Phase Equilibrium Data Reconciliation Using Multi‐Objective Differential Evolution with Tabu List

Bonilla‐Petriciolet, Adrián; Sharma, Shivom; Rangaiah, Gade Pandu

doi:10.1002/9781118341704.ch10

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…For example, Punnapala et al [70] employed MOO for parameter estimation in phase equilibrium; both the objectives are RMSE, one for activity coefficients, and another for heat of mixing. Bonilla-Petriciolet et al [71] applied MOO for reconciling phase equilibrium data using RMSEs for temperature, pressure, and vapor/liquid mole fractions as separate objectives. Soares et al [72] applied the RMSE of molar fraction, the mass of ethanol product, and the total mass collected from each batch to formulate a triple objective optimization to estimate the process and equipment parameters of an ethanol distillation process model based on their experimental data.…”

Section: Objectives Used For Moomentioning

confidence: 99%

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

2020

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This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of the 5 steps to create, solve, and then select the optimum result. Unlike single-objective optimization, the fifth step of selection or ranking of solutions is often overlooked by the authors of papers dealing with MOO applications. It is necessary to undertake a multi-criteria analysis to choose the best solution. A review of the recent publications using MOO for chemical process engineering problems shows a doubling of publications between 2016 and 2019. MOO applications in the energy area have seen a steady increase of over 20% annually over the last 10 years. The three key methods for solving MOO problems are presented in detail, and an emerging area of surrogate-assisted MOO is also described. The objectives used in MOO trade off conflicting requirements of a chemical engineering problem; these include fundamental criteria such as reaction yield or selectivity; economics; energy requirements; environmental performance; and process control. Typical objective functions in these categories are described, selection/ranking techniques are outlined, and available software for MOO are listed. It is concluded that MOO is gaining popularity as an important tool and is having an increasing use and impact in chemical process engineering.

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Section: Objectives Used For Moomentioning

confidence: 99%

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

2020

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“…Of these, the last two reviews cover more than 300 applications reported in chemical engineering journals from 2000 to 2012. There are also two monographs on MOO in chemical engineering. , Our recent work identified 65 journal articles of interest to chemical engineers, published during January 2013 and February 2015. This and earlier reviews clearly show that the focus of the studies on MOO in chemical engineering has been on finding the nondominated solutions for the application under study.…”

Section: Introductionmentioning

confidence: 99%

Application and Analysis of Methods for Selecting an Optimal Solution from the Pareto-Optimal Front obtained by Multiobjective Optimization

Wang

Rangaiah

2017

Ind. Eng. Chem. Res.

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228

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Process optimization often has two or more objectives which are conflicting. For such situations, multiobjective optimization (MOO) provides many optimal solutions, which are equally good from the perspective of the given objectives. These solutions, known as Paretooptimal front and as nondominated solutions, provide deeper insights into the trade-off among the objectives and many choices for implementation. In the past 20 years, researchers have applied MOO to numerous applications in chemical engineering. However, selection of an optimal solution from the set of nondominated solutions has not received much attention in the chemical engineering literature. In the present study, 10 methods for selecting an optimal solution from the Pareto-optimal front are carefully chosen and implemented in an MS Excel-based program. Then, they are applied to the selection of an optimal solution in many benchmark or mathematical problems and chemical engineering applications, and their effectiveness and similarities are analyzed. Results of analysis indicate that, among the 10 methods studied, technique for order of preference by similarity to ideal solution, gray relational analysis, and simple additive weighting are better for choosing one of the Pareto-optimal solutions.

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Parameter identification in synthetic biological circuits using multi-objective optimization * *This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1). Y.B. thanks grant FPI/2013-3242 of Universitat Politècnica de València and Becas Iberoamérica of Santander Group, Spain 2015. G.R.M. thanks the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Develop

et al. 2016

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Phase Equilibrium Data Reconciliation Using Multi‐Objective Differential Evolution with Tabu List

Cited by 4 publications

References 21 publications

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

Application and Analysis of Methods for Selecting an Optimal Solution from the Pareto-Optimal Front obtained by Multiobjective Optimization

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