Anaerobic Co-Digestion of Brewers’ Spent Grain from Craft Beer and Cattle Manure for Biogas Production

Abstract

The brewing industry generates significant organic waste, much of which remains underutilized despite its potential for energy recovery. This study assesses the feasibility of anaerobic co-digestion (AcoD) using brewers’ spent grain (BSG) from the craft beer production process and cattle manure from feedlots. Thermogravimetric analysis confirmed similar volatile solids content in both substrates, validating BSG as a viable feedstock. AcoD trials were conducted in 20 L biodigesters under dry and ambient conditions over 40 days. Methane yields reached 25 mL CH₄ gVS⁻¹ at a 1:1 inoculum–substrate ratio fresh matter basis and 67.33 mL CH₄ gVS⁻¹ at 2.5:1, indicating that higher inoculum levels enhance methane production. Kinetic modeling using Modified Gompertz, Logistic, and other microbial growth-based models showed that the Logistic model best represented the methane production trends. The detection of hydrogen sulfide in the biogas emphasizes the need for effective filtration. Overall, this work highlights AcoD as a promising approach for organic waste valorization and renewable energy generation in the craft brewing sector, supporting circular economy practices and contributing to environmental and economic sustainability.

1. Introduction

Sustainable waste and resource management are of paramount importance in the global environmental agenda. Despite organic waste being a significant source of pollution, it possesses substantial potential as a raw material to produce value-added bioproducts. Within this framework, anaerobic digestion (AD) has emerged as an effective strategy for treating organic waste such as food residues, excreta, and crop by-products, enabling the capture of methane that would otherwise be released into the atmosphere during natural biomass decomposition, thereby mitigating greenhouse gas emissions [1]. AD facilitates the degradation of biodegradable organic matter in solid waste, leading to the generation of biogas, a methane-rich energy source with diverse applications in renewable energy production [2]. This process also supports biohydrogen generation, further enhancing its sustainability and potential in circular bioeconomy frameworks [3].

Biogas is primarily composed of methane (CH₄, 50–70%) and carbon dioxide (CO₂, 30–50%), with trace amounts of hydrogen sulfide (H₂S), oxygen (O₂), hydrogen (H₂), and other minor gases [4]. Randazzo et al. [5] demonstrated that AD of green waste (GW) at the laboratory scale produces biogas enriched with terpenes, short-chain alkanes, alkenes, and cyclic compounds. Aromatic and halogenated compounds—recognized for their environmental and toxicological risks—were also consistently detected. The occurrence of benzene and toluene was found to be influenced by municipal solid waste management practices, particularly when hazardous waste is co-disposed with other waste streams [6]. These findings indicate that the inclusion of GW in municipal solid waste landfills increases the presence of terpenes and xenobiotic compounds in landfill gas, thereby enhancing its potential environmental toxicity.

AD has a positive energy balance, enabling both pollution mitigation and renewable energy recovery from organic biomass [7]. Anaerobic co-digestion (AcoD), which involves digesting two or more substrates simultaneously, improves process efficiency by enhancing hydrolysis, reducing retention time, and stabilizing biogas production through the dilution of inhibitory compounds [8,9]. It also increases nutrient availability and organic load, contributing to higher methane yields [9,10]. Common AcoD mixtures include animal manure and energy crops such as sorghum, wheat straw, and corn stalks [11]. This strategy optimizes substrate use and supports sustainable waste management and bioenergy generation from diverse organic residues [10,12].

The brewing industry plays a crucial role in the food sector, given its prominent economic position and the global demand for beer as one of the most consumed beverages. In this context, Mexico has emerged as the fourth-largest producer and the leading exporter of beer worldwide [13,14]. However, the escalating volume of waste generated by the brewing industry presents increasingly costly and challenging treatment issues [15]. For every 1000 tons of brewing product, an estimated 137 to 173 tons of waste are generated. It is estimated that approximately 45 kg of BSG, the primary constituent of brewery waste, is produced per 100 L of beer [16]. Effective waste management in the brewing industry is essential for achieving sustainable and circular production. Drosou et al. [17] emphasized the importance of prioritizing environmentally friendly methods over conventional approaches, advocating for the integration of efficient wastewater treatment and waste utilization strategies to enhance sustainability and support a circular economy. Craft breweries, in particular, encounter resource limitations in terms of technical expertise, financial resources, and qualified personnel. Various strategies for BSG disposal and recycling have been proposed in the scientific literature, encompassing applications in animal feed, composting, fungi and enzyme production, absorbent utilization, incorporation into construction materials like concrete and ceramics, paper manufacturing, brick production, and bioethanol generation, among others. BSG has been found to contain a substantial amount of cellulose and non-cellulosic polysaccharides, making it a suitable resource for biogas production [18].

Moreover, the brewing industry has a high thermal energy demand, primarily met through non-renewable sources such as natural gas and liquefied petroleum gas. The integration of AD systems for biogas production presents a viable alternative to reduce operational costs and dependency on fossil fuels while improving the sector’s sustainability [19]. From the industrial scenario, 7.38% of the electricity and 6.86% of the heat required to produce beer could be obtained from the dry AD of BSG [20].

From an economic perspective, the utilization of BSG for bioenergy and fertilizer production has demonstrated profitability, offering a reasonable payback period and return on investment [21]. Mesophilic conditions (~35 °C) have been identified as optimal for BSG degradation, leading to increased biogas yields [19,22]. The supplementation of trace elements and nutrients can further stabilize the digestion process and enhance methane production [23].

Co-digestion of BSG with other organic substrates, such as sewage sludge, has been reported to enhance biogas production; however, outcomes are highly dependent on specific substrate compositions and operational parameters [10,24]. Anaerobic co-digestion studies indicate that methane yield and biogas production are optimized when the BSG-to-co-substrate ratio is carefully adjusted. For instance, Edunjobi et al. [25] reported that a 60:40 ratio of BSG to poultry manure under mesophilic conditions (37 °C, 30-day retention) produced 400.34 mL CH₄ per gram of volatile solids (VS), with biogas and methane yields increasing by 46% and 52%, respectively. Similarly, co-digestion with cattle dung at a 1:3 ratio achieved a methane content of 54.7 ± 10.74% [26]. Further studies have explored the impact of different secondary substrates on methane yields. Polastri et al. [27] observed a methane yield of 249.87 mL CH₄ gVS⁻¹ using bovine ruminal waste at a 75:25 ratio, while Sganzerla et al. [19] reported the highest methane yield with a 12.5% BSG addition to brewery wastewater and sludge. These findings suggest that the optimal co-substrate ratio is not fixed but varies based on the secondary substrate type and operational conditions.

The fundamental aspect of kinetic evaluation in AD analysis is the methane accumulation curve over time, obtained from the Biochemical Methane Potential (BMP) test. This serves as a valuable analytical tool for assessing both the biological methane generation capacity and the degradability of substrates [28]. To enhance digester performance and improve process understanding, dynamic modeling describing AD behavior is essential [29]. These mathematical models are typically based on equations that describe the reaction rate dependency on substrate concentration [30]. Numerous studies have demonstrated the efficacy of integrating BMP test data with microbial growth models, including first-order, second-order, and modified kinetic models such as Logistic, Richards and Gompertz models. These models effectively describe the anaerobic fermentation process of various biomasses, yielding kinetic parameters that align with experimental data [31,32,33,34,35,36,37,38,39,40]. However, the applicability and practicality of such models should be continually reassessed and validated across a diverse range of experimental conditions [30].

Alkhrissat et al. [40] investigated the impact of various organic loading rates on the AD of cow manure. They observed an increase in methane production with higher organic loading and found that the Modified Gompertz and Logistic models demonstrated strong agreement with experimental findings. The Gompertz model describes cell concentration changes over fermentation time; however, its application can be challenging with complex substrates. Similarly, the Logistic model exhibits limitations in implementation when applied to heterogeneous substrates [35]. The differential equations underlying both models describe the rate of change in biogas production, with parameters adjusted to reflect microbiological relevance [41]. The Logistic model follows the characteristic pattern of biogas production kinetics, comprising an initial exponential growth phase followed by stabilization at a maximum production level [42]. This model assumes that the biomethane production rate is proportional to microbial activity, as indicated by methane yield and substrate concentration. In contrast, the Gompertz model postulates that gas production rate is also proportional to microbial activity but follows first-order kinetics due to decreasing fermentation efficiency over time [43].

The Modified Gompertz and Logistic models are widely employed in AD due to their characteristic “S”-shaped curves, effectively capturing the three distinct fermentation phases: lag, exponential, and stationary phases [35,36]. The accurate prediction of these kinetic stages is crucial for optimizing AD processes and enhancing methane yields. Moreover, the development and scalability of biogas processes heavily depend on accurate kinetic modeling. Analytical methods such as life cycle and cost analysis necessitate reliable scaling approaches for evaluating emerging technologies. Furthermore, a robust kinetic model is essential for system design, process optimization, and long-term operational stability [32]. Despite extensive research on BMP testing and efforts to standardize procedures, no universal mathematical model comprehensively describes biomethane formation kinetics under all conditions [30]. Therefore, continuous model refinement and validation are imperative to improving AD performance and maximizing methane production efficiency.

The growing popularity of craft beer in recent years has led to a substantial increase in the number of craft breweries. It is noteworthy that BSG generated by craft breweries often remains underutilized within the craft sector’s economy, with its commercial value being frequently insignificant. These residues are commonly directed toward animal feed without added value and occasionally disposed of in open landfills [18].

Although AcoD has gained recognition as a viable approach for the valorization of organic waste, experimental studies on the biogas production potential of waste streams from the craft brewing industry remain scarce. Furthermore, the lack of standardized methodologies and kinetic assessments for predicting methane generation from brewery residues underscores the need for further research in this domain.

This study aims to experimentally evaluate the methane production potential of residual wort from a small-scale craft brewery co-digested with feedlot cattle manure, a solid lignocellulosic waste primarily composed of cattle excreta mixed with straw.

To enhance the understanding of the degradation process, kinetic modeling approaches, including the Modified Gompertz, Logistic, and other widely applied models, will be utilized to describe and predict methane production kinetics. The novelty and significance of this study reside in generating empirical data under AD conditions directly relevant to the emerging craft brewing industry. This sector has experienced consistent growth in recent years, particularly in regions where waste management infrastructure remains limited. As a result, large volumes of BSG are generated by small and medium-sized breweries that often lack access to integrated co-digestion facilities or advanced pretreatment options. The integration of experimental and modeling approaches will facilitate the identification of key process parameters, optimize operational conditions, and contribute to the development of scalable biogas production strategies for the sustainable management of brewery waste. The outcomes of this research will be instrumental for stakeholders involved in the planning and implementation of AD systems, particularly within the craft brewing industry. The findings will support the effective valorization of BSG and other brewing residues within a circular economy framework, enabling resource optimization and reducing environmental impacts. Additionally, the knowledge generated will provide valuable insights for policymakers, environmental agencies, and brewery operators, promoting cleaner and more sustainable production practices in the craft brewing sector.

2. Materials and Methods

2.1. Biomass Sampling Procedure

BSG samples were obtained from Santo Negro, a craft brewery located in the municipality of Chihuahua (28°38′10.64″ N, 106°4′59.16″ W). The brewery produces a variety of craft beer styles. The production process begins with the grinding of the grain, followed by heating water, adding the grain to the liquid, boiling the mixture, and incorporating hops and adjuncts. After cooling, the mixture undergoes fermentation, maturation, and is then barreled, carbonated, bottled, labeled, and finally distributed for sale. This process takes approximately one and a half months to produce between 1500 and 1800 L of craft beer. Concurrently, cattle manure samples used for feedlot conditioning were procured from the pens of the Chihuahua Livestock Union (28°35′30.2″ N, 106°08′22.2″ W). All samples were carefully collected following standardized procedures, placed in individual polypropylene bags to avoid contamination, and transported under controlled conditions to the laboratory for further analysis.

2.2. Preparation of Bioreactors

High-density polyethylene containers with a 20 L capacity were employed as bioreactors for the AcoD experiments. Two experimental scenarios of AcoD were executed, incorporating residual wort BSG and cattle manure with varying ISR in triplicate. Both experiments were conducted together in the same period with the same temperature fluctuation. The experimental period lasted 45 days and was conducted from early October to mid-November under uncontrolled ambient temperature conditions. This duration was selected based on the observed stabilization of cumulative methane production and the absence of further gas release (<10%) in all reactors. Mean daily temperatures during the experiment ranged from 20 °C to 24 °C for daytime maxima and from 3 °C to 13 °C for nighttime minima, as shown in Supplementary Figures S1 and S2. These figures illustrate the gradual seasonal decline in temperature typical of the study region. Each biodigester was filled with a specific mixture of the aforementioned biomasses, as detailed in

Table 1. Components of AcoD experiments.

Experiment	BSG (kg)	Inoculum (kg)	Cattle Manure (kg)	Inoculum-to-Substrate Ratio SV
R1	0.50	1.0	0.5	1:1
R2	0.34	1.7	0.34	2.5:1

, and 2 L of water were introduced to establish a dry AD process in each reactor. Wet AD is fermentation with less than 15% dry material in the fermenter. Fermenters with a high content of dry material (>15%) are considered dry systems.

VS, moisture content, and ash content of the biomass samples were determined by thermogravimetric analysis (TGA) using a STA Regulus 2500 instrument (Netzsch Gerätebau GmbH, Selb, Germany). Approximately 10 mg of fresh biomass was placed in an alumina crucible and subjected to a heating ramp of 10 °C·min⁻¹ from 20 °C to 1000 °C under a continuous flow of high-purity helium (99.999%) at 50 mL·min⁻¹. The VS fraction was determined between 110 °C and 550 °C, following the standard test method ASTM E1131-08 [44].

Furthermore, to ensure an oxygen-free environment, a commercially available antacid (Alka-Seltzer) was added to each reactor. This antacid contains sodium bicarbonate, citric acid, and acetylsalicylic acid. The reaction between sodium bicarbonate and citric acid releases carbon dioxide, effectively displacing oxygen and facilitating anaerobic conditions. Although acetylsalicylic acid does not contribute to the AD process, its presence does not interfere with biogas production.

2.3. Inoculum Selection

The inoculum utilized in the AcoD process was obtained from an active reactor within a municipal wastewater treatment system. A stable AD system was selected to ensure a well-adapted microbial community capable of efficiently degrading organic matter. In the current study, experiments were conducted using different ISRs, specifically 1:1 and 2.5:1, based on volatile solids in the biomass mixture. These ratios were assessed to determine their impact on biogas yield and process performance.

2.4. Biogas Generation Measurement

The biogas generation potential of the biomass samples was assessed through a Biochemical Methane Potential (BMP) test adapted to ambient temperature. BMP is a standardized method used to quantify methane yield per unit of added volatile solids under controlled anaerobic conditions [45]. This approach allows for the evaluation of batch anaerobic digesters’ performance and the detection of potential microbial inhibitions resulting from organic overload or excessive accumulation of fatty acids [46]. By employing this approach, the study aimed to quantify the methane production capacity of the selected substrates under the experimental conditions, providing insights into their suitability for anaerobic digestion and potential application in bioenergy production. A continuous monitoring process was implemented to track the biogas production in the bioreactors under ambient temperature conditions. The concentrations of the major biogas components, including CH₄, CO₂, and H₂S, were measured using a Landtec Biogas 5000 gas analyzer. Additionally, biogas flow rates were recorded using a KG-2 flow meter to quantify gas production dynamics over time. This monitoring approach enabled real-time evaluation and analysis of the biogas production process, providing valuable insights into the performance and dynamics of the AD system.

2.5. Statistical Analysis

A statistical analysis was performed to evaluate differences in methane yield during the AcoD of brewery by-products. Analysis of variance (ANOVA) was applied to identify statistically significant factors and interactions between variables, with a significance level set at p ≤ 0.05. This analysis provided a quantitative assessment of the effects of different ISR and operational conditions on biogas production. The statistical computations were conducted using Jamovi 2.3.28, an open-source statistical software, ensuring rigorous data processing and interpretation.

2.6. Kinetic Modeling and Data Evaluation

Methane production kinetics were evaluated using four mathematical models: Logistic (L), Modified Logistic (ML), Modified Gompertz (MG), and Weibull (W), see

Table 2. Sigmoid models used to represent the kinetics of methane production.

Model	Equation	Number of Parameters
Logistic model (L)	$y={\displaystyle \frac{a}{1+{be}^{\left(c-x\right)}}}$	3
Modified Logistic (ML)	$y={\displaystyle \frac{a}{1+e^{\left(\frac{4b}{a}\right)(\left(\lambda -x\right)+2)}}}$	3
Modified Gompertz (MG)	$y=a\ast e^{-e^{\left(\frac{b\ast e}{a}\right)\ast \left(\lambda -x\right)+1}}$	3
Weibull (W)	$y=a-b{e}^{{(-cx)}^d}$	4

. These models were applied to describe the cumulative methane production trends and assess the dynamics of anaerobic digestion under the experimental conditions.

The kinetic analysis was conducted using CurveExpert Professional 2.6.5, a software specialized in nonlinear regression and curve fitting, to determine key parameters, including maximum methane yield (a), methane production rate (b), and lag phase duration (λ). The number of independent parameters in each model defined the degrees of freedom, influencing both predictive accuracy and model flexibility. Models with a higher number of parameters provided better adaptability to experimental data but required careful optimization to prevent overfitting. The obtained kinetic parameters facilitated the evaluation of substrate biodegradability and the overall efficiency of the AcoD process.

To determine the most suitable kinetic model for methane production, the Residual Sum of Squares (RSS) was calculated for each experimental condition. Additionally, the determination coefficient (R²) and the standard regression error (S) were evaluated to assess the correlation between the model predictions and the experimental data. These statistical parameters were analyzed within a 95% confidence interval to evaluate the goodness of fit [31]. R² is a critical parameter in AD kinetics, indicating how well the model fits experimental data. A high R² value demonstrates accurate representation of the process, essential for predicting biogas production and optimizing conditions to maximize methane yield and system efficiency. Furthermore, the second-order Akaike Information Criterion (AIC) test was conducted to examine the trade-off between model bias and variance, ensuring a robust selection of the best-fitting model [47]. A lower value of RSS, S and AIC for a model indicates a better model fit. By integrating these statistical assessments, the accuracy and predictive performance of the applied kinetic models were evaluated.

3. Results and Discussion

3.1. Material Characterization

The proximate analysis results of the inoculum, BSG, and excreta are presented in

Table 3. Proximate analysis of biomass.

Biomass	% Volatile Solids (Fresh Matter)	% Ash	% Total Solids (Fresh Matter)
Inoculum	26 ± 3.6	4 ± 1	29.71 ± 1.57
BSG	50.66 ± 3.21	2 ± 1.2	54.96 ± 1.36
Cattle manure	52.14 ± 3.75	21 ± 0.8	89 ± 3.8

. This analysis provides essential insights into the composition and properties of the materials employed in this study. By characterizing these materials, valuable information regarding their moisture content, volatile matter, and other significant parameters that influence the AcoD process can be obtained. Additionally, we conducted a one-way ANOVA to statistically assess the differences in VS, ash and Total Solids content between the biomass sources. The results confirmed that the differences were statistically significant (p ˂ 0.001).

According to McCarthy et al. [48], BSG dry residues are composed of 14.2–26.7% proteins, 3.9–13.3% lipids, 12.0–25.4% cellulose, 21.8–40.2% hemicellulose, and 4.0–27.8% lignin, along with other phenolic compounds. In this study, the VS content was assessed in two specific biomasses: BSG from the craft brewery and animal excreta, aiming to ascertain their potential for anaerobic degradation and biogas generation. VS serves as an indicator of organic matter content, representing the biodegradable fraction that can be harnessed by microbial consortia. An extensive literature review revealed a substantial variation in VS values across different biomasses, spanning from 9.96% to 96.96% in various organic materials [37,38,39,49,50,51]. These findings underscore the substantial influence of substrate composition on its biodegradability and biogas generation potential. The outcomes obtained in this investigation align with the reported variability in the literature. The VS analyses revealed comparable values for both BSG (50.66%) and animal excreta (52.14%), indicating the presence of a significant proportion of biodegradable organic matter in both biomass sources that can be effectively harnessed for biogas production through anaerobic digestion. According to Sganzerla et al. [20], BSG exhibited a volatile solids content of 73.62 ± 0.58% and a total solids content of 96.68 ± 0.21%. These values were determined from dried biomass, which accounts for the differences observed when compared to the results of the present study. Hilgert et al. [52] reported volatile solids contents ranging from 60.14% to 81% in samples of dairy cow manure and fattening pig manure collected from barn storage systems.

It is noteworthy that cattle manure exhibited a higher ash content compared to BSG. This disparity may be attributed to the presence of metallic oxides and other recalcitrant compounds in the excreta. Elevated concentrations of these compounds could potentially have detrimental effects on soils, underscoring the importance of proper waste management to mitigate potential adverse impacts. Collectively, the findings of this study endorse the utility of VS as indicators of organic matter content in anaerobic digester substrates. Both BSG and animal excreta display comparable biodegradable potential, suggesting that both materials are suitable substrates for AD and biogas generation.

3.2. Experimental AcoD

The experimental AcoD assays yielded average methane and carbon dioxide concentrations of 41.66% and 34.00%, respectively, for experiment R1, and 32.65% and 37.01%, respectively, for experiment R2. Detailed gas composition data are provided in the Supplementary Information.

Concentrations of H₂S in the biogas were identified in the range of 132 to 1621 ppm in this study. H₂S is recognized for its highly detrimental impact on the infrastructure components involved in biogas generation and utilization, including pipelines and blowers. The presence of H₂S in biogas can induce significant issues related to the performance and durability of the equipment employed in the biogas utilization process. Corrosion and deterioration of pipes and other system components can be initiated by the acidic nature of H₂S, resulting in potential leakage, interruptions in production, and elevated maintenance costs.

In order to ensure effective and safe utilization of biogas, a filtration treatment is necessary to eliminate or reduce the H₂S content in the biogas. Specialized filtration systems can be employed to remove H₂S and other undesirable compounds, thereby safeguarding the infrastructure and optimizing the performance of biogas generation and utilization processes.

Biogas purification employs several methods to remove CO₂ and H₂S, enhancing methane content. Physical absorption scrubs biogas with water at high pressure (~10 bar), dissolving impurities in a packed column; the water is regenerated in a stripper, achieving up to 98% methane purity. Adsorption uses activated carbon or modified zeolites to remove H₂S efficiently, with impregnated activated carbon showing superior results. Organic solvents like Genosorb absorb impurities physically and allow long solvent reuse. Chemical wet scrubbing employs solutions such as NaOH, alkanolamines, or iron compounds to chemically remove H₂S, with regenerable absorbents. Cryogenic separation cools and compresses biogas to separate gases but requires expensive equipment. Membrane technology separates gases by selective permeability but has lower methane recovery and higher costs. Biological oxidation uses sulfur-oxidizing bacteria to convert H₂S into elemental sulfur with high efficiency and low operating costs. The choice of method depends on impurity levels, costs, and methane purity targets. According to Kapłan et al. [53], the use of a developed carbon mixture (activated carbon) with turf ore (iron compounds) allows for 100% desulfurization of raw agricultural biogas under process conditions for mesophilic fermentation. These methods are designed to efficiently remove H₂S and other gaseous contaminants, ensuring the production of high-quality biogas while mitigating the risks associated with infrastructure corrosion and deterioration. The implementation of suitable filtration systems is pivotal in effectively eliminating this acid and other contaminants, thereby protecting the infrastructure required for biogas generation and utilization and maximizing the potential of biogas as a renewable energy source.

The results of the methane potential are presented in

Table 4. Results of methane potential experimentation.

Experiment	Lag Phase Day	Maximum Methane Production mL CH4 gVS−1	Day of Maximum Methane Production Rate Day
R1	10	27.22	25
R2	20	66.78	33

. The extended lag phase observed in both experiments can be attributed to the combination of factors that limited initial microbial activity. These include the use of a non-acclimated inoculum, the recalcitrant nature of the lignocellulosic substrates (BSG and cattle manure), and the dry digestion conditions applied under fluctuating ambient temperatures. Such factors are known to delay hydrolysis and the onset of methanogenesis, resulting in a prolonged adaptation period, as reflected in the kinetic parameters.

In the context of biogas production from organic waste biotransformation, numerous studies have been conducted to evaluate the potential of biogas generation from diverse biomass sources. These studies have yielded valuable insights into biogas production from different substrates and operating conditions. Kafle and Chen measured the BMP of five different types of livestock manure, obtaining a range of 155–323 mL CH₄ gVS⁻¹ [54]. These findings underscore the potential of bovine manure as a promising biomass source for biogas production. Furthermore, Moset et al. [49] investigated various biomasses, including cellulose, corn silage, wheat straw, and cattle excreta, and observed biogas yields of 333.9, 283.8, 246.3, and 210.9 mL CH₄ gVS⁻¹, respectively. These results indicate the varying biogas production potential among different biomass types. Almomani et al. [33] focused on co-digestion of agricultural solid waste and cow excreta, achieving a biogas yield of 297.99 mL CH₄ gVS⁻¹. This highlights the significance of co-digestion of different substrates for enhancing biogas production. Moreover, Pan et al. [55] discovered that co-digestion of food waste and other organic materials, such as agricultural waste, can reach a methane yield of up to 421 mL CH₄ gVS⁻¹. These laboratory-scale findings emphasize the potential of multi-substrate co-digestion for increasing biogas production.

In addition, Malakhova et al. [15] demonstrated the superiority of dry AD over the wet system for the co-digestion of BSG with Jerusalem artichoke. The dry system achieved a biodegradation rate of 63.5%, yielding 6.1% more methane than the wet system in a 40 L mesophilic continuously stirred tank reactor.

The results of this study indicate a numerical variation in methane production between the two experimental conditions. By day 40, experiment R2 achieved a cumulative yield of 67.33 mL CH₄ gVS⁻¹, while experiment R1 reached only 25 mL CH₄ gVS⁻¹. However, statistical analysis using ANOVA did not reveal significant differences between the conditions. This variation may be attributed to differences in the inoculum-to-substrate ratio and the ambient temperature fluctuations under which the experiments were conducted. Unlike studies performed under controlled mesophilic or thermophilic conditions, the present study was carried out at naturally fluctuating ambient temperatures, which likely influenced microbial activity and overall biogas production efficiency.

When comparing the obtained results with values reported in the literature, Sganzerla et al. [56] reported a higher methane yield of 88.02 mL CH₄ gVS⁻¹ using a 12.5% addition of brewers’ spent grain (BSG), highlighting the influence of substrate composition on methane production. In a separate study, the same authors reported a cumulative methane yield of 10.53 mL CH₄ gVS⁻¹ from the dry anaerobic digestion (AD) of BSG [20]. In general, dry AD of agri-food by-products without pretreatment has shown relatively low methane yields, including apple pomace (2.75 mL CH₄ gVS⁻¹) [57], BSG (26.72 mL CH₄ gVS⁻¹) [58], açaí seeds (19.82 mL CH₄ gVS⁻¹), and poultry feathers (10.14 mL CH₄ gVS⁻¹) [56]. By contrast, Rico et al. [59] achieved a much higher yield of 470 mL CH₄ gVS⁻¹ for dry AD of food waste.

The low methane content observed in this study may be attributed to suboptimal degradation conditions, such as the use of a non-acclimated inoculum, dry digestion conditions, and fluctuating ambient temperatures, all of which can inhibit microbial activity and methanogenesis. Moreover, the elevated H₂S concentrations likely result from the degradation of sulfur-containing compounds naturally present in both BSG and manure under anaerobic conditions. Previous studies conducted by our research group under ambient temperature conditions in 20 L reactors yielded 17.17 to 29.58 mL CH₄ gVS⁻¹ through anaerobic co-digestion of whey with cattle manure and colostrum with cattle-equine manure [39], further supporting the variability in performance depending on substrate characteristics and operating conditions. It is important to note that the present study did not include a control reactor containing inoculum alone, which limits the ability to quantify the net methane contribution attributable solely to the substrate. Future work should incorporate such controls to accurately subtract the background methane production of the inoculum and provide a more precise assessment of substrate-specific biodegradability. In this investigation, it was observed that increasing the ISR to 2.5:1 led to an augmentation in methane production in the experiments. According to Holliger, while it is not obligatory for the inoculum to be specifically adapted to the substrates employed in the trials, employing an inoculum adapted to such substrates can expedite the testing duration. Previous investigations have demonstrated that substrates with lower degradability, such as lignocellulosic organic matter found in animal excreta, benefit from an ISR equal to or greater than one [44]. Polastri et al. [27] determined that an optimal mixture of 25% bovine ruminal waste with 75% BSG (VS), using an ISR of 3.25, resulted in a methane yield of 249.87 mL CH₄ gVS⁻¹, as identified through a desirability test. According to Achinas et al. [31], an optimal ISR of 2 ensures biogas production. These comparisons highlight the potential for further optimization of substrate ratios and operational conditions to enhance methane production efficiency. Increasing the size of the inoculum has been linked to positive effects, such as enhanced microbial populations and greater buffering capacity. Nevertheless, it is essential to consider the adverse consequences associated with excessive inoculum, as it occupies space and reduces the reactor’s utilization efficiency. The observed increase in methane yield with higher ISR is relevant for industrial applications, as it improves process stability and microbial activity. However, elevated ISR reduces substrate loading capacity, affecting reactor efficiency. Thus, optimizing ISR is crucial to balance biogas productivity and operational feasibility in full-scale AcoD systems. The efficiency of methane production during AcoD has been widely assessed using various kinetic models, including the L, ML, MG, and W models. In this study, these models were applied to predict methane production, as presented in

Table 5. Kinetic models and parameters applied to methane production from the AcoD of brewery by-products.

Model	Parameters	R1	R2
Logistic (L)	a (mL)	31.121	70.033
	Difference	6.121	2.703
	R2	0.9957	0.992
	b	177.109	202.645
	AICC	−17.635	21.492
	S	0.5257	1.937
	RSS	3.316	45.039
Modified Logistic (ML)	a (mL)	15.1	79.033
	Difference	9.9	11.703
	λ (h)	43	3.251
	R2	0.686	0.992
	b	43.799	3.251
	AICC	46.945	21.492
	S	4.525	1.937
	RSS	245.768	45.039
Modified Gompertz (MG)	a (mL)	15.1	10.33
	Difference	9.9	57
	λ (h)	11.449	208,152
	R2	0.686	0.000000
	b	11.449	−42,713.54
	AICC	46.945	94.718
	S	4.525	22.246
	RSS	245.768	5938.88
Weibull (W)	a (mL)	30.55	73.365
	Difference	5.55	6.035
	R2	0.9956	0.9906
	b	30.389	72.788
	AICC	−13.981	27.842
	S	0.557	2.248
	RSS	3.423	55.633

.

The final methane yield obtained from the BMP assays was compared with the maximum methane production predicted by the applied kinetic models. The estimated methane production parameter demonstrated high accuracy in certain models, with the W model exhibiting a percentage deviation of less than 20% between predicted and experimental values.

These findings suggest that specific kinetic models effectively described methane production dynamics during the AcoD of brewery by-products, with the L model providing the most accurate predictions for the tested substrate, followed by the W model. Statistical evaluation using the R2, RSS, S and AIC identified the L model as the most accurate and efficient for fitting the experimental data under the conditions tested (Figure 1). The MG model is identified as inadequate for the current experimental data.

However, it is important to acknowledge that each study presents unique conditions, and, as highlighted by Sganzerla et al. [55], there is no universal consensus in the literature regarding the most appropriate kinetic model for methane production prediction.

The current challenge in AD lies in achieving an efficient, safe, and sustainable process that maximizes substrate utilization and yields optimal characteristics in the resulting byproducts.

It is imperative to consider the life cycle assessment of biomass in order to identify the generated byproducts and their potential applications, thereby establishing models for a circular economy. Pan et al. [54] highlighted that the solid fraction within the digestate can be converted into biochar through pyrolysis, offering opportunities for its utilization as a soil amendment or as a solid fuel. Biochar has garnered significant attention due to its multifaceted functions, including nutrient management, enhanced nitrogen and phosphorus retention in soils, and regulation of the carbon cycle through increased CO₂ mineralization capacity in soil environments.

The findings of this study demonstrate the significant potential for biogas generation through the co-digestion of BSG and cattle manure. The optimization of temperature conditions was found to play a crucial role in enhancing biogas production from these feedstocks. This highlights the importance of maintaining appropriate temperature levels during the anaerobic digestion process to maximize gas yield. In addition, the study observed a positive correlation between increasing the ISR and methane production. This suggests that a higher proportion of inoculum in relation to the substrate can enhance the conversion of organic matter into methane, leading to increased methane production. These findings have implications for achieving sustainable production and energy generation. Biomethane, as a renewable energy resource, offers significant advantages in terms of reducing greenhouse gas emissions and diversifying energy sources. By harnessing methane, industries can contribute to a more sustainable and environmentally friendly energy landscape. This study highlights the importance of biogas–methane production in a circular bioeconomy. It recognizes methane as a renewable energy resource that contributes to sustainable production. By integrating biogas–methane production into a circular bio economy and maximizing the utilization of byproducts, a more resource-efficient and environmentally sustainable system can be achieved.

4. Conclusions

This study provides preliminary evidence of the potential for anaerobic co-digestion of brewers’ spent grain and cattle manure under dry conditions at an ambient temperature, particularly for evaluating methane production kinetics and model performance. The results indicate that increasing the inoculum-to-substrate ratio enhances methane yields, highlighting the critical role of inoculum selection and acclimatization in optimizing the process.

Kinetic modelling provided critical insights into methane production dynamics, with the Weibull and Logistic models effectively describing the process. The Logistic model exhibited the highest predictive accuracy, supporting its application in AD process optimization. However, hydrogen sulfide was detected in the biogas, highlighting the necessity for filtration systems to prevent corrosion and ensure safe utilization.

The adoption of AD technologies in the craft brewing industry supports circular economy principles by enabling waste valorization, renewable energy production, and environmental sustainability. Additionally, the resulting digestate holds significant potential for agricultural applications, necessitating further assessment of its nutrient composition and soil amendment properties.

However, a key limitation of the study is the absence of a control reactor containing inoculum alone, which prevents accurate quantification of the net methane yield attributable exclusively to the substrate. This limitation has been acknowledged and should be addressed in future studies. Additionally, the monitoring of key operational parameters such as the carbon-to-nitrogen ratio and pH is recommended to improve the interpretation of anaerobic digestion performance and kinetic behavior.

Future research should prioritize the optimization of AcoD parameters under fluctuating ambient temperature conditions to enhance methane yield and process stability. Furthermore, large-scale feasibility studies are essential to evaluate the economic viability and practical integration of AD systems within brewery operations.