Biogas production has been suggested as a valid valorization solution for microalgal/bacteria biomass (MAB) grown on wastewater. This research is aimed at assessing the feasibility to use MAB grown in an outdoor raceway fed on piggery wastewater for biogas production. Batch and continuous anaerobic tests were conducted on the sole MAB and on a blend of MAB and carbonaceous substrates (deproteinated cheese whey and cellulose) to improve the carbon/nitrogen ratio. Results of batch biochemical methane potential tests confirmed that the sole microalgal/bacteria biomass was poorly degradable (119 NmL_CH4·g_COD⁻¹), while blending it with deproteinated cheese whey or cellulose (80% of carbonaceous material and 20% of MAB, as chemical oxygen demand (COD)) had no synergistic effects on the methane yield, although slight improvements in the degradation kinetics were observed. Continuous anaerobic degradation tests (at an organic loading rate of 1.5 g_COD·L⁻¹·d⁻¹, 35 °C and 30 days of hydraulic retention time) increased the overall methane yield from 81 NmL_CH4·g_COD⁻¹ (sole MAB) to 216 NmL_CH4·g_COD⁻¹ (MAB and deproteinated cheese whey) and 122 NmL_CH4·g_COD⁻¹ (MAB and cheese whey). However, data confirm that no evident synergistic effects were obtained.

The Biochemical Methane Potential (BMP) of primary and biological sludge vary in a wide range, mostly depending on location, sewer characteristics, wastewater treatment plant design and operating conditions. BMP tests are useful to verify the performance of a full scale digester, but they are not yet a common procedure in the operation of most Italian facilities because of cost and test duration. Changes in the composition of sewage sludge can lead to a high variation of biogas production. Aimed at developing BMP predictive models based on low cost and fast analyses, this study investigated the chemical composition of 20 sludge samples by means of Principal Component and Multiple Linear Regression Analyses. Three preliminary predictive models were developed based on soluble organic nitrogen, volatile solids, carbohydrates, proteins, lipids and an operational parameter, the sludge retention time: the explained variance and the standard errors of prediction of BMP are in the range 77–81% and 21–34 NmL_CH4·g_VS⁻¹, respectively. Models were evaluated on 5 additional samples: errors ranged 2–15% for four samples and about 54% for one sample, collected from a peculiar facility. Further data and variables describing the operation mode of the waterline would certainly improve the reliability and robustness of the models.

Anaerobic co-digestion in wastewater treatment plants is looking increasingly like a straightforward solution to many issues arising from the operation of mono-digestion. Process modelling is relevant to predict plant behavior and its sensitivity to operational parameters, and to assess the feasibility of simultaneously feeding a digester with different organic wastes. Still, much work has to be completed to turn anaerobic digestion modelling into a reliable and practical tool. Indeed, the complex biochemical processes described in the ADM1 model require the identification of several parameters and many analytical determinations for substrate characterization. A combined protocol including batch Biochemical Methane Potential tests and analytical determinations is proposed and applied for substrate influent characterization to simulate a pilot-scale anaerobic digester where co-digestion of waste sludge and expired yogurt was operated. An iterative procedure was also developed to improve the fit of batch tests for kinetic parameter identification. The results are encouraging: the iterative procedure significantly reduced the Theil’s Inequality Coefficient (TIC), used to evaluate the goodness of fit of the model for alkalinity, total volatile fatty acids, pH, COD, volatile solids, and ammoniacal nitrogen. Improvements in the TIC values, compared to the first iteration, ranged between 30 and 58%.