Recent Developments in Computational Approaches to Optimization under Uncertainty and Application in Process Systems Engineering

Geletu, Abebe; Li, Pu

doi:10.1002/cben.201400013

Cited by 17 publications

(18 citation statements)

References 184 publications

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“…In addition, each process has to coincide itself to be softly compatible with changing in its condition to keep flexibility and reduce extra cost which might impose to process for adjusting with new conditions (Ehrgott et al, 2014;Haobo et al, 2015). For instance, in the relevant process in management science, customer needs (Gasior & Józefczyk, 2009), external diplomatic rules, economical pressure, local and global environmental policies (Geletu & Li, 2014) and managing rules can be changed over time and it changes the process goals and ideal points of responses. So, it is the duty of engineers to design flexible processes which can be adjusted immediately coincide to new circumstances as soon as possible.…”

Section: Basic Informationmentioning

confidence: 99%

“…In this context, we concentrate more in Taguchi philosophy for the uncertain and noisy condition of the problem in the real world. Recent comprehensive overview of historical and technical aspects of robust optimization methods can be found in (Bertsimas et al, 2011;Beyer & Sendhoff, 2007;Dellino et al, 2015;Gabrel et al, 2014;Geletu & Li, 2014;Wang & Shan, 2011).…”

Section: Uncertaintymentioning

confidence: 99%

“…The application of robust design optimization has been contributed by great researchers to quality improvement of various processes or product design in practice, and several appropriate studies have been reviewed the application of Taguchi methodology in real case studies, (e.g. Beyer & Sendhoff, 2007;Dellino et al, 2015;Gabrel et al, 2014;Geletu & Li, 2014;Park & Lee, 2006;Wang & Shan, 2011). In current reviewing of studies, the application of robust design methodology on optimizing the process in two types of production and project management were considered, whose results are described in section 4.…”

Section: Positive and Negative Points Of View On Taguchi Approachmentioning

confidence: 99%

“…Bertsimas et al, 2011;Beyer & Sendhoff, 2007;Gabrel et al, 2014;Geletu & Li, 2014). Undoubtedly, Min-Max and two-stage approach have been widely used in region of robust optimization problems (Geletu & Li, 2014).…”

Section: Robust Optimization Based On Uncertainty Scenario Setsmentioning

confidence: 99%

See 3 more Smart Citations

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

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Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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Section: Basic Informationmentioning

confidence: 99%

Section: Uncertaintymentioning

confidence: 99%

Section: Positive and Negative Points Of View On Taguchi Approachmentioning

confidence: 99%

Section: Robust Optimization Based On Uncertainty Scenario Setsmentioning

confidence: 99%

See 2 more Smart Citations

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…uncertainty in the control vari-able runaway boundaries, and the inherent random disturbances of the control variable setpoint. 5,8,9,[32][33][34][35][36] On the other hand, the plant optimal operating policy should be considered as a trade-off between economic, environmental, and safety objectives. By using a new "safe operation criterion" introduced by Maria and Dan,[10][11][12][13][14] based on the sum of two failure probability indices related to uncertainty in the reactor runaway boundaries and random disturbances in the operating parameters, a multi-objective optimization can be formulated by defining the sustainability by simultaneously considering technological, economic, and safety constraints.…”

Section: Introductionmentioning

confidence: 99%

Derivation of Pareto Optimal Operating Policies Based on Safety Indices for a Catalytic Multi-tubular Reactor Used for Nitrobenzene Hydrogenation

Maria

Khwayyir

Dinculescu

2016

Chem.Biochem.Eng.Q.

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When highly exothermic/hazardous reactions are conducted in the presence of parametric uncertainty, derivation of optimal operating policies for a chemical reactor has to simultaneously consider several objectives of sustainability. The paper uses an original methodology to generate the Pareto optimal solutions when reactor productivity and safety objectives (expressed in probabilistic terms) are simultaneously considered using the process and reactor model in a simple way, in the presence of technological constraints, uncertainty in safety boundaries, and random fluctuations in control variables. An example is provided for the industrial fixed-bed tubular reactor used for the catalytic hydrogenation of nitrobenzene to aniline in vapour-phase.

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Liver function as an engineering system

et al. 2016

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Process Systems Engineering has tackled a wide range of problems including manufacturing, the environment, and advanced materials design. Here we discuss how tools can be deployed to tackle medical problems which involve complex chemical transformations and spatial phenomena looking in particular at the liver system, the body's chemical factory. We show how an existing model has been developed to model distributed behavior necessary to predict the behavior of drugs for treating liver disease. The model has been used to predict the effects of suppression of de novo lipogenesis, stimulation of b-oxidation and a combination of the two. A reduced model has also been used to explore the prediction of behavior of hormones in the blood stream controlling glucose levels to ensure that levels are kept within safe bounds using interval methods. The predictions are made resulting from uncertainty in two key parameters with oscillating input resulting from regular feeding. V C 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3285-3297, 2016 Keywords: mathematical modeling, biomedical engineering, systems biology, medical, controlThe liver, its regulation, and its diseases Why systems engineering of the liver?Systems Engineering is the discipline of the management of complex engineering systems over their whole life cycle. Process Systems Engineering is the study of complex process engineering systems involving chemical and physical change. Prof Roger Sargent was a pioneer from the 1950s in seeing the potential that computers could have to revolutionize the way that we tackled these problems.1 He also saw the way Chemical Engineering was broadening its base into molecular and biological systems to improve manufacturing. 1,2The liver is one part of a very complex processing system which ensures that all parts of the body receive the energy and nutrients that they need, that waste products are removed efficiently from the various streams, and that short and long term well being are maintained. It is the body's central chemical processing organ. Considering the liver system as one which controls chemical and physical change within the body, particularly of nutrition, makes it a legitimate area of study for Process Systems Engineers. A number of Chemical Engineers have also contributed to the use for Process Systems Engineering to a range of medical applications. Bogle 3 reviews recent

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Recent Developments in Computational Approaches to Optimization under Uncertainty and Application in Process Systems Engineering

Cited by 17 publications

References 184 publications

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Derivation of Pareto Optimal Operating Policies Based on Safety Indices for a Catalytic Multi-tubular Reactor Used for Nitrobenzene Hydrogenation

Liver function as an engineering system

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