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Prediction of brine evaporation rate based on response surface methodology and artificial neural network
Li, Zhiwei; Fu, Zhenhai; Li, Chengbao; Zhao, Dongmei; Zhang, Yongming; Ma, Yanfang; Zhang, Zhihong
第一作者Li, Zhiwei
2021-08
发表期刊DESALINATION AND WATER TREATMENT
ISSN1944-3994
卷号231页码:143-151
摘要In this study, a Box-Behnken design was carried out to investigate the effects of radiation intensity, environment temperature, relative humidity, brine temperature, wind speed and brine concentration on the brine evaporation rate. The predictive abilities of response surface methodology and artificial neural networks were compared. The results showed that root mean square error for new data by the response surface method and artificial neural network models is 0.265 and 0.125, respectively; whereas the coefficient of determination is 0.773 and 0.940, respectively; and the standard error of prediction is 29.26% and 13.77%, respectively. It indicating that the artificial neural network model has much higher modeling abilities and generalization abilities than the response surface methodology model. Thus, the artificial neural network model is much more stable and accurate to be used in predicting brine evaporation rate in comparison to the response surface methodology model.
DOI10.5004/dwt.2021.27508
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文献类型期刊论文
条目标识符http://ir.isl.ac.cn/handle/363002/34355
专题盐湖资源与化学实验室
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GB/T 7714
Li, Zhiwei,Fu, Zhenhai,Li, Chengbao,et al. Prediction of brine evaporation rate based on response surface methodology and artificial neural network[J]. DESALINATION AND WATER TREATMENT,2021,231:143-151.
APA Li, Zhiwei.,Fu, Zhenhai.,Li, Chengbao.,Zhao, Dongmei.,Zhang, Yongming.,...&Zhang, Zhihong.(2021).Prediction of brine evaporation rate based on response surface methodology and artificial neural network.DESALINATION AND WATER TREATMENT,231,143-151.
MLA Li, Zhiwei,et al."Prediction of brine evaporation rate based on response surface methodology and artificial neural network".DESALINATION AND WATER TREATMENT 231(2021):143-151.
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