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Journal of Chemistry
Volume 2015 (2015), Article ID 313105, 10 pages
Research Article

Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm

1Department of Environmental Engineering, Anhui Jianzhu University, Hefei 230022, China
2Key Laboratory of Water Pollution Control and Wastewater Resource of Anhui Province, Hefei 230601, China
3School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China

Received 10 December 2014; Accepted 23 January 2015

Academic Editor: Jian Lu

Copyright © 2015 Guang-zhou Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.