Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Andújar, José Manuel; Bravo, José Manuel

doi:10.1016/j.fss.2004.03.023

Cited by 29 publications

(19 citation statements)

References 21 publications

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“…Consequently, considering the maturity of computational intelligence (CI) techniques and new enabler technologies, CI methods represent an attractive alternative to develop accurate modelling and control solutions for highly complex models [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Taking into account the need to obtain precise models of complex systems which are applicable in a wide operating range, the CI techniques are an ideal method to reproduce the behaviour of a complex system [2,5,6] or, as it will be presented in this work, to analyze the dynamics of systems.…”

Section: Introductionmentioning

confidence: 99%

“…One of the main reasons for the use of nonlinear models is based on the dynamics of linear systems, since conventional mathematical formulations are not rich enough to reproduce a series of phenomena that usually appear in the real life [2,16,17]. The dynamic behaviour of a linear system, without considering its order, is basically governed by the eigenvalues of the corresponding state matrix [8].…”

Section: Introductionmentioning

confidence: 99%

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About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

et al. 2018

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This work presents a straightforward methodology based on neural networks (NN) which allows to obtain relevant dynamic information of unknown nonlinear systems. It provides an approach for cases in which the complex task of analyzing the dynamic behaviour of nonlinear systems makes it excessively challenging to obtain an accurate mathematical model. After reviewing the suitability of multilayer perceptrons (MLPs) as universal approximators to replace a mathematical model, the first part of this work presents a system representation using a model formulated with state variables which can be exported to a NN structure. Considering the linearization of the NN model in a mesh of operating points, the second part of this work presents the study of equilibrium states in such points by calculating the Jacobian matrix of the system through the NN model. The results analyzed in three case studies provide representative examples of the strengths of the proposed method. Conclusively, it is feasible to study the system behaviour based on MLPs, which enables the analysis of the local stability of the equilibrium points, as well as the system dynamics in its environment, therefore obtaining valuable information of the system dynamic behaviour.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

et al. 2018

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“…The formulation and solution of such "fuzzy-parameterized" models is a subdomain of fuzzy set theory that has received relatively little attention, with much more work having been focused on the use of fuzzy logic and reasoning methods. For example, just within the field of chemical engineering, there have been many applications of fuzzy logic and reasoning, in process control (e.g., Gromov et al, 1995;Chen et al, 2001;Andujar & Bravo, 2005;Chen & Chang, 2006;Sanjuan et al, 2006;Zhang et al, 2006;Kaucsár et al, 2007), safety and reliability analysis (e.g., Yu & Lee, 1991;Takeda et al, 1994;Guimarães & Lapa, 2004;Meel & Seider, 2006;Yong et al, 2007;Hassana et al, 2009), knowledge processing (e.g., Arva & Csukas, 1987;Vrba, 1991;Dohnal et al, 1994;Gromov et al, 1996;Johansen & Foss, 1997;Schmitz & Aldrich, 1998;Tsekouras et al, 2002;Claudel et al, 2003;Stephane & Marc, 2008), and other areas.…”

Section: Introductionmentioning

confidence: 99%

Computing fuzzy trajectories for nonlinear dynamic systems

Măceş

Stadtherr

2013

Computers & Chemical Engineering

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One approach for representing uncertainty is the use of fuzzy sets or fuzzy numbers. A new approach is described for the solution of nonlinear dynamic systems with parameters and/or initial states that are uncertain and represented by fuzzy sets or fuzzy numbers. Unlike current methods, which address this problem through the use of sampling techniques and do not account rigorously for the effect of the uncertain quantities, the new approach is not based on sampling and provides mathematically and computationally rigorous results. This is achieved through the use of explicit analytic representations (Taylor models) of state variable bounds in terms of the uncertain quantities. Examples are given that demonstrate the use of this new approach and its computational performance.

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“…3. This graphical fuzzy rule can be interpreted as follows: IF x is small or big and y is average THEN z is very small.The authors have demonstrated that this computer tool is capable of providing interpretations of the behavior of variables in complex systems, which were not visible through a classical statistical analysis of the measurements,Grande et al (2005a,Andújar and Barragán (2005),Andújar and Bravo (2005),Andújar et al (2006Andújar et al ( , 2007,Aroba et al (2007),Jiménez et al (2009).…”

mentioning

confidence: 99%

Fuzzy Modeling of the Spatial Evolution of the Chemistry in the Tinto River (SW Spain)

Grande

Andújar

Aroba

et al. 2010

Water Resour Manage

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The oxidation of sulfide minerals generates acid waters containing high concentrations of sulfates, metals and metalloids. The overriding chemical mechanism of AMD has now been reasonably well understood, although accurate assessment or prediction of its ecological impact are still difficult due to the complexity of the processes involved. The main objective of this work is to characterize the behavior of As conditioned by the presence of other elements which are characteristic of AMD pollution, both in the generating milieu, and in the receiving milieu of the Tinto river, and thus to be able to establish the differences in behavior at both sites by using a fuzzy computer tool. The extremely low As values are due to different circumstances at each sampling site: the pH in generating milieu may reach extremely high values while in receiving milieu it does not rise above medium-high values. The differences remain for extremely high As values, proving very much conditioned by the temperature in the generating milieu and only compatible with extremely high values for this, while in the receiving milieu it has barely any influence. It is the redox potential especially that presents a more pronounced difference, while in generating

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Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Cited by 29 publications

References 21 publications

About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

Computing fuzzy trajectories for nonlinear dynamic systems

Fuzzy Modeling of the Spatial Evolution of the Chemistry in the Tinto River (SW Spain)

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