Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm
Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential s...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2015/313105 |
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| author | Guang-zhou Chen Jia-quan Wang Ru-zhong Li |
| author_facet | Guang-zhou Chen Jia-quan Wang Ru-zhong Li |
| author_sort | Guang-zhou Chen |
| collection | DOAJ |
| description | Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS) algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms. |
| format | Article |
| id | doaj-art-07e419d6a63d4784a5ad29b79290f642 |
| institution | Kabale University |
| issn | 2090-9063 2090-9071 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Chemistry |
| spelling | doaj-art-07e419d6a63d4784a5ad29b79290f6422025-02-03T05:59:21ZengWileyJournal of Chemistry2090-90632090-90712015-01-01201510.1155/2015/313105313105Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search AlgorithmGuang-zhou Chen0Jia-quan Wang1Ru-zhong Li2Department of Environmental Engineering, Anhui Jianzhu University, Hefei 230022, ChinaSchool of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, ChinaParameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS) algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.http://dx.doi.org/10.1155/2015/313105 |
| spellingShingle | Guang-zhou Chen Jia-quan Wang Ru-zhong Li Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm Journal of Chemistry |
| title | Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm |
| title_full | Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm |
| title_fullStr | Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm |
| title_full_unstemmed | Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm |
| title_short | Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm |
| title_sort | parameter identification of the 2 chlorophenol oxidation model using improved differential search algorithm |
| url | http://dx.doi.org/10.1155/2015/313105 |
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