Backpropagation neural network model for detecting artificial emotions with color

Author(s):  
Min-Feng Lee ◽  
Guey-Shya Chen
2013 ◽  
Vol 838-841 ◽  
pp. 2525-2531
Author(s):  
Fu Quan Jia ◽  
Zhang Wei He ◽  
Zhu Jun Tian ◽  
Zhao Bo Chen ◽  
Hong Cheng Wang ◽  
...  

Prediction and optimization on water quality parameters (WQPs) have become more and more important to the wastewater treatment system (WWTs). In this study, the genetic algorithm backpropagation neural network model (GA-BPNN) had been used to predict and optimize WQPs of a low-strengthen complex wastewater treatment system (LSCWWTs). Results showed that the correlation coefficients between the predicted values and measured values were R2 =0.946 for COD, R2=0.962 for BOD, R2=0.933 for TN, R2=0.985 for NH3-N, R2=0.969 for TP, and R2=0.968 for SS, indicating the predictive values by the GA-BPNN model well fitted the mesured values of effluent WQPs. The optimal effluent WQPs were COD=27.6mg/L, BOD=7.1mg/L, TN=5.4mg/L, NH3-N=0.9mg/L, TP=0.11mg/L and SS=9.25mg/L, respectively. And the corresponding operating parameters were MLSS=3045.4mg/L, MLVSS=2405.9mg/L, T=23.2 °C, R=1.4, SRT=12.5d, HRT=17.3h, CODin =643.3mg/L, BODin=342.2mg/L, TNin=54.2mg/L, NH3-Nin=45.3mg/L, TPin=4.9mg/L, SSin=452.6mg/L, which could be beneficial to the operation optimization of LSCWWTs.


2021 ◽  
pp. 1-25
Author(s):  
Saipraneeth Gouravaraju ◽  
Jyotindra Narayan ◽  
Roger A. Sauer ◽  
Sachin Singh Gautam

Sign in / Sign up

Export Citation Format

Share Document