PMID- 34467491
OWN - NLM
STAT- MEDLINE
DCOM- 20220120
LR  - 20220120
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 29
IP  - 5
DP  - 2022 Jan
TI  - Predicting flocculant dosage in the drinking water treatment process using Elman 
      neural network.
PG  - 7014-7024
LID - 10.1007/s11356-021-16265-4 [doi]
AB  - Predicting the flocculant dosage in the drinking water treatment process is 
      essential for public health. However, due to the complexity of water quality and 
      flocculation, many difficulties remain. The present study aimed to report on 
      using artificial intelligence, namely, the Elman neural network (ENN), to predict 
      the flocculant dosage and explore the applications of the proposed model in 
      waterworks. The flocculation process of drinking water was introduced in this 
      study, and four typical models were developed based on multiple linear regression 
      (MLR), the radial basis function neural network (RBFNN), the least squares 
      support vector machine (LSSVM), and the ENN. To improve the prediction accuracy, 
      a mixed term including long-term data and short-term data was proposed to capture 
      the periodic and time-varying characteristics of water quality data. The weights 
      of each part are updated adaptively according to the comparison of effluent 
      turbidity and set values. The results demonstrate that the proposed ENN model 
      performed better than the other three models in terms of the prediction 
      performance. With the ENN model of flocculant dosage, the root mean square error 
      (RMSE), mean absolute percentage error (MAPE), and coefficient of determination 
      (R(2)) of the test data were 1.8917, 5.0067, and 0.8999, which were improved by 
      36.9%, 41.5%, and 14.0% in comparison with the best one (RBFNN) of the other 
      three models, respectively. The effluent turbidity of sedimentation tank was more 
      stable under the control of proposed ENN model of flocculant dosage than the 
      other three models. Considering its performance, the ENN model can be taken as a 
      preferred data intelligence tool for predicting the drinking water flocculant 
      dosage.
CI  - (c) 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Wang, Dongsheng
AU  - Wang D
AUID- ORCID: 0000-0003-4307-0992
AD  - College of Automation & College of Artificial Intelligence, Nanjing University of 
      Posts and Telecommunications, Nanjing, 210023, China. wdsnjupt@163.com.
AD  - Jiangsu Engineering Laboratory for Internet of Things and Intelligent Robots, 
      Nanjing University of Posts and Telecommunications, Nanjing, 210023, China. 
      wdsnjupt@163.com.
FAU - Chang, Xiao
AU  - Chang X
AD  - College of Automation & College of Artificial Intelligence, Nanjing University of 
      Posts and Telecommunications, Nanjing, 210023, China.
AD  - Jiangsu Engineering Laboratory for Internet of Things and Intelligent Robots, 
      Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
FAU - Ma, Kaiwei
AU  - Ma K
AD  - College of Automation & College of Artificial Intelligence, Nanjing University of 
      Posts and Telecommunications, Nanjing, 210023, China. makaiwei@live.com.
AD  - Jiangsu Engineering Laboratory for Internet of Things and Intelligent Robots, 
      Nanjing University of Posts and Telecommunications, Nanjing, 210023, China. 
      makaiwei@live.com.
LA  - eng
GR  - 52170001/National Natural Science Foundation of China/
GR  - 2020056/Science and Technology Project of Water Conservancy of Jiangsu Province/
GR  - 2012ZX07403-001/Major Science and Technology Program for Water Pollution Control 
      and Treatment/
GR  - NY220140/NUPTSF/
PT  - Journal Article
DEP - 20210831
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Drinking Water)
SB  - IM
MH  - *Artificial Intelligence
MH  - *Drinking Water
MH  - Flocculation
MH  - Neural Networks, Computer
MH  - Water Quality
OTO - NOTNLM
OT  - Drinking water
OT  - Elman neural network
OT  - Flocculant dosage
OT  - Least squares support vector machine
OT  - Multiple linear regression
OT  - Radial basis function neural network
EDAT- 2021/09/02 06:00
MHDA- 2022/01/21 06:00
CRDT- 2021/09/01 07:37
PHST- 2021/03/02 00:00 [received]
PHST- 2021/08/26 00:00 [accepted]
PHST- 2021/09/02 06:00 [pubmed]
PHST- 2022/01/21 06:00 [medline]
PHST- 2021/09/01 07:37 [entrez]
AID - 10.1007/s11356-021-16265-4 [pii]
AID - 10.1007/s11356-021-16265-4 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2022 Jan;29(5):7014-7024. doi: 
      10.1007/s11356-021-16265-4. Epub 2021 Aug 31.