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Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling of the biogas production rate from a real full-scale sludge digestion process. In the computational analysis, five process-related parameters such as influent sludge flow rate, total solids content, total volatile solids content, alkalinity, and volatile fatty acids concentration were considered as the input variables for the proposed models. In the ANN-based modeling, a benchmark comparison of 11 backpropagation (BP) algorithms was employed in the first step, and the scaled conjugate gradient algorithm was chosen as the best BP algorithm in terms of their respective mean squared errors. According to the selected BP algorithm (scaled conjugate gradient BP), the number of neurons at the hidden layer was optimized as 14, and the coefficient of determination (R2) was obtained as 0.65 for the optimal three-layer ANN structure (5:14:1). In the second part of the study, a MISO (multiple-input single-output)-type fuzzy-logic model was developed, and five input variables were fuzzified in a knowledge-based manner. For the fuzzy subsets, trapezoidal membership functions were implemented with 10 and 20 levels, and a Mamdani-type fuzzy inference system (FIS) was used to employ 394 rules in the if–then form. The product, summation, and centroid methods were employed, respectively, for implication, aggregation, and defuzzification processes conducted in the FIS. Fuzzy logic-produced forecasts were compared with the estimations obtained from both ANN-based model and a polynomial multiple regression-based model (employed as the third part of this study) derived in this study. Findings of this study clearly indicated that compared to ANN model and conventional multiple regression approach, the proposed MISO fuzzy logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting biogas production rate from a full-scale treatment plant with a satisfactory R2 value of 0.88. [ABSTRACT FROM AUTHOR]

Drying of poultry abattoir sludge (PAS) in thin-layer procedure was investigated with solar drying during summer conditions (radiation intensity 415 W/m2) at 1, 2, and 5 cm of sludge depths and 0.00 and 2.00 m/s of air flow velocity with auxiliary solar heated air (60 °C) applied beneath the sludge layer under open surface drying conditions. Lower height of PAS together with hot air blowing from drying bed increased the drying rate and decreased the time required for achieving constant dry matter with equilibrium humidity below 10.0%. Hot air blowing significantly accelerated water evaporation from sludge particle and the drying period was reduced from 8 to 6 h with 2.00 m/s air flow intensity for the sludge depth of 1 cm. Seven established solar drying models were explored to characterize the drying behavior of PAS under thin-layer open surface solar drying conditions assisted with hot air blowing below the sludge bed. Among the implemented models, Page model and logarithmic model were found to best fit the results, respectively, for the experiments with ventilation (hot air blowing application) and without ventilation considering several statistical evaluators such as determination coefficient (R2), sum of square error (SSE), and the square root of the mean squares of error (RMSE). Based on the achieved dry matter results, findings of this study demonstrated that the open surface solar drying assisted with solar heated air during high intensity solar radiation conditions could be an effective method for drying of PAS. Article Highlights: Drying behavior of the poultry abattoir sludge (PAS) was explored for the first time. Drying time decreased by half when thickness of PAS was decreased by five times. Thin-layer drying kinetics was best represented by Page and logarithmic models. Air circulation increased drying rate of PAS and decreased total drying time by 26%. Renewable solar energy could be a sustainable method for drying of by-product sludges. [ABSTRACT FROM AUTHOR]

This study aimed to perform a comparative analysis of the performance of five models (Gompertz, logistic, Richards, the first-order, artificial neural networks) in predicting methane production rate from anaerobic digestion of livestock manures. The input variables were fermentation time, digestion temperature, biogas temperature, ambient temperature, pH, and specific biogas production rate. The physicochemical compositions of cow manure and sheep manure showed that volatile solid (VS) contents were close to each other in manure compositions (77.6% and 64.7%, respectively), while the potential of methane production from cow manure (673.44 mL CH4/g VS) was greater than that from sheep manure (320.32 mL CH4/g VS). The determination coefficients (R2) for logistic function, Gompertz, Richards, the first-order, and ANN models were obtained as 0.968, 0.967, 0.975, 0.825, and 0.995 for the cow manure, respectively. In case of the sheep manure, the R2 values obtained from these models were 0.976, 0.979, 0.981, 0.968 and 0.991, respectively. Although the determination coefficients of all models were in satisfactory agreement with the experimental data, the ANN model showed competitive lower RMSE values of 0.111 and 0.164 for cow and sheep manure data sets, respectively, indicating its superior performance than other models. [ABSTRACT FROM AUTHOR]

Adsorption potential of the struvite (recovered from the high-strength ammonium-rich synthetic wastewater) was explored for the first time for the removal of an anthraquinone dye (Remazol Brilliant Blue R, RBBR) from aqueous phases. According to the experimental findings obtained from the RBBR-struvite adsorption system, the maximum dye removal efficiency (≈ 79%) was achieved at pH of 2.0, adsorbent dose of 40 g/L, initial dye concentration of 80 mg/L, contact time of 60 min, and temperature of 60 °C. Adsorption isotherms showed that both Langmuir and Freundlich isotherm models fitted very well to the experimental data (R2 > 0.990), and adsorption kinetics were satisfactorily described by the Ho and McKay's PSO kinetic model (R2 ≈ 0.990). Based on the Langmuir isotherm model, the maximum adsorption capacity of the struvite for the RBBR dye was determined as 21.73 mg/g. The sample exhibited a mesoporous structure and had a BET surface area of 16.35 m2/g. Thermodynamic parameters (ΔG°, ΔH°, and ΔS°) confirmed that the adsorption of the RBBR dye onto the struvite was spontaneous and endothermic in nature and reflected the enhanced degree of disorderliness at the solid/liquid interface, respectively. A MIMO (multiple inputs and single output) Mamdani-type fuzzy inference system was developed for the first time in modeling of the RBBR-struvite adsorption system. The proposed fuzzy logic model showed a superior prediction performance (R2 ≈ 0.980) when compared to the conventional multiple regression methodology. Overall experimental and statistical results confirmed that struvite could be used as a promising adsorbent material for anthraquinone dye removal from aqueous solutions. [ABSTRACT FROM AUTHOR]