請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48718
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 駱尚廉(Shang-Lien Lo) | |
dc.contributor.author | Home-Ming Chen | en |
dc.contributor.author | 陳宏銘 | zh_TW |
dc.date.accessioned | 2021-06-15T07:10:09Z | - |
dc.date.available | 2010-10-22 | |
dc.date.copyright | 2010-10-22 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-10-18 | |
dc.identifier.citation | 1. Achleitner, S., Möderl, M., and Rauch, W. (2007). City drain - an open source approach for simulation of integrated urban drainage system. Environmental Modelling & Software, 22(8), 1184-1195.
2. APHA, AWWA, WEF, (1995). Standard Methods for the Examination of Water and Wastewater, 19th ed. American Public Health Association/American Water Works Association/Water Environment Federation, Washington DC. 3. Benedetti, L., Dirckx, G., Bixio, D., Thoeye, C., and Vanrolleghem, P.A. (2008). Environmental and economic performance of the integrated urban wastewater system. Journal of Environmental Management, 88(4), 1262-1272. 4. Bjorlenius, B. and Reinius, L.G. (1998). Use of on-line data to evaluate the activity in the biological stage at a wastewater treatment plant. Water Science and Technology, 37 (9), 33-40. 5. Braden, J.B., and Johnston, D.M. (2004). Downstream economic benefits from storm-water management. Journal of Water Resources Planning and Management, 130(6), 498-505. 6. Brombach, H., Weib, G. and Fuchs, S. (2005). A new database on urban runoff pollution: comparison of separate and combined sewer systems. Water Science & Technology, 51(2), 119-128. 7. Buerge, I.J., Poiger, T., Muller, M.D., and Buser, H.R. (2006). Combined sewer overflows to surface waters detected by the anthropogenic marker caffeine. Environmental Science & Technology, 40(13), 4096-4102. 8. Chang, L.C. (2002). Dynamic Optical Ground Water Remediation Including Fixed and Operation Costs. Groundwater, 40(5), 481-490. 9. Chang, N.B., Chen, W.C. and Shieh, W.K. (2001). Optimal control of wastewater treatment plants via integrated neural network and genetic algorithms. Civil Engineering and Environmental Systems 18, 1-17. 10. Chang, N.B. and Wang, S.F. (1995). A grey nonlinear programming approach for planning coastal wastewater treatment and disposal systems. Water Science and Technology, 32 (2), 19-29. 11. Chang, N.B. and Wang, S.F. (1997). Integrated analysis of recycling and incineration programs by goal programming techniques. Waste Management Research , 15(2), 121-36. 12. Chang, N.B. and Wang, S.F. (1996). Managerial fuzzy optimal planning for solid waste management systems. Journal of Environmental Engineering, 122(7), 649-658. 13. Chang, N.B. and Wang, S.F. (1996). Solid waste management system analysis by multi-objective mixed integer programming model. Journal of Environmental Management. 48, 17-43. 14. Chang, N.B., Wen, C.G., Chen, Y.L. and Yong, Y.C. (1996). Optimal planning of the reservoir watershed by grey fuzzy multi-objective programming (I): theory. Water Research, 30(10), 2329-2334. 15. Chen, H.W. and Chang, N.B. (2000). Prediction analysis of solid waste generation based on grey fuzzy dynamic modelling. Resources Conservation and Recycling, 29(1-2), 1-18. 16. Chen, J.C., Chang, N.B., and Shieh, W.K. (2003a). Assessing Wastewater Reclamation Potential by Neural Networks Model. Engineering Applications of Artificial Intelligence, 16(2), 149-157. 17. Chen, J.C., Chang, N.B. (2007). Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process. Engineering Applications of Artificial Intelligence, 20(1), 959-969. 18. Chen, W.C., Chang, N.B. and Chen, J.C. (2003b). Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment. Water Research, 37 (2003), 95-107. 19. Chen, W.C. and Chang, N. B., Member, ASCE, and Shieh, Wen K. (2001). Advance hybrid fuzzy-neural controller for industrial wastewater treatment. Journal of Environmental Engineering, 127(11), 1048-1059. 20. Choi, D.J. and Park, H. (2001). A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. Water Research, 35 (16), 3959-3967. 21. Cote, M., Grandjean, B.P.A., Lessard, P. and Thibault J. (1995). Dynamic modeling of activated sludge process: improving prediction using neural networks. Water Research, 29 (4), 995-1004. 22. Deng, J. (1989). Introduction to grey system theory. The Journal of Grey System, 1 (1), 1-24. 23. Deng, J. (1989). Properties of multivariable grey model GM (1,N). The Journal of Grey System, 1(1), 25-41. 24. De Veaux, R.D., Brain, R. and Ungar, L.H. (1999). Hybrid neural network models for environmental process control. Environmetrics, 10 (3), 225-236. 25. Djebbar, Y. and Kadota, P.T. (1998). Estimating sanitary flows using neural networks. Water Science & Technology, 38(10), 215-222. 26. Downing, P.B. (1984). Environmental Economics and Policy, Little, Brown Company, Boston, USA, pp. 28-30. 27. Fronteau, C., Buwens, W. and Vanrolleghem, A. (1997). Integrated modelling: comparison of state variables, processes and parameters in sewer and wastewater treatment plant models. Water Science & Technology, 36(5), 373-380. 28. Fu, C. and Poch, M. (1995). System identification and real-time pattern recognition by neural networks for an activated sludge process. Environment International, 21 (1), 57-69. 29. Gontarski, C.A., Rodrigues, P.R., Mori, M. and Prenem, L.F. (2000). Simulation of an industrial wastewater treatment plant using artificial neural networks. Computers and Chemical Engineering, 24, 1719-1723. 30. Gujer, W., Henze, M., Mino, T. and van Loosdrecht, M. C. M. (1999). Activated sludge model No. 3. Water Science & Technology, 39(1), 183-193. 31. Häck, M. and Köhne, M. (1996). Estimation of wastewater process parameters using neural networks. Water Science & Technology, 33 (1), 101-115. 32. Hagan, M.T., Demuth, H.B. and Beale, M. (1996). Neural Network Design, PWS Publishing, Boston. 33. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation, second ed. Prentice-Hall, NJ, USA, pp. 205-207. 34. Henze, M., Grady, Jr. C. P. L., Gujer, W., Marais, G. V. R. and Matsuo, T. (1987). Activated sludge model No.1 Scientific and technical report No. 1. International Association on Water Pollution Research and Control, London. 35. Henze, M., Gujer, W., Mino, T., Matsuo, T., Wentzel, M. C., Marais, G. V. R. and van Loosdrecht, M.C.M. (1999). Activated sludge model No.2d, ASM2d, Water Science & Technology, 39 (1), 165-182. 36. Henze, M., Gujer, W., Mino, T. and van Loosdrecht, M. C. M. (2000). Activated sludge models: ASM1, ASM2, ASM2d and ASM3, IWA, London. 37. .Henze, M., Gujer ,W., Mino T., Wentzel, M.C. and Marais, G.V.R. (1995). Activated sludge model No.2, IWAQ Scientific and technical report No.3. IWAQ, London. 38. Hong, Y.S. and Bhamidimarri, R. (2003). Evolutionary self-organising modeling of a municipal wastewater treatment plant. Water Research, 37(2003), 1199-1212. 39. Huang, Y.P. and Huang, C.C. (1996). The integration and application of fuzzy and grey modeling methods. Fuzzy Sets Syst, 78, 107-119. 40. Huang, Y.P. and Huang, C.H. (1997). Read-valued genetic algorithms for fuzzy grey prediction system. Fuzzy Sets Syst, 87, 265-276. 41. Lee, D.S. and Park, J.M. (1999). Neural network modeling for on-line estimation of nutrient dynamics in a sequentially operated batch reactor. Journal of Biotechnology, 75, 229-239. 42. Lewis, C.D.(1982). International and Business Forecasting Methods, London: Butterworths. 43. Kalker, T.J.J. (1999). Fuzzy control of aeration in an activated sludge wastewater treatment plant:design simulation and evaluation . Water Science & Technology, 39 (4), 71-79. 44. Manesis, S.A., Sapidis, D.J. and King, R.E. (1998). Intelligent control of wastewater treatment plants. Artificial Intelligence in Engineering, 12(3), 275-281. 45. Molga, E. & Cherbanski, R. (2006). Modeling of an Industrial Full-Scale Plant for Biological Treatment of Textile Wastewaters: Application of Neural Networks. Industrial Engineering Chemistry Research, 45, 1039-1046. 46. Nebolsine R., and Vercelli G.L. (1974). Is the separation of sewers desirable? Kentucky University, Office of Research and Engineering Services, Bulletin., USA, pp. 115-123. 47. Ning, S.K., Chang, N.B., Yang, L., Chen, H.W., and Hsu, H.Y. (2001). Assessing pollution prevention program by QUAL2E simulation analysis for the Kao-Ping river basin, Taiwan. Journal of Environmental Management, 61(1), 61-76. 48. Olsson, G. and Newell, B. (1999). Wastewater Treatment Systems, Modelling, Diagnosis and Control, IWA, Publishing. 49. Pai, T. Y., Tsai, Y. P., Chen, S.W., Chiou, R. J. and Tsai, C. H. (2005), Prediction of effluent quality from an industrial wastewater treatment plant of deep oxidation ditch process using grey model. 1st IWA-ASPIRE Regional Conference and Exhibition, Singapore. 50. Pai, T.Y., Tsai, Y.P., Lo, H.M., Tsai, C.H. and Lin, C.Y. (2007). Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent. Computers & Chemical Engineering , 31 (10), 1272-1281. 51. Rauch, W. (1999). G.A. in real time control applied to minimize transient pollution for urban wastewater systems. Water Research, 33 (5), 1265. 52. Rauch, W., Krejci, V., and Gujer, W. (2002). Rebka - a software tool for planning urban drainage on the basis of predicted impacts on receiving waters. Urban Water, 4(4), 355-361. 53. Rodrigo, M. A., Seco, A. J., Ferrer Penya-roja, J.M. and Valverde, J. L. (1999). Nonlinear control of an activated sludge aeration process: use of fuzzy techniques for tuning PID controllers. ISA Transactions, 38: 231-241. 54. Spall, J.C. and Cristion, J.A. (1997). A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment. IEEE Trans Syst, Man Cybern-Part B: Cybernetics, 27(3), 369-375. 55. Syu, M.J. and Chen, B.C. (1998). Backpropagation neural network adaptive control of a continuous wastewater treatment process. Industrial and Engineering Chemistry Research, 37 (9), 3325-3630. 56. Tay, J.H. and Zhang, X. (1999). Neural fuzzy modeling of anaerobic biological wastewater treatment systems. Journal of Environmental Engineering, ASCE 125 (12), 1149-1159. 57. Tay, J.H. and Zhang, X. (2000). A fast predicting neural fuzzy model for highrate anaerobic wastewater treatment systems. Water Research, 34 (11), 2849-2860. 58. Tien, T.L. and Chen, C.K. (1997). The indirect measurement of fatigue limits of structural steel by the deterministic grey dynamic model DGDM(1,1,1). Appl Math Modelling, 21(10):611-619. 59. Tsai, Y.P., Ouyang, C.F., Chiang, W.L., Wu, M.Y. (1994). Construction of an on-line fuzzy controller for the dynamic activated sludge process.Water Research, 28 (4), 913-921. 60. Tsai, Y. P., Ouyang, C. F., Wu, M. Y. and Chiang, W. L. (1996). Effluent suspended solid control of activated sludge process by fuzzy control approach. Water Environment Research, 68(6), 1045- 1053. 61. Tchobanoglous, G. (1981). Sewage engineering: Collection and pumping of sewage. 2nd edn, Metcalf & Eddy, Inc., USA, pp. 100-113. 62. Toffol, S.D., Engelhard, C., and Rauch, W. (2007). Combined sewer system versus separate system- a comparision of ecological and economical performance indicators. Water Science & Technology, 55(4), 255-264. 63. Van der Tak, L., Wycoff, R., Brueckner, T., and Albert, P. (1993). Economic optimization analysis of combined sewer overflow controls in the Narragansett Bay, Ruode Island. Journal of New England Water Environment Association, 27(2), 164-186. 64. Wareham, D.G., Hall, K.J. and Mavinic, D.S. (1993). Real-time control of aerobic-anoxic sludge digestion using ORP. Journal of Environmental Engineering, 119 (1), 120-136. 65. Wang, W. L. and Ren, M. (2002). Soft-sensing Method for Wastewater Treatment Based on BP Neural Network. Proceedings of the 4th World Congress on Intelligent Control and Automation. June 10-14, 2330-2332. Shanghai, P. R. China. 66. Wu, C.C. and Chang, N.B. (2003a). Grey input–output analysis and its application for environmental cost allocation. European Journal of Operational Research, 145 (1), 175-201. 67. Wu, C.C. and Chang, N.B. (2003b). Global strategy for optimizing textile dyeing manufacturing process via GA-based grey nonlinear integer programming. Computers and Chemical Engineering, 27 (6), 833-854. 68. Wu, C.C. and Chang, N.B. (2004). Corporate optimal production planning with varying environmental costs: A grey compromise programming approach. European Journal of Operational Research, 155 (1), 68-95. 69. Yamada, K., Umehara, T., and Ichiki, A. (1993). Study on statistical characteristics of nonpoint pollutants deposited in urban area. Water Science & Technology, 28(3-5), 283-290. 70. Yen, J. and Langari, R. (1999). Fuzzy logic: intelligence, control, and information, Prentice-Hall, Inc. Upper Saddle River, New Jersey. 71. Zhang, Q. and Stanley, S.J. (1999). Real-time water treatment process control with artificial neural networks. Journal of Environment Engineering, ASCE, 125(2), 153-160. 72. Zhu, J., Zurcher, J., Rao, M. and Meng, M.Q.H. (1998). An on-line wastewater quality predication system based on a time-delay neural network. Engineering Applications of Artificial Intelligence, 11, 747-758. 73. 王晉中,2005,MATLAB 7 在工程上的應用,高立圖書有限公司,台北。 74. 白子易,蔡嘉和,廖婉君,邱智慧,蘇昭郎,呂鴻光,歐陽嶠暉,2002,以機制模式及類神經網路預測TNCU程序出流水質之比較,第二十七屆廢水處理技術研討會論文集,中華民國環境工程學會,台北。 75. 邱智慧,2006,徵收水污染防治費對河川水質之影響,國立台灣大學環境工程研究所,碩士論文。 76. 陳筱華,1989,河川污染特性及水質數學模式之探討-以基隆河為例,國立台灣大學環境工程研究所,碩士論文。 77. 郭振泰,1994,基隆河整治對河川影響及監測系統之評估 (二),台北市政府工務局養護工程處,台北。 78. 許銘熙、郭義雄、郭振泰、柳文成,1998,淡水河感潮段垂直二維理與水質動態傳輸(一),行政院國科會專題計畫研究成果報告。 79. 柳文成,1990,截流系統對基隆河水質影響之研究,國立台灣大學農學工程學研究所,碩士論文。 80. 歐陽嶠暉,2000,下水道工程學(三版),長松文化公司,台北。 81. 歐陽嶠暉,1971,淡水河水系水污染調查及河川自淨能力之研究,台灣水利,第19卷3期。 82. 盧瑞山、駱尚廉,2000,類神經網路於土壤復育工程之應用,財團法人中興工程顧問社。 83. 駱尚廉,2002,基隆河水污染防治群體研究-非點源污染與水質模式,海峽兩岸新世紀水的關懷-台北論壇手冊,時報文教基金會。 84. 駱尚廉,1995,環境數學,茂昌圖書有限公司,台北。 85. 駱尚廉,2006,環境經濟分析,曉園出版社,台北。 86. 焦李成,1991,類神經網路理論,格致圖書公司,台北。 87. 蔡勇斌、吳明洋、歐陽嶠暉、蔣偉寧,1993,動態活性污泥程序即時操作控制之研究,中國土木水利工程學刊,8(2),台北。 88. 蔡瑞煌,1995,類神經網路概論,三民書局,台北。 89. 蔡嘉和,白子易,陳世偉,蔡勇斌,孔慧雯,2003,以GM (1, 1) 模型及類神經網路預測工業區廢水廠出流水水質之比較,第十六屆環境規劃與管理研討會,中華民國環境工程學會,台中。 90. 蘇木春、張孝德,1999,機械學習:類神經網路、模糊系統以及基因演算法則,全華科技圖書股份有限公司,台北。 91. 溫坤禮、黃宜豊、陳繁雄、李元秉、連志峰、賴家瑞,2002,灰預測原理與應用,全華科技出版社,台北。 92. 溫坤禮、張簡士琨、葉鎮愷、王建文、林慧珊,2008,MATLAB在灰色系統理論的應用,全華圖書股份有限公司,台北。 93. 鄧聚龍、郭洪,1996,灰預測原理與應用,全華科技出版社,台北。 94. 鄧聚龍,1999,灰色系統理論與應用,高立圖書有限公司,台北。 95. 蕭代基、黃宗煌、陳明健、劉錦添、鄭欽龍、薛立敏,1988,環境經濟學與政策,聯經出版社,台北。 96. 羅華強,2001,類神經網路-MATLAB的應用,清蔚科技出版,台北。 97. 葉怡誠,1998,類神經網路模式應用與實作,儒林圖書有限公司,台北。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48718 | - |
dc.description.abstract | 為加速對河川水質改善,分析下水道管網最適化建設,及配合管網之逐步建設而建立污水處理廠水量、水質預測模式,為本研究之兩大重點。最適化建設經濟分析為以即將完成全面用戶接管之下水道系統為案例,採用邊際防治成本(MCC)等於邊際防治效益(MBC)之觀念,輔以河川BOD5為指標,以探討分流式下水道之最適化用戶接管普及率及最適化經濟建設。
分流式下水道建設期間,由於污水處理廠仍為初期營運階段,為提升進入污水處理廠之水量及其污染濃度,以加速對河川水質之改善,除原有之用戶接管工作外,常輔以增建簡易合流式下水道截流設施以加速達成之。但此段期間,污水處理廠之進廠水量、水質,仍常不如預期,且其變異性頗大,不易穩定操作,故本研究根據每年之管網建設資料,如用戶接管普及率、污水處理率及進出廠水量、水質等參數,採用倒傳遞類神經網路(BPNN),建立進出污水處理廠之水量、水質等4種基本預測模式(即A0、A1、A2及A3),並與灰色模式(GM)比較,前述基本模式經結合,如A1與A2結合即為A1+A2多階倒傳遞類神經網路(MBPNN),此多階模式可提供更多之訊息以利污水處理廠之操作及控制。 當達到最適化用戶接管普及率時,河川BOD5指標,已由嚴重或中度污染改善達到接近輕度污染,故辦理分流式下水道建設,當達到最適化用戶接管普及率時,有其建設之成效。同時該分流式下水道,考量加速對河川水質改善,除持續用戶接管工作外,建議餘人口較稀疏未接管區域可因地制宜採用建設成本較低之簡易合流式下水道截流設施,統稱為階段性結合式下水道系統(Hybrid sewer system),並配合不易接管區域之聚落污水處理設施,處理都市廢污水。 污水處理廠放流水質預測模式(A0)與灰色模式之預測效果接近,惟前者需大量資料,後者各分項水質如BOD5、COD及SS等,僅需少量資料即可達到預測效果,由每年建設之管網資料,所建立之進廠水量、水質預測模式(A1),及由進廠水量、水質建立之放流水量、水質預測模式(A2),以及由進出廠水量、水質建立之污泥預測模式(A3),經研究分析均獲得甚佳之預測效果,最後由A1及A2所結合建立之多階模式(Multi-model)可直接由建設使用中之下水道系統之相關建設參數預測放流水量及水質。經綜合分析各個模式之預測效果,上述各模式對水量Q、BOD5、污泥量之預測結果最佳。 | zh_TW |
dc.description.abstract | The objective of this study was to apply the concept that Marginal Cost of Control (MCC) equals to Marginal Benefits of Control (MBC) to develop a method for studying the optimal percentage of household connection to a separate sewer system and the most cost-effective construction of the separate sewer system. Mathematical models were also developed to provide useful information for operating the end-of-the-pipe wastewater treatment plants to meet discharge standards, and for managing the water quality of water bodies that receive the effluent discharges from hybrid sewer systems.
Base on the progress of the construction, back-propagation neural network (BPNN) was applied to predict the wastewater quantity and quality. Four basic models are included in this network: (1) A0 (PIQ)model for predicting influent quality, (2) A1(PIQQ)model for predicting influent quantity and quality, (3) A2(PEQQ)model for predicting effluent quantity and quality, and (4) A3(PQWCWS)model for predicting the quantity and water content of waste sludge. The multi-model (A1+A2), a multi-back-propagation neural network (MBPNN) formed by combining the A1 and A2 models, was used for estimating A2 output parameters by using A1 input parameters directly. Comparing to the A0 model, the predicting results suggest that GM (Grey model) can be used to predict the variation of municipal effluent with insufficient effluent data. The results also indicate that BPNN (back-propagation neural network) and MBPNN are suitable for predicting the wastewater quantity and quality, especially for Q, BOD5, sludge amount, and the water content of sludge in an under-constructed sewer system. The validity and applicability of the method proposed in this study have been demonstrated by analyzing the optimal household connection percentage to assess the most cost-effective construction of the separate sewer. The results of that the receiving water quality can be improved in a cost-effective manner. The optimal percentage of household connection to the separate sewer will lead to the most cost-effective stage when the stream Biochemical Oxygen Demand (BOD5) meets the water quality standards. For more accurate analyses, the effect of other factors such as human health protection, and animal and plant production should be quantified. The Scenario Analysis Method can be applied for evaluating the total benefits of control (TBC). Once the economic cost of construction is calculated, the relatively more expensive section of the separate sewer will not be constructed. Instead, it will be switched over to a less expensive combined sewer system to make the whole system a hybrid sewer system. This study also reveals that during the initial construction phase of the separate sewer more household connection will lead to significant BOD5 reduction in the receiving water body. However, at a later stage, additional increase of the household connection will not further improve the river quality as much as it has previously; the receiving body water quality will reach a steady state thereafter. The receiving river water quality as expressed by BOD5 is improved from near “serious pollution” to “moderate pollution”, and it continues to approach “light pollution” when the optimal household connection was reached. This concept of the hybrid sewer system has been implemented for the other cities to alleviate the financial burden of constructing the sewer system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T07:10:09Z (GMT). No. of bitstreams: 1 ntu-99-D92541003-1.pdf: 3074477 bytes, checksum: 8954f6ecace0f1a438d5ed646469eb59 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 第一章 緒論 1
1.1 研究緣起 1 1.2 研究目的 2 1.3 研究內容 3 第二章 文獻回顧 5 2.1 分流式下水道及合流式下水道 5 2.2 邊際防治成本及邊際防治效益 9 2.3 數學模式及建立目的 13 2.4 類神經網路及灰色模式 16 2.4.1 類神經網路 16 2.4.2 灰色模式 25 2.5 研究範疇之資料蒐集 29 第三章 研究方法 37 3.1 最適化建設經濟分析 37 3.1.1 總防治成本與邊際防治成本 37 3.1.2 總防治效益與邊際防治效益 40 3.1.3 邊際防治成本曲線與邊際防治效益曲線之關係 41 3.1.4 目標函數及邊際條件 41 3.2 倒傳遞類神經網路水質預測模式(A0模式)及灰色模式 44 3.3 倒傳遞類神經網路水量、水質預測模式 48 3.3.1 建設成果變化量預測進廠水量、水質(A1模式) 48 3.3.2 進廠水量、水質預測放流水量、水質(A2模式) 49 3.3.3 進出廠水量、水質預測污泥量及含水率(A3模式) 50 3.4 多階倒傳遞類神經網路水量、水質預測模式 53 3.5 模式預測評估及誤差分析 55 3.6 相關係數(R) 55 第四章 結果與討論 57 4.1 最適污染排放量及經濟最適化分析 57 4.2 類神經網路放流水質預測模式(A0模式)與各類灰色模 式比較 68 4.2.1 放流水質BOD5之模擬預測結果 68 4.2.2 放流水質COD之模擬預測結果 71 4.2.3 放流水質SS之模擬預測結果 73 4.2.4 放流水質預測模式整體說明 75 4.3 基本水量、水質預測模式最適化結構篩選 75 4.4 基本水量、水質預測模式驗證及結果確認 81 4.4.1 A1模式之模擬預測結果 81 4.4.2 A2模式之模擬預測結果 91 4.4.3 A3模式之模擬預測結果 100 4.5 多階預測模式及下水道系統管理 104 4.5.1 多階A1+A2模式之模擬預測結果 104 4.5.2 下水道系統管理模式 111 4.6 綜合結果與討論 111 第五章 結論與建議 114 5.1 結論 114 5.2 建議 115 參考文獻 116 附錄 …… 126 | |
dc.language.iso | zh-TW | |
dc.title | 分流式下水道最適化建設經濟分析及水量水質預測
模式之研究 | zh_TW |
dc.title | Economic Analyses for Optimizing the Construction of Separate Sewer and Water Quantity/Water Quality Prediction Modelling | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-1 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 李公哲(Kung-Cheh Li),林正芳(Cheng-Fang Lin),歐陽嶠暉(Chaio-Fuei Ouyang),張添晉(Tien-Chin Chang),謝啟萬(Chiwan Wayne Hsieh) | |
dc.subject.keyword | 邊際防治成本,邊際防治效益,結合式下水道,倒傳遞類神經網路,灰色模式, | zh_TW |
dc.subject.keyword | marginal benefits of control (MBC),marginal cost of control (MCC),hybrid sewer system,back-propagation neural network (BPNN),grey model, | en |
dc.relation.page | 152 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-10-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 環境工程學研究所 | zh_TW |
顯示於系所單位: | 環境工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-99-1.pdf 目前未授權公開取用 | 3 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。