期刊
WATER RESEARCH
卷 164, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.watres.2019.114888
关键词
Artificial neural network; Genetic algorithm; Drinking water treatment; Water production
资金
- National Key Research and Development Program of China [2016YFA0203104/2017YFE0107200]
- Natural Science Foundation of China [51708280]
- Veolia and Projects on Social Development of Jiangsu Province of China [BE2016734]
Stringent regulations and deteriorating source water quality could greatly influence the water production capacity of drinking water treatment plants (DWTPs). Using models to predict the performance of DWTPs under stress provides valuable information for decision making and future planning. A hybrid statistic model named HANN was established by combining artificial neural network (ANN) with genetic algorithm (GA) aiming at forecasting the overall performance of DWTPs nationwide in China. Monthly data from 45 DWTPs across China was employed. Water quality parameters like temperature and chemical oxygen demand (COD) and operational parameters like electricity consumption and chemical consumption were selected as input variables, while drinking water production was employed as the output. Both preliminary data analysis and principal component analysis (PCA) suggested a clear nonlinear relationship between the input and output variables. The structure of the HANN model was optimized by employing the lowest mean squared error (MSE) as the indicator. The resultant HANN model performed well when simulating the training datasets. Its predictive accuracy for the independent test datasets was enhanced when feeding more training datasets and the performance was constantly higher than the independent multi-layered ANN models using the coefficient of determination (R-2) as the indicator, indicating the HANN model was capable of capturing complex non-linear relationship and extrapolation. Results from Accuracy test, Garson sensitivity analysis and Analysis of Variance (ANOVA) suggested the quantity of water produced by DWTPs was closely linked to water quality and operational parameters. The scenario analysis showed that the HANN model was capable of predicting water production variation based on the parameter variations, indicating that the HANN model could be a general management tool for decision makers and DWTP managers to make plans in advance of regulatory changes, source water quality variations and market demand. (C) 2019 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据