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To enhance the codigestion of degradation and improve biomethane production potential, sugarcane bagasse and filter mud were pretreated by sodium hydroxide NaOH 1 N at 100°C for 15, 30, and 45 minutes, respectively. Biomethane generation from 1-liter batch reactor was studied at mesophilic temperature (37 ± 1)°C, solid concentrations of 6%, and five levels of mixing proportion with and without pretreatment. The results demonstrate that codigestion of filter mud with bagasse produces more biomethane than fermentation of filter mud as single substrate; even codigested substrate composition presented a better balance of nutrients (C/N ratio of 24.70) when codigestion ratio between filter mud and bagasse was 25 : 75 in comparison to filter mud as single substrate (C/N ratio 9.68). All the pretreatments tested led to solubilization of the organic matter, with a maximum lignin reduction of 86.27% and cumulative yield of biomethane (195.8 mL·gVS−1, digestion of pretreated bagasse as single substrate) obtained after 45 minutes of cooking by NaOH 1 N at 100°C. Under this pretreatment condition, significant increase in cumulative methane yield was observed (126.2 mL·gVS−1) at codigestion ratio of 25 : 75 between filter mud and bagasse by increase of 81.20% from untreated composition.

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The present research emphasized the utilization of a novel sequential thermochemical and sonication pretreatment technology to enhance methane production from corn stover. The corn stover was thermochemically pretreated with sodium hydroxide to enhance its lignocellulosic digestibility. Due to thermochemical pretreatment, 65.45% lignin removal and 36.33% hemicellulose solubilization was observed and further five sonication levels were employed (25, 45, 60, 90, and 120 min). All pretreatments were found significant (P < 0.05) to enhance methane production from 14.78% to 73.72% while thermo-NaOH pretreatment with 90 min sonication time was proven as the optimum pretreatment with specific methane production of 320 mL/g volatile solids (VS). Anaerobic digestion process stability was deeply monitored at 3 day intervals via total volitile fatty acids, alcohol production, pH, chemical oxygen demand, and VS removal.

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To study the optimum process conditions for pretreatments and anaerobic codigestion of oil refinery wastewater (ORWW) with chicken manure, L9 (34) Taguchi's orthogonal array was applied. The biogas production (BGP), biomethane content (BMP), and chemical oxygen demand solubilization (CODS) in stabilization rate were evaluated as the process outputs. The optimum conditions were obtained by using Design Expert software (Version 7.0.0). The results indicated that the optimum conditions could be achieved with 44% ORWW, 36°C temperature, 30 min sonication, and 6% TS in the digester. The optimum BGP, BMP, and CODS removal rates by using the optimum conditions were 294.76 mL/gVS, 151.95 mL/gVS, and 70.22%, respectively, as concluded by the experimental results. In addition, the artificial neural network (ANN) technique was implemented to develop an ANN model for predicting BGP yield and BMP content. The Levenberg-Marquardt algorithm was utilized to train ANN, and the architecture of 9-19-2 for the ANN model was obtained.

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To survey the anaerobic co-digestion (AcoD) of oil refinery wastewater (ORWW) with sugarcane bagasse (SCB), six different AcoD compositions were evaluated. Results including cumulative biogas production (BGP), bio-methane contents (BMP), and soluble chemical oxygen demand (CODs) removal rate were experimentally obtained. The negligible BGP by ORWW mono-digestion revealed that it could not support any microbial activity. However, increasing the SCB ratio in the AcoD compositions led to increased BGP and BMP contents. By considering the statistical test (LSD0.05) results for the kinetic parameters, the 1:4 ratio treatment was the most favorable AcoD composition. Moreover, the CODs removal rate from 22.34 ± 1.63% for the SCB mono-digestion was improved to 49.67 ± 0.38% for the 2:3 AcoD composition and BMP content from 54.12 ± 0.45% for the SCB mono-digestion was enhanced to 62.69 ± 1.22% for the 1:4 AcoD composition with 20% lower SCB usage. The results computed by applying three mathematical models determined that the modified Gompertz model provided the best fit. Also, implementing artificial neural network algorithms to model the BGP data revealed that the Back Propagation algorithm was the best suited for the experimental BGP data, with 0.6444 and 0.9658 for MSE and R2, respectively.