Dye Removal,Energy Consumption and Operating Cost of Electrocoagulation of Textile Wastewater as a Clean Process

In this research, the efficiency of electrocoagulation treatment process using aluminum electrodes to treat synthetic wastewater containing Reactive Red198 (RR198) was studied. The effects of parameters such as voltage, time of reaction, electrode connection mode, initial dye concentration, electrolyte concentration, and inter electrode distance on dye removal efficiency were investigated. In addition, electrical energy consumption, electrode consumption, and operating cost at optimum condition have been investigated. The results showed that dye and chemical oxygen demand removals were 98.6 and 84%, respectively. Electrode consumption, energy consumption and operating cost were 0.052 kg/m³, 1.303 kWh/m³ and 0.256 US$/m³, respectively. Dye removal kinetic followed first order kinetics. It can be concluded that electrocoagulation process by aluminum electrode is very efficient and clean process for reactive dye removal from colored wastewater.     

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer

Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.     

Performance Evaluation of Constructed Wetlands Treating Wastewater Treatment Plant Effluent in Taihu Lake,China

Constructed wetlands are often used for advanced treatment of the secondary effluent of municipal wastewater treatment plants (WWTPs). Through assessing wetlands based on economic, technical, environmental, and social impacts, an optimal process is selected. In this study, a set of assessment methods for wetland treatment technology is established: The analytic hierarchy process (AHP) is used to establish the evaluation index system; the entropy weight method is employed to calculate the index weights; and the preference ranking organization method for enrichment evaluation (PROMETHEE) is utilized for ranking of the selected treatment technologies. Then four processes applied in Taihu Lake basin, China are evaluated. The results show the following ranking: Vertical‐flow wetland–ecological pond–surface‐flow wetland–horizontal‐flow wetland, vertical‐flow wetland–horizontal‐flow wetland, ecological pond–horizontal‐flow wetland–surface‐flow wetland, and ecological ditch–ecological pond. The wetland exhibits certain universality and space portability with regard to treatment of municipal WWTP effluent. From the view of comprehensive benefits, the ranking of the treatment technology based on the vertical‐flow wetland is high (Φ values between 0.0224 and 0.0349), whereas that based on the ecological pond is low (Φ values between ?0.2086 and ?0.2652), owing to the mechanism of the process itself and the role of microorganisms in the system. Moreover, for organic matter removal, a vertical‐flow wetland process is recommended (48%), whereas for the removal of N contamination, an integrated‐flow wetland process is suggested (31.2% for NH₃‐N, 32.4% for TN removals).     

Neural network modelling for mean velocity and turbulence intensities of steep channel flows

The main purpose of this study is to evaluate the potential of simulating the profiles of the mean velocity and turbulence intensities for the steep open channel flows over a smooth boundary using artificial neural networks. In a laboratory flume, turbulent flow conditions were measured using a fibre‐optic laser doppler velocimeter (FLDV). One thousand and sixty‐four data sets were collected for different slopes and aspect ratios at different locations. These data sets were randomly split into two subsets, i.e. training and validation sets. The multi‐layer functional link network (MFLN) was used to construct the simulation model based on the training data. The constructed MFLN models can almost perfectly simulate the velocity profile and turbulence intensity. The values of correlation coefficient (γ) are close to one and the values of root mean square error (RMSE) are close to zero in all conditions. The results demonstrate that the MFLN can precisely simulate the velocity profiles, while the log law and Reynolds stress model (RSM) are less effective when used to simulate the velocity profiles close to the side wall. The simulated longitudinal turbulence intensities yielded by the MFLN were also fairly consistent with the measured data, while the simulated vertical turbulence intensities by the RSM were not consistent with the measured data. Copyright © 2007 John Wiley & Sons, Ltd.     

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul,Turkey

Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d₂) for the testing data were 0.90, 8.78 µg/m³, 11.15 µg/m³, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM₁₀, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d₂ did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.     

Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater

Groundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r² and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd.     

Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models

Abstract

Much of the prairie region in North America is characterized by relatively flat terrain with many depressions on the landscape. The hydrological response (runoff) is a combination of the conventional runoff from the contributing areas and the occasional overflow from the non-contributing areas (depressions). In this study, we promote the use of a hybrid modelling structure to predict runoff generation from prairie landscapes. More specifically, the Soil and Water Assessment Tool (SWAT) is fused with artificial neural networks (ANNs), so that SWAT and the ANN module deal with the contributing and non-contributing areas, respectively. A detailed experimental study is performed to select the best set of inputs, training algorithms and hidden neurons. The results obtained in this study suggest that the fusion of process-based and data-driven models can provide improved modelling capabilities for representing the highly nonlinear nature of the hydrological processes in prairie landscapes.

Editor D. Koutsoyiannis; Associate editor L. See     

Adjusting Chlorine Dosage and to Prevent Bio‐Growth and Minimize Trihalomethanes in Reverse Osmosis Filtrate in a Wastewater Reclamation Process

Feasibility of effluent reclamation for the Futian municipal WWTP in Taichung Taiwan was evaluated using an “SF‐UF‐RO” pilot plant. The optimal parameters of each unit were obtained during the pilot plant test. The pilot plant started the operation in late October 2008 and operated until January 2011. The reverse osmosis (RO) system produces 75 m³ water daily, and the produced water quality was comparable to the city water in Taichung. Chlorine dosed in the sand filtration (SF) inlet and ultrafiltration (UF) backwash had the most significant effect on the stability of system performance. When the chlorine was underdosed, biofilm clogged the bag filter (prefilter of UF) and led to the flow rate decay of the UF. The prefilter needed replacement every 1 or 2 weeks resulting in increased process cost. On the other hand, when the chlorine dosage was increased to mitigate the biofilm growth, the residual chlorine not only reacted with TOC and derived trihalomethanes (THMs) in the RO product water (more than 20 µg/L), but it also damaged the RO membrane. After trial and error, the chlorine concentration was optimized as 0.7 mg/L in SF inlet to prevent growth of biofilm as well as to control the residual chlorine in the RO inlet and THMs in the RO product water. It is suggested that cautiously adjusting chlorine dosage is essential for stably operating such a hybrid membrane system to reclaim the municipal wastewater.     

Basin‐scale stream‐flow forecasting using the information of large‐scale atmospheric circulation phenomena

It is well recognized that the time series of hydrologic variables, such as rainfall and streamflow are significantly influenced by various large‐scale atmospheric circulation patterns. The influence of El Niño‐southern oscillation (ENSO) on hydrologic variables, through hydroclimatic teleconnection, is recognized throughout the world. Indian summer monsoon rainfall (ISMR) has been proved to be significantly influenced by ENSO. Recently, it was established that the relationship between ISMR and ENSO is modulated by the influence of atmospheric circulation patterns over the Indian Ocean region. The influences of Indian Ocean dipole (IOD) mode and equatorial Indian Ocean oscillation (EQUINOO) on ISMR have been established in recent research. Thus, for the Indian subcontinent, hydrologic time series are significantly influenced by ENSO along with EQUINOO. Though the influence of these large‐scale atmospheric circulations on large‐scale rainfall patterns was investigated, their influence on basin‐scale stream‐flow is yet to be investigated. In this paper, information of ENSO from the tropical Pacific Ocean and EQUINOO from the tropical Indian Ocean is used in terms of their corresponding indices for stream‐flow forecasting of the Mahanadi River in the state of Orissa, India. To model the complex non‐linear relationship between basin‐scale stream‐flow and such large‐scale atmospheric circulation information, artificial neural network (ANN) methodology has been opted for the present study. Efficient optimization of ANN architecture is obtained by using an evolutionary optimizer based on a genetic algorithm. This study proves that use of such large‐scale atmospheric circulation information potentially improves the performance of monthly basin‐scale stream‐flow prediction which, in turn, helps in better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd.     

Modeling of occurrence frequencies of ion conics as a function of altitude and conic angle

The occurrence frequencies of dayside ion conics with various conic angles are obtained as a function of altitude from Exos-D (Akebono) observations. We made a model calculation of ion conic evolution to match the observation results. The observed occurrence frequencies of ion conics with 80° to 90° conic angle are used as an input to the model and the occurrence frequencies of ion conics with smaller conic angles are numerically calculated at higher altitudes. The calculated occurrence frequencies are compared with the observed ones of ion conics with smaller conic angles. We take into account conic angle variation with altitude in both adiabatic and non-adiabatic cases, horizontal extension of ion conics due to E × B drift, and evolution to elevated conics and ion beams in the model. In the adiabatic case, the conic angle decreases with increasing altitude much faster than was observed. The occurrence frequency of small-angle conics is much larger than the observed value without E × B drift and evolution to the other UFIs. An agreement is obtained by assuming non-adiabatic variation of conic angles with altitude and an ion E × B drift to gyro velocity ratio of 0.08 to 0.6, depending on geomagnetic activities.     

Removal of Natural Free Estrogens and their Conjugates in a Municipal Wastewater Treatment Plant

Removal of natural free estrogens and estrogen conjugates in a municipal wastewater treatment plant (WWTP) was investigated and analyzed by GC‐MS, in which estrogen conjugates were first transformed to their corresponding free estrogens with an acid solvolysis procedure before their analysis. Natural free estrogens, E1‐3‐sulfate (E1‐3S), and E3‐3‐sulfate (E3‐3S) were detected with high concentrations in both the influent and effluent of the primary settling tank (PS), while no estrogen glucuronides were detected in any of the monitored wastewater samples. Regarding their removal efficiencies, all were almost completely removed, except for E1 with only a minor decrease. The estrogenic/androgenic removal of the same WWTP was also evaluated with estrogen receptor (androgen receptor) (ER (AR))‐binding assays, in which the removal efficiencies for E2 equivalents (EEQ) or testosterone equivalents (TEQ) were 68.5 and 72.2%. In addition, the chemically calculated EEQ from natural estrogens were about 20.6–39.3% that of the ER‐binding assay, in which E3 contributed the biggest proportion in both the influent and PS, while the calculated value of E1 increased from only 6.7% in the influent to as high as 20.6% in the effluent.     

Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment

ABSTRACT

Climate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important.

Editor M.C. Acreman; Associate editor S. Kanae     

Modeling of terracette‐hillslope soil moisture as a function of aspect,slope and vegetation in a semi‐arid environment

In the semi‐arid western United States, water availability plays a defining role in land use. Soil moisture, vegetation, and microtopography are key variables in the hydrologic function of these ecosystems. Previous research has not addressed the influence of site‐specific aspect, vegetation, or slope gradient on terracette soil moisture patterns in semi‐arid rangelands. Therefore, the objectives of this study were to: (1) assess the influence of terracette site aspect, vegetation cover, and slope on soil moisture; (2) conceptualize conditions at the hillslope scale given terracette morphology; and (3) estimate the extent of terracettes at a regional scale. The Simultaneous Heat and Water (SHAW) model was used to simulate soil water dynamics of terracettes given variations in site conditions. These results were coupled with time‐of‐flight laser scans to quantify terracette bench and riser percent‐area, and statewide assessments of terracette extent using digital orthoimagery and a geographical information system (GIS). Modeling results indicated site aspect had minimal influence (±0.005 m³ m^?3) on terracette soil moisture. Vegetation, represented as leaf area index (LAI), had the single‐most influential effect on terracette volumetric water content (θ _v) demonstrated by an inverse relationship of LAI to mean terracette hillslope θ _v; and slope increases of ≥15% on northern azimuths increased mean θ _v which contrasted with southern azimuths for similar slope increases. Laser scanning results indicated bench width and riser length could be estimated from mean site slope (R ² = 0.82 risers and R ² = 0.93 benches). Aerial orthoimagery/GIS assessments estimated >159 000 ha of terracettes throughout the State of Idaho, with >41 000 ha (~26%) occurring on lands managed as grazing allotments. These findings provide an increased understanding of rangeland hydrologic processes as influenced by cattle density, vegetation, and terracettes which can aide land managers in their selection and application of best management practices on these lands. Copyright © 2017 John Wiley & Sons, Ltd.     

Natural 222Rn as a tracer of mixing and volatilization in a shallow aquifer during a CO2 injection experiment

This study aims to evaluate the application of ²²²Rn in groundwater as a tracer for monitoring CO₂ plume migration in a shallow groundwater system, which is important to detect potential CO₂ leakage in the carbon capture and storage (CCS) project. For this research, an artificial CO₂-infused water injection experiment was performed in a shallow aquifer by monitoring hydrogeochemical parameters, including ²²²Rn. Radon in groundwater can be a useful tracer because of its sensitivity to sudden changes in subsurface environment. To monitor the CO₂ plume migration, the data were analysed based on (a) the influence of mixing processes on the distribution of ²²²Rn induced by the artificial injection experiment and (b) the influence of a carrier gas role by CO₂ on the variation of ²²²Rn. The spatio-temporal distributions of radon concentrations were successfully explained in association with horizontal and vertical mixing processes by the CO₂-infused water injection. Additionally, the mixing ratios of each monitoring well were calculated, quantitatively confirming the influence of these mixing processes on the distribution of radon concentrations. Moreover, one monitoring well showed a high positive relationship between ²²²Rn and Total dissolved inorganic carbon (TIC) by the carrier gas effect of CO₂ through volatilization from the CO₂ plume. It indicated the applicability of ²²²Rn as a sensitive tracer to directly monitor CO₂ leakage. When with a little effect of carrier gas, natural ²²²Rn in groundwater can be used to compute mixing ratio of CO₂-infused water indicative of CO₂ migration pathways. CO₂ carrier gas effect can possibly increase ²²²Rn concentration in groundwater and, if fully verified with more field tests, will pose a great potential to be used as a natural tracer for CO₂.