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The use of various advanced oxidation methods in the treatment of wastewater has become the subject of many studies published in recent years. In particular, it is exceedingly significant to compare these treatment methods for industrial wastewater to reduce environmental effects and optimize plant operations and economics. The present study is the first to deal with the treatability of real epoxy paint wastewater (EPW) using MW- and UV-assisted Fenton processes within an optimization framework. A three-factor, three-level Box–Behnken experimental design combined with response surface methodology (RSM) was conducted for maximizing the chemical oxygen demand (COD) and color removal efficiencies of ultraviolet (UV)/Fenton and microwave (MW)/Fenton processes in the treatment of the real epoxy paint wastewater (EPW, initial COD = 4600 ± 90 mg/L, initial color = 114 ± 4 Pt-Co), based on 15 different experimental runs. Three independent variables (reaction time ranging from 20 to 60 min (UV) and from 5 to 15 min (MW), power ranging from 20 to 40 W (UV) and from 300 to 600 W (MW), and H2O2/Fe2+ ratio ranging from 0.2 to 0.6 (for both UV and MW)) were consecutively coded as A, B, and C at three levels (−1, 0, and 1), and four second-order polynomial regression equations were then derived to estimate the responses (COD and color removals) of two distinct systems. The significance of the independent model components and their interrelations were appraised by means of a variance analysis with 99% confidence limits (α = 0.01). The standardized differences of the independent variables and the consistency between the actual and predicted values were also investigated by preparing normal probability residual plots and experiment-model plots for all processes. The optimal operating conditions were attained by solving the quadratic regression models and analyzing the surface and contour plots. UV/Fenton and MW/Fenton processes, which constitute combined Fenton processes, were performed using advanced oxidation methods, while Fenton processes were utilized as the standard method for wastewater treatment. When UV/Fenton and MW/Fenton processes were applied separately, the COD removal efficiencies were determined to be 96.4% and 95.3%, respectively. For the color parameter, the removal efficiencies after the application of both processes were found to exceed 97.5%. While these efficiencies were achieved in 1 h with a 38 W UV unit, they were achieved in 15 min with a MW power of 570 W. According to the RSM-based regression analysis results, the R2 values for both processes were greater than 0.97 and p values were less than 0.003. [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]

In this study, an adaptive neuro-fuzzy inference system (ANFIS) including five process variables, such as influent chemical oxygen demand, influent flow rate, influent total Kjeldahl nitrogen, effluent volatile fatty acids and effluent bicarbonate, was described to predict the effluent chemical oxygen demand load from a full-scale expanded granular sludge bed reactor (EGSBR) treating corn processing wastewater. The proposed ANFIS model 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 validated using various descriptive statistical indicators, such as root mean-square error, index of agreement, the factor of two, fractional variance, proportion of systematic error, etc. The lowest root mean square error (RMSE = 0.03655) and the highest determination coefficient (R2 = 0.958) were achieved with the subtractive clustering of a first-order Sugeno type fuzzy inference system. ANFIS predicted results were compared with the outputs of multiple nonlinear regression analysis-based models derived in the scope of the present work. Statistical performance indices computed for the testing data set proved that the developed ANFIS-based model exhibited a very good precision in predicting the effluent chemical oxygen demand, load for the EGSBR system. Due to high capability of the ANFIS model in capturing the dynamic behavior and non-linear interactions, it was demonstrated that a complex system, such as anaerobic digestion, could be easily modeled. [ABSTRACT FROM AUTHOR]

Two three-layer artificial neural network (ANN) models were respectively developed to predict biogas and methane production rates in a pilot-scale mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Eight process-related variables such as volumetric organic loading rate (OLR), influent and effluent pH, operating temperature, influent and effluent alkalinity, effluent chemical oxygen demand (COD), and volatile fatty acid (VFA) concentrations were selected for the implementation of an artificial intelligence-based approach. A tangent sigmoid transfer function (tansig) at the hidden layer and a linear transfer function (purelin) at the output layer were conducted for the proposed ANN models. Several benchmark comparisons were conducted to obtain an optimal algorithm for training the network. After backpropagation training combined with principal component analysis (PCA), the scaled conjugate gradient algorithm (trainscg) was found as the best of the 11 training algorithms. The number of neurons in the hidden layer was optimized as nine and 12 with the minimum mean squared errors (MSE) of 0.06238 and 0.06488, respectively, for the estimation of biogas and methane production rates. ANN-predicted results were also compared to the outputs of two non-linear regression models by means of several statistical indicators, such as determination coefficient (R2), unsystematic root mean-square error (RMSEU), index of agreement (IA), and fractional variance (FV). Computational results clearly demonstrated that, compared to the conventional multiple regression-based methodology, the proposed ANN-based models produced smaller deviations and exhibited superior predictive accuracy with satisfactory determination coefficients of about 0.935 and 0.924, respectively, for the forecasts of biogas and methane production rates. [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]

Purpose: A multiple inputs and multiple outputs (MIMO) fuzzy-logic-based model was proposed to estimate color and chemical oxygen demand (COD) removal efficiencies in the post-treatment of anaerobically pretreated poultry manure wastewater effluent using Fenton's oxidation process. Three main input variables including initial pH, Fe, and HO dosages were fuzzified in a new numerical modeling scheme by the use of an artificial intelligence-based approach. Materials and methods: 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 70 rules in the IF-THEN format. The product (prod) and the center of gravity (centroid) methods were applied as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two first-order polynomial regression models derived in the scope of this study. Estimated results were also compared to the multiple regression approach by means of various descriptive statistical indicators, such as root mean-squared error, index of agreement, fractional variance, proportion of systematic error, etc. Results and discussion: Results of the statistical analysis clearly revealed that, compared to conventional regression models, the proposed MIMO fuzzy-logic model produced very smaller deviations and demonstrated a superior predictive performance on forecasting of color and COD removal efficiencies with satisfactory determination coefficients over 0.98. Conclusions: Due to high capability of the fuzzy-logic methodology in capturing the non-linear interactions, it was demonstrated that a complex dynamic system, such as Fenton's oxidation, could be easily modeled. [ABSTRACT FROM AUTHOR]

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]

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]

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]

• Orchis mascula (OM) was used as a new coagulant in synthesized bilge water treatment. • More than 90 % of COD, TU, and O&G removals were achieved at pH 5.0 and 4 mg/L of OM. • C-F with OM was effectively described by FCCCD-RSM and MARS models for the first time. • Adsorption and inter-particle bridging mechanism were predominant for C-F with OM. • Cost-effectiveness and naturalness of OM showed its potential for industrial scale. Applicability of coagulation-flocculation process by the Orchis mascula tuber starch as a novel natural coagulant was investigated for the first time in the treatment of oily-saline wastewater. Three inputs variables (pH, coagulant dose, and contact time) and two outputs of chemical oxygen demand (COD) and turbidity (TU) were studied for the proposed system. Orchis mascula tuber starch showed a remarkable performance on treatment of bilge water at the optimum conditions (4 mg L−1 of coagulants dose, pH of 5.0, and contact time of 15 min), with COD and TU removal efficiencies of 92.21 % and 90.63 %, respectively. Also, this material could remove the surfactant and oil-grease up to 23 % and 93 %, respectively. The face-centered central composite design-response surface methodology (FCCCD-RSM) and multivariate adaptive regression splines (MARS), which were used comparatively for the first time in the quantitative evaluation of the studied coagulation-flocculation process, revealed satisfactory predictive performances (R 2 > 0.97) for both COD and TU removals. The kinetic study concluded that the second-order model performance was superior to the first-order model. Moreover, the bonding between the particles was also observed from the Fourier-transform infrared spectroscopy (FTIR) analysis of the Orchis mascula tuber starch. [ABSTRACT FROM AUTHOR]