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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-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]