Statistical optimization of methane production from brewery spent grain: Interaction effects of temperature and substrate concentration

Towards a circular bioeconomy to produce methane by co-digestion of coffee and brewery waste using a mixture of anaerobic granular sludge and cattle manure as inoculum

Coffee processing wastes, such as solid (pulp and husk) and wastewater, co-digested with industrial brewery wastewater, serve as excellent substrates for generating methane in the anaerobic digestion process. This study compared methane production using different compositions of cattle manure (CM) and granular sludge from an Upflow Anaerobic Sludge Blanket (UASB) reactor used in poultry wastewater treatment (GS). Four anaerobic batch reactors (500 mL) were assembled, A (50% CM and 50% GS), B (60% CM and 40% GS), C (70% CM and 30% of GS) and D (60% CM and 40% GS). Equal concentrations of substrates were added to all reactors: pulp and husk pretreated by hydrothermolysis (1 g L-1), coffee (10 g COD L-1) and brewery (1.5 g COD L-1) wastewaters. Assays A, B and C were supplemented with 2 g L-1 of yeast extract, except for assay D. The reactors were operated at 37 °C and pH 7.0. In assay B, the highest CH4 production of 759.15 ± 19.20 mL CH4 g-1 TS was observed, possibly favored by the synergistic interactions between cellulolytic bacteria Christensenellaceae_R-7_group and Methanosaeta archaea, as inferred by genes encoding enzymes related to acetoclastic methanogenesis (acetyl-CoA synthetase). Consequently, the electricity production potential of assay B (45614.08 kWh-1 year-1) could meet the energy demand of a farm producing coffee and beer, contributing to a positive energy balance concerning methane generation.

Biomethane recovery through co-digestion of cheese whey and glycerol in a two-stage anaerobic fluidized bed reactor: Effect of temperature and organic loading rate on methanogenesis

Anaerobic digestion for CH₄ recovery in wastewater treatment has been carried out with different strategies to increase process efficiency, among which co-digestion and the two-stage process can be highlighted. In this context, this study aimed at evaluating the co-digestion of cheese whey and glycerol in a two-stage process using fluidized bed reactors, verifying the effect of increasing the organic loading rate (OLR) (2-20 g-COD.L^-1.d^-1) and temperature (thermophilic and mesophilic) in the second stage methanogenic reactor. The mesophilic methanogenic reactor (R-Meso) (mean temperature of 22 °C) was more tolerant to high OLR and its best performance was at 20 g-COD.L^-1.d^-1, resulting in methane yield (MY) and methane production (MPR) of 273 mL-CH₄.g-COD^-1 and 5.8 L-CH₄.L^-1.d^-1 (with 67% of CH₄), respectively. Through 16S rRNA gene massive sequencing analysis, a greater diversity of microorganisms was identified in R-Meso than in R-Thermo (second stage methanogenic reactor, 55 °C). Firmicutes was the phyla with higher relative abundance in R-Thermo, while in R-Meso the most abundant ones were Proteobacteria and Bacteroidetes. Regarding the Archaea domain, a predominance of hydrogenotrophic microorganisms could be observed, being the genera Methanothermobacter and Methanobacterium the most abundant in R-Thermo and R-Meso, respectively. The two-stage system composed with a thermophilic acidogenic reactor + R-Meso was more adequate for the co-digestion of cheese whey and glycerol than the single-stage process, promoting increases of up to 47% in the energetic yield (10.3 kJ.kg-COD^-1) and 14% in organic matter removal (90.5%).

Inoculum-to-substrate ratio and solid content effects over in natura spent coffee grounds anaerobic digestion

Coffee is the second most traded commodity worldwide, and its production is associated with the generation of a large number of residues, which are underused and disposed of in landfills. Notably, the coffee industry annually generates approximately 6 million tons of industrial spent coffee ground (ISCG) when extracting coffee flavorings to produce soluble coffee. That resource loss scenario has been highlighted in sustainable waste management contexts as an opportunity to improve the coffee circular economy. Despite ISCG bioconversion to methane potentially meets the waste-to-energy purposes of reducing residues disposal in landfills, decreasing greenhouse gas (GHG) emissions, and increasing renewable energy sources, data about anaerobic digestion (AD) of ISCG remains quite restricted. That limitation becomes more apparent owing to the lack of data focusing on AD key parameters for ISCG as substrate. This study assessed the influence of inoculum-to-substrate ratio (ISR) and the solid content influences on mesophilic (37 °C) ISCG-AD throughout the Response Surface Methodology (RSM) and Central Composite Design (CCD). Results revealed that both factors, ISR and solid content, should be kept above a certain threshold of 0.5 and 6.0 gTVS L^-1 to ensure experimental reliability, as well as reproductively and above 1.0 and 8.0 gTVS L^-1 to avoid underestimation on the MY potential achieved. Concerning ISCG-AD kinetics, the quadratic model optimum condition was at 1.36 and 14.83 gTVS L^-1 for ISR and solid content, respectively. This optimum range for ISR and solid content could guide further development of process configurations for mono- and co-digestion of ISCG, avoiding underestimation of the MY potential and extended incubation periods.

Subcritical water pretreatment enhanced methane-rich biogas production from the anaerobic digestion of brewer's spent grains

ABSTRACTThis study evaluated the effectiveness of a semi-continuous flow-through subcritical water hydrolysis (SWH) pretreatment of brewer's spent grains (BSG) for subsequent application in the anaerobic digestion (AD) process. BSG pretreatment was conducted at 160 °C and 15 MPa with a flow rate of 10 mL water min-1 and 15 g water g-1 BSG. The results revealed that SWH attacked the hemicellulose structure, releasing arabinose (46.54 mg g-1) and xylose (39.90 mg g-1) sugars, and proteins (34.89 mg g-1). The start-up of anaerobic reactors using pretreated BSG (747.71 L CH4 kg-1 TVS) increased the methane yield compared with the reactor without pretreatment (53.21 L CH4 kg-1 TVS). For the process with pretreatment, the generation of electricity (134 kWh t-1 BSG) and heat (604 MJ t-1) are responsible for the mitigation of 43.90 kg CO2 eq t-1 BSG. The adoption of SWH as an eco-friendly pretreatment of biomass for AD could be a technological route to increase methane-rich biogas and bioenergy production, supporting the circular economy transition by reducing the carbon footprint of the beer industry.

Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification ma-chine learning (ML) modelling. Six different ML models were developed; Model 1 (M1) and M2 were developed using the NIR absorbance values (100 inputs from 1596–2396 nm) and e-nose (nine sensor readings) as inputs, respectively, to classify the samples into control, low and high concentration of faults. Model 3 (M3) and M4 were based on NIR and M5 and M6 based on the e-nose readings as inputs with 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neu-ron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 95.6%, M2 = 95.3%, M3 = 98.9%, M4 = 98.3%, M5 = 96.8%, and M6 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.