Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators
Though a global optimization procedure using a randomized algorithm and a commercial process simulator is relatively easy to implement for complex design problems (i.e., intensified design processes), a dominant problem is their heavy computation load. As the process simulation is repeatedly execute...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2021/02/matecconf_apcche21_06004.pdf |
| _version_ | 1819058025507323904 |
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| author | Cabrera-Ruiz Julian Hasebe Shinji Alcantara-Avila J. Rafael |
| author_facet | Cabrera-Ruiz Julian Hasebe Shinji Alcantara-Avila J. Rafael |
| author_sort | Cabrera-Ruiz Julian |
| collection | DOAJ |
| description | Though a global optimization procedure using a randomized algorithm and a commercial process simulator is relatively easy to implement for complex design problems (i.e., intensified design processes), a dominant problem is their heavy computation load. As the process simulation is repeatedly executed to calculate the objective function, it is inevitable to spend long computation time to derive the optimal solution. Also, the randomized algorithms consider the treatment of all variables as continuous. Thus, the reduction of the number of iterations is crucial for such optimization procedures that include integer variables. In this work, an estimation procedure of the objective function having integer design variables is proposed. In the proposed procedure, the values of the objective function at the nodes of hyper-triangle that includes the suggested next search point are used to estimate the objective function, at the same time normalization of the design optimization variables is recommended. The procedure was implemented on the simulated annealing stochastic algorithm with a trivial case of a binary mixture in order to know the optimal solution and compare the traditional optimizations procedures and the proposed one. The proposed procedure show improvement not only for reducing the number iterations, but also for an increase of accuracy of finding the optimal solution. |
| first_indexed | 2024-12-21T13:48:38Z |
| format | Article |
| id | doaj.art-b85a6669536a4a6496de07256b9b592d |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-21T13:48:38Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-b85a6669536a4a6496de07256b9b592d2022-12-21T19:01:47ZengEDP SciencesMATEC Web of Conferences2261-236X2021-01-013330600410.1051/matecconf/202133306004matecconf_apcche21_06004Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process SimulatorsCabrera-Ruiz Julian0Hasebe Shinji1Alcantara-Avila J. Rafael2Chemical Engineering Department, University of Guanajuato, Guanajuato Campus, Noria Alta s/nDeparment of Chemical Engineering, Kyoto University, Katsura CampusDeparment of Chemical Engineering, Kyoto University, Katsura CampusThough a global optimization procedure using a randomized algorithm and a commercial process simulator is relatively easy to implement for complex design problems (i.e., intensified design processes), a dominant problem is their heavy computation load. As the process simulation is repeatedly executed to calculate the objective function, it is inevitable to spend long computation time to derive the optimal solution. Also, the randomized algorithms consider the treatment of all variables as continuous. Thus, the reduction of the number of iterations is crucial for such optimization procedures that include integer variables. In this work, an estimation procedure of the objective function having integer design variables is proposed. In the proposed procedure, the values of the objective function at the nodes of hyper-triangle that includes the suggested next search point are used to estimate the objective function, at the same time normalization of the design optimization variables is recommended. The procedure was implemented on the simulated annealing stochastic algorithm with a trivial case of a binary mixture in order to know the optimal solution and compare the traditional optimizations procedures and the proposed one. The proposed procedure show improvement not only for reducing the number iterations, but also for an increase of accuracy of finding the optimal solution.https://www.matec-conferences.org/articles/matecconf/pdf/2021/02/matecconf_apcche21_06004.pdf |
| spellingShingle | Cabrera-Ruiz Julian Hasebe Shinji Alcantara-Avila J. Rafael Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators MATEC Web of Conferences |
| title | Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators |
| title_full | Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators |
| title_fullStr | Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators |
| title_full_unstemmed | Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators |
| title_short | Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators |
| title_sort | improvement of the optimal design procedure using randomized algorithm and process simulators |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2021/02/matecconf_apcche21_06004.pdf |
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