TY - JOUR
T1 - A state of art review on applications of multi-objective evolutionary algorithms in chemicals production reactors
AU - Al Ani, Zainab
AU - Gujarathi, Ashish M.
AU - Al-Muhtaseb, Ala’a H.
N1 - Funding Information:
This work was supported by Sultan Qaboos University, Sultanate of Oman under Grant IG/ENG/PCED/19/01.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.
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PY - 2022/7/19
Y1 - 2022/7/19
N2 - Chemical reactors are employed to produce several materials, which are utilized in numerous applications. The wide use of these chemical engineering units shows their importance as their performance vastly affects the production process. Thus, improving these units will develop the process and/or the manufactured material. Multi-objective optimization (MOO) with evolutionary algorithms (EA’s) has been used to solve several real world complex problems for improving the performance of chemical reactors with conflicting objectives. These objectives are of different nature as they could be economy, environment, safety, energy, exergy and/or process related. In this review, a brief description for MOO and EA’s and their several types and applications is given. Then, MOO studies, which are related to the materials’ production via chemical reactors, those were conducted with EA’s are classified into different classes and discussed. The studies were classified according to the produced material to hydrogen and synthesis gas, petrochemicals and hydrocarbons, biochemical, polymerization and other general processes. Finally, some guidelines are given to help in deciding on future research.
AB - Chemical reactors are employed to produce several materials, which are utilized in numerous applications. The wide use of these chemical engineering units shows their importance as their performance vastly affects the production process. Thus, improving these units will develop the process and/or the manufactured material. Multi-objective optimization (MOO) with evolutionary algorithms (EA’s) has been used to solve several real world complex problems for improving the performance of chemical reactors with conflicting objectives. These objectives are of different nature as they could be economy, environment, safety, energy, exergy and/or process related. In this review, a brief description for MOO and EA’s and their several types and applications is given. Then, MOO studies, which are related to the materials’ production via chemical reactors, those were conducted with EA’s are classified into different classes and discussed. The studies were classified according to the produced material to hydrogen and synthesis gas, petrochemicals and hydrocarbons, biochemical, polymerization and other general processes. Finally, some guidelines are given to help in deciding on future research.
KW - Chemical reactors
KW - Differential Evolution
KW - Evolutionary algorithms
KW - Genetic algorithms
KW - Industrial Applications
KW - Review
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UR - https://www.mendeley.com/catalogue/06d207bd-7270-3e35-86f5-435025ec394e/
U2 - 10.1007/s10462-022-10219-z
DO - 10.1007/s10462-022-10219-z
M3 - Article
AN - SCOPUS:85134543905
SN - 0269-2821
VL - 56
SP - 2435
EP - 2496
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 3
M1 - 3
ER -