Search Results (465)

Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative and quantitative synthesis. The interplay between headspace volume fraction ${\mathrm{V}}_{\mathrm{H}\mathrm{S}}/{\mathrm{V}}_{\mathrm{t}\mathrm{o}\mathrm{t}}$, operating pressure, and normalized methane yield was assessed, explicitly integrating safety and instrumentation requirements. In laboratory settings, maintaining a headspace volume fraction (HSVF) of 0.30–0.50 with continuous pressure monitoring P(t) and gas chromatography reduces volumetric uncertainty to below 5–8% and establishes reference yields of 300–430 NmL CH₄ g⁻¹ VS at 35 °C. At the pilot scale, operation at 3–4 bar absolute increases the CH₄ fraction by 10–20 percentage points relative to ~1 bar, while maintaining yields of 0.28–0.35 L CH₄ g COD⁻¹ and production rates of 0.8–1.5 Nm³ CH₄ m⁻³ d⁻¹ under OLRs of 4–30 kg COD m⁻³ d⁻¹, provided pH stabilizes at 7.2–7.6 and the free NH₃ fraction remains below inhibitory thresholds. At full scale, gas domes sized to buffer pressure peaks and equipped with continuous pressure and flow monitoring feed predictive models (AUC > 0.85) that reduce the incidence of foaming and unplanned shutdowns, while the integration of desulfurization and condensate management keep corrosion at acceptable levels. Rational sizing of HS is essential to standardize BMP tests, correctly interpret the physicochemical effects of HS on CO₂ solubility, and distinguish them from intrinsic methanogenesis. We recommend explicitly reporting standardized metrics (Nm³ CH₄ m⁻³ d⁻¹, NmL CH₄ g⁻¹ VS, L CH₄ g COD⁻¹), absolute or relative pressure, HSVF, and the analytical method as a basis for comparability and coupled thermodynamic modeling. While this review primarily focuses on batch (discontinuous) anaerobic digesters, insights from semi-continuous and continuous systems are cited for context where relevant to scale-up and headspace dynamics, without expanding the main scope beyond batch systems. Full article

This study compares the carbon footprints of two municipal solid waste treatment technologies—anaerobic digestion and a gas recovery system—with the aim of evaluating their potential for biogas recovery and greenhouse gas (GHG) mitigation. The analysis applies the 2006 IPCC model to real operational data from the Paracambi Waste Treatment Complex (Rio de Janeiro, Brazil), integrating carbon footprint estimation and environmental compensation modeling through tree planting. From a different perspective, this work evaluates the replacement of biogas recovery with a biologically controlled system based on material segregation. Within the limits and parameters defined for the system, anaerobic digestion achieved net emissions of 0.0029 tCO₂eq per ton of organic waste, compared to 1.14 tCO₂eq per ton for the biogas recovery system. This represents a potential 393-fold reduction in GHG emissions. However, this result is specific to the modeled conditions and does not consider the full life cycle impacts of non-organic waste fractions. The results suggest that anaerobic digestion, when integrated into an efficient selective collection system, can significantly improve energy recovery and mitigate the carbon footprint of waste management systems. Full article

Biogas production plays a key role in the development of renewable energy systems; however, forecasting biomethane yield remains challenging due to the nonlinear nature of anaerobic digestion. The objective of this study was to develop a predictive model based on Radial Basis Function Neural Networks (RBF-NN) to approximate biomethane production using operational data from the Przybroda biogas plant in Poland. Two separate models were constructed: (1) the relationship between process temperature and daily methane production, and (2) the relationship between methane fraction and total biogas flow. Both models were trained using Gaussian activation functions, individually adjusted neuron parameters, and a zero-level correction algorithm. The developed RBF-NN models demonstrated high approximation accuracy. For the temperature-based model, root mean square error (RMSE) decreased from 531 m³ CH₄·day⁻¹ to 52 m³ CH₄·day⁻¹, while for the methane-fraction model, RMSE decreased from 244 m³ CH₄·day⁻¹ to 27 m³ CH₄·day⁻¹. The determination coefficients reached R² = 0.99 for both models. These results confirm that RBF-NN provides an effective and flexible tool for modeling complex nonlinear dependencies in anaerobic digestion, even when only limited datasets are available, and can support real-time monitoring and optimization in biogas plant operations. Full article

This study investigates the energy performance of anaerobic digesters in a municipal wastewater treatment plant by integrating empirical data from two tanks located at different distances from the heat source with simulation results. The analysis of measurements enabled the determination of heat transferred to the raw sludge, total heat losses of both systems, and provided input data for an hourly simulation of the thermal balance of the digester envelope. An analytical model was developed, including separate equations for the sludge and biogas phases, considering heat losses caused by mass transfer, conduction, convection, and radiation, as well as solar heat gains. The results show that the temperature difference between sludge and biogas exhibits seasonal variation, with a maximum value of 10.5 K, while the desired operational temperature of sludge fermentation is maintained at 38 °C. The total annual heat balance of the anaerobic digester in 2024 was estimated at 202.8 MWh, with the following structure: aboveground walls 46%, ground-contact partitions 30%, and dome 24%. Model validation using data from one of the digesters indicated a total system energy demand of 1812.0 MWh, distributed as follows: heat transferred to raw sludge 88.6%, heat transfer losses 0.2%, and digester envelope balance 11.2%. Replacing the thermal insulation of the aboveground section could reduce heat losses by 70.7 MWh, decreasing the total energy demand of the system by 3.9%. Comparison with the second digester revealed an energy gap of 166.3 MWh, which may be attributed to higher transmission losses or degradation of the insulation layer. Full article

The amount of municipal solid waste (MSW) generated worldwide is constantly growing. In many countries, anaerobic digestion (AD) is the recommended process for converting organic waste, playing a crucial role in the transition to a circular economy. Capturing and using biogas helps to reduce greenhouse gas emissions. This paper summarizes the results of comprehensive studies conducted at three municipal waste biogas plants (MWBPs) located in Poland. These studies include measurements related to concentrations of odor (c_od) and odorants (C) as well as microclimate parameters. We statistically analyzed the research obtained. However, the microclimatic parameters were not used in a final PCA model and were only used in exploratory correlation. Principal component analysis (PCA) is one of the methods of statistical factor analysis, which allows for the organization of a large set of data from three objects from the annual study. The use of PCA allowed us to determine which substance at a specific biogas plant is primarily responsible for odor nuisance and to estimate the percentage of variability contained in the first two principal components. The obtained results clearly indicate the influence of the technological regime and the type of fermentation feed on the determining effect of a specific odorant. In connection with the vision of creating new MWBPs that are consistent with circular economy assumptions, it seems advisable to extend the conducted analysis to include an immission study outside the plant boundaries. This study could play a crucial role in public consultations and serve as a tool for minimizing odor nuisance. Full article

Background: Municipal solid waste management is a relevant component of climate and air quality policy, yet published life cycle assessments report inconsistent conclusions on whether sanitary landfilling, waste-to-energy incineration, composting, or anaerobic digestion yields the lowest greenhouse gas and co-pollutant impacts because results depend strongly on methodological choices and local context. Objective: To synthesize and critically evaluate how key life cycle assessment assumptions and boundary decisions influence reported emissions across major waste management pathways, with primary emphasis on the United States and selected comparison to European Union policy frameworks. Methods: Peer-reviewed life cycle assessment studies and supporting technical and regulatory sources were reviewed and compared, focusing on functional unit definition, system boundaries, time horizon, energy substitution and crediting methods, and treatment of methane, nitrous oxide, and air pollutant controls; drivers of variability were identified through structured cross study comparison and sensitivity-focused interpretation. Results: Reported pathway rankings vary primarily with landfill gas collection and utilization assumptions, the carbon intensity of displaced electricity or heat for waste-to-energy systems, and the representation of biological process emissions across active and curing stages; harmonized comparisons reduce variability but do not yield a single consistently superior pathway across all plausible settings. Conclusions: Comparative conclusions are context-dependent and policy-relevant interpretation requires transparent reporting and sensitivity analysis for capturing efficiency, substitution factors, and biological emission controls, along with clear alignment between modeled scenarios and real-world operating conditions. Full article

Broccoli waste (Brassica oleracea), comprising non-commercialized stems and leaves, represents a valuable substrate for bioenergy and commodity recovery within agro-industrial systems. This study evaluates the potential of dark fermentation (DF) to produce hydrogen (H₂) and carbon dioxide (CO₂) from unpretreated broccoli residues. Batch experiments (120 mL) yielded maximum gas production rates of up to 166 mL/L·d, with final compositions of 41.43 mol% and 58.56 mol% of H₂ and CO₂, respectively. Based on these results, two biorefinery models were simulated using COCO v3.10 and SuperPro Designer^® v12.0, incorporating absorption and cryogenic separation technologies in the purification stage. Two scenarios were considered: Option A (169.82 kmol/day; H₂: 0.5856 mol fraction, CO₂: 0.4143 mol fraction) and Option B (72.84 kmol/day; H₂: 0.6808 mol fraction, CO₂: 0.3092 mol fraction). In both configurations, the purities of the final streams were the same, being 99.8% and 99.8% for both H₂ and CO₂, respectively. However, energy consumption was 43.76% higher in the cryogenic H₂/CO₂ separation system than in the absorption system. Noteworthily, this difference does not depend on the stream’s composition. Furthermore, from a financial standpoint, the cryogenic system is more expensive than the absorption system. These findings confirm the feasibility of designing biorefineries for H₂ production with high CO₂ recovery from broccoli waste. However, the economic viability of the process depends on the valorization of the secondary effluent from the fermentation reactor, which may require subsequent anaerobic digestion stages to complete the degradation of residual organic matter and enhance overall resource recovery. Full article

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models—support vector machine (SVM), random forest (RF), and artificial neural network (ANN)—were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m³/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (>85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (“sensor → prediction → programmable logic controller (PLC)/operation → feedback”), the ANN model was associated with a reduction in gas-yield fluctuation from approximately ±18% to ±5%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12–15 USD/t lower operating cost, 8–10% energy savings, and 5–7% CO₂ reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation. Full article

In the face of increasingly severe water environmental pollution and energy shortages, anaerobic digestion (AD) technology has demonstrated immense potential for the resource recovery of wastewaters such as food waste leachate (FWL). However, the inherent drawback of the long experimental period required for AD severely constrains research efficiency. Existing studies often rely on either kinetic models with high interpretability or machine learning models with strong generalization capabilities, rarely integrating both. To address this, this study innovatively investigated the anaerobic co-digestion of enzyme-modified biodegradable plastics (BPs) and FWL, and constructed a novel Physics-Informed Neural Network (PINN) based on a dataset of 261 experimental observations. The results indicated that, among the three kinetic models, the Modified Gompertz model exhibited the best prediction accuracy (R² approaching 0.99), stability, and universality. Among the four machine learning models, the Artificial Neural Network (ANN) demonstrated optimal generalization ability (Test set R² = 0.958). Notably, the constructed Modified Gompertz PINN model achieved superior predictive performance (Test set R² = 0.994), reducing the Root Mean Square Error (RMSE) by 74.0% compared to the ANN model. Shapley analysis further confirmed the PINN retained strong biological rationality, indicating that the hydrolysis process significantly impacts methane production. This work provides a robust hybrid model for efficient co-digestion prediction and offers a new approach for the resource valorization of enzyme-modified BPs and FWL. Full article

The massive arrival of pelagic sargassum on the Gulf of Mexico coast has become an environmental and socioeconomic challenge, generating high management costs and affecting tourism, fisheries, and coastal ecosystems. In this context, its valorization through anaerobic digestion represents a sustainable alternative for renewable energy production. This study assessed its valorization through anaerobic digestion as a renewable energy route. Pelagic sargassum (Sargassum natans/Sargassum fluitans) was collected, mechanically pretreated, and digested in batch mode using ruminal fluid as inoculum. Two inoculum:substrate ratios (2:1 and 3:1, v/v) were operated for 7 days, and daily cumulative biogas production was recorded. The 3:1 ratio reached 10.6 mL of cumulative biogas, approximately twice the 5.0 mL obtained at 2:1, and its production curve did not plateau by day 7, suggesting ongoing activity. Elemental analysis of the sargassum showed a low C/N ratio (6.9:1) and high moisture (~95%), both of which constrain performance. Boyle’s model was used to estimate theoretical CH₄ and CO₂ yields and as expected, largely overpredicted the experimental volumes because it assumes ideal conversion. These results indicate that ruminal fluid enhances early-stage biogas formation but also highlight process limitations associated with biomass quality and short retention time. Future work should include extended digestion, co-digestion strategies to adjust the C/N ratio, and full monitoring of pH, soluble COD, VFAs, and volatile solids consumption. Full article

Artificial intelligence (AI) has emerged as an innovative approach to the computer modeling and optimization of anaerobic digestion (AD) and anaerobic co-digestion (AcoD) processes. AI-based algorithms are ideally suited to capture the complex nonlinear behavior of these processes. Compared to conventional methods and models, AI-based algorithms have made modeling these processes much easier. Various AI algorithms, including multivariate statistical analyses, tree-based machine learning, nature-inspired optimization, support vector machine, and artificial neural networks (ANN) have been widely used to model the AD and AcoD processes. Researchers have successfully used stand-alone and hybrid ANMs to predict biogas yield and composition, as well as for efficient process monitoring and control. Furthermore, the development of advanced optimization algorithms, including genetic algorithms and particle swarm optimization, helps to optimize the ratio of mixing of co-substrates in AcoD and important process parameters (i.e., temperature (T), pH, retention time, total solids and volatile solids). This review discusses AI applications for AD and AcoD process modeling, optimization, prediction of unknown parameters and variables, and real-time monitoring and control. A critical comparison is made with some of the popular mathematical models and algorithms for monitoring and optimization designed on their basis. The review presents also future research directions in this area and some of our own results. Full article

Sheep manure and beet waste (the uneatable leaf part of the beet) are promising feedstock for biogas production due to their abundance and organic richness. However, their high lignocellulosic content reduces anaerobic digestibility and controls methane yield. This study investigates the effect of various pretreatment strategies, namely physical, thermal, and combined physical–thermal methods, on the Biochemical Methane Potential (BMP) of sheep manure and beet waste. Batch anaerobic digestion experiments were conducted under mesophilic conditions, with BMP values recorded for each treatment. The highest BMP for sheep manure, 125 Nml CH₄/g VS, was achieved using combined physical and thermal pretreatment. This approach enhanced methane production by 16%, 25%, and 60% compared to physical pretreatment (PP) alone, thermal pretreatment (TP) alone, and no pretreatment, respectively, while the one BMP for beet waste is 80 Nml CH₄/g VS and obtained with thermal pretreatment. To predict BMP outcomes, three machine learning approaches are applied, namely Linear Regression (LM), Random Forest Regression (RFR), and Gradient Boosting Machine (GBM), using digestion time (N days), total solids (Ts), volatile solids (Vs), pretreatment type, and biomass type. The variance analysis confirmed that the interaction between pretreatment and biomass type significantly improved model performance. While diagnostic checks revealed non-linear patterns limiting the linear model, ensemble methods achieved stronger results. The RFR model explained 79.5% of the variance with a Root Mean Square Error (RMSE) of about 15.7, whereas the GBM model achieved the lowest RMSE of 5.05. GBM captures complex non-linear interactions. In addition, variable importance analyses identified digestion time, solid content, and pretreatment as the most influential factors for methane yield, with the combined chemical and physical pretreatment producing the highest biogas outputs. These findings underscore the potential of advanced machine learning models, particularly GBM (Gradient Boosting Machine), for optimizing anaerobic digestion strategies and maximizing biogas recovery from sheep manure and beet waste. Full article

Tomato processing and dairy industries generate significant effluents worldwide, contributing to environmental pollution and nutrient loss. Anaerobic digestion (AD) offers a sustainable solution by treating these effluents while recovering nutrients and producing biomethane. Substrate composition and temperature play a key role in AD efficiency. This study investigates the batch co-digestion of tomato waste (TW) and cheese whey (CW) under mesophilic (37 °C) and thermophilic (55 °C) conditions over 20 days. Fresh cow manure (CM) served as the inoculum, maintaining a substrate-to-inoculum ratio of 1 (S/I = 1) across all digesters. The co-digestion ratios (CDRs), expressed as CW/TW (gVS/gVS), were set at 4.6, 1.7, 0.8 and 0.3. Co-digestion of TW with CW produced 2.5 times higher methane yield than mono-digestion of TW in both temperature conditions. Similarly, among all digesters set under both temperature conditions, digester 2 (CDR = 4.6) exhibited the highest performance, producing 44 mL/gVS-added cumulative methane under mesophilic conditions and 182.5 mL/gVS-added under thermophilic conditions. Across all CDRs, thermophilic digesters outperformed mesophilic ones, generating three times more biomethane. The modified Gompertz model effectively described the experimental data, achieving R² values between 0.97 and 1, confirming an excellent fit. Full article

Biomethane plays a key role in the green transition, offering a renewable, carbon-neutral substitute for natural gas while enabling the storage and use of intermittent renewable energy. This work presents a techno-economic assessment of biomethane production through the Power-to-Biomethane concept, which combines electrolytic hydrogen from renewable electricity with the direct catalytic methanation of raw biogas from anaerobic digestion. The main objective of this study is to identify the optimal plant size and configuration, taking into account the different operational management strategies of the system’s constituting units. The analysis integrates thermochemical modeling with a techno-economic optimization procedure. Three different configurations for renewable energy production, photovoltaic-based, wind-based, and hybrid photovoltaic–wind, were evaluated for a case study in Southern Italy. Results show that the hybrid configuration provides the best techno-economic balance, achieving the highest annual biomethane output (≈2288 t) and the lowest levelized cost of biomethane (EUR 97.4/MWh). While current biomethane production costs exceed natural gas prices, the proposed pathway represents a viable long-term solution for renewable integration and climate-neutral gas supply. Full article

Anaerobic digestion (AD) is increasingly recognized as a key technology for renewable energy generation and sustainable waste management within the circular economy. However, its performance is highly sensitive to feedstock variability and environmental fluctuations, making stable operation and high methane yields difficult to sustain. Conventional monitoring and control systems, based on limited sensors and mechanistic models, often fail to anticipate disturbances or optimize process performance. This review discusses recent progress in electrochemical, optical, spectroscopic, microbial, and hybrid sensors, highlighting their advantages and limitations in artificial intelligence (AI)-assisted monitoring. The role of soft sensors, data preprocessing, feature engineering, and explainable AI is emphasized to enable predictive and adaptive process control. Various machine learning (ML) techniques, including neural networks, support vector machines, ensemble methods, and hybrid gray-box models, are evaluated for yield forecasting, anomaly detection, and operational optimization. Persistent challenges include sensor fouling, calibration drift, and the lack of standardized open datasets. Emerging strategies such as digital twins, data augmentation, and automated optimization frameworks are proposed to address these issues. Future progress will rely on more robust sensors, shared datasets, and interpretable AI tools to achieve predictive, transparent, and efficient biogas production supporting the energy transition. Full article