Analysis of the Applications of Particle Swarm Optimization and Genetic Algorithms on Reaction Kinetics: A Prospective Study for Advanced Oxidation Processes

被引:6
作者
Acosta-Angulo, Bryan [1 ]
Diaz-Angulo, Jennyfer [2 ]
Lara-Ramos, Jose [1 ]
Torres-Palma, Ricardo [3 ]
Martinez-Pachon, Diana [4 ]
Moncayo-Lasso, Alejandro [4 ]
Machuca-Martinez, Fiderman [1 ]
机构
[1] Univ Valle, Grp Invest Proc Avanzados Tratamientos Quim & Bio, Escuela Ingn Quim, Valle Del Cauca, Colombia
[2] GITAM, Res & Technol Dev Water Treatment Proc Modelling, Cauca, Colombia
[3] Univ Antioquia, Grp Invest Remediac Ambiental & Biocatalisis GIRA, Inst Chem, Fac Exact & Nat Sci, Antioquia, Colombia
[4] Univ Antonio Narino, Grp Invest Ciencias Biol & Quim, Fac Sci, Bogota, Colombia
关键词
Bibliometrics; Electrochemistry; Forecasting; Kinetics; Photocatalysis; ELECTROCHEMICAL ADVANCED OXIDATION; MONTE-CARLO-SIMULATION; ELECTRO-FENTON PROCESS; WASTE-WATER; ORGANIC POLLUTANTS; PHOTOCATALYTIC DEGRADATION; NUMERICAL-SIMULATION; PARAMETER-ESTIMATION; REACTION-MECHANISM; UV/H2O2; OXIDATION;
D O I
10.1002/celc.202200229
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
A bibliometric analysis of the Scopus database was implemented to assess the spread of two types of evolutionary algorithms (EAs), genetic algorithms (GA) and particle swarm optimization (PSO), on the study of reaction kinetics. Particular attention was given to applications for advanced oxidation processes (AOPs). The collaborations between countries and authors, as well as the keywords co-occurrences, were investigated. Finally, the Gompertz and Logistic Substitution models (LSM) were employed to forecast future scenarios. It was observed that GA methods were the preferred algorithms for reaction kinetic studies, and the USA was the most influential country in terms of collaboration, followed by China. On the other hand, there was still poor collaboration for most countries; this was also observed for authors' collaboration. In addition, literature concerning AOPs was still scarce. The forecasting suggested growth for implementing evolutionary algorithms, especially for the PSO, increasing its popularity regarding GA methods.
引用
收藏
页数:15
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