Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective...

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Vydáno v:Engineering optimization Ročník 50; číslo 4; s. 716 - 731
Hlavní autoři: Guo, Zhan, Yan, Xuefeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 03.04.2018
Taylor & Francis Ltd
Témata:
Adaptive algorithms
Algorithms
Chemical industry
Computer simulation
differential evolution algorithm
Evolutionary algorithms
Machine learning
multi-objective optimization problems
Multiple objective analysis
Optimization
Oxidation
p-Xylene oxidation process
Population (statistical)
population-based incremental learning algorithm
self-adaptive
Statistical analysis
Terephthalic acid
Xylene
ISSN:0305-215X, 1029-0273
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Shrnutí:Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2017.1337756