Economic Analyses for Optimizing the Construction of Separate Sewer and Water Quantity/Water Quality Prediction Modelling

博士 === 國立臺灣大學 === 環境工程學研究所 === 99 === 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-eff...

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Bibliographic Details
Main Authors: Home-Ming Chen, 陳宏銘
Other Authors: Shang-Lien Lo
Format: Others
Language:zh-TW
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/43901301981660855907
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Summary:博士 === 國立臺灣大學 === 環境工程學研究所 === 99 === 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.