Search

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]

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: 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]