Share This Article:

Research and Application of Pollution Control in the Middle Reach of Ashe River by Multi-Objective Optimization

DOI: 10.4236/gep.2013.12001    3,041 Downloads   5,988 Views  

ABSTRACT

Based on one-dimensional water quality model and nonlinear programming, the point source pollution reduction model with multi-objective optimization has been established. To achieve cost effective and best water quality, for us to optimize the process, we set pollutant concentration and total amount control as constraints and put forward the optimal pollution reduction control strategy by simulating and optimizing water quality monitoring data from the target section. Integrated with scenario analysis, COD and ammonia nitrogen pollution optimization wasstudiedin objective function area from Mountain Maan of Acheng to Fuerjia Bridge along Ashe River. The results showed that COD and NH3-N contribution has been greatly reduced to AsheRiverby 49.6% and 32.7% respectively. Therefore, multi-objective optimization by nonlinear programming for water pollution control can make source sewage optimization fairly and reasonably, and the optimal strategies of pollution emission are presented.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Wang, Y. , Guo, L. , Wang, Y. , Ran, M. , Liu, J. and Wang, P. (2013) Research and Application of Pollution Control in the Middle Reach of Ashe River by Multi-Objective Optimization. Journal of Geoscience and Environment Protection, 1, 1-6. doi: 10.4236/gep.2013.12001.

References

[1] Chinese Ministry of Environmental Protection (2010). National “Twelve Five” plan of focus on water pollution prevention (2011-2015). http://wfs.mep.gov.cn/zdlyshew/
[2] Rongsun, Q. (2008). Further strengthen the control and management of water environment capacity. China Construction News.
[3] Momtahen, S., & Dariane, A. B. (2007). Direct search approaches using genetic algorithms for optimization of water reservoir operating policies. Journal of Water Resources Planning and Management-Asce, 133, 202-209. http://dx.doi.org/10.1061/(ASCE)0733-9496(2007)133:3(202)
[4] Dong, S., & Yang, W. (2007). Genetic algorithm solution of a gray nonlinear water environment management model developed for the liming river in Daqing, China. Journal of Environmental Engineering Asce, 133, 287-293. http://dx.doi.org/10.1061/(ASCE)0733-9372(2007)133:3(287)
[5] Zou, R. (2007). An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling. Water Resources Research. http://dx.doi.org/10.1029/2006WR005158
[6] Hua, L. B. (2011). Water quality monitoring survey of Ashe River. Heilongjiang Science and Technology Information, 8, 81-85.
[7] Fei, L. J., Qiang, F., & Guo, M. Q. (2012). Basin pollution situation analysis and discussion of Ashe River. Water Conservancy Science and Technology and Economy, 3, 50-54.
[8] Feng, H. X., Guo, S. D., & Quan, G. W. (2008). Research of rivers emission rights allocation model by multi-objective optimization. Journal of Hydraulic Engineering, 1, 75-80.
[9] Feng, L. G. (2006). Based on fairness, efficiency and multi-objective optimization method of river pollution load distribution. D. Thesis, Guangzhou: Zhongshan University.
[10] Gang, T. X., Ting, J. M., & Jian, H. (2010). Multi-objective programming model for sewage pollution control programs in the riparian Application—Minjiang River in Yibin City as an example. Resour- ces and Environment in the Yangtze Basin, 10, 156-162.
[11] GB3838-2002, Surface water quality standards. http://datacenter.mep.gov.cn/trs/query.action
[12] Yi, W., Wen, L. J., & Xue, S. (2012). Computational intelligence based optimization study on the watershed discharge of sewage. China Environmental Science, 1, 173-180.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.