Search

A rapid and efficient method for the simultaneous monitoring and recovery of Auramine O (AO) and Methylene Blue (MB) dyes from water samples is presented. This method, named ultrasound-assisted dispersive-magnetic nanocomposites-solid-phase microextraction (UA-DMN-μSPE), utilizes NiCoMn/Fe 3 O 4 @C composite sorbents. Response surface methodology (RSM) combined with artificial neural networks (ANN) and generalized regression artificial neural network (GRNN) under central composite design (CCD) was employed to optimize various parameters for efficient extraction, followed by further refinement using desirability function analysis (DFA) and genetic algorithms (GA). Under optimized conditions, the method achieved exceptional recovery rates (99.5 ± 1.2% for AO and 99.8 ± 1.1% for MB) with acetone as the eluent. Additionally, a high preconcentration factor of 45.50 and 47.30 for AO and MB, respectively, was obtained. Low detection limits of 0.45 ng mL⁻1 (AO) and 1.80 ng mL⁻1 (MB) were achieved with wide linear response ranges (5–1000 and 5–2000 ng mL⁻1 for AO and MB, respectively). The method exhibited good stability with RSDs below 3% for five recycling runs, and minimal interference from various ions was observed. This UA-DMN-μSPE-UV/Vis method offers significant advantages in terms of efficiency, preconcentration, and detection limits, making it a valuable tool for the analysis of AO and MB in water samples. [Display omitted] • UA-DMN-μSPE method optimized using RSM, ANN, and GRNN ensures high-performance MB and AO extraction. • Optimized UA-DMN-μSPE-UV/Vis method, developed with DFA and GA, enhances recovery, preconcentration, and selectivity for MB and AO. • Concurrent monitoring & recovery offers sustainable solution for dye removal. • Potential to address environmental & health concerns from non-degradable dyes. [ABSTRACT FROM AUTHOR]

The waste collection process is an issue where numerous studies have already been conducted in the existing literature especially based on finding the optimal routes for the garbage trucks assigned in this service process. In this manner, A Mixed-Integer Linear Programming (MILP) based route optimization model has been conducted as the first attempt for waste collection-oriented electric garbage trucks routing process in this study. Even different studies exist in the literature for the route optimization of conventional fuels-based garbage trucks as mentioned above, no studies devote to considering the electric garbage trucks to the best of our knowledge. Besides, it is not easy to reach the detailed garbage collection area information in the literature. In this manner, data have been obtained by real field measurements in a region within the service area of Bakirkoy Municipality, Istanbul, Turkey. A unit energy consumption value that can be considered as a reference in the future has also been obtained using real data. Besides, real road information data have been integrated to the data used as input while assessing the optimization approach and the system analyses have been conducted in a more realistic concept. The proposed concept has led to an increased reality of nearly 38% for the analysis of the results under conditions closer to real-time, and a decrement of nearly 32% has been obtained. It is expected that this study may lead a conceptual input to an enhanced and greener waste collection process. • Mixed-Integer Linear Programming was introduced for electric garbage trucks routing process. • A unit energy consumption value was obtained as a new reference via real field measurements. • Average electric energy consumption was obtained as 0.86 kWh/km as a more realistic case. • Proposed approach decreased energy consumption for the collection area by nearly 32%. • Proposed approach provided techno-economic contribution for sustainable energy management. [ABSTRACT FROM AUTHOR]

The large consumption of natural gas, one of the most important energy sources in the world, necessitates reliable, precise, and accurate calculation of gas flow rate and amount in order to use this resource in an efficient and sustainable way. The present computational study investigates the possibilities of several soft-computing strategies in estimating the mass flow rate of natural gas flow stream (kg/h) (output variable) based on four input variables of orifice plate diameter ratio, differential pressure of orifice plate (kPa), operating pressure of the natural gas (bar), and operating temperature of the natural gas (°C). A genotype/phenotype genetic algorithm (gene expression programming (GEP) technique), two decision tree-based methods (random forest (RF), random tree (RT) models), and two kernel-based approaches (Gaussian process regression (GPR) and support vector machines (SVM) methods) were applied for the first time to predict gas mass flow rate. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), Scattering index (SI), Nash–Sutcliffe efficiency (NSE), and mean absolute relative error (MARE) were computed as the statistical performance evaluators to determine of the best-performing soft-computing approach. The performance assessment indices corroborated the superiority of the Pearson VII universal kernel function-based GPR model (GPR-PUKF) model (CC = 0.9997, MAE = 64.8091 kg/h, RMSE = 248.7584 kg/h, SI = 0.0237, and NSE = 0.9993 for the testing dataset) over other data-intelligent models in predicting the gas mass flow rate. In addition, statistical results revealed that the predictions of the RF method were better than those of the GEP- and RT-based models, but the GEP approach showed the lowest performance among all applied models. Although the CC values of all models were satisfactory (>0.993), the percentile deviation of GPR model (1.7325%) from the actual values showed competitive lower values, indicating its superior performance than other models (GEP = 15.1436%, RF = 6.5403%, RT = 9.5576%, and SVM = 3.2107%). This study highlighted the significance of employing advanced soft-computing approaches in determining the mass flow rate of natural gas, a vital source of energy, as well as its value to the gas sector. [Display omitted] • Soft-computing implementation for prediction of natural gas mass flow rate. • Benchmarking of genetic/decision tree/kernel-based data-intelligent models. • Lower deviations (1.73% and 0.36%) of GPR-PUKF over other methods. • Superiority of RF (6.54%) over GEP (15.14%) and RT (9.56%) in terms of errors. • Flexibility of soft computation in highly nonlinear real-world gas measurement. [ABSTRACT FROM AUTHOR]

Struvite precipitation is widely used as an established and promising physicochemical treatment method due to its higher effectiveness for removing and recovering excess nitrogen and phosphorus from wastewater, and production of a beneficial byproduct at the end of the process. However, the majority of the literature focus not on the economic aspects of the process, but rather on the effect of different chemical combinations and changing operating conditions. In order to fulfill this gap, this study aims at a comprehensive feasibility analysis of struvite recovery process for a full-scale fertilizer production industry with a 500 m 3 /day capacity. For quantitative assessment, the experimental conditions and chemical combinations that will allow the receiving environment discharge standard for ammonium nitrogen are optimized by taking into account a large number of economic and operating parameters. The effect of change in the struvite sale price on the profit share is examined for the optimum conditions, and the investment and operating costs are calculated by getting the latest up-to-date values from the market. It is determined that when the struvite sale price is raised to 560 €/ton, the facility will obtain a net profit of €445.62/day, and be able to pay for itself in approximately six years. The findings of this study corroborate the economic feasibility of struvite recovery process as a clean and eco-friendly technology at the plant level for a sustainable nutrient management. Clearly, it appears likely that the struvite precipitation method will become more widespread in the future as an effective and non-polluting process. [ABSTRACT FROM AUTHOR]

Utilization of the struvite recovered from sheep slaughterhouse wastewater was explored for the first time in sustainable cement production and fire-resistant wooden structure design. Sheep abattoir-originated struvite precipitation process was optimized using a chemical combination of MgCl 2.6H 2 O + NaH 2 PO 4.2H 2 O, a molar ratio of Mg2+:NH 4 +-N:PO 4 3--P = 1.2:1:1, a reaction pH of 9.0, an initial ammonium concentration of 240 mg NH 4 +-N/L, and a reaction time of 15 min. Based on both American (ASTM C305-14) and Turkish (TS EN 196–1) standard methods, struvite was used in proportions of 10–30% by weight for struvite-substituted cement production. The best compressive strength values were achieved with 85.5% cement clinker (C), 4.5% gypsum (G), and 10% struvite (S) for the struvite-replaced cement (C85.5G4.5S10). According to the US EPA's greenhouse gas protocol, it was estimated that producing 10% struvite-substituted cement would result in 9.97% lower absolute CO 2 emissions than producing 100% Portland cement. It was also found that slaughterhouse-derived struvite could compete with commercial water-based fire retardant solution and exhibit acceptable flame resistance behavior for wooden structures. The versatility of sheep abattoir-oriented struvite was confirmed as an environmentally sustainable and clean by-product for different structural uses. [Display omitted] • First time use of SSW-derived struvite in ecologically friendly cement production. • Novel application of SSW-sourced struvite in fire resistant wood structure design. • More than 70% of NH 4 +-N removal/recovery from SSW via struvite precipitation. • Attractiveness and versatility of struvite in sustainable abattoir waste management. • Noticeable impact of partial cement substitution on reduction of CO 2 emissions. [ABSTRACT FROM AUTHOR]

In the present paper, the removal of ethidium bromide (EtBr) from aqueous solutions in a batch system using natural (NP) and aluminium-coated pumice (ACP) as alternative low-cost adsorbents was investigated. The maximum adsorption capacity, q m (mg/g) was 58.82 and 76.92 mg/g for NP and ACP, respectively, operating at an initial pH of 8, an adsorbent dose of 8 g/L, a contact time of 210 min and an initial EtBr concentration of 30 mg/L. The equilibrium data of both adsorbents fitted the Freundlich isotherm model, indicating the heterogeneity of the adsorbent surface. In addition, the adsorption rate of both adsorbents was well described by the pseudo-second-order kinetics model. This indicated chemisorption was the rate-controlling step of the adsorption process which occurred by ion exchange. Within the performed study, a three-layer artificial neural network (ANN) model was also developed to predict the efficiency of EtBr removal. Computational results clearly demonstrated that the ANN model was able to predict the combined effect of initial pH, adsorbent dose, contact time and initial EtBr concentration on the adsorption efficiency with a very high determination coefficient ( R 2 = 0.998) and a low relative error (RE = 0.037). [ABSTRACT FROM AUTHOR]

Worldwide ageing infrastructures which are vulnerable to seismic lateral loads and located in high seismicity regions have arrested the interest of many researchers to find alternative materials and techniques to strengthen in bending and shear, for example reinforced concrete (RC) beams, slabs, columns, etc. There are several strengthening/repair techniques and materials in literature. Although the method of strengthening concrete structures with fiber reinforced polymers (FRP) is a relatively new technique, it has existed for more than two decades. In this context, several confinement models have been developed for FRP-confined concrete for the prediction of stress–strain response and several researchers have developed various constitutive models to measure the increase in the axial strength of concrete due to the confinement effect of FRP laminates. In this study, RC columns wrapped with carbon FRP (CFRP) considering some existing confinement models in the literature have been investigated. Moreover, based on the experimental data set in the literature, a new artificial intelligence-based algorithm (a Mamdani-type fuzzy inference system) was implemented to model the strength enhancement of CFRP confined RC columns using fuzzy logic methodology. Fuzzy logic predicted results were compared with the outputs of a non-linear regression analysis-based exponential model derived in the scope of the present work. The best predictive performances of the models were assessed by means of various descriptive statistical indicators. The comparison of the proposed prognostic approach with existing empirical and experimental data exhibits a very good precision of the developed artificial intelligence-based model in predicting the lateral confinement coefficient in CFRP wrapped RC columns. [ABSTRACT FROM AUTHOR]

Abstract: The main aim of this paper was the development of a new empirical model based on the composite desirability function methodology for predicting mass condensate flux of a condenser in a seawater greenhouse located at Al-Hail, Muscat, Oman. The computational analyses were conducted for simulating relative humidity of inlet dry air, inlet temperature of humid air (dry bulb temperature), temperature of the seawater at the inlet, mass flow of humid air input, and mass flow of seawater at the inlet. The performance of the proposed empirical model was assessed by means of various descriptive statistical indicators. The comparison of the proposed approach with existing condensation data exhibits very good precision of the developed composite desirability function-based model in predicting the mass condensate flux for the condenser process. The proposed mathematical formulation eliminated the various complex variable interactions and many unit conversions usually carried out in the theoretical approach. [Copyright &y& Elsevier]

Highlights: [•] LECA modified by aqueous solution of MgCl2 and H2O2 are used to remove toxic chromium. [•] Spent natural and surface modified LECA with MgCl2 and H2O2 are best regenerated with HCl acid. [•] XRD and FTIR studies of surface modified LECA do not show any structural distortion. [•] The modification of adsorbents improve specific surface area and metal sorption capacity. [•] Studied adsorbents partially increase the solution conductivity and turbidity. [Copyright &y& Elsevier]

[Display omitted] • CFRP-wrapped R/S RC columns were simulated using soft-computing methodology. • GPR, SVM, and M5P were inter-compared in prediction of K s for the first time. • GPR/SVM-based kernels and (un)pruned M5P were used for the first time for K s. • GPR-PUKF model outperformed than SVM and M5P models with lower deviations. • The total thickness of CFRP was the most effective parameter for predicting the K s. In this study, various soft-computing models (Gaussian process regression (GPR) and support vector machines (SVM) based on the polynomial kernel function (PKF), Pearson VII universal kernel function (PUKF), and radial basis kernel function (RBKF), as well as pruned/unpruned M5P tree models) were simultaneously applied for the first time in prediction of the lateral confinement coefficient (K s) of CFRP-wrapped rectangular/square (R/S) RC columns, and their corresponding predictive successes were appraised statistically. For this aim, short side of the column section (b), long side of the column section (h), total thickness of CFRP (t), compressive strength of the unconfined concrete (f' c 0), and the elastic modulus of CFRP (E CFRP) were used as independent input variables whereas the K s was the output variable. Results indicated that the performance of the Pearson VII kernel function-based Gaussian process regression (GPR-PUKF) model was superior to other models for the training and testing stages. A sensitivity investigation showed that the total thickness of CFRP (t) was the most effective parameter for predicting the K s using GPR-PUKF-based model. Findings of the present computational assessment obviously revealed that the employed soft-computing strategy had the capability of accurately estimating the K s of R/S RC columns wrapped with CFRP. [ABSTRACT FROM AUTHOR]

Abstract: An integrated multi-objective optimization approach within the framework of nonlinear regression-based kinetic modeling and desirability function was proposed to optimize an up-flow anaerobic sludge blanket (UASB) reactor treating poultry manure wastewater (PMW). Chen–Hashimoto and modified Stover–Kincannon models were applied to the UASB reactor for determination of bio-kinetic coefficients. A new empirical formulation of volumetric organic loading rate was derived for the first time for PMW to estimate the dimensionless kinetic parameter (K) in the Chen–Hashimoto model. Maximum substrate utilization rate constant and saturation constant were predicted as 11.83gCOD/L/day and 13.02gCOD/L/day, respectively, for the modified Stover–Kincannon model. Based on four process-related variables, three objective functions including a detailed bio-economic model were derived and optimized by using a LOQO/AMPL algorithm, with a maximum overall desirability of 0.896. The proposed optimization scheme demonstrated a useful tool for the UASB reactor to optimize several responses simultaneously. [Copyright &y& Elsevier]

Abstract: Oil composition and properties including density, viscosity, asphaltene, saturate, aromatics and resin contents are responsible factors for the formation of water-in-crude-oil emulsions. These factors can be used to develop an stability index which determines states of water-in-oil emulsion in terms of either an unstable, entrained, mesostable or stable conditions. It is important to note that most of the regression models cannot capture the non-linear relationships involved in the formation of these emulsions. This study deals with the prediction of water-in-oil emulsions stability by an adaptive neuro-fuzzy inference system (ANFIS) with basic compositional factors such as density, viscosity and percentages of SARA (saturates, aromatics, resins, and asphaltenes) components. In the computational method, grid partition and subtractive clustering fuzzy inference systems were tried to generate the optimum fuzzy rule base sets. The stability estimation was conducted by applying hybrid learning algorithm and the model performance was tested by the means of distinct test data set randomly selected from the experimental domain. The ANFIS-based predictions were also compared to the conventional regression approach by means of various descriptive statistical indicators, such as root mean-square error (RMSE), index of agreement (IA), the factor of two (FA2), fractional variance (FV), proportion of systematic error (PSE), etc. With trying various types of fuzzy inference system (FIS) structures and several numbers of training epochs ranging from 1 to 100, the lowest root mean square error (RMSE=2.0907) and the highest determination coefficient (R 2 =0.967) were obtained with subtractive clustering method of a first-order Sugeno type FIS. For the optimum ANFIS structure, input variables were fuzzified with four Gaussian membership functions, and the number of training epochs was computed as 21. In the computational analysis, the predictive performance of the ANFIS model was examined for the following ranges of the clustering parameters: range of influence (ROI)=0.45–0.60, squash factor (SF)=1.20–1.35, accept ratio (AR)=0.40–0.55, and reject ratio (RR)=0.10–0.20. Results indicated that ROI, SF, AR and RR were obtained to be 0.54, 1.25, 0.50 and 0.15, respectively, for the best FIS structure. It was clearly concluded that the proposed ANFIS model demonstrated a superior predictive performance on forecasting of water-in-oil emulsions stability. Findings of this study clearly indicated that the neuro-fuzzy modeling could be successfully used for predicting the stability of a specific water-in-oil mixture to provide a good discrimination between several visual stability conditions. [Copyright &y& Elsevier]

Abstract: A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate (R V), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 (±3)% and an average volumetric TCOD removal rate of 6.87 (±3.93)kg TCODremoved/m3-day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98. [ABSTRACT FROM AUTHOR]

Abstract: A three factor, three-level Box–Behnken experimental design combining with response surface modeling (RSM) and quadratic programming (QP) was employed for maximizing Pb(II) removal from aqueous solution by Antep pistachio (Pistacia vera L.) shells based on 17 different experimental data obtained in a lab-scale batch study. Three independent variables (initial pH of solution (pH0) ranging from 2.0 to 5.5, initial concentration of Pb(II) ions (C 0) ranging from 5 to 50ppm, and contact time (t C) ranging from 5 to 120min) were consecutively coded as x 1, x 2 and x 3 at three levels (−1, 0 and 1), and a second-order polynomial regression equation was then derived to predict responses. The significance of independent variables and their interactions were tested by means of the analysis of variance (ANOVA) with 95% confidence limits (α =0.05). The standardized effects of the independent variables and their interactions on the dependent variable were also investigated by preparing a Pareto chart. The optimum values of the selected variables were obtained by solving the quadratic regression model, as well as by analysing the response surface contour plots. The optimum coded values of three test variables were computed as x 1 =0.125, x 2 =0.707, and x 3 =0.107 by using a LOQO/AMPL optimization algorithm. The experimental conditions at this global point were determined to be pH0 =3.97, C 0 =43.4ppm, and t C =68.7min, and the corresponding Pb(II) removal efficiency was found to be about 100%. [Copyright &y& Elsevier]

A comprehensive analysis of Turkish antibiotic data was conducted to evaluate potential environmental risks associated with antibiotic consumption in Turkey for year 2007. Antibiotics were defined for systemic use or group J01 of the WHO Anatomical Therapeutic Chemical (ATC) classification system. Total emissions and prescriptions for each ATC group were classified separately into 17 different J01 categories and three forms of medication (capsule/tablets, injectables and suspensions). Capsules and tablets were found as the most emitted form of medication in year 2007, with a total emission rate of about 585.5tons/year (76%). Total antibiotic emission rates including all forms of medications were determined to be about 664.2tons/year (86%) and 110.1tons/year (14%) for adult and pediatric patients, respectively. An environmental risk assessment of 8 human antibiotics was conducted according to the EU draft guidance (CEC/III/5504/94, draft 6, version 4) and the risk was indicated by the ratio of predicted environmental concentration (PEC) to predicted no effect concentration (PNEC) for the aquatic environment. Available acute and chronic toxicity data were collected from the open peer-reviewed literature to derive PNEC. Risk quotients (PEC/PNEC) were then calculated for 8 pharmaceutical substances. PEC/PNEC ratio exceeded 1.0 for β-lactams (cephalosporins and penicillins), fluoroquinolones, macrolides and aminoglycosides. The findings of this study concluded that the release of these compounds from wastewater treatment plants may potentially be of an important environmental concern based on today''s use of antibiotics in Turkey. [Copyright &y& Elsevier]

Magnesium ammonium phosphate hexahydrate (MgNH4PO4·6H2O, MAP) precipitation was studied on up-flow anaerobic sludge blanket (UASB) pretreated poultry manure wastewater in a lab-scale batch study. To recover high strength of ammonium nitrogen (NH4 +–N=1318mg/L) from UASB effluent, three combinations of chemicals including MgCl2·6H2O+KH2PO4, MgSO4·7H2O+NaHPO4·7H2O, and MgO+85% H3PO4 were first applied at the stoichiometric ratio (Mg2+:NH4 +–N:PO4 3−–P=1:1:1) and at different pH levels ranging from 4.45 to 11. Preliminary test results indicated that maximum NH4 +–N removal, as well as maximum chemical oxygen demand (COD) and color reductions, were obtained as 85.4%, 53.3% and 49.8% at pH 9.0 with the addition of MgCl2·6H2O+KH2PO4, respectively. The paired experimental data obtained from batch studies were statistically evaluated by a non-parametric Mann–Whitney test and a two-sample t-test. Based on the previous results, another batch experiments were then performed at pH 9.0 using MgCl2·6H2O+KH2PO4 for different molar ratios applied as overdose (1.2:1:1, 1.5:1:1, 1:1:1.2, 1:1:1.5) and underdose (0.5:1:1, 0.8:1:1, 1:1:0.5, 1:1:0.8). In the final step, the fertility of the MAP precipitate as struvite was also tested on the growth of three test plants including purslane (Portulaca oleracea), garden cress (Lepidum sativum) and grass (Lolium perenne). Findings of this experimental study clearly confirmed the recovering of NH4 +–N from UASB pretreated poultry manure wastewater by MAP precipitation, and also the application of recovered MAP sludge as a valuable slow release fertilizer for agricultural use. [Copyright &y& Elsevier]

Abstract: The performance of electrocoagulation (EC) technique for decolorization and chemical oxygen demand (COD) reduction of anaerobically pretreated poultry manure wastewater was investigated in a laboratory batch study. Two identical 15.7-L up-flow anaerobic sludge blanket (UASB) reactors were first run under various organic and hydraulic loading conditions for 216 days. Effects of operating parameters such as type of sacrificial electrode material, time of electrolysis, current density, initial pH, and electrolyte concentration were further studied to optimize conditions for the post-treatment of UASB pretreated poultry manure wastewater. Preliminary tests conducted with two types of sacrificial electrodes (Al and Fe) resulted that Al electrodes were found to be more effective for both COD and color removals than Fe electrodes. The subsequent EC tests performed with Al electrodes showed that optimal operating conditions were determined to be an initial pH of 5.0, a current density of 15mA/cm2, and an electrolysis time of 20min. The results indicated that under the optimal conditions, about 90% of COD and 92% of residual color could be effectively removed from the UASB effluent with the further contribution of the EC technology used as a post-treatment unit. In this study, the possible acute toxicity of the EC effluent was also evaluated by a static bioassay test procedure using guppy fish (Lebistes reticulatus). Findings of this study clearly indicated that incorporation of a toxicological test into conventional physicochemical analyses provided a better evaluation of final discharge characteristics. [Copyright &y& Elsevier]

Abstract: A three-layer artificial neural network (ANN) model was developed to predict the efficiency of Pb(II) ions removal from aqueous solution by Antep pistachio (Pistacia Vera L.) shells based on 66 experimental sets obtained in a laboratory batch study. The effect of operational parameters such as adsorbent dosage, initial concentration of Pb(II) ions, initial pH, operating temperature, and contact time were studied to optimise the conditions for maximum removal of Pb(II) ions. On the basis of batch test results, optimal operating conditions were determined to be an initial pH of 5.5, an adsorbent dosage of 1.0g, an initial Pb(II) concentration of 30ppm, and a temperature of 30°C. Experimental results showed that a contact time of 45min was generally sufficient to achieve equilibrium. After backpropagation (BP) training combined with principal component analysis (PCA), the ANN model was able to predict adsorption efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm (LMA) was found as the best of 11 BP algorithms with a minimum mean squared error (MSE) of 0.000227875. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of about 0.936 for five model variables used in this study. [Copyright &y& Elsevier]

A nonlinear modeling study was carried out to evaluate the performance of UASB reactors treating poultry manure wastewater under different organic and hydraulic loading conditions. Two identical pilot scale up-flow anaerobic sludge blanket (UASB) reactors (15.7L) were run at mesophilic conditions (30–35°C) in a temperature-controlled environment with three hydraulic retention times (θ) of 15.7, 12 and 8.0 days. Imposed volumetric organic loading rates (L V) ranged from 0.65 to 4.257kg COD/(m3 day). The pH of the feed varied between 6.68 and 7.82. The hydraulic loading rates (L H) were controlled between 0.105 and 0.21m3/(m2 day). The daily biogas production rates ranged between 4.2 and 29.4L/day. High volumetric COD removal rates (R V) ranging from 0.546 to 3.779kg CODremoved/(m3 day) were achieved. On the basis of experimental results, two empirical models having a satisfactory correlation coefficient of about 0.9954 and 0.9416 were developed to predict daily biogas production (Q g) and effluent COD concentration (S e), respectively. Findings of this modeling study showed that optimal COD removals ranging from 86.3% to 90.6% were predicted with HRTs of 7.9, 9.5, 11.2, 12.6, 13.7 and 14.3 days, and L V of 1.27, 1.58, 1.78, 1.99, 2.20 and 2.45kg COD/(m3 day) for the corresponding influent substrate concentrations (S i) of 10,000, 15,000, 20,000, 25,000, 30,000 and 35,000mg/L, respectively. [Copyright &y& Elsevier]

Abstract: The applicability of Fenton''s oxidation as an advanced treatment for chemical oxygen demand (COD) and color removal from anaerobically treated poultry manure wastewater was investigated. The raw poultry manure wastewater, having a pH of 7.30 (±0.2) and a total COD of 12,100 (±910)mg/L was first treated in a 15.7L of pilot-scale up-flow anaerobic sludge blanket (UASB) reactor. The UASB reactor was operated for 72 days at mesophilic conditions (32±2°C) in a temperature-controlled environment with three different hydraulic retention times (HRT) of 15.7, 12 and 8.0 days, and with organic loading rates (OLR) between 0.650 and 1.783kgCOD/(m3 day). Under 8.0 days of HRT, the UASB process showed a remarkable performance on total COD removal with a treatment efficiency of 90.7% at the day of 63. The anaerobically treated poultry manure wastewater was further treated by Fenton''s oxidation process using Fe2+ and H2O2 solutions. Batch tests were conducted on the UASB effluent samples to determine the optimum operating conditions including initial pH, effects of H2O2 and Fe2+ dosages, and the ratio of H2O2/Fe2+. Preliminary tests conducted with the dosages of 100mg Fe2+/L and 200mg H2O2/L showed that optimal initial pH was 3.0 for both COD and color removal from the UASB effluent. On the basis of preliminary test results, effects of increasing dosages of Fe2+ and H2O2 were investigated. Under the condition of 400mg Fe2+/L and 200mg H2O2/L, removal efficiencies of residual COD and color were 88.7% and 80.9%, respectively. Under the subsequent condition of 100mg Fe2+/L and 1200mg H2O2/L, 95% of residual COD and 95.7% of residual color were removed from the UASB effluent. Results of this experimental study obviously indicated that nearly 99.3% of COD of raw poultry manure wastewater could be effectively removed by a UASB process followed by Fenton''s oxidation technology used as a post-treatment unit. [Copyright &y& Elsevier]

• Available literature on LCA and structural characteristics of buildings is reviewed. • Low-rise buildings have received more attention than high-rise buildings. • Energy use and greenhouse gas emissions are the main focus of the existing literature. • The use phase of buildings accounts for the majority of life cycle impacts. • The construction phase may play a large role, particularly in energy efficient buildings. An overview of current status of the available literature on Life Cycle Energy, Life Cycle Greenhouse gasses, and Conventional Life Cycle Assessment of commercial and residential buildings was presented with respect to their height. A narrative literature review was carried out to provide a comprehensive overview as well as to highlight the recent contributions related to the environmental evaluation of high-rise and low-rise buildings. The study was carried out by searching the databases of the Scopus and Elsevier in conjunction with ScienceDirect and Google Scholar databases. The reason for this was to cover the published papers in this field up to the highest degree of accuracy. By means of the search of publications quoting the use of LCA in construction sector for the period from 1997 to 2018, more than 230 peer-reviewed publications referencing the use of life cycle assessment in buildings have been identified. The review shows that low-rise buildings (1~5 floors) compared to high-rise ones (≥ 5 floors) received significant attention as the studies focusing on the life cycle assessment of low-rise buildings were about twice in number more than the studies related to the life cycle assessment of high-rise buildings. In case of high-rise buildings, commercial buildings gained more attention by over 60% of the reviewed studies, while for low-rise buildings, residential buildings took the leverage by accounting to over 70% of the reviewed studies. The more frequently studied life cycle stages were those related to the manufacturing and use phases. Similarly, the most considered impact categories were the global warming potential and embodied energy. The reported values for embodied energy of high-rise buildings had a great variation ranging from 0.533 MJ/m2 to 883.1 GJ/m2, while the same values for low-rise buildings ranged from 0.21 to 374.4 GJ/m2. In terms of global warming potential, high-rise buildings emitted 10 to 10,010 kg CO 2 -eq/m2 per year, however, some studies revealed the potential of timber structure in emission reduction by values ranging from 234.8 to 1338 kg CO 2 -eq/m2. The emissions associated by low-rise buildings ranged from 0.07 to 35,765 kg CO 2 -eq/m2, and the respective values for emission reduction by timber structures were between 12.9 and 361 kg CO 2 -eq/m2. The results also indicate that a wide range of building's lifespan varying from 20 to over 100 years were utilized in life cycle assessment of different types of buildings. Functional unit was also another parameter that showed a broad variation both in terms of unit and definition. While the majority of researchers considered "m2" as the functional unit (61%), "whole building" was also considered as the functional unit in almost 20% of the reviewed studies, indicating the lack of standardized definition for functional unit for more practical outcomes. Ecoinvent was the most referred inventory database (65%) for life cycle assessment of buildings followed by University of Bath ICE (11%), U.S. database (9%), and Australian material inventory database (7%). SimaPro dominated computer-aided softwares with 40% of citations among the reviewed studies. ATHENA Impact Estimator and GaBi software gathered the attention of the reviewed studies by 7.5% and 4%, respectively. The review finally highlights that the variations in building design (structure and materials), lifetime, functional unit, and scope restrict to compare the findings and results of studies with each other. [ABSTRACT FROM AUTHOR]

The efficiency of new and nano-scale adsorbents including multi-walled carbon nanotubes (MWCNTs) and γ-alumina in the removal of cobalt(II) from aqueous solutions was experimentally evaluated in a batch-system reactor. To the best of our knowledge, no previous study has specifically attempted to introduce a hybrid strategy based on artificial neural network and genetic algorithm techniques for modelling and optimizing adsorptive removal of cobalt(II) from aqueous solutions via the proposed nanoparticles. The analyses of SEM, TEM, and FTIR were used to characterize both adsorbents. The response surface methodology (RSM) approach suggested a second-order polynomial model with a p -value < 0.0001 and R 2 of 0.9980 for MWCNTs adsorbent and a p -value < 0.0001 and R 2 of 0.9992 for γ-alumina adsorbent. The artificial neural network (ANN) approach suggested a three-layered feed-forward backpropagation model with R 2 of 0.9794 for MWCNTs adsorbent and R 2 of 0.9823 for γ-alumina adsorbent. The results linked to optimization by RSM showed that the maximum cobalt(II) removal efficiency of about 90% was achieved in the case of the MWCNTs adsorbent under the conditions of pH = 10, contact time = 38.6 min, MWCNTs dosage = 1.57 mg/L, and initial cobalt(II) concentration = 56.57 mg/L. About 93% of cobalt(II) removal could be obtained in the case of γ-alumina adsorbent under the conditions of pH = 10, contact time = 35.5 min, γ-alumina dosage = 1.63 g/L, and initial cobalt(II) concentration = 52.15 mg/L. The optimization values using the genetic algorithm (GA) technique were almost the same as those obtained from the RSM method. The kinetic model of Ho and McKay's pseudo-second order (PSO) and the isotherm model of Dubinin–Radushkevich were found to be the best-fitted to the experimental for both MWCNTs and γ-alumina. In addition, the maximum monolayer adsorption capacity of MWCNTs and γ-alumina adsorbents for the adsorption of cobalt(II) was 78.94 mg/g and 75.78 mg/g, respectively. Also, a thermodynamic study exhibited a favorable and spontaneous adsorption process for both materials. The present study clearly concluded that the proposed adsorbents could be effectively used for the removal of cobalt(II) from aqueous solutions at lower adsorbent dose and shorter contact times than various adsorbents reported in literature. Unlabelled Image • Adsorptive removal of cobalt (II) was explored via MWCNTs and nano-alumina. • ANN and genetic algorithm were proposed for this first time in cobalt (II) adsorption. • Isotherms and kinetic data followed Dubinin–Radushkevich and PSO models, respectively. • A cobalt (II) removal efficiency above 90% was possible at pH = 10 and 1 g adsorbent/L. • Favorable and spontaneous removal was corroborated by thermodynamic study. [ABSTRACT FROM AUTHOR]

The present analysis was conducted as the first study to investigate the biochemical methane potential of four different agro-industrial wastewaters originating from chocolate, slaughterhouse, gum, and beet sugar industries under the same anaerobic fermentation conditions. To the best of our knowledge, no previous study has specifically attempted to pinpoint a hybrid programming strategy for making a quantitative description of the anaerobic biodegradability of these waste streams. Thus, considering the scarcity of the literature in this field, a comprehensive study was conducted to evaluate the amount of bio-methane obtainable from the investigated organic wastes and to predict their kinetics using three different sigmoidal microbial growth curve models (modified Gompertz equation, transference function (reaction curve-type model), and logistic function) within the framework an original MATLAB®-based coding scheme. The results showed that methane productions started immediately after 4 h of incubation for all substrates and reached their maximum rates of 118, 116, 108, 34 mL CH 4 /g VS/day, respectively, for wastewaters from chocolate, slaughterhouse, gum, and beet sugar industries. The corrected mean steady state methane contents were 61.7%, 73.4%, 62.8%, and 62.1% in the respective order. The highest methane yield (943 mL CH 4 /g VS) was obtained from the slaughterhouse wastewater, and this value was 1.32, 1.58, and 4.56 times higher than those obtained in the anaerobic digestion of chocolate, gum, and beet sugar wastewaters, respectively. Among the three kinetic models tested, the logistic function best explained the behavior of the observed data of all substrates using a Quasi-Newton cubic line search procedure (R 2 = 0.987–0.996) with minimum number of non-linear iterations and function counts. Deviations between the measured and the outputs of the best-fit kinetic model were less than 4.3% in prediction of methane production potentials, suggesting that the proposed computational methodology could be used as a well-suited and robust approach for modeling and optimization of a highly non-linear biosystem. Image 1038 • Biochemical methane potential of four agro-industrial substrates was explored. • A new hybrid computational strategy was proposed to estimate the kinetic parameters. • Logistic model with Quasi-Newton algorithm showed the best fit of the observed data. • Highest methane production potential was obtained from slaughterhouse wastewater. • Studied agro-industrial feedstocks showed potential energy/economic value added. [ABSTRACT FROM AUTHOR]

In the present study, defluoridation study of liquid phase with the help of multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs) were well investigated and elucidated. The impact of different experimental conditions such as solution pH, initial fluoride concentration, adsorbent dose, and contact time was well studied and optimized for the maximum fluoride removal from water. The experimental data were fitted by the Freundlich, Langmuir and Dubinin–Radushkevich (D–R) isotherm models and the related equilibrium constants were calculated. The results of the isotherm studies showed that fluoride removal by both adsorbents followed the Freundlich isotherm model. Kinetic studies were conducted and the results demonstrated that the experimental data were fitted well with the pseudo-second order kinetic model. Furthermore, two multiple regression-based equations were also derived to model the removal of fluoride from aqueous solutions by the carbon nanotubes. This study demonstrated that the polynomial equations satisfactorily described the behavior of the present defluoridation process for both MWCNTs ( R 2 = 0.913) and SWCNTs ( R 2 = 0.941). [ABSTRACT FROM AUTHOR]

Abstract: One-dimensional (1D) advection–dispersion transport modeling was conducted as a conceptual approach for the estimation of the transport parameters of fourteen different phenolic compounds (phenol, 2-CP, 2-MP, 3-MP, 4-MP, 2-NP, 4-NP, 2,4-DNP, 2,4-DCP, 2,6-DCP, 2,4,5-TCP, 2,4,6-TCP, 2,3,4,6-TeCP, PCP) and three different inorganic contaminants (Cu, Zn, Fe) migrating downward through the several liner systems. Four identical pilot-scale landfill reactors (0.25m3) with different composite liners (R1: 0.10+0.10m of compacted clay liner (CCL), Le =0.20m, ke =1×10−8 m/s, R2: 0.002-m-thick damaged high-density polyethylene (HDPE) geomembrane overlying 0.10+0.10m of CCL, Le =0.20m, ke =1×10−8 m/s, R3: 0.002-m-thick damaged HDPE geomembrane overlying a 0.02-m-thick bentonite layer encapsulated between 0.10+0.10m CCL, Le =0.22m, ke =1×10−8 m/s, R4: 0.002-m-thick damaged HDPE geomembrane overlying a 0.02-m-thick zeolite layer encapsulated between 0.10+0.10m CCL, Le =0.22m, ke =4.24×10−7 m/s) were simultaneously run for a period of about 540days to investigate the nature of diffusive and advective transport of the selected organic and inorganic contaminants. The results of 1D transport model showed that the highest molecular diffusion coefficients, ranging from 4.77×10−10 to 10.67×10−10 m2/s, were estimated for phenol (R4), 2-MP (R1), 2,4-DNP (R2), 2,4-DCP (R1), 2,6-DCP (R2), 2,4,5-TCP (R2) and 2,3,4,6-TeCP (R1). For all reactors, dispersion coefficients of Cu, ranging from 3.47×10−6 m2/s to 5.37×10−2 m2/s, was determined to be higher than others obtained for Zn and Fe. Average molecular diffusion coefficients of phenolic compounds were estimated to be about 5.64×10−10 m2/s, 5.37×10−10 m2/s, 2.69×10−10 m2/s and 3.29×10−10 m2/s for R1, R2, R3 and R4 systems, respectively. The findings of this study clearly indicated that about 35–50% of transport of phenolic compounds to the groundwater is believed to be prevented with the use of zeolite and bentonite materials in landfill liner systems. [Copyright &y& Elsevier]

[Display omitted] • Electricity consumption is mapped using a combined cartographic methodology. • State of the sky impact is explored for the first time on energy management strategies. • LF strategy provides the best load monitoring by minimizing the generator uptime. • Energy management strategies play a pivotal role as hybrid solar system control tools. A novel optimized sizing and management strategy of a grid-connected hybrid photovoltaic (PV)-solar-battery-group system were proposed for the electrification of residential consumers in Northwest Africa (a case of Mauritania), and the influence of the state of the sky (clear, moderately overcast, and overcast) was analyzed according to the load flowing (LF) and the cycle charging (CC) strategies. In order to mitigate the pressure on the national grid, consolidate the consumer autonomy, minimize the cost of medium- and long-term consumption bills, and CO 2 -related emissions, a cartographic approach was conducted as the first attempt to map the electricity consumption potential for buildings in the city of Nouakchott (Mauritania) using a geo-referenced database. ArcGIS®, HOMER Pro®, and MATLAB® softwares were used for the establishment of the load profile, optimized sizing of the PV-batteries-group-grid system, and calculation of the lightness index, respectively. The LF strategy provided the best monitoring of the load throughout the day by minimizing the generator uptime. The techno-economic analysis revealed the values of cost of energy (COE) and net present cost (NPC) as follows: COE = $0.0549/kWh and NPC = $24,796 for the LF PV-batteries-grid strategy, COE = 0.0646 $/kWh and NPC = $23,380 for the CC PV-batteries-grid strategy, and COE = $0.17/kWh and NPC = $23,262 for the case of the grid on its own. [ABSTRACT FROM AUTHOR]

Due to its importance in flow measurement and instrumentation, as well as its frequent application in differential pressure flowmeters, orifice discharge coefficient (C d) needs to be estimated precisely. In this study, different soft computing models (including multiple linear regression (MLR), group method of data handling (GMDH), multivariate adaptive regression splines (MARS), M5P tree model, and random forest (RF)) were employed for the first time in estimation of the C d value, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), scattering index (SI), and Nash–Sutcliffe model efficiency coefficient (NSE) were used as the statistical indicators for validating the performance of each soft computing model. The statistical indicators approved the superiority of the RF model over the other models, while the MARS model also showed a competitive prediction potential over M5P, GMDH, and MLR models. The findings of this computational study clearly demonstrated that the implemented soft computing strategy had the capability to be used in precise estimation of the C d of the orifice meter, specifically, in situations where the measurement of the parameters in deterministic equation is not practically feasible. [Display omitted] • Differential pressure flowmeters were simulated using soft computing methodology. • MLR, GMDH, MARS, M5P, and RF were used to estimate C d for the first time. • Effectiveness of models was tested by CC, MAE, RMSE, SI, and NSE metrics. • RF outperformed other models, and MARS showed competitive prediction accuracy. • RF-based soft computation was of potential to be used in precise estimation of C d. [ABSTRACT FROM AUTHOR]

This study introduced an investigation to evaluate spatial and temporal variations of the wind potential for the techno-economic feasibility analysis of the energy production in Northwest Africa (a case of Mauritania). The present research was introduced as the first attempt to appraise the spatio-temporal influence of the wind energy production in Mauritania, and particularly focused on analyzing seasonal, daily, and turbulence index on the available wind potential in this region. Data measured every 10 min over one-year period were collected from eight sites (with three different height levels) located mainly on the west coast of Mauritania, and the annual average of the wind characteristics were determined. Power density, Weibull parameters, turbulence indices, and power-law exponents were estimated based on seasonal and daily wind analyses. Comparative studies of the power density potential of the wind on different sites were also conducted while investigating the influence of seasons, height of the wind turbines, wind directional distributions, and daily characteristics. Investigations regarding the generated energy from the wind turbine and the related capacity factor were performed based on eight particular wind turbines (Ecotècnia-44, Ecotècnia-48, Nordex-N50, Neg-Micon, Vestas-V66, Power-Wind-90, Bonus-2MW, and Vestas-V90). Results showed that the power-law exponent was higher where the turbulence index was low. The analysis of the power distribution allowed concluding on the energy availability according to the influent variables. Findings of the present techno-economic analysis (for electricity generation from the planned wind energy systems) revealed that the best cost of energy (ranging from 0.0187 €/kWh to 0.0596 €/kWh) was observed for the wind turbine Ecotècnia-48 on all sites. Image 1 • Spatio-temporal variations of the wind potential were explored in Mauritania for the first time. • Wind potential for electricity generation was characterized for eight sites with three height levels. • Wind direction was more stable in the rainy season than in dry season on all sites. • Annual mean turbulence index and power law exponent showed an opposite relationship. • Techno-economic feasibility analysis revealed that the best cost of energy was 0.0187 €/kWh. [ABSTRACT FROM AUTHOR]

• Poultry abattoir sludge stimulates nodulation, nitrogen fixation, and chickpea yield. • PAS application rate of 50 kg N ha−1 markedly enriches biomass and grain yields. • PAS amendment boosts chickpea yield up to 45% in low fertility and high pH soil. • Image analysis plays a pivotal role to clarify effects of PAS on chickpea yield. • PAS application to marginal lands suggests a sustainable way for chickpea production. Sustainable development goals imply environmentally sound management of all wastes to minimize the waste generation through prevention, reduction, recycling, and reuse. In particular, the poultry industry produces nutrient-rich waste that requires proper management. Additionally, the recycling of bio-wastes in agricultural lands is still a key technology for the sustainable use of nutrients as a renewable fertilizer. Currently, there are very few studies on the utilization of agro-industrial bio-wastes, such as poultry abattoir sludge (PAS), for crop cultivation in soils containing low organic matter and high pH. In this context, it is necessary to make a more particular assessment of poultry industry-oriented and locally available nutrient-rich organic wastes for nodulation, physiological adaptation, and crop yield. Considering the scarcity of the literature in this field, the present study aimed to fulfill the apparent gap by focusing on the applicability of recycled PAS to low fertility soil in the growth of chickpea selected as a model legume, thereby contributing to the development of an agricultural and sustainable industrial management strategy for the relevant sectors. In this study, leaf chlorophyll content and nodule color were also investigated by the image analysis methodology to describe the effects of bio-waste on closing chickpea yield gap in a marginal land with high soil pH and low organic matter. Two-year consecutive field experiments were carried out to explore the effect of the PAS with the application rates of 25 kg N ha−1 (T 2), 50 kg N ha−1 (T 3), and 100 kg N ha−1 (T 4) along with unamended (T 0) and fertilized control (T 1). The results indicated that the PAS treatments significantly differed in chlorophyll content, nodulation parameters, and biomass and grain yields. The chlorophyll content was correlated (r = 0.910) with the red color value (RGB color model) of nodule image analysis in the response to bio-waste. Based on the two-year average, it was concluded that chickpea yield could be increased 45% by amending with the PAS (T 3). The present study clearly demonstrated that the image analysis could be a useful digital tool for the evaluation of chlorophyll content, nitrogen fixation efficiency, and forecasting biomass and grain yields of chickpea. The results also confirmed that the PAS application to low fertility soil could prominently contribute to establish sustainable waste management and crop production alternatives for closing chickpea yield gap. [ABSTRACT FROM AUTHOR]

Humic substances (HSs) are a group of complex macromolecular polymeric compounds originating from the decomposition of plant residuals and other organic matter. Within the presence of micro-pollutants and heavy metals, HSs negatively act upon potable water quality by contributing to aesthetic problems such as yellowish or brownish color and annoying taste and odor. They are also responsible for re-growth of pathogenic microorganisms and fouling of membranes in water distribution systems. More importantly, these high-molecular-weight polymers have been noted to be the major contributor to the formation of disinfection by-products (DBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs). Considering these harmful effects, removal of HSs is one of the significant tasks in drinking water treatment. For this purpose, this study aimed to explore the effects of various operating parameters (initial concentration, initial pH, electrical conductivity, pulse time, pulse number, and process time) on the humic acid (HA) removal efficiency and energy consumption. In this study, a new current supply method called alternating pulse current electrocoagulation-flotation (APC-ECF) process was proposed, and a detailed comparative optimization of four different ECF processes (direct current (DC)-simple electrode, DC-perforated electrode, pulse current-simple electrode, and pulse current-perforated electrode) was conducted within the framework of Taguchi-based experimental design methodology. According to scanning electron microscopy (SEM), the morphology of electrode surfaces with APC and perforated electrode showed less disordered (irregular) pores and a regular structure of aluminum compared to the DC, which confirmed the difference in the corrosion rates. Moreover, the proposed APC-ECF method led to the production of less dewatered and dense sludge. The results of the performance analysis revealed that the APC with a perforated electrode provided 3.2-fold lower energy consumption and 2.5-fold lower aluminum consumption compared to the DC with a simple electrode. Considering the expenses associated with power consumption and sludge disposal costs for the electrocoagulation unit, the experimental findings corroborated that the proposed APC-ECF process could be used as a promising and cost-effective technology in water treatment for the removal of HSs. [ABSTRACT FROM AUTHOR]

A novel application of artificial neural networks (ANN) combined with Taguchi orthogonal experimental design methodology (27 runs, 3 levels, 6 factors) was introduced for modeling and optimization of a new alternating pulse current electrocoagulation-flotation (APC-ECF) process for the removal of humic acid (HA) from aqueous media. Two different ANN architectures, such as multilayer perceptron (MLP NN) and generalized feed forward (GFF NN), were proposed and trained to describe the nonlinear behavior of a laboratory-scale batch APC-ECF reactor. Various operating parameters, such as initial HA concentration (C0), initial pH (pH0), electrical conductivity (EC0), current density (CD), and number of pulses (Npls), were used as inputs for the proposed networks, and the HA removal was selected as the output. According to the goodness-of-fit criteria, the computational results showed that the single hidden-layered GFF NN (5:6:1), where a sigmoid axon transfer function was used at its hidden layer and its output layer was trained by the Levenberg–Marquardt algorithm, showed the best performance (R2 = 0.999, MSE = 0.00006). For the optimal conditions of C0 = 42 mg/L, pH0 = 6.63, CD = 24.3 A/m2, EC0 = 856 μS/cm, and Npls = 3, the maximum HA removal was obtained based on the predicted outputs of the best ANN model (GFF NN). The results of the computational analysis clearly corroborated that ANN integrated design of experiments (DOE)-based modeling was rapidly and effectively used for predicting the optimum performance of a complex electrochemical process in removal of HA from water using aluminum electrodes in monopolar arrangement. [ABSTRACT FROM AUTHOR]

• A new alternating pulse current electrocoagulation-flotation process is introduced. • APC-ECF system is first modeled for removal of humic acid from water using ANN/DOE. • About 100% of HA is removed from water with an energy consumption of 1.08 kWh/kg HA. • ANN/DOE-based approach can describe behavior of a complex electrochemical process. A novel application of artificial neural networks (ANN) combined with Taguchi orthogonal experimental design methodology (27 runs, 3 levels, 6 factors) was introduced for modeling and optimization of a new alternating pulse current electrocoagulation-flotation (APC-ECF) process for the removal of humic acid (HA) from aqueous media. Two different ANN architectures, such as multilayer perceptron (MLP NN) and generalized feed forward (GFF NN), were proposed and trained to describe the nonlinear behavior of a laboratory-scale batch APC-ECF reactor. Various operating parameters, such as initial HA concentration (C 0), initial pH (pH 0), electrical conductivity (EC 0), current density (CD), and number of pulses (N pls), were used as inputs for the proposed networks, and the HA removal was selected as the output. According to the goodness-of-fit criteria, the computational results showed that the single hidden-layered GFF NN (5:6:1), where a sigmoid axon transfer function was used at its hidden layer and its output layer was trained by the Levenberg–Marquardt algorithm, showed the best performance (R 2 = 0.999, MSE = 0.00006). For the optimal conditions of C 0 = 42 mg/L, pH 0 = 6.63, CD = 24.3 A/m2, EC 0 = 856 μS/cm, and N pls = 3, the maximum HA removal was obtained based on the predicted outputs of the best ANN model (GFF NN). The results of the computational analysis clearly corroborated that ANN integrated design of experiments (DOE)-based modeling was rapidly and effectively used for predicting the optimum performance of a complex electrochemical process in removal of HA from water using aluminum electrodes in monopolar arrangement. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the applicability of the adsorption process for the removal of organophosphorus pesticide malathion 57% from water by using multi-walled carbon nanotubes (MWCNTs). The impact of various experimental conditions such as pH, quantity of adsorbent, concentration of pesticides, contact time and temperature was studied and optimized for the maximum removal of malathion. Unlike conventional optimization, a limited number of experiments (26 steps) were performed in a cost-effective manner for different independent variables such as MWCNTs concentration (0.1–0.5 g/L), the malathion (57%) concentration (6 mg/L and 10 mg/L), contact time (2–30 min) and pH (neutral range). Based on the experimental data obtained in a lab-scale batch study, a three-factor response surface modeling (RSM) approach was implemented in order to optimize the conditions for maximum removal of malathion, and compare experimental results with standardized malathion samples. The optimized conditions to achieve the maximum removal of malathion (100%) were determined to be a malathion concentration of 6 mg/L, an initial MWCNTs concentration of 0.5 g/L, and a contact time of 30 min. Findings of this study clearly indicated that 100% of the malathion could be cost-effectively removed by MWCNTs in conditions predicted by the proposed optimization methodology. [ABSTRACT FROM AUTHOR]

This study focuses on predicting the output power of wind turbines (WTs) using the wind speed and WT operational characteristics. The main contribution of this work is a model identification method based on an adaptive neuro-fuzzy inference system (ANFIS) through multi-source data fusion on a moving window (MoW). The proposed ANFIS-MoW-based approach was applied to data in different time series windows, namely the very short-term, short-term, medium-term and long-term time horizons. Data collected from a 30-MW wind farm on the west coast of Nouakchott (Mauritania) were used in the computational analysis. In comparison to nonparametric models from the literature and models employing artificial intelligence machine learning techniques, the proposed ANFIS-MoW model demonstrated superior predictions for the output power of the WT with the fusion of very few data collected from different WTs. Moreover, for various time series windows (TSW) and meteorological conditions, additional benchmarking demonstrated that the ANFIS-MoW-based method outperformed five existing ANFIS-based models, including grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), fuzzy c-means clustering (ANFIS-FCM), genetic algorithm (ANFIS-GA), and particle swarm optimization (ANFIS-PSO). The results indicated that the suggested methodology is a promising soft-computing tool for accurately estimating the WT output power for WTs' sustainability through better control of their operation. [Display omitted] • A novel ANFIS-based moving window approach for wind power prediction is proposed. • The proposed model is capable of wind power forecasting for different time series horizon. • Real dataset from 30-MW wind farm in Africa is employed for the modeling study. • Performance of the applied neuro-fuzzy tool is validated on various climatic conditions. • Superiority of the ANFIS-MoW model is approved compared to existing models. [ABSTRACT FROM AUTHOR]

Azinphos-methyl and chlorpyrifos are the organophosphorous pesticides which pose serious threats to the environment including their detrimental effect on humans and, therefore, their removal from the environment is a must. Hence, in the present paper the ultrasound technique was applied to remove the above-mentioned hazardous compounds. For this, the effect of influential parameters such as pH, initial pesticide concentration, frequency, electric power and treatment time on the ultrasound degradation of azinphos-methyl and chlorpyrifos was well investigated and elucidated. The results obtained showed that azinphos-methyl and chlorpyrifos were effectively and rapidly degraded by the ultrasound technique. Thus, the operating optimal conditions (initial pH 9, initial pesticide concentration 1 mg/L, frequency 130 kHz, electric power 500 W and treatment time 20 min) lead to a degradation of 78.50% for azinphos-methyl and of 98.96% for chlorpyrifos with in 20 min of contact time. Two multiple regression-based equations were derived to describe the degradation process of the pesticides by the ultrasound treatment. The result of this study showed that the polynomial equations satisfactorily described the behavior of the present process for various operating conditions. [ABSTRACT FROM AUTHOR]

Owing to its importance in extraction of natural gas from underground gas storage as well as its crucial role in determination of final gas mixture in the production facilities of gas/oil industry, the dry content of wet gas mixture needs to be calculated precisely. The present study explores the potential of different soft-computing techniques in estimation of the dry gas flow rate (kg/h) (output variable) of wet gas mixture based on two input variables of wet gas flow rate (kg/h) and absolute gas humidity (g/m3). Decision tree-based methods (M5P tree, random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), kernel function-based approaches (Gaussian process regression (GPR) and support vector machines (SVM)), and non-parametric regression-based technique (multivariate adaptive regression splines (MARS)) were implemented for the first time to estimate the dry gas flow rate, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), Legates and McCabe's index (LMI), and Willmott's Index (WI) were used as the statistical indicators for validating the performance of each soft-computing model. While M5P model (MAE = 122.2382 kg/h, RMSE = 580.5626 kg/h, CC = 0.9875 for the testing data set) was better than other tree-based models (MAE = 363.2802–542.6119 kg/h, RMSE = 871.9363–1025.3444 kg/h, CC = 0.9587–0.9706 for the testing data set) and MARS model (MAE = 128.0083 kg/h, RMSE = 622.9515 kg/h, CC = 0.9852 for the testing data set), the statistical indicators approved the superiority of the radial basis kernel function-based GPR model (GPR-RBKF) model (MAE = 163.3266 kg/h, RMSE = 483.1359 kg/h, CC = 0.9915 for the testing data set) over other implemented models in predicting the dry gas flow rate. The findings highlighted the potential of soft-computing methodologies in precise estimation of dry gas flow rate in wet gas mixture, particularly, in situations where the measurement of such parameters with traditional deterministic models is practically not possible. [Display omitted] • Soft-computing methods for estimation of dry content of wet gas mixture. • Performance evaluation of M5P, RF, RT, REPT, GPR, SVM, and MARS. • Superiority of GPR-RBKF over other models in terms of accuracy. • Superiority of M5P over other tree decision tree models in terms of error. • Higher precision of Gaussian processing with kernel-based regression vector. [ABSTRACT FROM AUTHOR]

The gas--solid cyclone has been remarkably widely used among all types of industrial gas-cleaning devices. The objective of this work was to present the nonlinear optimization modeling of series and parallel cyclone arrangement for 1D3D, 2D2D and 1D2D cyclones of the Nitrogen, Phosphorus, and Potassium (NPK) fertilizer plant. The numerical solutions were carried out using the General Algebraic Modeling System (GAMS) software for different case studies. The optimal number of cyclone with the minimum total cost for two types of arrangements was obtained. From the economic point of view, the results of mathematical programming models showed that the small cyclone with a small number of the cyclone arrangement could be implemented for space-limited conditions on the field, and the pressure drop across the cyclone was directly related to the fan power requirement. [ABSTRACT FROM AUTHOR]

This study proposes an original adaptive neuro-fuzzy inference system modeling approach to predict the output power of a wind turbine. The model's input includes the wind speed, turbine rotational speed, and mechanical-to-electrical power converter's temperature. The structure of the adaptive neuro-fuzzy inference system-based model was first identified using the processed data gathered from wind turbine number 1 of a 30-MW wind farm in Nouakchott (Mauritania). Then, the proposed data-driven model was trained and validated according to two new scenarios based on the data set from four identical wind turbines operated in the same climatic conditions and the data set from the same wind turbines operated under different climatic conditions. Benchmarking involved the proposed model, existing approaches in the literature, and five adaptive neuro-fuzzy inference system-based models, including grid partition, subtractive clustering, fuzzy C-means clustering, genetic algorithm, and particle swarm optimization, on the same data set to validate their prediction performance. Compared with existing adaptive neuro-fuzzy inference system-based models, the proposed approach was proven to be a promising methodology with higher accuracy for estimating the output power of wind turbines operating in different climatic conditions. According to the results from two different scenarios, the lowest value of the fitting rate and the highest values of the normalized mean square error, normalized mean absolute error, and root mean square error for the validating period were 0.9977, 0.0047, 0.0473, and 46.5831 kW, respectively. Moreover, the proposed model showed superior forecasting performance and thus better accuracy in estimating wind power output compared to other adaptive neuro-fuzzy inference system-based models. [Display omitted] • Original ANFIS approach was developed for wind turbine (WT) output power prediction. • Scenarios-based validation with real data from wind farm enhanced model building. • Machine learning embedded fuzzy logic boosted the model identification strategy. • Benchmarking of existing models corroborated the superiority of preprocessed ANFIS. • Neuro-fuzzy tool accurately estimated WT output power in various climatic conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • AgFeO 2 /GO interface synthesized by ultrasonic hydrothermal and Hummer's techniques. • High LMF removal efficiency achieved with low-energy consumption in limited duration. • Bifunctional properties toward photodegradation and disinfection in an aqueous system. • Heterojunction system that facilitates the photo-induced electron charge carriers. The bi-functional nanoscale catalysts are of growing attention to address environmental problems. The present work successfully fabricated the hexagonal silver delafossite oxide bridged graphene oxide ultrathin nanosheets interfaces (AgFeO 2 /GO) to target photodegradation of an antibiotic and inactivation of model waterborne pathogens for the first time. The AgFeO 2 /GO composite was prepared via facile hydrothermal treatment followed by ultrasonic agitation by varying the GO loading. The AgFeO 2 /GO composite and their parent constituents were verified by advanced analytical, microscopic, and spectroscopic techniques like FESEM, EDX, HR-TEM, DLS, XRD, FTIR, XPS, UV–Vis, and PL. The AgFeO 2 /GO3 composite showed excellent photocatalytic activity towards mineralization of a broad-spectrum antibiotic, lomefloxacin (LMF) under the irradiation of visible light (30 W, LED). A maximum of ~88 % of LMF (20 mg/L) was degraded within 75 min of catalysis. The superoxide and hydroxyl radicals were predominant in LMF breakdown. In addition, AgFeO 2 /GO3 was proved effective up to three consecutive trials retaining 78.85 % efficiency. On the other hand, AgFeO 2 /GO3 heterojunctions demonstrated fair bactericidal activity against pathogens, Escherichia coli and Staphylococcus aureus. Disc diffusion method of antimicrobial assay confirmed the activity of AgFeO 2 /GO3 on par with standard drugs. Therefore, in both the functionalities, the GO incorporation along with AgFeO 2 standpoints enhanced photoactivity and catalytic inactivation making it a sought-after, bi-functional, and efficient environmental catalyst. [ABSTRACT FROM AUTHOR]

Abstract: Four identical pilot-scale landfill reactors with different alternative composite liners were simultaneously operated for a period of about 540days to investigate and to simulate the migration behaviors of phenolic compounds (phenol, 2-CP, 2-MP, 3-MP, 4-MP, 2-NP, 4-NP, 2,4-DNP, 2,4-DCP, 2,6-DCP, 2,4,5-TCP, 2,4,6-TCP, 2,3,4,6-TeCP, PCP) and heavy metals (Pb, Cu, Zn, Cr, Cd, Ni) from landfill leachate to the groundwater. Alternative landfill liners of four reactors consist of R1: Compacted clay liner (10cm+10cm, k=10−8 m/sn), R2: Geomembrane (2mm HDPE)+compacted clay liner (10cm+10cm, k=10−8 m/sn), R3: Geomembrane (2mm HDPE)+compacted clay liner (10cm, k=10−8 m/sn)+bentonite liner (2cm)+compacted clay liner (10cm, k=10−8 m/sn), and R4: Geomembrane (2mm HDPE)+compacted clay liner (10cm, k=10−8 m/sn)+zeolite liner (2cm)+compacted clay liner (10cm, k=10−8 m/sn). Wastes representing Istanbul municipal solid wastes were disposed in the reactors. To represent bioreactor landfills, reactors were operated by leachate recirculation. To monitor and control anaerobic degradation in the reactors, variations of conventional parameters (pH, alkalinity, chloride, conductivity, COD, TOC, TKN, ammonia and alcaly metals) were also investigated in landfill leachate samples. The results of this study showed that about 35–50% of migration of organic contaminants (phenolic compounds) and 55–100% of migration of inorganic contaminants (heavy metals) to the model groundwater could be effectively reduced with the use of bentonite and zeolite materials in landfill liner systems. Although leachate contaminants can reach to the groundwater in trace concentrations, findings of this study concluded that the release of these compounds from landfill leachate to the groundwater may potentially be of an important environmental concern based on the experimental findings. [Copyright &y& Elsevier]

In Section 2.3 on page 105, it was stated that: "Two L-shaped aluminium electrodes were embedded in the reactor in unipolar mode with an effective surface area of 100cm SP 2 sp at 1cm distance from each other, and 20 pores with a diameter of 4mm were created on the surface of each electrode." In Section 3.3.2 on page 109, it was stated that: "At the beginning of the process, the removal efficiency of HA versus time is increased with a steep slope, so that the highest HA removal rate in experiments shown in Fig. This sentence should be replaced by: "At the beginning of the process, the removal efficiency of HA versus time is increased with a steep slope, so that the highest HA removal rate in experiments shown in Fig. [Extracted from the article]

• Zn 5 (OH) 8 Cl 2 ·H 2 O nanostructure was hydrothermally synthesized under acidic environment. • Bioactive Zn based nanostructure shows potential enhancement of biomethanation process. • Zn 5 (OH) 8 Cl 2 ·H 2 O nanostructure for efficient in bio-conversion of waste into energy. Zn based bioactive Zn 5 (OH) 8 Cl 2 ·H 2 O nanostructure was synthesized by sol-gel technique and characterized. Characterization results confirmed desired functionalities such as polycrystalline structure, simonkolleite phase, functional groups, high stability and 2-D flakes like nanostructures. Zn based nanostructure influenced the microbial CH 4 production rate at dose ranges of 0.2–0.4 g that was attributed to increased microbial metabolic activities under anaerobic condition. Whereas, less production of CH 4 with increased dosages of 0.6–1.2 g was observed due to inhibitory effects of Zn active agents released by nanostructure. It indicated that CH 4 production rate was predominantly influenced by availability and bioactive phase of Zn required for robust anaerobic biomethanation. [ABSTRACT FROM AUTHOR]

Despite the remarkable potential for biogas production from livestock and organic wastes, the number of constructed biogas digesters for the continent is in the order of thousands and in case of Mauritania is lower than the number of the provinces. While majority of the existing digesters left far behind the expected efficiency, lack of a comprehensive economic assessment along with an efficient design questions their practical application. The present study introduced the first assessment to evaluate the biogas potential from livestock manures and waste from slaughterhouses in Northwest Africa (a case of Mauritania). Using ArcGIS® software, a database was conducted to build maps that show the amount of waste products, the potential of biogas, and equivalent amounts of energy. These were used to assess the potential of biogas and the corresponding potential energy for each geographical department in Mauritania. The results indicated that the southern provinces had the highest biogas potential with the maximum and the average values of 520 and 258.7 (±125.8) × 106 m3/year. On the other hand, the lowest biogas production potential (27.7 × 106 m3/year) was recorded for northern provinces with the maximum and the average values of 135 and 76.4 (±39.7) × 106 m3/year. The results showed that 63,579 × 106 kg of waste associated with livestock (cattle) and slaughterhouse applications were annually produced in the country. It was determined that this quantity could generate 2451 × 106 m3 of biogas per year, corresponding to an energy potential of 52,704 × 106 MJ/year. Considering the rapid depletion of conventional energy sources and the significance of biogas as a renewable fuel, a detailed feasibility analysis (for the biogas production in each province of Mauritania by means of community type fixed-dome digesters) was also performed in the scope of this study. The results of the comprehensive cost breakdown analysis revealed that the revenues obtained from sale of biogas-generated electricity and digested slurry (as fertilizer) could able to pay the initial investment within approximately 6.5 years without subsidy. The findings of this study, as the first of its own, could be used to comprehend how utilization of information such as slaughterhouse and livestock population, nutrition habitats, land-usage maps and geographic information system (GIS) can be employed to germinate a model for more comprehensive assessment of biogas production potential from livestock manure and slaughterhouse wastes, specifically in case of northern African countries. Moreover, in case of biogas plant, this model could be employed as a decision-making tool to identify the highly qualified location for construction of the biogas plant. • Mapping of biogas production potential is conducted in Mauritania for the first time. • 63,579 × 106 kg of waste can generate 2451 × 106 m3 of biogas per year in the country. • The southern provinces are found to have approximately 4 times more biogas potential. • Energy production in Mauritania heavily depends on oil, natural gas, and wood fuels. • A nationwide biogas digester project can pay for itself in 6.5 years without subsidy. [ABSTRACT FROM AUTHOR]