Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/5280
標題: 物理性擋水牆與地下水抽取處理系統最佳配置之研究
Optimal Configuration of Physical Barrier System and Pump-and-Treat Groundwater Remediation System
作者: 賴彌輝
Lai, Mi-Hui
關鍵字: Genetic algorithms;遺傳演算法;groundwater remediation;optimization;MODFLOW;MT3DMS;physical barrier;pump-and-treat.;地下水污染;最佳化;MODFLOW;MT3DMS;物理性擋水牆;抽取處理
出版社: 環境工程學系所
引用: 李姵穎,「啟發式演算法在地下水復育優選問題之應用」,碩士論文,中興大學環境工程學研究所,2006。 林文輝,「石化廠址地下污染流體堵解之研究」,碩士論文,國立高雄第一科技大學環境與安全衛生學系,2004。 林妙貞,「遺傳演算法在地下水復育系統的不確定性分析之應用」,碩士論文,中興大學環境工程學研究所,1997。 梁家明,「遺傳演算法於配水管網設計最佳化之應用」,碩士論文,中興大學環境工程學研究所,2005。 陳莉、張斐章,「遺傳演算法優選水庫運用規線之研究」,農業工程學報,第四十一卷,第四期,pp. 20-29,1995。 黃佳雯,「地下水模式工具於污染控制場址範疇界定之研析」,碩士論文,國立台灣大學環境工程學研究所,2005。 Aguado, E., Remson, I., Pikul, M. F., & Thomas, W. A. (1974). Optimal pumping for aquifer dewatering.100, 869-877. Anderson, M. P., & Woessner, W. W. (1992). Applied Groundwater modeling: Simulation of Flow and Advective Transport. Academic Press. Bayer, P., Finkel, M., & Teutsch, G. (2005). Cost-optimal contaminant plume management with a combination of pump-and-treat and physical barrier systems. Ground Water Monitoring and Remediation, 25(2), 96-106. Bedient, P. B., & Rifal, H. S., & Newell, C. J. (1999). Groundwater contamination :transport and remediation. Prentice-Hall. Chan, N. (1993). Robustness of the multiple realization method for stochastic hydraulic aquifer management. Water Resources Research, 29(9), 3159-3168. Chiang, W. H., & Kinzelbach, W. (2001). 3D-Groundwater Modeling with PMWIN. Springer-Verlag Berlin Heidelberg Gorelick, S. M. (1983). A review of distributed parameter groundwater management modeling methods. Water Resources Research, 19(2), 305-319. Gorelick, S. M., Voss, C. I., Gill, P. E., Murray, W., Saunders, M. A., & Wright, M. H. (1984). Aquifer reclamation design: the use of contaminant transport simulation combined with nonlinear programing. Water Resources Research, 20(4), 415-427. Harte, P. T., Konikow, L. F., & Hornberger, G. Z. (2006). Simulation of solute transport across low-permeability barrier walls. Journal of contaminant hydrology, 85(3-4), 247-270. Hilton, A. B. C., & Culver, T. B. (2005). Groundwater remediation design under uncertainty using genetic algorithms. Journal of Water Resources Planning and Management, 131(1), 25-34. Hsiao, C., & Chang, L. (2002). Dynamic optimal groundwater management with inclusion of fixed costs. Journal of Water Resources Planning and Management, 128(1), 57-65. Jeong, I.-K., and Lee, J.-J. (1996). Adaptive simulated annealing genetic algorithm for system identification. Engineering Applications of artificial intelligence, 9(5), 523-532. Lin, M., & McKinney, D. C. (1995). Optimal remediation of DNAPL contaminated aquifers using surfactant enhanced pump-and-treat systems. Proceedings of the International Symposium on Groundwater Management, 41-45. Lin, M., & McKinney, D. C. (1995). Optimization of the surfactant enhanced pump-and-treat remediation systems. Proceedings of the 22nd Annual Conference on Integrated Water Resources Planning for the 21st Century, 864-867. Maskey, S., Jonoski, A., & Solomatine, D. P. (2002). Groundwater remediation strategy using global optimization algorithms. Journal of Water Resources Planning and Management, 128(6), 431-440. McKinney, D. C., Gates, G. B., & Lin, M. (1994). Aquifer remediation design: Nonlinear programming and genetic algorithms. Proceedings of the 21st Annual Conference on Water Policy and Management: Solving the Problems, 254-257. McKinney, D. C., & Lin, M. (1996). Pump-and-treat ground-water remediation system optimization. Journal of Water Resources Planning and Management, 122(2), 128-136. Reed, P., & Kollat, J. (2005). Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design. Advances in Water Resources, 29, 792-807. Ritzel, B. J., Eheart, J. W., & Ranjithan, S. (1994). Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resources Research, 30(5), 1589-1604. Singh, A., & Minsker, B. (2003). Modeling and characterization of uncertainty for optimization of groundwater remediation at the umatilla chemical depot. American Society of Civil Engineers (ASCE) Environmental & Water Resources Institute (EWRI) World Water & Environmental Resources Congress, Singh, R. M., & Datta, B. (2006). Identification of groundwater pollution sources using GA-based linked simulation optimization model. Journal of Hydrologic Engineering, 11, 101. Storck, P., Eheart, J. W., & Valocchi, A. J. (1997). A method for the optimal location of monitoring wells for detection of groundwater contamination in three-dimensional heterogenous aquifers. Water Resources Research, 33, 2081-2088. Tiedeman, C., & Gorelick, S. M. (1993). Analysis of uncertainty in optimal groundwater contaminant capture design. Water Resources Research, 29(7), 2139-2154. Wagner, B., & Gorelick, S. (1989). Reliable aquifer remediation in the presence of spatially variable hydraulic conductivity: From data to design. Water Resources Research, 25(10), 2211-2225. Wu, J., Zheng, C., Chien, C. C., & Zheng, L. (2006). A comparative study of monte carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty. Advances in Water Resources, 29(6), 899-911.
摘要: 
有鑑於地下水復育工作之整治時間及費用相當可觀,本研究乃應用地下水模擬模式結合遺傳演算法,針對自行建置的均質與非均質油品污染研究案例,評估各種地下水復育方案包括:零方案、抽取處理以及物理性擋水牆等方案所需之最小成本,以提供決策者參考。此外,本研究也搜集相關成本資料加以彙整,建置了本土化的抽取井設置、維護之成本函數。同時本研究也分析不同的模式決策變數組合對於最佳解之影響。研究結果顯示,遺傳演算法能成功地求解地下水復育優選問題,而模式參數如:復育井位、抽水量、復育井數與復育時間等,若能成為決策變數之形式由模式進行優選,則可以得到更好之結果。在物理性擋水牆方面,加入物理性擋水牆使污染物較為拘限,然而污染物之去除率僅較單純使用抽取處理系統者略高,並無顯著的差別。

Due to the remediation of groundwater contamination is generally a long-term and costly task, this study integrated groundwater simulators and genetic algorithms to evaluate the minimal-cost strategies for different groundwater remediation alternatives including: do-nothing, pump-and-treat and physical barriers. Two virtual contaminated sites (homogeneous and heterogeneous) were used as case studies. Furthermore, localized cost functions for well installation, operation and maintenance were also developed. The impacts of different combinations of decision variables on the quality of optimal solution were also investigated. The results indicated that the genetic algorithms can successfully solve the optimization problems of groundwater remediation. Better results will be obtained if the parameters of the groundwater simulator were treated as decision variables and determined by the optimization model. For the results of installing physical barriers, a better performance of confining the contaminant plumes was observed, and the removal efficiency is not significantly higher than using pump-and-treat system alone.
URI: http://hdl.handle.net/11455/5280
其他識別: U0005-1308200717330300
Appears in Collections:環境工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.