A Comparative Study of Fuzzy Techniques to Handle Uncertainty: An Industrial Grinding Process

Fuzzy‐based approaches like fuzzy chance constrained programming (FCCP) and fuzzy expected value model (FEVM) have been applied to a multi‐objective optimization problem of the industrial grinding process to carry out the uncertainty analysis. Results are compared with respect to the power of risk a...

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Bibliographic Details
Published in:Chemical engineering & technology Vol. 39; no. 6; pp. 1031 - 1039
Main Authors: Virivinti, Nagajyothi, Mitra, Kishalay
Format: Journal Article
Language:English
Published: Weinheim WILEY-VCH Verlag 01.06.2016
WILEY‐VCH Verlag
Subjects:
Chance constrained programming
Chemical engineering
Constraints
Fuzzy
Fuzzy credibility theory
Grinding
Industrial grinding process
Mathematical models
Optimization
Optimization under uncertainty
Programming
Uncertainty
ISSN:0930-7516, 1521-4125
Online Access:Get full text
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Summary:Fuzzy‐based approaches like fuzzy chance constrained programming (FCCP) and fuzzy expected value model (FEVM) have been applied to a multi‐objective optimization problem of the industrial grinding process to carry out the uncertainty analysis. Results are compared with respect to the power of risk averseness adopted in the approaches used. The extent of constraint satisfaction due to the presence of uncertain parameters can be accommodated assuming credibility of constraint satisfaction under the FCCP framework whereas the robust set of parameters in the FEVM approach is determined by considering the expectation terms for objectives and constraints. Nonlinear relation of uncertain parameters has been handled by adopting simulation‐based approaches while computing the credibility. These approaches are very generic and can be applied for the study of parametric sensitivity for any process model in a novel manner. Multi‐objective optimization of an industrial grinding process under uncertainties in various process parameters was performed. The uncertain optimization problem was converted into an equivalent deterministic optimization problem by credibility‐based fuzzy chance constrained programming. The proposed approaches can be applied to study parametric sensitivity for any process model.
Bibliography:ArticleID:CEAT201400577
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ISSN:0930-7516
1521-4125
DOI:10.1002/ceat.201400577