Predicting the Performance of Gorgan Wastewater Treatment Plant Using ANN-GA, CANFIS, and ANN Models

A reliable model for any wastewater treatment plant (WWTP) is essential to predict its performance and form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This study applied artificial neural network-genetic algorithm (ANN-GA) and co-active neuro-fuzzy logic inference system (CANFIS) in comparison with ANN for predicting the performance of WWTP. The result indicated that the GA produces more accurate results than fuzzy logic technique. It was found that GA components increased the ANN ability in predicting WWTP performance. The normalized root mean square error (NRMSE) for ANN-GA in predicting chemical oxygen demand (COD), total suspended solids (TSS) and biochemical oxygen demand (BOD) were 0.15, 0.19 and 0.15, respectively. The corresponding correlation coefficients were 0.891, 0.930 and 0.890, respectively. Comparing these results with other studies showed that despite the slightly lower performance of the current model, its requirement for a lower number of input parameters can save the extra cost of sampling

Comparisons of GM (1,1), and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan

AbstractThis paper represents the first study to compare seven types of first–order and one–variable grey differential equation model [abbreviated as GM (1, 1)] and back-propagation artificial neural network (BPNN) for predicting hourly particulate matter (PM) including PMio and PM2.5 concentrations in Dali area of Taichung City, Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) was 16.76%, 132.95, and 11.53, respectively for PM10 prediction. For PM2.5 prediction, the minimum MAPE, MSE, and RMSE value of 21.64%, 40.41, and 6.36, respectively could be achieved. All statistical values revealed that the predicting performance of GM (1, 1, x(0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) could predict the hourly PM variation precisely even comparing with BPNN

Developing magnetic functionalized multi-walled carbon nanotubes-based buckypaper for the removal of Furazolid

Magnetic f-MWCNTs-based BP/PVA membrane was fabricated and utilized for the elimination of furazolidone (FZD) from aqueous solution. Characterisation and adsorption studies were performed to evaluate the performance and adsorptive efficiency, respectively of the membrane. Furthermore, statistical and machine learning technique were also applied to predict the removal efficiency of FZD on the membrane. The results revealed that magnetic f-MWCNTs-based BP/PVA membrane has the potential to be used as an efficient membrane for practical applications

Application of data mining techniques to predict the performance of matured Vertical Flow Constructed Wetlands Systems treating urban wastewater

The rapid urbanisation and industrialisation, due to technological advancement, led to severe environmental pollution. The environmental pollution in the last few decades resulted in an adverse impact on the environment causing massive accumulation of wastewater. Wastewater is one of the closest sources of environmental problems, at the same time water scarcity is becoming alarming due to its high demand as the global population is increasing. Hence, the application for managing available water resources becomes crucial. The ever-increasing demand for water brings the need for wastewater treatment as an alternative source of water. Constructed Wetlands (CW) have gained broader research attention due to their environmental and safety benefits for wastewater treatment. In this study, over three years of monitoring performance data from 03rd December 2014 to 28th March 2018 (thirty-nine months) of the vertical flow vertical wetlands system, receiving and treating domestic wastewater, were collected and utilised to assess and investigate the treatment performance efficiency of the Vertical Flow Constructed Wetland Systems (VFCWs) for removing pollutants from wastewater. Different laboratory-scale vertical-flow constructed wetlands filters filled with gravel and planted with common reed were built to remove removal from wastewater. The overall evaluation of the system treatment performance was calculated using percentage removal efficiency. The results were recorded it was observed that all vertical flow constructed wetland filters had recorded high removal performance for the water quality parameters, irrespective of filter set-up and operation. The system was discovered to be very useful in pollutants removal (water quality parameters) with significant efficiency. However, the high cost of analysis laboratory tests, time-consuming parameters couple with uncertainties associated with an analysis of water quality variables, lead to the development of two data mining technique models Multiple Linear Regressions (MLR) and Multilayer Perceptron (MLP). To predict the wastewater treatment performance of CW by predicting selected output water quality parameters these include Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), orthophosphate phosphorous (PO4-P), ammonium nitrogen (NH4-N) and suspended solids (SS) with respect to other known input parameters that will provide comfortable, reliable and cost-effective methods. Correlation analysis was conducted to select the most highly correlated input parameters to be used for the model development (prediction of output parameter). The monitoring dataset of all the parameters used was divided into training dataset to build prediction models (MLR and MLP) and testing dataset to validate the models constructed. In this current work, 70% of the whole data was used as a training dataset while the remaining 30% of the data set was used as a testing dataset. The prediction models built were evaluated and compared using two model evaluation criteria: graphical model evaluation (scatter plot and hydrograph) and numerical model error evaluation criteria using five model evaluation criteria, these include: Root Mean Square Error (RMSE), regression coefficient (r), Relative Absolute Error (RAE), mean absolute error (MAE) and root relative squared error (RRSE). The results obtained indicated that the predicted values of output parameters were in good agreement and relationship with their respective measured parameters. Thus, this showed that the two models built yielded satisfactory predictions and both models had performed reasonably well in predicting output variables concentrations accurately given the value of input dependent variable. Furthermore, the comparison between the model's outcomes showed that MLP model prediction performance was discovered to be better than the MLR model in a majority of water quality parameters. Both models built could be effectively used as a tool for predicting removal of water quality parameters efficiency of vertical flow constructed wetlands treating domestic wastewater and in predicting constructed wetland performance in wastewater treatment process in term of pollutants removal. The results demonstrated the potentiality of vertical flow constructed wetlands to treat domestic wastewater and remove pollutants for future reuse

Simple tools for improved management of small wastewater treatment plants

Ph. D. ThesisThe treatment performance of small WWTPs (< 250 PE) in England is not well understood and their ecological impact may be underestimated. However, the critical role such systems play in ensuring sustainable wastewater management, means they can no longer be neglected. The aim of this thesis, therefore, was to provide new data, understanding and analytical approaches to improve the management of existing, small WWTPs. Firstly, through an extensive sampling campaign, we found a significant difference (p < 0.05) between the effluent quality discharged from twelve small and three larger WWTPs across a range of abiotic parameters. Specifically, mean removal rates at the small plants were 67.3 ± 20.4%, 80 ± 33.9% and 55.5 ± 30.4% for sCOD, TSS and NH4-N (± standard deviation), respectively, whereas equivalent rates for larger plants were 73.3 ± 17.6%, 91.7 ± 4.6% and 92.9 ± 3.7%. A Random Forest classification model accurately predicted the likelihood of a small WWTP becoming unreliable. Among the important predictors was population equivalence, suggesting the smallest WWTPs may require particularly stringent management. Quantifying, in the raw and treated wastewater samples, three genetic faecal markers targeting Bacteroides and two targeting E. coli, revealed that humanassociated Bacteroides markers have the greatest potential as alternative performance metrics at small WWTPs, however, all markers were influenced by seasonality. Next, the problem of predicting flows at small scales was overcome using an inverse approach to solve a linear reservoir function (NSE = 0.77 – 0.93). The model was combined with the field data to generate pollutant loads and investigate the effect of influent peak loading of COD on the final effluent quality at small discharges. Simple tools developed, here, provide wastewater managers with new techniques to improve the operation and increase the understanding of small WWTPs. Growing awareness of the need for sustainable wastewater and water resources management makes the work both timely and of global relevance.Engineering and Physical Sciences Research Council (EP/M50791X/1), Northumbrian Water Limite

Optimizing Fenton-like process, homogeneous at neutral pH for ciprofloxacin degradation: Comparing RSM-CCD and ANN-GA

Author's accepted manuscriptAntibiotics are considered among the most non-biodegradable environmental contaminants due to their genetic resistance. Considering the importance of antibiotics removal, this study was aimed at multi-objective modeling and optimization of the Fenton-like process, homogeneous at initial circumneutral pH. Two main issues, including maximizing Ciprofloxacin (CIP) removal and minimizing sludge to iron ratio (SIR), were modeled by comparing central composite design (CCD) based on Response Surface Methodology (RSM) and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA). Results of simultaneous optimization using ethylene diamine tetraacetic acid (EDTA) revealed that at pH ≅ 7, optimal conditions for initial CIP concentration, Fe2+ concentration, [H2O2]/[Fe2+] molar ratio, initial EDTA concentration, and reaction time were 14.9 mg/L, 9.2 mM, 3.2, 0.6 mM, and 25 min, respectively. Under these optimal conditions, CIP removal and SIR were predicted at 85.2% and 2.24 (gr/M). In the next step, multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) were developed to model CIP and SIR. It was concluded that ANN, especially multilayer perceptron (MLP-ANN) has a decent performance in predicting response values. Additionally, multi-objective optimization of the process was performed using Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to maximize CIP removal efficiencies while minimizing SIR. NSGA-II optimization algorithm showed a reliable performance in the interaction between conflicting goals and yielded a better result than the GA algorithm. Finally, TOPSIS method with equal weights of the criteria was applied to choose the best alternative on the Pareto optimal solutions of the NSGA-II. Comparing the optimal values obtained by the multi-objective response surface optimization models (RSM-CCD) with the NSGA-II algorithm showed that the optimal variables in both models were close and, according to the absolute relative error criterion, possessed almost the same performance in the prediction of variables.acceptedVersio

Hydraulic head and groundwater 111Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS)

In this study, hydraulic head and 111Cd interpolations based on the geo-adaptive neuro-fuzzy inference system (Geo-ANFIS) and empirical Bayesian kriging (EBK) were performed for the alluvium unit of Karabağlar Polje in Muğla, Turkey. Hydraulic head measurements and 111Cd analyses were done for 42 water wells during a snapshot campaign in April 2013. The main objective of this study was to compare Geo-ANFIS and EBK to interpolate hydraulic head and 111Cd content of groundwater. Both models were applied on the same case study: alluvium of Karabağlar Polje, which covers an area of 25 km2 in Muğla basin, in the southwest of Turkey. The ANFIS method (called ANFISXY) uses two reduced centred pre-processed inputs, which are cartesian coordinates (XY). Geo-ANFIS is tested on a 100-random-data subset of 8 data among 42, with the remaining data used to train and validate the models. ANFISXY and EBK were then used to interpolate hydraulic head and heavy metal distribution, on a 50 m2 grid covering the study area for ANFISXY, while a 100 m2 grid was used for EBK. Both EBK- and ANFISXY-simulated hydraulic head and 111Cd distributions exhibit realistic patterns, with RMSE &lt; 9 m and RMSE &lt; 8 μg/L, respectively. In conclusion, EBK can be considered as a better interpolation method than ANFISXY for both parameters.Keywords: ANFIS, EBK, interpolation, hydraulic head, metal, 111Cd, alluvium, Muğl