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Abstract
Understanding pollutant transformation in sewers is important in controlling odor emission from pressure mains as well as in assessing organic pollutant removal capacity of gravity sewers. Sewer process models have thus been developed to quantify the pollutant transformation processes under various sewer conditions. The quantification largely depends on model parameter values, in particular the kinetic and stoichiometric parameters related to microbial activities. The current approaches not only involve a large amount of experimental work but also may induce significant errors when microbial reactions cannot be differentiated effectively during the experiments. Therefore, this study is aimed at developing a new method that can reduce experimental work significantly. The proposed method utilizes a genetic algorithm (GA) to enable the determination with a single set of batch experiments. To study the feasibility of the proposed method, a set of 72-hr batch experiments was first conducted for determining the parameters of a sewer model developed in this study, which adopted a full version of the International Water Association (IWA) Activated Sludge Model No. 3 (ASM3) to describe the microbial activities in sewers. The results were then verified with two different sets of the batch experiments. Furthermore, dynamic variation data of dissolved oxygen level were collected at the outlet of a 1.5-km gravity sewer to validate the determined parameters. All the results showed that the proposed parameter determination method is effective.