Artificial neural network: An alternative approach for assessment of biochemical oxygen demand of the Damodar River, West Bengal, India

Sludge bulking is the most common solids settling problem in wastewater treatment plants, which is caused by the excessive growth of filamentous bacteria extending outside the flocs, resulting in decreasing the wastewater treatment efficiency and deteriorating the water quality in the effluent. Previous studies using molecular techniques have been widely used from the microbiological aspects, while the mechanisms have not yet been completely understood to form the deterministic cause-effect relationship. In this study, system identification techniques based on the analysis of the inputs and outputs of the activated sludge system are applied to the data-driven modeling. Principle component regression (PCR) and artificial neural network (ANN) were identified using the data from Chongqing wastewater treatment plant (CQWWTP), including temperature, pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SSs), ammonia (NH4+), total nitrogen (TN), total phosphorus (TP), and mixed liquor suspended solids (MLSSs). The models were subsequently used to predict the sludge volume index (SVI), the indicator of the bulking occurrence. Comparison of the results obtained by both models is also presented. The results showed that ANN has better prediction power (R2=0.9) than PCR (R2=0.7) and thus provides a useful guide for practical sludge bulking control.

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Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

Marine and Freshwater Research ◽

10.1071/mf16327 ◽

2017 ◽

Vol 68 (11) ◽

pp. 2070 ◽

Cited By ~ 12

Author(s):

Manh-Ha Bui ◽

Thanh-Luu Pham ◽

Thanh-Son Dao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Adaptive Learning ◽

Transfer Functions ◽

Learning Algorithm ◽

Oxygen Demand ◽

Cyanobacterial Blooms ◽

Cyanobacteria Bloom ◽

Feed Forward Back Propagation ◽

Artificial Neural

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg–Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6–16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error=0.108) and high correlation coefficient values (R=0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.

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Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models

Water Science & Technology ◽

10.2166/wst.2021.035 ◽

2021 ◽

Vol 83 (5) ◽

pp. 1250-1264

Author(s):

B. L. Dinesha ◽

Sharanagouda Hiregoudar ◽

Udaykumar Nidoni ◽

K. T. Ramappa ◽

Anilkumar Dandekar ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Wastewater Treatment ◽

Response Surface ◽

Dairy Industry ◽

Oxygen Demand ◽

Ann Models ◽

Artificial Neural ◽

Industry Wastewater

Abstract The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) for efficient treatment of dairy industry wastewater using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models. The nano-adsorbents were synthesized using chemical precipitation method and characterized by using scanning electron microscope with elemental detection sensor (SEM-EDS) and atomic force microscope (AFM). Maximum %RBOD (96.71 and 87.56%) and %RCOD (90.48 and 82.10%) for CZnO and CTiO2 nano-adsorbents were obtained at adsorbent dosage of 1.25 mg/L, initial biological oxygen demand (BOD) and chemical oxygen demand (COD) concentration of 100 and 200 mg/L, pH of 7.0 and 2.00, contact time of 100 and 60 min, respectively. The results obtained for both the nano-adsorbents were subject to RSM and ANN models for determination of goodness of fit in terms of sum of square errors (SSE), root mean square error (RMSE), R2 and Adj. R2, respectively. The well trained ANN model was found superior over RSM in prediction of the treatment effect. Hence, the developed CZnO and CTiO2 nano-adsorbents could be effectively used for dairy industry wastewater treatment.

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Experimental and Artificial Neural Network Modeling of a Upflow Anaerobic Contactor (UAC) for Biogas Production from Vinasse

International Journal of Chemical Reactor Engineering ◽

10.1515/ijcre-2016-0025 ◽

2016 ◽

Vol 14 (6) ◽

pp. 1241-1254 ◽

Cited By ~ 8

Author(s):

Ousman R. Dibaba ◽

Sandip K. Lahiri ◽

Stephan T’Jonck ◽

Abhishek Dutta

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Anaerobic Digestion ◽

Volatile Fatty Acids ◽

Biogas Production ◽

Oxygen Demand ◽

Pilot Scale ◽

Organic Loading Rate ◽

Neural Network Modeling ◽

Artificial Neural

Abstract A pilot scale Upflow Anaerobic Contactor (UAC), based on upflow sludge blanket principle, was designed to treat vinasse waste obtained from beet molasses fermentation. An assessment of the anaerobic digestion of vinasse was carried out for the production of biogas as a source of energy. Average Organic loading rate (OLR) was around 7.5 gCOD/m3/day in steady state, increasing upto 8.1 gCOD/m3/day. The anaerobic digestion was conducted at mesophilic (30–37 °C) temperature and a stable operating condition was achieved after 81 days with average production of 65 % methane which corresponded to a maximum biogas production of 85 l/day. The optimal performance of UAC was obtained at 87 % COD removal, which corresponded to a hydraulic retention time of 16.67 days. The biogas production increased gradually with OLR, corresponding to a maximum 6.54 gCOD/m3/day (7.4 % increase from initial target). A coupled Artificial Neural Network-Differential Evolution (ANN-DE) methodology was formulated to predict chemical oxygen demand (COD), total suspended solids (TSS) and volatile fatty acids (VFA) of the effluent along with the biogas production. The method incorporated a DE approach for the efficient tuning of ANN meta-parameters such as number of nodes in hidden layer, input and output activation function and learning rate. The model prediction indicated that it can learn the nonlinear complex relationship between the parameters and able to predict the output of the contactor with reasonable accuracy. The utilization of the coupled ANN-DE model provided significant improvement to the study and helps to study the parametric effect of influential parameters on the reactor output.

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Artificial neural network prediction of chemical oxygen demand in dairy industry effluent treated by electrocoagulation

Separation and Purification Technology ◽

10.1016/j.seppur.2014.05.053 ◽

2014 ◽

Vol 132 ◽

pp. 627-633 ◽

Cited By ~ 15

Author(s):

G.F.S. Valente ◽

R.C.S. Mendonça ◽

J.A.M. Pereira ◽

L.B. Felix

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Chemical Oxygen Demand ◽

Dairy Industry ◽

Oxygen Demand ◽

Neural Network Prediction ◽

Network Prediction ◽

Artificial Neural ◽

Industry Effluent

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Analysis of Water Environmental Quality Using BP Artificial Neural Network in Weihe River Baoji Segment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.401-403.2147 ◽

2013 ◽

Vol 401-403 ◽

pp. 2147-2150 ◽

Cited By ~ 1

Author(s):

Heng Xing Xie

Keyword(s):

Neural Network ◽

Water Quality ◽

Artificial Neural Network ◽

Surface Water ◽

Oxygen Demand ◽

Surface Water Quality ◽

Bp Artificial Neural Network ◽

Weihe River ◽

Artificial Neural ◽

Water Environmental Quality

The BP artificial neural network model in type 7-5-5 was constructed with the surface water quality standard (GB3838-2002) and the surface water quality items such as BOD5 (5 day biochemical oxygen demand), COD (chemical oxygen demand), permanganate index, fluoride, NH3-N, TP (total phosphorus) and TN (total nitrogen), and the water environmental quality evaluation was conducted using the trained BP artificial neural network with the water contamination concentration data in 6 sections of Weihe river Baoji segment in year 2009. Results showed that the water quality were GradeIand GradeII in Lin Jia Cun section and Sheng Li Qiao section, and Grade III in the rest section (Wo Long Si Bridge, Guo Zhen Bridge, Cai Jia Po Bridge and Chang Xing Bridge).

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