Application of Artificial Neural Network for Modeling Wastewater Treatment Process

2019 ◽  
pp. 143-154
Author(s):  
A. Sebti ◽  
B. Boutra ◽  
M. Trari ◽  
L. Aoudjit ◽  
S. Igoud
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Wastewater Treatment Process ◽  
Artificial Neural

scholarly journals A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zehua Huang ◽  
Renren Wu ◽  
XiaoHui Yi ◽  
Hongbin Liu ◽  
Jiannan Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Biogas Production ◽  
Anaerobic Treatment ◽  
Production Rates ◽  
Wastewater Treatment Process ◽  
Effluent Quality ◽  
Anaerobic Wastewater Treatment ◽  
Novel Model

The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.

scholarly journals Sludge Bulking Prediction Using Principle Component Regression and Artificial Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Inchio Lou ◽  
Yuchao Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Suspended Solids ◽  
Oxygen Demand ◽  
Volume Index ◽  
Principle Component ◽  
Principle Component Regression ◽  
Sludge Bulking ◽  
Artificial Neural

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.

scholarly journals Artificial Neural Network-Cuckoo Optimization Algorithm (ANN-COA) for Optimal Control of Khorramabad Wastewater Treatment Plant, Iran

2016 ◽  
Vol 2 (11) ◽  
pp. 555-567 ◽  
Author(s):  
Samaneh Khademikia ◽  
Ali Haghizadeh ◽  
Hatam Godini ◽  
Ghodratollah Shams Khorramabadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Mean Square Error ◽  
Optimization Algorithm ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Mean Square ◽  
Cuckoo Optimization Algorithm ◽  
Artificial Neural

In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP.

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

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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