Multilinear Regression Model for Biogas Production Prediction from Dry Anaerobic Digestion of OFMSW

Comparing the effects of Al-based coagulants in waste activated sludge anaerobic digestion: Methane yield, kinetics and sludge implications

Due to its effectiveness and ease of application, the process of flocculation and coagulation is often used for pollution removal in wastewater treatment. Most of these coagulants precipitate and accumulate in waste activated sludge (WAS), and could negatively affect sludge treatments, as observed for anaerobic digestion. Nowadays, wastewater treatment plants (WWTPs) are widely discussed because of the current paradigm shift from linear to circular economy, and the treatments performed at the facility should be planned to avoid or reduce adverse effects on other processes. The aim of this study was to compare the impact of poly aluminum chloride (PAC) and aluminum sulfate (AS) on WAS anaerobic digestion, by feeding replicate serum reactors with different levels of coagulant (5, 10 and 20 mg Al/g TS). Reactors without the addition of any coagulants represented the control group. Results revealed that Al-based coagulants inhibited methane production, which decreased as the coagulant addition increased. The inhibition was much more severe in AS-conditioned reactors, showing average reductions in methane yield from 14.4 to 31.7%, compared to the control (167.76 ± 1.88 mL CH4/g VS). Analytical analysis, FTIR and SEM investigations revealed that the addition of coagulants affected the initial conditions of the anaerobic reactors, penalizing the solubilization, hydrolysis and acidogenesis phases. Furthermore, the massive formation of H2S in AS-conditioned reactors played a key role in the suppression of methane phase. On the other hand, the use of coagulant can promote the accumulation and recovery of nutrient in WAS, especially in terms of phosphorus. Our findings will expand research knowledge in this field and guide stakeholders in the choice of coagulants at full scale plant. Future research should focus on reducing the effect of coagulants on methane production by modifying or testing new types of flocculants.

Influences of straw alkaline pretreatment on biogas production and digestate characteristics: artificial neural network and multivariate statistical techniques

Anaerobic digestion is one of the best options for producing valuable end products (biogas and biofertilizer). The aim of this study was to investigate the influences of thermoalkaline pretreatment of wheat straw on biogas production and digestate characteristics from codigestion with waste-activated sludge. Different alkaline conditions (NaOH, KOH and Na2CO3) and pretreatment durations (1, 3 and 5 h) were used for straw pretreatment. Batch anaerobic codigestion of sludge and pretreated straw was conducted under different pretreatment conditions. A feedforward neural network (FFNN) model, logistic model and statistical analysis were applied to the experimental data to predict biogas and investigate the significance and relationships among the variables. NaOH pretreatment for 5 h showed the best treatment conditions: biogas yield was 6.59 times higher than that without treatment. Moreover, the proportions of total solids, total volatile solids, chemical oxygen demand and microbial count removed reached 63.52%, 74.60%, 78.15% and 82.22%, respectively. The methane content was 67.50%, indicating that the biogas had a high quality. The thermoalkaline pretreatment significantly affected biogas production and digestate characteristics, allowing it to be used as a biofertilizer. Experimental data were successfully modelled for predicting biogas production using the applied models. The R2 values reached 0.985 and 0.999 for the logistic and FFNN models, respectively.

Multiple-layer statistical methodology for developing data-driven models of anaerobic digestion process

When modelling anaerobic digestion, ineffective data handling and inadequate designation of modelling parameters can undermine the model reliability. In this study, a multilayer statistical technique, which employed a machine learning technique using regression models, was introduced to systematically support the development of anaerobic digestion models. Layer-by-layer statistical techniques including cubic smoothing splines (missing data reconstruction), principal component analysis (identifying correlated parameters), analysis of variance (analysing differences among datasets), and linear regression (developing data-driven models) were used to develop and validate anaerobic digestion models. Experimental data collected from the long-term operation of lab-scale (operated for 350 days), pilot-scale (operated for 150 days), and full-scale reactors (operated for 750 days) were used to demonstrate the modelling process. The multivariate models based on a data-driven modelling technique were developed by subjecting the experimental and monitored data to a modelling process. The developed models could predict the biogas production and effluent chemical oxygen demand during anaerobic digestion. Statistical analyses verified the modelling hypotheses, evaded invalid model development, and ensured data integrity and parameter validity. Multiple linear regression of principal components demonstrated that the performance of biogas production using food waste was influenced by the variances of the nitrogen and organic concentrations, but not by the chemical oxygen demand to total nitrogen (C/N) ratio. In the validation process, the model developed with lab-scale reactor data showed relatively high accuracy with R2, SSE, and RMSE values of 0.86, 34.45, and 0.72.

Anaerobic Digestion of Pig Slurry in Fixed-Bed and Expanded Granular Sludge Bed Reactors

Anaerobic digestion of animal manure is a potential bioenergy resource that avoids greenhouse gas emissions. However, the conventional approach is to use continuously stirred tank reactors (CSTRs) with hydraulic retention times (HRTs) of greater than 30 d. Reactors with biomass retention were investigated in this study in order to increase the efficiency of the digestion process. Filtered pig slurry was used as a substrate in an expanded granular sludge bed (EGSB) reactor and fixed-bed (FB) reactor. The highest degradation efficiency (ηCOD) and methane yield (MY) relative to the chemical oxygen demand (COD) were observed at the minimum loading rates, with MY = 262 L/kgCOD and ηCOD = 73% for the FB reactor and MY = 292 L/kgCOD and ηCOD = 76% for the EGSB reactor. The highest daily methane production rate (MPR) was observed at the maximum loading rate, with MPR = 3.00 m3/m3/d at HRT = 2 d for the FB reactor and MPR = 2.16 m3/m3/d at HRT = 3 d for the EGSB reactor. For both reactors, a reduction in HRT was possible compared to conventionally driven CSTRs, with the EGSB reactor offering a higher methane yield and production rate at a shorter HRT.