Artificial Intelligence for U.S. Army Wastewater Treatment Plant Operation and Maintenance

1988 ◽  
Author(s):  
B.J. Kim ◽  
J.T. Bandy ◽  
K.K. Gidwani ◽  
S.P. Shelton
Keyword(s):  
Artificial Intelligence ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Plant Operation ◽  
Operation And Maintenance

scholarly journals Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach

2018 ◽  
Vol 78 (10) ◽  
pp. 2064-2076 ◽  
Author(s):  
Vahid Nourani ◽  
Gozen Elkiran ◽  
S. I. Abba
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Weighted Averaging ◽  
Neural Network Ensemble ◽  
Ensemble Models ◽  
Non Linear

Abstract In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff), chemical oxygen demand (CODeff) and total nitrogen (TNeff). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BODeff, the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both CODeff and TNeff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.

Traitement des eaux usées par coagulation-floculation avec recirculation des boues chimiques : Performance générale et stabilité du procédé

2000 ◽  
Vol 27 (4) ◽  
pp. 702-718
Author(s):  
Frédéric Monette ◽  
François G Brière ◽  
Michel Létourneau ◽  
Marc Duchesne ◽  
Robert Hausler
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Normal Plant ◽  
Plant Operation ◽  
Load Variations ◽  
Sludge Recycling ◽  
The Stability ◽  
Chemical Sludge ◽  
Flocculation Process

Six series of tests were carried out to have a better understanding of the stability and efficiency of a coagulation-flocculation process with chemical sludge recycling. The tests consisted in sequential sludge recycling in 100-L pilot reactors. Other tests were performed to examine the stability following wastewater loading variations. Results showed that stability was reached immediately during the first recycling sequences. Furthermore, to obtain improved results compared with those of a classical coagulation-flocculation process, the flocculant concentration must be increased according to the sludge recycling load. Results also revealed that recycling sludge does not absorb wastewater load variations. Consequently, the implementation of sludge recycling in a wastewater treatment plant would not cause effluent degradation or entail major changes in a normal plant operation routine. The predominant coagulation-flocculation mechanisms that explained the increase in efficiency, in comparison with the classical process, were identified as enmeshment and sweep flocculation. Finally, the recycled sludge produced were conditioned and dewatered in a fashion similar to that of a classical process.Key words: recycling, sludge, preformed flocs, coagulation-flocculation, treatment, wastewater, stability.

scholarly journals A Knowledge Discovery Assistance Method Using Multivariate Statistics for Efficient Wastewater Treatment Plant Operation

2012 ◽  
Vol 10 (2) ◽  
pp. 87-99
Author(s):  
Osamu YAMANAKA ◽  
Akihiro NAGAIWA ◽  
Yukio HIRAOKA ◽  
Katsuya YAMAMOTO ◽  
Katsumi SANO ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Knowledge Discovery ◽  
Multivariate Statistics ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Plant Operation
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