A preliminary approach based on numerical simulation modelling and evaluation of permeable reactive barrier for aquifer remediation susceptible to selenium contaminant.

In this study, numerical groundwater modelling software (GMS) was applied for a 2D transient state predictive (flow and contaminant fate and transport) conceptual model for heavy metal (Selenium in this research) contaminated groundwater, Imamzadeh-Jafar Aquifer, Kohgiluyeh and Boyer-Ahmad Province, Iran. The performances of permeable reactive barrier (PRB) in pollutant removal in the contaminated aquifers were studied by helping the MODFLOW-MT3DMS model. The spatiotemporal distribution of Selenium (Se) contaminant over the aquifer was illustrated using the calibrated flow and contaminant model. According to the findings, the downward movement of Se has resulted in an unsafe and undesirable water quality status in the Imamzadeh-Jafar aquifer, which is supported by field data. The sensitivity analysis of PRB layouts, geometric features, and reactant material characteristics was conducted in groundwater remediation. The numerical model results illustrated that the PRB thickness, ranging from 10 to 500 m, manifested the drop in Se concentration approximately from 40 to 46%. The results shed light on the hydraulic conductivity variations of reactant materials have effects less than 0.5% in Se removals. Furthermore, the decay rate variations in the ranges from 0.0001 to 0.01 d-1 could result in Se removal from 5 to 100%. According to studies, if the contaminant sources are prevented, in a) installation of PRB and b) not installation of PRB scenarios, the Imamzadeh-Jafar aquifer remediation will take 6 months and 84 months, respectively.

Equilibrium strategy based waste load allocation using simulated annealing optimization algorithm.

This study focuses on development of equilibrium strategy based on simulated annealing (SA) algorithm for balancing economic and environmental concerns in waste load allocation (WLA) problem. To resolve conflicts among various stakeholders, including Iran Department of Environment (DoE) as governmental authority and industrial and municipal dischargers, Stackelberg and Nash bargaining games have been applied in this WLA problem and the results have been compared. SA algorithm has been coupled to QUAL2Kw model to derive optimal WLA program and the environmental penalty tariff (EPT) in Nash bargaining and Stackelberg games. The proposed tools and methodologies were illustrated in a case study of multi-stakeholders WLA problem in Gheshlagh River, Sanandaj, Kordestan, Iran. The results indicate that lower BOD removal rates are allocated to the pollutant dischargers in the Stackelberg game compared to the Nash bargaining game. Furthermore, the EPT assigned by Iran DoE in Stackelberg and Nash bargaining games are 11.25 and 3.6 Rials/(gr/month), respectively. The estimated EPT in the Stackelberg game is close to the current tariff (10 Rials/(gr/month)) specified by Iran DoE on impermissible BOD discharges.

Predicting coagulation-flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models.

Three artificial intelligence (AI) data-driven techniques, including artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were applied for modeling and predicting turbidity removal from water using graphene oxide (GO). Based on partial mutual information (PIM) algorithm, pH, GO dosage, and initial turbidity were selected as the input variables for developing the models. The prediction performance of the AI-based models was compared with each other and with the response surface methodology (RSM) model, previously reported by the authors, as well. The models' estimation accuracy was assessed through statistical measures, including mean-squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Among the evaluated models, ANN had the highest estimation accuracy as it showed the highest R2 for the validation data (0.949) and the lowest MSE, RMSE, and MAE values. Furthermore, ANN predicted 76.1% of data points with relative errors (RE) less than 10%. In contrast, the weakest prediction performance belonged to the SVR model with the lowest R2 for both calibration (0.712) and validation (0.864) data. Besides, only 57.1% of the SVR's predictions were characterized by RE < 10%. The ANFIS and RSM models exhibited a more or less similar performance in terms of R2 for the validation data (0.877 and 0.871, respectively) and other statistical parameters. According to the results, the ANN technique is proposed as the best option for modeling the process. Nevertheless, as the RSM technique provides valuable information about the contribution of the independent operational parameters and their complex interaction effects using the least number of experiments, simulating the process by this technique before modeling by ANN is inevitable.

Fuzzy pattern recognition method for assessing soil erosion.

In this paper a PSIAC-based multi-parameter fuzzy pattern recognition (MPFPR) model is proposed and applied for classifying and ranking the potential soil erosion (PSE). In this approach, standard value matrix is used to define the membership degrees of each catchment to each class and the feature values are used for alternative ranking. The characteristic of PSE for each class is expressed by linguistic variables. The proposed method is straightforward, easy to understand, very practical, and its results may easily be interpreted. To assess the performance of the model, the results of PSIAC MPFPR and original PSIAC method are interpreted and compared with the observed data. It is shown that the proposed approach reflects the fuzzy nature of the soil erosion more efficiently and is quite robust for application in real world cases.

Coagulation-flocculation of turbid water using graphene oxide: simulation through response surface methodology and process characterization.

It was aimed to precisely investigate the coagulation properties of graphene oxide (GO) as a novel coagulant for turbidity removal from water. For this purpose, the process was simulated through response surface methodology (RSM) to determine the effect of the preselected independent factors (pH, GO dosage, and initial turbidity) and their interaction effects on the process. Based on the results, increased turbidity removal efficiencies were obtained as pH decreased from 10 to 3. Besides, increase of GO dosage within the test range (2.5-30 mg/L) was highly beneficial for enhancing the process performance. However, a slight overdosing of GO was observed for dosages of more than 20 mg/L under pH values of less than about 4. For initial turbidity with test range of 25-300 NTU, there was an optimum range (approximately 120-200 NTU) out of which the removal efficiency declined. According to the results of the analysis of variance (ANOVA), pH and GO dosage, orderly, had the strongest individual effect on the process performance. The most significant interaction effect was also observed between pH and GO dosage. The optimal coagulation conditions with GO dosage of 4.0 mg/L, pH of 3.0, and initial turbidity of 193.34 NTU led to a turbidity removal efficiency of about 98.3%, which was in good agreement with RSM results. Under basic pH levels, the sweeping effect was recognized as the main coagulation mechanism occurred between the negatively surface charged particles of GO and soil. However, according to zeta potential (ZP) analysis results, under acidic pH conditions in addition to the sweep coagulation, the electric double layer compression, and the subsequent ZP reduction also contributed significantly to the process. Scanning electron microscopy (SEM) images showed that the layered structure of GO particles provided an appropriate platform on which the flocs were formed.