fuzzy systems, artificial neural networks and expert systems, are currently employed to overcome the complexity of water treatment processes (Dharman, Chandramouli, & Lingireddy, 2012;Wu & Lo, 2010;Zhang, Achari, Li, Zargar, & Saqid, 2013).…”

Section: Introductionmentioning

confidence: 99%

<b>Intelligent system for improving dosage control

et al. 2017

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ABSTRACT. Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant.Keywords: water treatment plant, process control, coagulant dosage, artificial neural networks, optimization.Inteligência artificial aplicada ao controle da dosagem de coagulante em plantas de tratamento de água RESUMO. A coagulação é um dos processos mais importantes em uma planta de tratamento de tratamento de água e é utilizado para desestabilizar as impurezas contidas na água para possibilitar a floculação. Atualmente, algumas técnicas têm sido utilizadas na área para determinar a melhor dosagem, como ensaios de jar-teste, medidas do potencial zeta e métodos de inteligência artificial, como Redes Neurais Artificiais, Fuzzy e Sistemas Especialistas, as quais permitem combinações de técnicas para auxiliar os operadores e engenheiros no processo de tratamento de água. Este artigo apresenta uma abordagem de rede neural artificial para a avaliação da dosagem de coagulante otimizada em vários cenários de qualidade da água bruta, utilizando alguns parâmetros, tais como a cor da água bruta, turbidez das águas bruta, decantada e filtrada, além de uma entrada calculada, denominada Dose Rate para monitorar o processo de filtração. Outra característica presente nesta abordagem é a utilização do método backpropagation nas redes neurais artificiais para estimar a melhor dosagem coagulante em dois pontos da instalação de tratamento de água simultaneamente. Os resultados da simulação foram comparados com a dosagem atual e apontaram que o sistema proposto pode trazer redução nos custos de matéria-prima na estação de tratamento de água.Palavras-chave: planta de tratamento de água, controle de processos, dosagem de coagulante, redes neurais artificiais, otimização.

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Multi-model adaptive control of a simulated pH neutralization process

Cited by 92 publications

References 14 publications

Supervised multi-model adaptive control of neuromuscular blockade with off-set compensation

Supervised multi-model adaptive control of neuromuscular blockade with off-set compensation

A Multimodel Approach for Complex Systems Modeling based on Classification Algorithms

<b>Intelligent system for improving dosage control

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