期刊
WATER QUALITY RESEARCH JOURNAL
卷 57, 期 1, 页码 20-39出版社
IWA PUBLISHING
DOI: 10.2166/wqrj.2021.018
关键词
grid partitioning; hydrogen sulfide; intelligent predictive model; sewer system; subtractive clustering
资金
- Department of Civil and Environmental Engineering and Research and Development Office, Prince of Songkla University (PSU), Thailand
- Ministry of Higher Education, Science, Research and Innovation, Thailand, through the Researcher Development Scholarship Scheme under the 2021 PSU President Scholarship [REVPD64043]
The study aimed to develop a predictive model to estimate hydrogen sulfide (H2S) emission from gravity sewers using two different adaptive neuro-fuzzy inference system (ANFIS) models. Results showed that the ANFIS-GP model, with fewer rules and parameters, outperformed the ANFIS-SC model in forecasting H2S emission.
A predictive model to estimate hydrogen sulfide (H2S) emission from sewers would offer engineers and asset managers the ability to evaluate the possible odor/corrosion problems during the design and operation of sewers to avoid in-sewer complications. This study aimed to model and forecast H2S emission from a gravity sewer, as a function of temperature and hydraulic conditions, without requiring prior knowledge of H2S emission mechanism. Two different adaptive neuro-fuzzy inference system (ANFIS) models using grid partitioning (GP) and subtractive clustering (SC) approaches were developed, validated, and tested. The ANFIS-GP model was constructed with two Gaussian membership functions for each input. For the development of the ANFIS-SC model, the MATLAB default values for clustering parameters were selected. Results clearly indicated that both the best ANFIS-GP and ANFIS-SC models produced smaller error compared with the multiple regression models and demonstrated a superior predictive performance on forecasting H2S emission with an excellent R-2 value of >0.99. However, the ANFIS-GP model possessed fewer rules and parameters than the ANFIS-SC model. These findings validate the ANFIS-GP model as a potent tool for predicting H2S emission from gravity sewers.
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