scholarly journals Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 784
Author(s):  
Jeong-Hee Kang ◽  
JiHyeon Song ◽  
Sung Soo Yoo ◽  
Bong-Jae Lee ◽  
Hyon Wook Ji
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Prediction Accuracy ◽  
Treatment Plant ◽  
Ann Model ◽  
Odor Concentration ◽  
Odor Emission ◽  
Artificial Neural Network Ann

The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.

scholarly journals Artificial neural network (ANN) approach for modeling of selected biogenic compounds in a mixture of treated municipal and dairy wastewater

2019 ◽  
Vol 100 ◽  
pp. 00077
Author(s):  
Joanna Struk-Sokołowska ◽  
Piotr Ofman ◽  
Sevgi Demirel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Extreme Values ◽  
Treatment Plant ◽  
Dairy Wastewater ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents artificial neural network (ANN) model of wastewater treatment plant, which was used for average monthly concentrations of N-NH4+, N-NO3-, N-NO2-, total Kiejdahl nitrogen (TKN), PO43- and SO42- approximation. ANN model was developed for wastewater treatment plant located in Bystre, Poland which treats municipal wastewater with a share of dairy wastewater. The object was chosen because of the unique location, in the Great Mazury Lakes area and the need for its special environmental protection. Input layer of developed ANN model consisted of BOD, COD, concentrations of total nitrogen and total phosphorus, total organic carbon, sulphates, wastewater temperature and pH., The developed model reflected extreme values observed during study period. Average error percentage with which output variables were approximated equalled to 35.35%; 8.99%; 21.23%; 5.08%; 10.99%; 3.02% respectively for N-NH4+, N-NO3-, N-NO2-, TKN, PO43- and SO42-.

Mapping soil nail loads using Federal Highway Administration (FHWA) simplified models and artificial neural network technique

2020 ◽  
Vol 57 (10) ◽  
pp. 1453-1471 ◽  
Author(s):  
Peiyuan Lin ◽  
Pengpeng Ni ◽  
Chengchao Guo ◽  
Guoxiong Mei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Limit States ◽  
Operational Conditions ◽  
Soil Nail ◽  
Federal Highway ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study compiles a broad database containing 312 measured maximum soil nail loads under operational conditions. The database is used to re-assess the prediction accuracies of the default Federal Highway Administration (FHWA) nail load model and its modified version previously reported in the literature. Predictions using the default and modified FHWA models are found to be highly dispersive. Moreover, the prediction accuracy is statistically dependent on the magnitudes of the predicted nail load and several model input parameters. The modified FHWA model is then recalibrated by introducing extra empirical terms to account for the influences of wall geometry, nail design configuration, and soil shear strength parameters on the evolvement of nail loads. The recalibrated FHWA model is demonstrated to have much better prediction accuracy compared to the default and modified models. Next, an artificial neural network (ANN) model is developed for mapping soil nail loads, which is shown to be the most advantageous one as it is accurate on average and the dispersion in prediction is low. The abovementioned dependency issue is also not present in the ANN model. The practical value of the ANN model is highlighted by applying it to reliability-based designs of soil nails against internal limit states.

scholarly journals Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis

2020 ◽  
Vol 12 (16) ◽  
pp. 6386 ◽  
Author(s):  
Farzin Golzar ◽  
David Nilsson ◽  
Viktoria Martin
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Heat Recovery ◽  
Historical Data ◽  
Treatment Plant ◽  
Artificial Neural Network Ann

Wastewater contains considerable amounts of thermal energy. Heat recovery from wastewater in buildings could supply cities with an additional source of renewable energy. However, variations in wastewater temperature influence the performance of the wastewater treatment plant. Thus, the treatment is negatively affected by heat recovery upstream of the plant. Therefore, it is necessary to develop more accurate models of the wastewater temperature variations. In this work, a computational model based on artificial neural network (ANN) is proposed to calculate wastewater treatment plant influent temperature concerning ambient temperature, building effluent temperature and flowrate, stormwater flowrate, infiltration flowrate, the hour of day, and the day of year. Historical data related to the Stockholm wastewater system are implemented in MATLAB software to drive the model. The comparison of calculated and observed data indicated a negligible error. The main advantage of this ANN model is that it only uses historical data commonly recorded, without any requirements of field measurements for intricate heat transfer models. Moreover, Monte Carlo sensitivity analysis determined the most influential parameters during different seasons of the year. Finally, it was shown that installing heat exchangers in 40% of buildings would reduce 203 GWh year−1 heat loss in the sewage network. However, heat demand in WWTP would be increased by 0.71 GWh year−1, and the district heating company would recover 176 GWh year−1 less heat from treated water.

scholarly journals Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

Hydrology ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 80
Author(s):  
Khurshid Jahan ◽  
Soni M. Pradhanang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Sodium Chloride ◽  
Stormwater Runoff ◽  
Quality Data ◽  
Ann Model ◽  
Water Quality Data ◽  
Artificial Neural Network Ann ◽  
Chloride Concentrations

Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl2), and calcium chloride (CaCl2)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.

scholarly journals An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Mingjun Li ◽  
Junxing Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Concrete Dam ◽  
Artificial Neural ◽  
Dam Deformation ◽  
Artificial Neural Network Ann

Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random loading, and strong nonlinear deformation of concrete dams. Conversely, the artificial neural network (ANN) model shows good adaptability to complex and highly nonlinear behaviors. This paper aims to evaluate the specific performance of the multiple linear regression (MLR) and artificial neural network (ANN) model in characterizing concrete dam deformation under environmental loads. In this study, four models, namely, the multiple linear regression (MLR), stepwise regression (SR), backpropagation (BP) neural network, and extreme learning machine (ELM) model, are employed to simulate dam deformation from two aspects: single measurement point and multiple measurement points, approximately 11 years of historical dam operation records. Results showed that the prediction accuracy of the multipoint model was higher than that of the single point model except the MLR model. Moreover, the prediction accuracy of the ELM model was always higher than the other three models. All discussions would be conducted in conjunction with a gravity dam study.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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