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.

Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant

2019 ◽  
Vol 30 (3) ◽  
pp. 593-608 ◽  
Author(s):  
Naceureddine Bekkari ◽  
Aziez Zeddouri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Activation Functions ◽  
Content Type ◽  
Hidden Layer

Purpose Modeling Wastewater Treatment Plant (WWTP) constitutes an important tool for controlling the operation of the process and for predicting its performance with substantial influent fluctuations. The purpose of this paper is to apply an artificial neural network (ANN) approach with a feed-forward back-propagation in order to predict the ten-month performance of Touggourt WWTP in terms of effluent Chemical Oxygen Demand (CODeff). Design/methodology/approach The influent variables such as (pHinf), temperature (TEinf), suspended solid (SSinf), Kjeldahl Nitrogen (KNinf), biochemical oxygen demand (BODinf) and chemical oxygen demand (CODinf) were used as input variables of neural networks. To determine the appropriate architecture of the neural network models, several steps of training were conducted, namely the validation and testing of the models by varying the number of neurons and activation functions in the hidden layer, the activation function in output layer as well as the learning algorithms. Findings The better results were achieved with an architecture network [6-50-1], hyperbolic tangent sigmoid activation functions at hidden layer, linear activation functions at output layer and a Levenberg – Marquardt method as learning algorithm. The results showed that the ANN model could predict the experimental results with high correlation coefficient 0.89, 0.96 and 0.87 during learning, validation and testing phases, respectively. The overall results indicated that the ANN modeling approach can provide an effective tool for simulating, controlling and predicting the performance of WWTP. Originality/value This work is the first of its kind in this region due to the remarkable development in terms of population and agricultural activity in the region, which drove to the increase of water pollutants, so it is necessary to use the modern technologies to modeling and controlling of WWTP.

Savings with upgraded performance through improved activated sludge denitrification in the combined activated sludge–biofilter system of the Southpest Wastewater Treatment Plant

2008 ◽  
Vol 57 (8) ◽  
pp. 1287-1293 ◽  
Author(s):  
A. Jobbágy ◽  
G. M. Tardy ◽  
Gy. Palkó ◽  
A. Benáková ◽  
O. Krhutková ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Wastewater Treatment Plant ◽  
Biological Treatment ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Process Efficiency ◽  
Initial Value ◽  
Initial Profile ◽  
Biological Treatment System

The purpose of the experiments was to increase the rate of activated sludge denitrification in the combined biological treatment system of the Southpest Wastewater Treatment Plant in order to gain savings in cost and energy and improve process efficiency. Initial profile measurements revealed excess denitrification capacity of the preclarified wastewater. As a consequence, flow of nitrification filter effluent recirculated to the anoxic activated sludge basins was increased from 23,000 m3 d−1 to 42,288 m3 d−1 at an average preclarified influent flow of 64,843 m3 d−1, Both simulation studies and microbiological investigations suggested that activated sludge nitrification, achieved despite the low SRT (2–3 days), was initiated by the backseeding from the nitrification filters and facilitated by the decreased oxygen demand of the influent organics used for denitrification. With the improved activated sludge denitrification, methanol demand could be decreased to about half of the initial value. With the increased efficiency of the activated sludge pre-denitrification, plant effluent COD levels decreased from 40–70 mg l−1 to < 30–45 mg l−1 due to the decreased likelihood of methanol overdosing in the denitrification filter

Evaluating the treatment performance of a full scale Activated Sludge Plant in Islamabad, Pakistan

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
S. S. Fatima ◽  
S. Jamal Khan
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Wastewater Treatment Plant ◽  
Suspended Solids ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Full Scale ◽  
Operational Parameter ◽  
Mixed Liquor ◽  
Treatment Performance

In this study, the performance of wastewater treatment plant located at sector I-9 Islamabad, Pakistan, was evaluated. This full scale domestic wastewater treatment plant is based on conventional activated sludge process. The parameters which were monitored regularly included total suspended solids (TSS), mixed liquor suspended solids (MLSS), mixed liquor volatile suspended solids (MLVSS), biological oxygen demand (BOD), and chemical oxygen demand (COD). It was found that the biological degradation efficiency of the plant was below the desired levels in terms of COD and BOD. Also the plant operators were not maintaining consistent sludge retention time (SRT). Abrupt discharge of MLSS through the Surplus Activated sludge (SAS) pump was the main reason for the low MLSS in the aeration tank and consequently low treatment performance. In this study the SRT was optimized based on desired MLSS concentration between 3,000–3,500 mg/L and required performance in terms of BOD, COD and TSS. This study revealed that SRT is a very important operational parameter and its knowledge and correct implementation by the plant operators should be mandatory.

Metro-environmental data approach for the prediction of chemical oxygen demand in new Nicosia wastewater treatment plant

2021 ◽  
Vol 221 ◽  
pp. 31-40
Author(s):  
A.S. Mubarak ◽  
Parvaneh Esmaili ◽  
Z.S. Ameen ◽  
R.A. Abdulkadir ◽  
M.S. Gaya ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Environmental Data
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