Optimization of dividing wall columns based on online Kriging model and improved particle swarm optimization algorithm
Dividing wall columns (DWCs) can effectively improve the thermodynamic efficiency of traditional distillation columns. However, DWCs have intricate structures and strong internal interactions. Numerous structural and operational variables are interrelated. This work presents an improved cellular par...
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| Veröffentlicht in: | Computers & chemical engineering Jg. 166; S. 107978 |
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| ISSN: | 0098-1354 |
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| Abstract | Dividing wall columns (DWCs) can effectively improve the thermodynamic efficiency of traditional distillation columns. However, DWCs have intricate structures and strong internal interactions. Numerous structural and operational variables are interrelated. This work presents an improved cellular particle swarm optimization based on the online Kriging model (KCPSO) algorithm, and applies it to the optimization of DWC with the objective of minimizing the total annual cost. The algorithm uses the information of particle swarm search to act on the online Kriging model, and reacts on the particle search through the information of the online Kriging model. Calculations demonstrate that the KCPSO algorithm is superior to standard particle swarm optimization (PSO) and cellular PSO (CPSO) algorithms due to its higher quality of iteration. The KCPSO algorithm can effectively overcome the difficulty of early convergence of the CPSO algorithm and the problem that the PSO algorithm is prone to falling into local optimal solutions. |
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| AbstractList | Dividing wall columns (DWCs) can effectively improve the thermodynamic efficiency of traditional distillation columns. However, DWCs have intricate structures and strong internal interactions. Numerous structural and operational variables are interrelated. This work presents an improved cellular particle swarm optimization based on the online Kriging model (KCPSO) algorithm, and applies it to the optimization of DWC with the objective of minimizing the total annual cost. The algorithm uses the information of particle swarm search to act on the online Kriging model, and reacts on the particle search through the information of the online Kriging model. Calculations demonstrate that the KCPSO algorithm is superior to standard particle swarm optimization (PSO) and cellular PSO (CPSO) algorithms due to its higher quality of iteration. The KCPSO algorithm can effectively overcome the difficulty of early convergence of the CPSO algorithm and the problem that the PSO algorithm is prone to falling into local optimal solutions. |
| ArticleNumber | 107978 |
| Author | Yuan, Xigang Jia, Shengkun Zhao, Kefan Qian, Xing Liang, Mengkun Song, Jiayin |
| Author_xml | – sequence: 1 givenname: Mengkun surname: Liang fullname: Liang, Mengkun organization: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China – sequence: 2 givenname: Jiayin surname: Song fullname: Song, Jiayin organization: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China – sequence: 3 givenname: Kefan surname: Zhao fullname: Zhao, Kefan organization: School of Chemical Engineering and Technology, Chemical Engineering Research Center, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300350, China – sequence: 4 givenname: Shengkun surname: Jia fullname: Jia, Shengkun email: jiask@tju.edu.cn organization: School of Chemical Engineering and Technology, Chemical Engineering Research Center, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300350, China – sequence: 5 givenname: Xing orcidid: 0000-0002-5208-2672 surname: Qian fullname: Qian, Xing email: qianx@mail.buct.edu.cn organization: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China – sequence: 6 givenname: Xigang surname: Yuan fullname: Yuan, Xigang organization: School of Chemical Engineering and Technology, Chemical Engineering Research Center, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300350, China |
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| Keywords | Dividing wall columns Heuristic algorithm Online surrogate model Particle swarm optimization algorithm Optimal design |
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| SubjectTerms | Dividing wall columns Heuristic algorithm Online surrogate model Optimal design Particle swarm optimization algorithm |
| Title | Optimization of dividing wall columns based on online Kriging model and improved particle swarm optimization algorithm |
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