417 research outputs found

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

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    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

    Evaluation of Greywater and A/C Condensate for Water Reuse: An Approach using Artificial Neural Network Modeling

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    Alternative resources play a vital role for water-sensitive infrastructures where consistent water supply is a challenge, and freshwater resources are limited. Greywater and A/C condensate are potentially new alternatives for increasing urban water supply. An advanced physical filtration system for greywater treatment was developed named as GAC-MI-ME. It is comprised coarse filtration (CR-F) followed by microfiltration (MF), granular activated carbon (GAC), ultrafiltration (UF), ultraviolet (UV), and reverse-osmosis (RO). GAC-MI-ME effluent-quality was analyzed for greywater from laundry, shower, and wash basin. High-grade effluent equivalent to unrestricted water reuse was observed at UF and RO units. A subsequent tool (GREY-ANN) was proposed for GAC-MI-ME effluent quality predictions. Artificial Neural Network (ANN) was applied to develop 5 unit models for selected parameters including Biochemical Oxidation Demand, pH, Total Dissolved Solids, Turbidity, and Oxidation-Reduction Potential to predict effluent quality at each stage of GAC-ME-MI treatment using water quality databases (developed from a series of experiments testing greywater of varying strength). The 15 days storage potential of GAC-MI-ME treated effluents were also analyzed and showed no significant quality depletion in UF and RO effluent quality. A hybrid modeling approach was applied to A/C condensate estimation, which included a psychrometric based “Air-Conditioner-Condensate” (ACON) model, and data-driven “Internal Load Analysis using Neural Network” (ILAN) model. The ACON model uses mass and energy balance approach for HVAC systems operating under steady state conditions. It accounts for psychometric states of different air parcels during the cooling and dehumidification process. The ILAN model was developed using ANN for the city of Doha to predict Internal Load at a daily time step for variable climatic conditions (temperature, relative humidity). The ACON- ILAN models were validated for a test building and applied for yearly condensate estimation for Doha. The virtual simulations of the hybrid model showed an annual condensate volume of 1370 and 3700 l/100 m^3 of cooling space for 20% and 100% outdoor-ventilation. The condensate quality (for limited water quality parameters) showed values within primary and secondary drinking water standards, except for copper, which had marginally higher concentrations. Overall, the GREY-ANN and ACON-ILAN may improve greywater and A/C condensate reuse potentials

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

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    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

    Generation of (synthetic) influent data for performing wastewater treatment modelling studies

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    The success of many modelling studies strongly depends on the availability of sufficiently long influent time series - the main disturbance of a typical wastewater treatment plant (WWTP) - representing the inherent natural variability at the plant inlet as accurately as possible. This is an important point since most modelling projects suffer from a lack of realistic data representing the influent wastewater dynamics. The objective of this paper is to show the advantages of creating synthetic data when performing modelling studies for WWTPs. This study reviews the different principles that influent generators can be based on, in order to create realistic influent time series. In addition, the paper summarizes the variables that those models can describe: influent flow rate, temperature and traditional/emerging pollution compounds, weather conditions (dry/wet) as well as their temporal resolution (from minutes to years). The importance of calibration/validation is addressed and the authors critically analyse the pros and cons of manual versus automatic and frequentistic vs Bayesian methods. The presentation will focus on potential engineering applications of influent generators, illustrating the different model concepts with case studies. The authors have significant experience using these types of tools and have worked on interesting case studies that they will share with the audience. Discussion with experts at the WWTmod seminar shall facilitate identifying critical knowledge gaps in current WWTP influent disturbance models. Finally, the outcome of these discussions will be used to define specific tasks that should be tackled in the near future to achieve more general acceptance and use of WWTP influent generators

    Stormwater detention and infiltration devices treating road runoff

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

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    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
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