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Application of Surrogate-Assisted Gray Wolf Optimization (Sagwo) Algorithm For Optimization of Large-Scale Process Plants With Computationally Expensive Evaluation – Gas To Liquids (Gtl) Process Case Study
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DOR
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20.1001.2.9919199705.1399.11.1.340.5
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نویسنده
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- - ,- - ,- -
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منبع
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كنگره مهندسي شيمي - 1399 - دوره : 11 - یازدهمین کنگره بین المللی مهندسی شیمی - کد همایش: 99191-99705
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چکیده
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Process optimization is necessary in order to decrease energy consumption and production costs# using surrogate models, rather than mathematical modeling or simulator software, is an effective method to decrease the calculations and the time needed for optimization# developing an offline data-based surrogate model for the whole response space requires generating a big data set# this itself involves numerous calculations and, therefore, would be too time-consuming# in this paper, the utilization of an online optimization algorithm is addressed for large-scale processes with a high computational burden# in this algorithm, by using of latin hypercube sampling (lhs) method and the grey wolf meta-heuristic optimization algorithm (gwo) in combination with the support vector machine (svm), a suitable balance between exploration and exploitation abilities is achieved# for comparison, the value of the objective function in the estimated global optimum point (gop) and the number of objective function evaluations (nfes) required to converge to gop are investigated# the large-scale gas to liquids (gtl) process plant is chosen as a case study# the results showed that in the online method, while decreasing nfes to less than one-tenth of the offline method, the gop is found with a relative error of 0#1 percent#
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کلیدواژه
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Process Optimization ,Grey Wolf Optimization (Gwo) ,Gas To Liquids (Gtl)
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آدرس
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Ferdowsi University Of Mashhad, Iran, Ferdowsi University Of Mashhad, Iran, Ferdowsi University Of Mashhad, Iran
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Authors
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