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Review

Recent Advances and Prospects in Methane Production from Anaerobic Digestion: Process Intensification, Additives, and Biogas Upgrading

by
Bonface O. Manono
1,* and
Felix Lamech Mogambi Ming’ate
2
1
Engagement and Extension, Colorado State University, Fort Collins, CO 80253, USA
2
Department of Environmental Studies and Community Development, Kenyatta University, Nairobi P.O. Box 43844-00100, Kenya
*
Author to whom correspondence should be addressed.
Methane 2026, 5(2), 13; https://doi.org/10.3390/methane5020013
Submission received: 10 March 2026 / Revised: 5 April 2026 / Accepted: 13 April 2026 / Published: 15 April 2026
(This article belongs to the Special Issue Innovations in Methane Production from Anaerobic Digestion)
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Abstract

Anaerobic digestion (AD) plays an important role in the circular bioeconomy by converting organic waste into renewable methane and nutrient-rich fertilizer. However, consistent, high-quality biomethane production is hindered by four main factors: hydrolysis limitations, fluctuating feedstock quality, microbial instability, and the high cost/energy demand of purification. This review explores three key areas that improve biomethane production: (i) process intensification (pretreatments and advanced reactors), (ii) microbial regulation through additives, and (iii) biogas upgrading for pipeline use. Anaerobic digestion can be greatly improved by combining thermal or hybrid pretreatments, staged digestion, high-solids technology, and electrochemical systems. These methods speed up hydrolysis and help the system handle higher amounts of organic material more effectively. However, actual performance benefits depend on specific substrate characteristics, heat integration, and control complexity. Optimizing the C:N ratio, buffering capacity, and trace-element supplementation, while simultaneously diluting toxic inhibitors, makes co-digestion an effective and adaptable approach to enhancing anaerobic digestion processes. Additives like carbon, iron nanoparticles, enzymes, and buffers can optimize digestion, but their performance is highly dependent on dosage and substrate. Additionally, they lack validation in long-term, industrial-scale applications. Conventional physicochemical techniques continue to be standard for generating high-quality biomethane, but biological methanation and microalgal systems are playing a growing role in integrating Power-to-Gas technology and using CO2 efficiently. Critical research needs to focus on four areas: (1) standardized reporting metrics, (2) AI-enabled monitoring and control, (3) coupled techno-economic and life-cycle analysis (TEA-LCA), and (4) long-term pilot or full-scale validation. Overall, comprehensive optimization of the entire flow is more effective than improving isolated parts.

1. Introduction

Anaerobic digestion (AD) is a biologically mediated conversion process in which microorganisms decompose organic matter in the absence of oxygen to produce biogas and nutrient-rich digestate [1]. Although widely deployed, AD performance remains strongly shaped by substrate composition, microbial ecology, and reactor design, which sustains active innovation in process engineering and microbial management [2]. The AD pathway proceeds through four tightly coupled stages, hydrolysis, acidogenesis, acetogenesis, and methanogenesis, whose kinetics and stability determine methane formation [3]. Hydrolysis is frequently the dominant bottleneck for lignocellulosic and some protein-rich substrates because structural recalcitrance restricts enzyme access and slows conversion to soluble intermediates [4]. Because intermediates produced in one phase propagate into the next, phase imbalance can rapidly translate into volatile fatty acid (VFA) accumulation, pH decline, and suppressed methanogenesis under high loading or heterogeneous feedstocks.
AD contributes to climate and circular-economy goals by capturing methane that would otherwise be released during unmanaged decomposition and by generating a renewable-energy carrier [5,6]. Raw biogas typically contains 55–65% methane and 35–45% CO2 with trace contaminants (e.g., H2S and moisture) that constrain direct use [7,8]. Upgrading to biomethane (>95% methane) broadens end-use options, enabling grid injection and transport fuels with potential emissions benefits relative to diesel [8,9]. In parallel, digestate can support nutrient recovery and reduce reliance on synthetic fertilizers when appropriately managed [10].
While promising, practical methane output is often limited by the slow hydrolysis of complex biomass, system instability due to VFA and ammonia buildup, and the technical demands of refining raw biogas into market-grade biomethane [7,11,12,13]. These constraints frequently interact, which is why improvements reported for single interventions may not transfer unless reactor conditions, microbial ecology, and gas handling are considered jointly. Accordingly, this review synthesizes recent advances in methane production from AD with a focus on process intensification, additive-enabled microbial/process regulation, and biogas upgrading technologies. It examines how pretreatment, co-digestion, and reactor design improve hydrolysis and process stability. It evaluates additives (e.g., direct interspecies electron transfer (DIET)-promoting materials) for specific control mechanisms and positions upgrade options within the context of operational performance and feasibility constraints.

2. Review Methods, Search Strategy and Scope

This structured review examines recent advances that directly improve methane production performance and biomethane quality in AD systems. The scope covers five tightly connected domains: (a) process intensification and pretreatment, (b) co-digestion and reactor design, (c) additives and conductive materials, (d) microbial monitoring and process control, and (e) biogas upgrading and techno-economic feasibility. To strengthen reproducibility, the review was organized as a structured scoping synthesis rather than a PRISMA-style systematic review or meta-analysis. Literature discovery focused primarily on peer-reviewed studies indexed through major scientific databases and citation tracking, with emphasis on work published from 2020 to March 2026 while retaining earlier foundational studies where necessary for mechanistic or technological context.
Search combinations paired core terms such as “anaerobic digestion”, “biogas”, and “biomethane” with topic-specific terms including pretreatment, co-digestion, reactor design, DIET, conductive materials, microbial community, metagenomics, ADM1, machine learning, biogas upgrading, techno-economic analysis, and life-cycle assessment. Studies were prioritized when they reported enough process detail to support interpretation, including feedstock type, reactor configuration, operating conditions, and at least one deployable outcome such as methane yield or rate, methane purity, VFA behavior, pH, ammonia, sulfide, microbial shifts, or techno-economic or environmental indicators. Studies focused only on hydrogen or volatile fatty acid production without a methane endpoint, reports with insufficient operating detail, and duplicate summaries of the same dataset were deprioritized. Review articles were retained when they offered mechanistic synthesis or comparative framing for fast-moving topics such as DIET-promoting additives, biological methanation, and AI-assisted process optimization [14,15,16].
Because AD studies differ substantially in substrate type, inoculum, reactor configuration, scale, and reporting metrics, the review uses a comparative synthesis rather than a pooled meta-analysis. Where studies allowed, comparisons were interpreted against decision-relevant variables such as solids content, organic loading rate, hydraulic retention time, methane concentration, methane slip, ammonia or sulfide pressure, and the presence or absence of heat or energy integration. Evidence was weighed qualitatively based on its transferability to real-world applications. High-confidence evidence included continuous, pilot-scale, long-duration, and system-integration studies. Batch and BMP studies were used primarily to clarify mechanisms, while emerging topics lacking long-term validation were classified as promising, lower-TRL (technology readiness level) pathways rather than mature best practices. The review therefore emphasizes patterns, contradictions, semi-quantitative performance ranges, and boundary conditions rather than single best-technology conclusions. Figure 1 provides a conceptual overview of the AD stages and the intervention points synthesized in this review.

3. Strategies for Process Intensification and Enhanced Methane Yield

Process intensification in AD has shifted from isolated pretreatment experiments toward integrated strategies that co-optimize substrate accessibility, reactor stability, and methane conversion efficiency [17,18]. Recent studies show that successful interventions do more than increase biodegradability; they stabilize processes by smoothing phase imbalances, enhancing microbial retention, or improving feedstock compatibility. Accordingly, intensification claims must be interpreted within explicit boundary conditions including substrate recalcitrance, solids content, loading rate, retention time, inhibitor burden, heat-integration options, and operator control capacity. Technologies that outperform a baseline in BMP or short-batch tests do not automatically deliver equivalent gains at continuous or industrial scale. This section therefore organizes intensification strategies by mechanism, transferability, and operational boundary conditions rather than by technology novelty alone.

3.1. Advancements in Physical, Chemical, and Biological Methods for Breaking Down Complex Organic Matter

Physical pretreatments are essential for overcoming hydrolysis limitations by increasing substrate accessibility through size reduction. However, mechanical methods like milling or grinding offer limited benefits unless paired with thermal or chemical conditioning [19]. Thermal hydrolysis is highly effective for sludge and food waste, as it breaks down cellular structures and increases solubilization. This pretreatment reduces lag phases and boosts methane production [20,21,22]. However, thermal intensification is only advantageous when heat integration and solids handling are well designed.
Chemical pretreatments provide stronger structural disruption for recalcitrant substrates, particularly lignocellulosic residues. While alkali and acid treatments increase cellulose accessibility by removing partial lignin and hemicellulose, oxidants like ozonation and KMnO4 improve sludge disintegration and speed up hydrolysis [23,24]. The critical limitation is not proof of biochemical benefit, but cost, chemical recovery, and compatibility with downstream biology under continuous operation.
Biological pretreatments and bioaugmentation strategies operate at milder conditions and align more naturally with AD microbiology. Enzyme additives (e.g., cellulase and amylase) often improve methane yield in cellulose-rich feedstocks, but results vary strongly with substrate composition, pH, exposure time, and pretreatment sequence [25,26]. Bioaugmentation can improve resilience or accelerate selected conversion steps, but reproducibility remains context-dependent because introduced strains must compete within established consortia [27]. The literature indicates that hybrid pretreatment design is the most adaptable practice. Combining moderate physical disruption with targeted chemical or enzymatic conditioning often outperforms severe, single-stage pretreatment. This approach boosts hydrolysis efficiency while minimizing chemical consumption and energy use [28]. Table 1 provides a comparative summary of major intensification pathways and their boundary conditions.

3.2. Synergistic Benefits of Co-Digestion

Co-digestion is a powerful way to enhance anaerobic digestion because it can address several constraints simultaneously: it corrects nutrient imbalances, improves alkalinity and pH buffering, provides essential trace elements, and dilutes inhibitory compounds [30,31]. Reported methane gains are often large, but direct comparison across studies is difficult because baseline mono-digestion conditions vary widely. The more transferable insight is mechanistic: co-digestion works when mixing shifts the composite feed toward operating windows that better match microbial demand, for example by moving the C:N ratio toward approximately 20–30:1, improving alkalinity, increasing micronutrient supply, and reducing the effective burden of free ammonia or other inhibitors per unit reactor volume [11,32,33,34]. Food waste and manure combinations illustrate this clearly. Food waste provides high biodegradability and carbon availability, while manure improves buffering capacity, contributes micronutrients, and often stabilizes microbial activity [32,33,34]. Similar logic applies to microalgae with carbon-rich co-substrates and to sludge fractions paired with fruit waste, where co-substrates improve pH behavior and VFA conversion [35,36,37].For optimal anaerobic digestion of lignocellulosic residues, co-digestion with manure or sludge is highly effective because it provides buffering, inoculation, and dilution of inhibitory intermediates [38]. However, a pretreatment step is still required to break down the complex, recalcitrant structure of many crop residues [39]. This reinforces a broader point: co-digestion is not a universal substitute for pretreatment, but a compatibility tool that improves biochemical resilience when blending is matched to substrate chemistry and reactor constraints. A persistent gap is that many co-digestion studies optimize methane yield while underreporting logistical constraints such as seasonal feedstock availability, preprocessing requirements, transport distance, and mixture-specific ammonia or long chain fatty acid (LCFA) risk. These factors often determine whether co-digestion remains advantageous outside laboratory or pilot settings. Representative co-digestion pairings and reported outcomes are synthesized in Table 2.

3.3. Novel Reactor Designs and Integrated Systems

Reactor innovation now focuses less on residence time alone and more on phase management, microbial habitat design, and controllability. Two-stage AD systems decouple acidogenesis from methanogenesis so each phase can operate under more favorable conditions, which often improves stability and methane quality as much as it improves yield [1,24,41]. Temperature-phased anaerobic digestion (TPAD) works well for feedstocks that acidify quickly. It uses two temperature stages to balance the process: a high-heat thermophilic first stage to speed up breakdown, followed by a cooler mesophilic second stage to keep methane production stable [22,42]. TPAD is particularly attractive where pathogen reduction and sludge stabilization are operational priorities, but its benefits depend on thermal integration and operator capability.
Other configurations including fixed-bed systems, high-solid AD (HS-AD), and microbial electrochemical systems reflect a broader, strategic move toward improving microbial retention and enhancing electron transfer [43,44,45]. While these systems improve methane yield and loading capacity, they also introduce greater operational complexity. In practice, reactor intensification should be selected based on the plant’s dominant constraint (hydrolysis, instability, water use, or gas upgrading integration), not on laboratory performance alone. These intensification mechanisms and their interactions are summarized in Figure 2, while key reactor configurations and deployment implications are compared in Table 3.

4. Role of Additives and Conductive Materials

In AD, additives have shifted from general supplements to targeted process-control tools. Recent research shows that specific additive classes map to distinct functional improvements: conductive materials for DIET and syntrophic efficiency, enzymes for accelerated hydrolysis, and buffering/trace-element systems for microbial stability [50,51]. This mechanistic framing is important because additive performance is rarely universal. Reported gains depend on substrate composition, baseline instability, additive dosage, and operating regime.

4.1. Nanoparticles and Nano-Catalysts

The addition of conductive materials, including biochar, graphene, and Fe3O4 nanoparticles, significantly enhances anaerobic digestion by promoting direct interspecies electron transfer (DIET) between syntrophic bacteria and methanogenic archaea [14,15,52]. In many systems, DIET reduces reliance on diffusional hydrogen transfer and can accelerate methanogenic conversion, particularly under stressed or high-loading conditions. Biochar is especially versatile because it combines conductivity, pH buffering, and microbial attachment surfaces [53]. However, DIET-promoting materials should currently be classified as emerging, lower-TRL intensification tools rather than as mature plant-wide strategies. Most evidence comes from batch or short-duration reactors, where changes in VFA turnover, lag phase, and methane rate are easier to observe than long-term operational reliability. While research highlights increased methane yields and hydrogenotrophic methanogen enrichment, transitioning these additives to real-world applications is limited by several factors. Specifically, unresolved issues with additive longevity, accumulation in digestate, poor recoverability, material costs, and sensitivity to mixed-feedstock variability impede industrial adoption [31,54,55,56]. In practice, DIET-oriented additives are best viewed as promising control tools for specific instability or high-loading scenarios pending long-duration pilot and full-scale validation.

4.2. Enzyme Additives

Enzyme additives directly target the hydrolysis bottleneck and are most useful where the dominant substrate polymers are known. Cellulase and amylase additions can improve hydrolysis and methane yield in cellulose-rich materials such as crop residues [25,26]. Their strongest contribution is often not a dramatic absolute yield increase but a reduction in lag time and improved process predictability. However, enzyme-based intensification is sensitive to operational conditions, especially pH, temperature, retention time, and substrate accessibility [57]. In mixed waste systems, enzyme benefits may be limited unless the substrate has already been physically or chemically preconditioned [58]. This is why enzyme addition is often more effective within a staged intensification strategy than as a standalone intervention [59,60].

4.3. pH and Nutrient Regulation

Buffering systems and trace-element supplementation remain among the most practical additive strategies because they address instability directly. Ash, biochar, and phosphate buffers can stabilize pH in reactors exposed to VFA accumulation, while trace elements such as Fe, Co, and Ni support enzymatic pathways required for methanogenesis [51,61,62]. Biochar and ash are particularly attractive because they can provide simultaneous buffering, adsorption, and sulfur-management effects; some studies also report lower H2S and a shift toward methanogenic dominance over sulfate-reducing populations [63]. Nonetheless, trace-element dosing windows are narrow and site-specific, and excessive supplementation can create toxicity or precipitation problems. To optimize anaerobic digestion, additives should not be selected arbitrarily, but rather to address the specific bottleneck or failure mode dominating the plant. In anaerobic digestion, conductive materials best address electron-transfer bottlenecks, enzymes combat rate-limiting hydrolysis, and buffers or trace elements resolve pH and micronutrient instability [64]. The dominant additive-mediated mechanisms are illustrated in Figure 3, and representative additive classes, effects, and caveats are summarized in Table 4.

5. Innovations in Biogas Upgrading to Biomethane

Biogas upgrading to biomethane is now a primary design factor for anaerobic digestion systems rather than an afterthought. Because methane purity, methane recovery, slip, impurity tolerance, and energy demand jointly shape both profitability and environmental performance, upgrading must be integrated into core process strategy rather than optimized as a separate end step [7,8]. Recent innovation is best interpreted as a shift from single-unit upgrading toward integrated gas-management strategies that account for impurities, CO2 valorization, and energy coupling. Accordingly, technology selection should be based on a multi-criteria screening framework that considers methane purity, recovery/slip, specific energy burden, pretreatment requirements, scale fit, and CO2 valorization potential rather than purity alone.

5.1. Physicochemical Upgrading Methods

Physicochemical upgrading remains the most mature route for achieving pipeline-grade biomethane. Due to its modular and scalable nature, membrane separation is a preferred technology. Recent advancements are maximizing efficiency through multi-stage trains and enhanced materials, which minimize methane leakage and improve separation selectivity [65,66,67]. The primary operational challenge in biogas upgrading is its sensitivity to impurities, specifically H2S and siloxanes, which necessitate extensive, costly pretreatment [68,69]. Cryogenic separation delivers very high methane purity and a concentrated CO2 stream, making it attractive where CO2 utilization is part of the business model [70,71]. However, its energy intensity and capital cost usually limit application to larger plants or specialized contexts.
Water scrubbing remains relevant because it is robust and comparatively simple, especially for small- to medium-scale plants [72,73,74]. Chemical absorption systems (e.g., amines) can achieve even higher methane purity, but solvent regeneration energy and chemical handling increase complexity [75,76]. Across these options, methane purity alone is an incomplete comparison metric; methane recovery, slip, impurity tolerance, and specific energy demand are more useful for technology selection.

5.2. Biological Biogas Upgrading

Biological upgrading is important because it converts CO2 to methane instead of only separating it. In in situ biological methanation, hydrogen is injected directly into the digester and hydrogenotrophic methanogens convert CO2 to CH4 [77,78]. This can raise methane concentration without a separate upgrading reactor, but gas–liquid mass transfer, pH shifts, and H2 control remain difficult under continuously changing digester conditions. Ex situ biological methanation addresses these constraints by separating methanation into a dedicated reactor supplied with H2 and biogas or CO2 [79,80]. This improves control and often stabilizes conversion efficiency, but it adds reactor infrastructure and depends on reliable H2 supply. Microalgae-based upgrading takes a different pathway by using photosynthesis for CO2 removal while generating biomass, but performance is highly sensitive to light, temperature, and harvesting logistics [8,81]. Biological upgrading is therefore best viewed as a carbon-management strategy linked to renewable hydrogen and co-product pathways, not simply as an alternative separator.

5.3. Hybrid and Integrated Systems

Hybrid upgrading systems combine multiple technologies to maximize methane recovery and overcome the limitations of individual methods [82,83]. Examples include membrane pre-concentration followed by scrubbing or biological polishing, and integrated trains that coordinate upgrading with renewable heat and electricity supply [84,85,86]. Upgrading to hybrid anaerobic digestion can enhance environmental performance, but it also requires sophisticated, integrated control systems and greater initial capital investment [87]. Modern hybrid anaerobic digestion focuses on optimizing the entire plant by linking biomethane production, CO2 valorization, and digestate management into a cohesive, circular value chain [88]. This approach is expected to lead future commercial adoption, as it connects upgrade decisions directly with local energy markets, hydrogen availability, and opportunities for carbon utilization. A comparative map of upgrading pathways is provided in Figure 4, and key performance characteristics across technologies are summarized in Table 5.

6. Microbial Community Dynamics and Process Monitoring

6.1. Microbial Community Dynamics and Methane Production

AD performance is fundamentally driven by microbial interactions, which are shaped by engineering conditions [89,90]. Hydrolytic, acidogenic, acetogenic, and methanogenic guilds must remain functionally coupled for methane production to be stable [91,92]. Methanosarcina is frequently associated with resilient systems because of metabolic flexibility, while Methanobacterium and related hydrogenotrophs often dominate when hydrogen transfer or DIET-linked pathways are favored [48,93,94,95]. Microbial community composition responds strongly to pH, temperature, loading rate, substrate type, trace-element availability, and inhibitor exposure [96,97,98,99]. This means microbial analysis is most informative when interpreted together with reactor kinetics and chemistry. Community data alone can suggest mechanisms, but causal inference requires coupling sequence-based diagnostics with methane rates, VFAs, pH, total/free ammonia nitrogen (TAN/FAN), sulfide, and gas composition.
Three classes of inhibitors warrant consideration, as they consistently influence methanogenic failure or alterations in metabolic pathways. First, total ammonia nitrogen and especially free ammonia can suppress acetoclastic methanogens, alter electron flow, and select for more ammonia-tolerant syntrophic or hydrogenotrophic consortia [11,12]. Second, sulfide or H2S can impair sensitive populations and change competition between methanogens and sulfur-cycling microorganisms, particularly in sulfur-rich feedstocks or sulfate-bearing wastewaters [51,63,91]. Third, substantial lipid loading and the buildup of long-chain fatty acids may adhere to biomass, impede mass-transfer processes, and extend lag phases, even though fats possess considerable theoretical potential for methane production [91]. These inhibitors are therefore not secondary operational details; they help determine whether intensified systems remain stable under higher loading and more heterogeneous feedstocks.
Recent microbial analyses are also moving beyond 16S-only descriptions. Metagenomic, metatranscriptomic, and broader multi-omics approaches increasingly link taxonomic shifts to functional genes, stress responses, and pathway re-routing, especially under ammonia stress or during process perturbation [12,91]. Their current value is strongest in mechanistic diagnosis, inoculum selection, and additive assessment, while routine deployment in full-scale plants remains constrained by sampling cost, turnaround time, and bioinformatic standardization.

6.2. Advanced Monitoring, ADM1, and Data-Driven Control

Monitoring is moving from periodic measurement toward continuous, model-informed control. Routine parameters such as pH, VFAs, alkalinity, gas composition, and temperature remain essential, but their value increases when paired with soft sensors, mechanistic models such as ADM1, and automated anomaly detection [100,101]. In practice, ADM1-type frameworks help reconcile sensor signals with state variables that are difficult to measure directly, including latent overloading, alkalinity stress, and changing substrate-conversion dynamics. This provides a biochemical backbone for control decisions rather than relying only on empirical trend interpretation. 16S rRNA-based microbial profiling has improved mechanistic understanding and can support troubleshooting, especially when evaluating additives, inocula, or process transitions [81,102,103]. However, routine plant use remains limited by cost and turnaround time, which is why soft-sensor and biosensor approaches remain especially promising for near-real-time estimation of hidden variables or toxicity risk [15,104,105].
Machine learning, particularly Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) models, and hybrid ML–mechanistic approaches, can improve yield prediction, fault detection, virtual sensing, and set-point optimization when sufficiently large, high-frequency, quality-controlled datasets are available [16,106,107,108]. However, plant-level usefulness depends on more than predictive accuracy. Robust deployment requires attention to sensor drift, missing data, narrow training domains, overfitting, interpretability, and the need to update models as feedstocks and operating regimes change [106,107,108,109]. The most credible near-term pathway is therefore not black-box replacement of process understanding, but hybrid control in which ML complements ADM1 or related biochemical models for forecasting, early warning, and operator decision support. Figure 5 summarizes the microbial interaction network and the main monitoring/control levers discussed.

7. Challenges, Prospects, and Economic Feasibility

7.1. Current Limitations to Large-Scale Implementation

The primary challenge for AD is not lab-scale efficiency, but ensuring consistent, economic performance in real-world, variable-feedstock applications [110,111,112]. Capital Expenditure (CAPEX) remains a major barrier when pretreatment, advanced reactors, and upgrading are combined, particularly in farm-scale and decentralized systems [111,113]. Operational complexity is equally important. Intensified systems increase the number of interacting control variables, which narrows the margin for error and raises demands on operator expertise [2,110,114]. As a result, technologies that perform well in pilot studies may underperform when maintenance, staffing, or feedstock quality management is inconsistent [115]. Process instability remains a persistent limitation, especially under variable loading, ammonia stress, or poor feedstock conditioning [12,116,117]. This underscores a core point of this review: future performance gains will depend as much on control architecture and monitoring as on any single pretreatment, additive, or reactor innovation.

7.2. Future Research Directions

Future AD research is moving toward integrated, digitally enabled process optimization, but the immediate goal should be validated hybrid control rather than algorithm proliferation. AI and machine learning will be most useful where they are linked to interpretable features, mechanistic models, and plant-relevant datasets rather than deployed as isolated black boxes [106,107,118,119]. A second priority is truly coupled techno-economic analysis (TEA) with life-cycle assessment (LCA). Performance gains from severe thermal or oxidative pretreatment, specialized additives, or H2-dependent upgrading are not automatically beneficial once parasitic energy demand, material-input burdens, methane slip, and capital intensity are counted [29,85,118,120]. For example, pretreatments that improve methane yield can still worsen net energy or climate performance when waste-heat recovery is absent, and biological methanation can become environmentally or economically unattractive when H2 is not both low-carbon and reliably available. A third priority is biorefinery integration, where biomethane is co-optimized with digestate valorization, CO2 conversion, and other product streams [121,122]. In this framework, profitability depends on value stacking rather than methane yield alone, and future claims of superiority should increasingly be supported by long-duration pilot or full-scale validation.

7.3. Economic Analysis and Feasibility Pathways

Economic viability in AD increasingly depends on portfolio economics. Biomethane revenue remains central, but digestate valorization, CO2 utilization, tipping fees, and policy incentives are often decisive for project bankability [10,123,124]. Digestate is particularly important because it links AD to agricultural nutrient management and can create stable local value if product quality is standardized [125]. Policy and market design strongly influence outcomes. Feed-in tariffs, renewable-gas standards, carbon pricing, and permitting frameworks frequently determine whether technically viable projects are financially attractive [121,126]. This means TEA results are not universally transferable without policy context.
A strong synthesis across economic studies is that AD should be evaluated as an integrated waste–energy–nutrient–carbon system. Projects that combine reliable methane recovery with digestate and CO2 valorization often outperform simpler waste-treatment alternatives, but only when feedstock logistics, uptime, and local market conditions are explicitly considered. Integrated biogas projects which combine reliable methane recovery with digestate and CO2 valorization often outperform simpler waste-treatment methods. However, their success hinges on careful planning regarding feedstock logistics, system uptime, and local market conditions [127,128]. Figure 6 summarizes major cost and value pathways, and Table 6 synthesizes economic findings, risks, and improvement strategies for intensified AD systems.

8. Conclusions

Recent advances show that methane production from AD is improved most reliably when process intensification, additive-enabled control, and biomethane upgrading are evaluated as an integrated design problem rather than as isolated unit operations. Pretreatment and reactor innovations can reduce hydrolysis and phase-instability constraints, co-digestion can improve biochemical compatibility, and additives can provide targeted support for electron transfer, hydrolysis, or methanogenic stability. However, the strength of evidence differs across interventions: co-digestion, staged reactors, and established physicochemical upgrading routes are comparatively mature, whereas DIET-promoting materials, AI-assisted control, and some biological or hybrid upgrading pathways remain promising but still under-validated at long duration and full scale. Future progress will depend on reproducible comparative reporting, hybrid ADM1–ML monitoring, and TEA-LCA-linked demonstration studies that determine when higher methane yield translates into better net energy, climate, and economic performance.

Author Contributions

B.O.M.: Conceptualization, Investigation, Methodology, Validation, Visualization, Writing—original draft, Supervision. F.L.M.M.: Investigation, Validation, Visualization. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data and materials used in this study are available within this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ADAnaerobic Digestion
AIArtificial Intelligence
ANNArtificial Neural Network
CAPEXCapital Costs
OPEXOperating Costs
DIETDirect Interspecies Electron Transfer
HS-ADHigh-Solid Anaerobic Digestion
LCALife-Cycle Analysis
LCFALong Chain Fatty Acid
LSTMLong Short-Term Memory
TAN/FANTotal/Free Ammonia Nitrogen
TEATechno-Economic Analysis
TPADTemperature-Phased Anaerobic Digestion
TRLTechnology Readiness Level
VFAVolatile Fatty Acid

References

  1. Simeonov, I.; Kabaivanova, L.; Chorukova, E. Two-stage anaerobic digestion of organic wastes: A review. Ecol. Eng. Environ. Prot. 2021, 3, 38–56. [Google Scholar] [CrossRef]
  2. Rocha-Meneses, L.; Zannerni, R.; Inayat, A.; Abdallah, M.; Shanableh, A.; Ghenai, C.; Kamil, M.; Kikas, T. Current progress in anaerobic digestion reactors and parameters optimization. Biomass Convers. Biorefin. 2025, 15, 29573–29596. [Google Scholar] [CrossRef]
  3. Shinde, P.R.; Mali, S.T. Investigation of phase equations for anaerobic digestion process of anaerobic bioreactor landfill. E3S Web Conf. 2020, 170, 06005. [Google Scholar] [CrossRef]
  4. Menzel, T.; Neubauer, P.; Junne, S. Role of microbial hydrolysis in anaerobic digestion. Energies 2020, 13, 5555. [Google Scholar] [CrossRef]
  5. Bruno, M.; Marchi, M.; Ermini, N.; Niccolucci, V.; Pulselli, F.M. Life cycle assessment and cost–benefit analysis as combined economic–environmental assessment tools: Application to an anaerobic digestion plant. Energies 2023, 16, 3686. [Google Scholar] [CrossRef]
  6. Torres-Sebastian, M.J.; Colli-Mull, J.G.; Escobedo-Sanchez, L.; Martinez-Fong, D.; Rios-Solis, L.; Gutierrez-Castillo, M.E.; López-Jiménez, G.; Moreno-Rivera, M.L.; Tovar-Galvez, L.R.; Espadas-Alvarez, A.J. Methane, a renewable biofuel: From organic waste to bioenergy. Biofuels 2022, 13, 907–917. [Google Scholar] [CrossRef]
  7. Antukh, T.; Lee, I.; Joo, S.; Kim, H. Hydrogenotrophs-based biological biogas upgrading technologies. Front. Bioeng. Biotechnol. 2022, 10, 833482. [Google Scholar] [CrossRef]
  8. Lóránt, B.; Tardy, G.M. Current status of biological biogas upgrading technologies. Period. Polytech. Chem. Eng. 2022, 66, 465–481. [Google Scholar] [CrossRef]
  9. Grande, C.A.; Morence, D.G.; Bouzga, A.M.; Andreassen, K.A. Silica gel as a selective adsorbent for biogas drying and upgrading. Ind. Eng. Chem. Res. 2020, 59, 10142–10149. [Google Scholar] [CrossRef]
  10. Lohosha, R.V.; Palamarchuk, V.D.; Krychkovskyi, V.Y. The economic and bioenergy efficiency of use of the biogas plant digestate in the cultivation of agricultural and vegetable crops in the context of European integration of Ukraine. Bus. Inf. 2022, 9, 40–52. [Google Scholar] [CrossRef]
  11. Mansour, M.N.; Lendormi, T.; Drévillon, L.; Naji, A.; Louka, N.; Maroun, R.G.; Hobaika, Z.; Lanoisellé, J.L. Influence of substrate/inoculum ratio, inoculum source and ammonia inhibition on anaerobic digestion of poultry waste. Environ. Technol. 2024, 45, 1894–1907. [Google Scholar] [CrossRef] [PubMed]
  12. Zhang, H.; Yuan, W.; Dong, Q.; Wu, D.; Yang, P.; Peng, Y.; Li, L.; Peng, X. Integrated multi-omics analyses reveal the key microbial phylotypes affecting anaerobic digestion performance under ammonia stress. Water Res. 2022, 213, 118152. [Google Scholar] [CrossRef] [PubMed]
  13. Nyamukamba, P.; Mukumba, P.; Chikukwa, E.S.; Makaka, G. Biogas upgrading approaches with special focus on siloxane removal—A review. Energies 2020, 13, 6088. [Google Scholar] [CrossRef]
  14. Jadhav, P.; Nasrullah, M.; Zularisam, A.W.; Bhuyar, P.; Krishnan, S.; Mishra, P. Direct interspecies electron transfer performance through nanoparticles (NPs) for biogas production in the anaerobic digestion process. Int. J. Environ. Sci. Technol. 2022, 19, 10427–10439. [Google Scholar] [CrossRef]
  15. Chen, L.; Fang, W.; Chang, J.; Liang, J.; Zhang, P.; Zhang, G. Improvement of direct interspecies electron transfer via adding conductive materials in anaerobic digestion: Mechanisms, performances, and challenges. Front. Microbiol. 2022, 13, 860749. [Google Scholar] [CrossRef]
  16. Onu, P.; Mbohwa, C.; Pradhan, A. An analysis of the application of machine learning techniques in anaerobic digestion. In 2023 International Conference on Control, Automation and Diagnosis (ICCAD); IEEE: New York, NY, USA, 2023; pp. 1–6. [Google Scholar] [CrossRef]
  17. Aworanti, O.A.; Ajani, A.O.; Agbede, O.O.; Agarry, S.E.; Ogunkunle, O.; Laseinde, O.T.; Kalam, M.A.; Fattah, I.M. Enhancing and upgrading biogas and biomethane production in anaerobic digestion: A comprehensive review. Front. Energy Res. 2023, 11, 1170133. [Google Scholar] [CrossRef]
  18. Kumar, M.; Dutta, S.; You, S.; Luo, G.; Zhang, S.; Show, P.L.; Sawarkar, A.D.; Singh, L.; Tsang, D.C. A critical review on biochar for enhancing biogas production from anaerobic digestion of food waste and sludge. J. Clean. Prod. 2021, 305, 127143. [Google Scholar] [CrossRef]
  19. Chen, G.; Cao, H.N.; Fan, X.Q.; Sun, Y.C.; Wang, J.; Dong, J.; Wu, P. Investigation of the Effect of Compaction on the Anaerobic Digestion Process. BioEnergy 2021, preprint. [Google Scholar] [CrossRef]
  20. Alam, S.; Rahman, K.S.; Rokonuzzaman, M.; Salam, P.A.; Miah, M.S.; Das, N.; Chowdhury, S.; Channumsin, S.; Sreesawet, S.; Channumsin, M. Selection of waste to energy technologies for municipal solid waste management—Towards achieving sustainable development goals. Sustainability 2022, 14, 11913. [Google Scholar] [CrossRef]
  21. Liyanage, T.H.; Babel, S. Enhancement of Methane Production in Anaerobic Digestion of Food Waste using Thermal Pretreatment. Environ. Nat. Resour. J. 2022, 20, 10–18. [Google Scholar] [CrossRef]
  22. Li, L.; Kong, Z.; Qin, Y.; Wu, J.; Zhu, A.; Xiao, B.; Ni, J.; Kubota, K.; Li, Y.Y. Temperature-phased anaerobic co-digestion of food waste and paper waste with and without recirculation: Biogas production and microbial structure. Sci. Total Environ. 2020, 724, 138168. [Google Scholar] [CrossRef]
  23. Cesaro, A.; Belgiorno, V. Ozone pretreatment for the anaerobic digestion of organic solid waste. Detritus 2020, 12, 51–56. [Google Scholar] [CrossRef]
  24. Tang, S.; Wang, Z.; Lu, H.; Si, B.; Wang, C.; Jiang, W. Design of stage-separated anaerobic digestion: Principles, applications, and prospects. Renew. Sustain. Energy Rev. 2023, 187, 113702. [Google Scholar] [CrossRef]
  25. Wang, X.; Cheng, S.; Li, Z.; Men, Y.; Wu, J. Impacts of cellulase and amylase on enzymatic hydrolysis and methane production in the anaerobic digestion of corn straw. Sustainability 2020, 12, 5453. [Google Scholar] [CrossRef]
  26. Ruan, M.; Zhang, J.; Niu, C.G.; Huang, J.; Zhang, W.; Zeng, Z.H.; Zhou, K.; Li, X. Efficient and convenient pretreatment to enhance waste activated sludge anaerobic hydrolysis by iron porphyrin biomimetic enzyme. IOP Conf. Ser. Earth Environ. Sci. 2020, 474, 052101. [Google Scholar] [CrossRef]
  27. Hernández-Vásquez, L.; Vallejo-Cantú, N.A.; Alvarado-Lassman, A.; Reyes-Rosas, S. Evaluation of the biodegradability of a heat-treated anaerobic inoculum. Renew. Energy Biomass Sustain. 2019, 1, 28–32. [Google Scholar] [CrossRef]
  28. Siami, S.; Aminzadeh, B.; Karimi, R.; Hallaji, S.M. Process optimization and effect of thermal, alkaline, H2O2 oxidation and combination pretreatment of sewage sludge on solubilization and anaerobic digestion. BMC Biotechnol. 2020, 20, 21. [Google Scholar] [CrossRef] [PubMed]
  29. Girotto, F.; Lavagnolo, M.C.; Acar, G.; Piazza, L. Bio-methane production from tomato pomace: Preliminary evaluation of process intensification through ultrasound pre-treatment. J. Mater. Cycles Waste Manag. 2021, 23, 416–422. [Google Scholar] [CrossRef]
  30. Paranjpe, A.; Saxena, S.; Jain, P. A review on performance improvement of anaerobic digestion using co-digestion of food waste and sewage sludge. J. Environ. Manag. 2023, 338, 117733. [Google Scholar] [CrossRef]
  31. Zhou, J.; Zhang, Y.; Khoshnevisan, B.; Duan, N. Meta-analysis of anaerobic co-digestion of livestock manure in last decade: Identification of synergistic effect and optimization synergy range. Appl. Energy 2021, 282, 116128. [Google Scholar] [CrossRef]
  32. Chow, W.L.; Chong, S.; Lim, J.W.; Chan, Y.J.; Chong, M.F.; Tiong, T.J.; Chin, J.K.; Pan, G.T. Anaerobic co-digestion of wastewater sludge: A review of potential co-substrates and operating factors for improved methane yield. Processes 2020, 8, 39. [Google Scholar] [CrossRef]
  33. Xing, B.S.; Cao, S.; Han, Y.; Wen, J.; Zhang, K.; Wang, X.C. Stable and high-rate anaerobic co-digestion of food waste and cow manure: Optimisation of start-up conditions. Bioresour. Technol. 2020, 307, 123195. [Google Scholar] [CrossRef]
  34. Song, Y.J.; Oh, K.S.; Lee, B.; Pak, D.W.; Cha, J.H.; Park, J.G. Characteristics of biogas production from organic wastes mixed at optimal ratios in an anaerobic co-digestion reactor. Energies 2021, 14, 6812. [Google Scholar] [CrossRef]
  35. Zhang, Y.; Caldwell, G.S.; Blythe, P.T.; Zealand, A.M.; Li, S.; Edwards, S.; Xing, J.; Goodman, P.; Whitworth, P.; Sallis, P.J. Co-digestion of microalgae with potato processing waste and glycerol: Effect of glycerol addition on methane production and the microbial community. RSC Adv. 2020, 10, 37391–37408. [Google Scholar] [CrossRef]
  36. Jensania, M.K.; Sadikina, A.N.; Ngadia, N.; Azmana, M.F.; Abd Muisb, Z.; Aslic, U.A. Anaerobic co-digestion of food waste with crude glycerol for biogas production. Chem. Eng. 2021, 83, 577–582. [Google Scholar] [CrossRef]
  37. Erdirencelebi, D.; Bayhan, C. Feasibility and potential of separate anaerobic digestion of municipal sewage sludge fractions. Water SA 2020, 46, 123–130. [Google Scholar] [CrossRef]
  38. Shah, F.A.; Mahmood, Q.; Rashid, N.; Pervez, A.; Raja, I.A.; Shah, M.M. Co-digestion, pretreatment and digester design for enhanced methanogenesis. Renew. Sustain. Energy Rev. 2015, 42, 627–642. [Google Scholar] [CrossRef]
  39. Sinbuathong, N.; Sombat, N.; Kratay, W. Sedge for biogas production and improving the process by pretreating sedge prior to co-digestion. Int. J. Glob. Warm. 2020, 20, 12–24. [Google Scholar] [CrossRef]
  40. Rawoof, S.A.; Kumar, P.S.; Vo, D.V.; Subramanian, S. Sequential production of hydrogen and methane by anaerobic digestion of organic wastes: A review. Environ. Chem. Lett. 2021, 19, 1043–1063. [Google Scholar] [CrossRef]
  41. Pasteris, A.M.; Heiermann, M.; Theuerl, S.; Plogsties, V.; Jost, C.; Prochnow, A.; Herrmann, C. Multi-advantageous sorghum as feedstock for biogas production: A comparison between single-stage and two-stage anaerobic digestion systems. J. Clean. Prod. 2022, 358, 131985. [Google Scholar] [CrossRef]
  42. Chen, H.; Chang, S. Dissecting methanogenesis for temperature-phased anaerobic digestion: Impact of temperature on commun ity structure, correlation, and fate of methanogens. Bioresour. Technol. 2020, 306, 123104. [Google Scholar] [CrossRef]
  43. Pampang, H.A.; Purnomo, C.W.; Cahyono, R.B. Enhancement of biogas production in anaerobic digestion from sludge of dairy waste with fixed bed reactor by using natural zeolite. Key Eng. Mater. 2020, 849, 27–33. [Google Scholar] [CrossRef]
  44. Wang, Y.; Gao, Y.; Lu, X.; Gadow, S.I.; Zhuo, G.; Hu, W.; Song, Y.; Zhen, G. Bioelectrochemical anaerobic membrane bioreactor enables high methane production from methanolic wastewater: Roles of microbial ecology and microstructural integrity of anaerobic biomass. Chemosphere 2023, 339, 139676. [Google Scholar] [CrossRef]
  45. Nguyen, H.T.; Noori, M.T.; Min, B. Accelerating anaerobic digestion process with novel single chamber microbial electrochemical systems with baffle. Bioresour. Technol. 2022, 359, 127474. [Google Scholar] [CrossRef]
  46. Abid, M.; Wu, J.; Seyedsalehi, M.; Hu, Y.Y.; Tian, G. Novel insights of impacts of solid content on high solid anaerobic digestion of cow manure: Kinetics and microbial community dynamics. Bioresour. Technol. 2021, 333, 125205. [Google Scholar] [CrossRef] [PubMed]
  47. Liu, X.; Liu, H.; Peng, L.; Su, H. Influence of ammonium acetate and betaine supplements on the anaerobic digestion under high salinity conditions. Energy Sci. Eng. 2020, 8, 2621–2627. [Google Scholar] [CrossRef]
  48. Park, J.G.; Lee, B.; Lee, U.J.; Jun, H.B. An anaerobic digester with microbial electrolysis cell enhances relative abundance of methylotrophic methanogens in bulk solution. Environ. Eng. Res. 2022, 27, 210666. [Google Scholar] [CrossRef]
  49. Xiao, J.; Liu, C.; Ju, B.; Xu, H.; Sun, D.; Dang, Y. Estimation of in-situ biogas upgrading in microbial electrolysis cells via direct electron transfer: Two-stage machine learning modeling based on a NARX-BP hybrid neural network. Bioresour. Technol. 2021, 330, 124965. [Google Scholar] [CrossRef]
  50. Rasapoor, M.; Young, B.; Brar, R.; Sarmah, A.; Zhuang, W.Q.; Baroutian, S. Recognizing the challenges of anaerobic digestion: Critical steps toward improving biogas generation. Fuel 2020, 261, 116497. [Google Scholar] [CrossRef]
  51. Paritosh, K.; Yadav, M.; Chawade, A.; Sahoo, D.; Kesharwani, N.; Pareek, N.; Vivekanand, V. Additives as a support structure for specific biochemical activity boosts in anaerobic digestion: A review. Front. Energy Res. 2020, 8, 88. [Google Scholar] [CrossRef]
  52. Gahlot, P.; Aboudi, K.; Ahmed, B.; Tawfik, A.; Khan, A.A.; Khursheed, A.; Tyagi, V.K. Direct interspecies electron transfer (DIET) via conductive materials in anaerobic digestion of organic wastes. In Clean Energy and Resources Recovery; Elsevier: Amsterdam, The Netherlands, 2021; pp. 227–252. [Google Scholar] [CrossRef]
  53. Anyebe, O.; Sadiq, F.K.; Manono, B.O.; Matsika, T.A. Biochar characteristics and application: Effects on soil ecosystem services and nutrient dynamics for enhanced crop yields. Nitrogen 2025, 6, 31. [Google Scholar] [CrossRef]
  54. Jang, H.M.; Brady, J.; Kan, E. Succession of microbial community in anaerobic digestion of dairy manure induced by manure-derived biochar. Environ. Eng. Res. 2021, 26, 200001. [Google Scholar] [CrossRef]
  55. Sun, J.; Zhang, S.; Luo, G. The role of hydrochar in promoting methane production from anaerobic digestion with different inocula. Fermentation 2023, 9, 433. [Google Scholar] [CrossRef]
  56. Zuo, T.O.; Qian, Y.; Zhang, H.A.; Liu, J.I.; Gong, L.; Zhou, J.; Yang, X.I. Impact of Fe3O4 nanoparticles on methane production from anaerobic digestion and kinetic analysis. Rev. Chim. SRL 2022, 73, 87–98. [Google Scholar] [CrossRef]
  57. Sahil, S.; Nanda, S. Process Intensification of Anaerobic Digestion of Biowastes for Improved Biomethane Production: A Review. Sustainability 2025, 17, 6553. [Google Scholar] [CrossRef]
  58. Bonilla, S.; Choolaei, Z.; Meyer, T.; Edwards, E.A.; Yakunin, A.F.; Allen, D.G. Evaluating the effect of enzymatic pretreatment on the anaerobic digestibility of pulp and paper biosludge. Biotechnol. Rep. 2018, 17, 77–85. [Google Scholar] [CrossRef] [PubMed]
  59. Elsayed, A.; Abdelrahman, A.M.; Al Saleh, M.; Zagloul, M.S.; Kakar, F.L.; Muller, C.; Bell, K.Y.; Santoro, D.; Norton, J.; Marcus, A.; et al. Alternative Configurations for the Intensification of the Anaerobic Digestion Process: A Comprehensive Review. Processes 2026, 14, 695. [Google Scholar] [CrossRef]
  60. Goria, K.; Vashishth, V.K.; Mathur, A.; Singh, A.; Saini, J.K.; Shankarayan, R. Synergism in Enzyme-Nanomaterial Constructs for Applications in Food and the Environment. In Enzyme Immobilization with Nanomaterials: Applications and Challenges; American Chemical Society: Washington, DC, USA, 2025; pp. 209–238. [Google Scholar] [CrossRef]
  61. Egwu, U.; Uchenna-Egwu, B.; Ezeokpube, G.C. Ash-extracts from plant residues can provide sufficient buffering alkalinity and trace elements required to prevent operation instability to guarantee optimum methane yield during anaerobic digestion of agricultural residues. J. Clean. Prod. 2021, 318, 128369. [Google Scholar] [CrossRef]
  62. Danmallam, A.M.; Agbaji, E.B.; Nwokem, N.C.; Sani, U.; Nwokem, C.O. Improved Methane Production from Anaerobic Digestion of Municipal Solid Waste by Trace Elements Supplementation. Islam. Univ. Multidiscip. J. (IUMJ) 2020, 2, 356–360. [Google Scholar]
  63. Xu, C.; Ding, Y.; Liu, J.; Huang, W.; Cheng, Q.; Fan, G.; Yan, J.; Zhang, S.; Song, G.; Xiao, B. Anaerobic digestion of sulphate wastewater mediated by biochar. Environ. Technol. 2023, 44, 1667–1678. [Google Scholar] [CrossRef]
  64. Wu, Y.; Wang, S.; Liang, D.; Li, N. Conductive materials in anaerobic digestion: From mechanism to application. Bioresour. Technol. 2020, 298, 122403. [Google Scholar] [CrossRef]
  65. Gkotsis, P.; Kougias, P.; Mitrakas, M.; Zouboulis, A. Biogas upgrading technologies–Recent advances in membrane-based processes. Int. J. Hydrogen Energy 2023, 48, 3965–3993. [Google Scholar] [CrossRef]
  66. Vilardi, G.; Bassano, C.; Deiana, P.; Verdone, N. Exergy and energy analysis of three biogas upgrading processes. Energy Convers. Manag. 2020, 224, 113323. [Google Scholar] [CrossRef]
  67. Tanvidkar, P.; Jonnalagedda, A.; Kuncharam, B.V. Investigation of cellulose acetate and ZIF-8 mixed matrix membrane for CO2 separation from model biogas. Environ. Technol. 2024, 45, 2867–2878. [Google Scholar] [CrossRef]
  68. Vali, S.A.; Moral-Vico, J.; Font, X.; Sanchez, A. Adsorptive removal of siloxanes from biogas: Recent advances in catalyst reusability and water content effect. Biomass Convers. Biorefin. 2024, 14, 23259–23273. [Google Scholar] [CrossRef]
  69. Piechota, G. Removal of siloxanes from biogas upgraded to biomethane by Cryogenic Temperature Condensation System. J. Clean. Prod. 2021, 308, 127404. [Google Scholar] [CrossRef]
  70. Baena-Moreno, F.M.; Le Saché, E.; Pastor-Perez, L.; Reina, T.R. Membrane-based technologies for biogas upgrading: A review. Environ. Chem. Lett. 2020, 18, 1649–1658. [Google Scholar] [CrossRef]
  71. Yusuf, N.; Almomani, F. Recent advances in biogas purifying technologies: Process design and economic considerations. Energy 2023, 265, 126163. [Google Scholar] [CrossRef]
  72. Wantz, E. Biogas Upgrading at Farm-Scale for the Production of Biomethane: Development of an Innovative High-Pressure Water Scrubbing Process. 2023. Available online: https://inis.iaea.org/records/c47vr-1fk67 (accessed on 6 February 2026).
  73. Salave, H.S.; Desai, A.D. The study and analysis of different biogas enrichment technologies. Int. J. Adv. Sci. Technol 2020, 29, 8709–8719. [Google Scholar]
  74. Ahmed, S.F.; Mofijur, M.; Tarannum, K.; Chowdhury, A.T.; Rafa, N.; Nuzhat, S.; Kumar, P.S.; Vo, D.V.; Lichtfouse, E.; Mahlia, T.M. Biogas upgrading, economy and utilization: A review. Environ. Chem. Lett. 2021, 19, 4137–4164. [Google Scholar] [CrossRef]
  75. Saputro, S.H.; Sudibandriyo, M. Feasibility Study of Biogas Upgrading Facility Development from Anaerobic Digestion of Palm Oil Mill Effluent in Indonesia. In Proceedings of the International Conference on Science, Technology & Environment (ICoSTE), Surabaya, Indonesia, 2–3 December 2020. [Google Scholar] [CrossRef]
  76. Vinjarapu, S.H.; Madelaine, L.; von Solms, N.; Fosbøl, P.L. New Technologies for Energy Reduction in Biogas Upgrading. In Proceedings of the 15th Greenhouse Gas Control Technologies Conference, Virtual, 15–18 March 2021; pp. 15–18. [Google Scholar] [CrossRef]
  77. Zhang, L.; Kuroki, A.; Tong, Y.W. A mini-review on in situ biogas upgrading technologies via enhanced hydrogenotrophic methanogenesis to improve the quality of biogas from anaerobic digesters. Front. Energy Res. 2020, 8, 69. [Google Scholar] [CrossRef]
  78. Smrithi, V.; Singh, S.; Mohan, S.V. Biogas upgrading via hydrogenotrophic methanogenesis and hematite-GAC assisted in-situ CO2-to-CH4 conversion. Chem. Eng. J. 2025, 526, 170830. [Google Scholar] [CrossRef]
  79. Calbry-Muzyka, A.S.; Schildhauer, T.J. Direct methanation of biogas—Technical challenges and recent progress. Front. Energy Res. 2020, 8, 570887. [Google Scholar] [CrossRef]
  80. Laguillaumie, L.; Rafrafi, Y.; Moya-Leclair, E.; Delagnes, D.; Dubos, S.; Spérandio, M.; Paul, E.; Dumas, C. Stability of ex situ biological methanation of H2/CO2 with a mixed microbial culture in a pilot scale bubble column reactor. Bioresour. Technol. 2022, 354, 127180. [Google Scholar] [CrossRef]
  81. Yang, W.; Li, S.; Qv, M.; Dai, D.; Liu, D.; Wang, W.; Tang, C.; Zhu, L. Microalgal cultivation for the upgraded biogas by removing CO2, coupled with the treatment of slurry from anaerobic digestion: A review. Bioresour. Technol. 2022, 364, 128118. [Google Scholar] [CrossRef] [PubMed]
  82. Yellezuome, D.; Zhu, X.; Liu, X.; Liu, X.; Liu, R.; Wang, Z.; Li, Y.; Sun, C.; Abd-Alla, M.H.; Rasmey, A.H. Integration of two-stage anaerobic digestion process with in situ biogas upgrading. Bioresour. Technol. 2023, 369, 128475. [Google Scholar] [CrossRef] [PubMed]
  83. Elsayed, A.; Kakar, F.L.; Abdelrahman, A.M.; Ahmed, N.; Alsayed, A.; Zagloul, M.S.; Muller, C.; Bell, K.Y.; Santoro, D.; Norton, J.; et al. Enhancing anaerobic digestion Efficiency: A comprehensive review on innovative intensification technologies. Energy Convers. Manag. 2024, 320, 118979. [Google Scholar] [CrossRef]
  84. Khan, M.U.; Lee, J.T.; Bashir, M.A.; Dissanayake, P.D.; Ok, Y.S.; Tong, Y.W.; Shariati, M.A.; Wu, S.; Ahring, B.K. Current status of biogas upgrading for direct biomethane use: A review. Renew. Sustain. Energy Rev. 2021, 149, 111343. [Google Scholar] [CrossRef]
  85. El-Hadary, M.I.; Senthilraja, S.; Zayed, M.E. A hybrid system coupling spiral type solar photovoltaic thermal collector and electrocatalytic hydrogen production cell: Experimental investigation and numerical modeling. Process Saf. Environ. Prot. 2023, 170, 1101–1120. [Google Scholar] [CrossRef]
  86. Yahaya, A.A.; AlMuhaini, M.; Heydt, G.T. Optimal design of hybrid DG systems for microgrid reliability enhancement. IET Gener. Transm. Distrib. 2020, 14, 816–823. [Google Scholar] [CrossRef]
  87. Andlar, M.; Belskaya, H.; Morzak, G.; Ivančić Šantek, M.; Rezić, T.; Petravić Tominac, V.; Šantek, B. Biogas production systems and upgrading technologies: A review. Food Technol. Biotechnol. 2021, 59, 387–412. [Google Scholar] [CrossRef]
  88. Huang, X. The promotion of anaerobic digestion technology upgrades in waste stream treatment plants for circular economy in the context of “Dual Carbon”: Global status, development trend, and future challenges. Water 2024, 16, 3718. [Google Scholar] [CrossRef]
  89. Lee, E.; Min, K.J.; Park, K.Y. Changes in anaerobic digestion performance and microbial community by increasing SRT through sludge recycling in food waste leachate treatment. Sci. Rep. 2025, 15, 19845. [Google Scholar] [CrossRef]
  90. Jiang, Q.; Wu, P.; Zhang, X.; Zhang, Y.; Cui, M.; Liu, H.; Liu, H. Deciphering the effects of engineered biochar on methane production and the mechanisms during anaerobic digestion: Surface functional groups and electron exchange capacity. Energy Convers. Manag. 2022, 258, 115417. [Google Scholar] [CrossRef]
  91. Harirchi, S.; Wainaina, S.; Sar, T.; Nojoumi, S.A.; Parchami, M.; Parchami, M.; Varjani, S.; Khanal, S.K.; Wong, J.; Awasthi, M.K.; et al. Microbiological insights into anaerobic digestion for biogas, hydrogen or volatile fatty acids (VFAs): A review. Bioengineered 2022, 13, 6521–6557. [Google Scholar] [CrossRef] [PubMed]
  92. Zhou, B.; Wu, W.; Li, X.; Dai, R.; Wang, Z. A state-of-the-art review on anaerobic digestion of sewage sludge based on microbial abundance: Correlations among microbiota, performance and process parameters. Crit. Rev. Environ. Sci. Technol. 2024, 54, 1093–1116. [Google Scholar] [CrossRef]
  93. Ferry, J.G. Methanosarcina acetivorans: A model for mechanistic understanding of aceticlastic and reverse methanogenesis. Front. Microbiol. 2020, 11, 1806. [Google Scholar] [CrossRef]
  94. Dellagnezze, B.M.; Bovio-Winkler, P.; Lavergne, C.; Menoni, D.A.; Mosquillo, F.; Cabrol, L.; Barret, M.; Etchebehere, C. Temperature increase affects acetate-derived methane production in Alaskan lake sediments and wetland soils. bioRxiv 2021, preprint. [Google Scholar] [CrossRef]
  95. Vítězová, M.; Kohoutová, A.; Vítěz, T.; Hanišákováx, N.; Kushkevych, I. Methanogenic microorganisms in industrial wastewater anaerobic treatment. Processes 2020, 8, 1546. [Google Scholar] [CrossRef]
  96. Manono, B.O. Carbon dioxide, nitrous oxide and methane emissions from the Waimate District (New Zealand) pasture soils as influenced by irrigation, effluent dispersal and earthworms. Cogent Environ. Sci. 2016, 2, 1256564. [Google Scholar] [CrossRef]
  97. Mazzurco Miritana, V.; Massini, G.; Visca, A.; Grenni, P.; Patrolecco, L.; Spataro, F.; Rauseo, J.; Garbini, G.L.; Signorini, A.; Rosa, S.; et al. Effects of sulfamethoxazole on the microbial community dynamics during the anaerobic digestion process. Front. Microbiol. 2020, 11, 537783. [Google Scholar] [CrossRef] [PubMed]
  98. Valenti, F.; Rojas-Sossa, J.P.; Zhong, Y.; Porto, S.M.; Toscano, A.; Marsh, T.; Dale, B.E.; Liu, Y.; Liao, W. Effects of Mediterranean agricultural residues on microbial community and anaerobic digestion performance. Biofuels Bioprod. Biorefin. 2022, 16, 523–536. [Google Scholar] [CrossRef]
  99. Zhen, X.; Luo, M.; Dong, H.; Fang, L.; Wang, W.; Feng, L.; Yu, Q. Effect of organic load regulation on anaerobic digestion performance and microbial community of solar-assisted system of food waste. Water Reuse 2022, 12, 260–273. [Google Scholar] [CrossRef]
  100. Rahavi, S.M.; Ahmadi, F.; Vahid, A.; Moinoddini, H.; Ghayour, M.; Tagliapietra, F. Design, development and validation of an automated gas monitoring equipment for measurement of the dynamics of microbial fermentation. MethodsX 2022, 9, 101641. [Google Scholar] [CrossRef]
  101. Pangallo, D.; Pedullà, A.; Zema, D.A.; Calabrò, P.S. Influence of the preliminary storage on methane yield of anaerobic digestion of the organic fraction of municipal solid waste. Processes 2021, 9, 2017. [Google Scholar] [CrossRef]
  102. Pereira, A.R.; Assis, N.V.; Paranhos, A.G.; Lima, D.R.; Baeta, B.E.; Aquino, S.F.; Silva, S.D. Effect of inoculum composition on the microbial community involved in the anaerobic digestion of sugarcane bagasse. Environ. Technol. 2024, 45, 2205–2217. [Google Scholar] [CrossRef]
  103. Alhraishawi, A.; Aslan, S. Effect of lipid content on anaerobic digestion process and microbial community: Review study. Eur. Sci. J. ESJ 2022, 8, 197–208. [Google Scholar] [CrossRef]
  104. Brown, F.C.; Burge, S.R.; Hristovski, K.D.; Burge, R.G.; Taylor, E.; Hoffman, D.A. Microbial potentiometric sensor technology for real-time detecting and monitoring of toxic metals in aquatic matrices. Maced. J. Chem. Chem. Eng. 2020, 39, 119–127. [Google Scholar] [CrossRef]
  105. Manono, B.O.; Mwami, B.; Mutavi, S.; Nzilu, F. Precision farming with smart sensors: Current state, challenges and future outlook. Sensors 2026, 26, 882. [Google Scholar] [CrossRef]
  106. Tisocco, S.; Weinrich, S.; Møller, H.B.; Ward, A.J.; Kilmartin, L.; Zhan, X.; Crosson, P. Machine learning vs. ADM1: Reliable biogas prediction with minimal data requirements in full-scale plants. Environ. Sci. Ecotechnol. 2026, 29, 100662. [Google Scholar] [CrossRef]
  107. Gupta, R.; Zhang, L.; Hou, J.; Zhang, Z.; Liu, H.; You, S.; Ok, Y.S.; Li, W. Review of explainable machine learning for anaerobic digestion. Bioresour. Technol. 2023, 369, 128468. [Google Scholar] [CrossRef]
  108. Kazemi, P.; Bengoa, C.; Steyer, J.P.; Giralt, J. Data-driven techniques for fault detection in anaerobic digestion process. Process Saf. Environ. Prot. 2021, 146, 905–915. [Google Scholar] [CrossRef]
  109. Walid, F.; El Fkihi, S.; Benbrahim, H.; Tagemouati, H. Modeling and optimization of anaerobic digestion: A review. E3S Web Conf. 2021, 229, 01022. [Google Scholar] [CrossRef]
  110. de Castro, T.M.; Cammarota, M.C.; Pacheco, E.B. Sustainability of the anaerobic digestion of oil refinery secondary sludge. Bioengineered 2023, 14, 181–196. [Google Scholar] [CrossRef] [PubMed]
  111. Nleya, Y.; Young, B.; Nooraee, E.; Baroutian, S. Opportunities and challenges for anaerobic digestion of farm dairy effluent. ChemBioEng Rev. 2023, 10, 924–940. [Google Scholar] [CrossRef]
  112. Manono, B.O. Effects of Irrigation, Effluent Dispersal and Organic Farming on Earthworms and Soil Microbes in New Zealand Dairy Farms. Ph.D. Thesis, University of Otago, Dunedin, New Zealand, 2014. Available online: https://hdl.handle.net/10523/5097 (accessed on 20 February 2026).
  113. Francini, G.; Lasagni, M.; Lombardi, L. Comparison of anaerobic digestion technologies: An Italian case study. Detritus 2020, 9, 94–104. [Google Scholar] [CrossRef]
  114. Czekała, W.; Nowak, M.; Bojarski, W. Anaerobic digestion and composting as methods of bio-waste management. Agric. Eng. 2023, 27, 173–186. [Google Scholar] [CrossRef]
  115. Manono, B.O. Small-scale farming in the United States: Challenges and pathways to enhanced productivity and profitability. Sustainability 2025, 17, 6752. [Google Scholar] [CrossRef]
  116. Guo, Y.; Xiao, F.; Yan, M.; Tang, S.; Duan, Z.; Sun, Y.; Li, Y. Effect of ammonia on anaerobic digestion: Focusing on energy flow and electron transfer. Chem. Eng. J. 2023, 471, 144638. [Google Scholar] [CrossRef]
  117. Seruga, P. The municipal solid waste management system with anaerobic digestion. Energies 2021, 14, 2067. [Google Scholar] [CrossRef]
  118. Cruz, I.A.; Andrade, L.R.; Bharagava, R.N.; Nadda, A.K.; Bilal, M.; Figueiredo, R.T.; Ferreira, L.F. An overview of process monitoring for anaerobic digestion. Biosyst. Eng. 2021, 207, 106–119. [Google Scholar] [CrossRef]
  119. Meola, A.; Weinrich, S. Hybrid modelling of dynamic anaerobic digestion process in full-scale with LSTM NN and BMP measurements. In Proceedings of the ESANN 2023, Bruges, Belgium, 4–6 October 2023. [Google Scholar] [CrossRef]
  120. Elviliana, E.; Suhartini, S.; Hidayat, N.; Oechsner, H. A mini-review on anaerobic digestion of Indonesian macroalgae biomass: Present scenario and future scope. Adv. Food Sci. Sustain. Agric. Agroindustrial. Eng. (AFSSAAE) 2022, 5, 154–170. [Google Scholar] [CrossRef]
  121. Sevillano, C.A.; Pesantes, A.A.; Peña Carpio, E.; Martínez, E.J.; Gómez, X. Anaerobic digestion for producing renewable energy—The evolution of this technology in a new uncertain scenario. Entropy 2021, 23, 145. [Google Scholar] [CrossRef] [PubMed]
  122. Rezaee, M.; Gitipour, S.; Sarrafzadeh, M.H. Different pathways to integrate anaerobic digestion and thermochemical processes: Moving toward the circular economy concept. Environ. Energy Econ. Res. 2020, 4, 57–67. [Google Scholar] [CrossRef]
  123. Bhatt, A.H.; Tao, L. Economic perspectives of biogas production via anaerobic digestion. Bioengineering 2020, 7, 74. [Google Scholar] [CrossRef] [PubMed]
  124. Jurgutis, L.; Šlepetienė, A.; Šlepetys, J.; Cesevičienė, J. Towards a full circular economy in biogas plants: Sustainable management of digestate for growing biomass feedstocks and use as biofertilizer. Energies 2021, 14, 4272. [Google Scholar] [CrossRef]
  125. Manono, B.O. Methane Emissions from Livestock Operations: Sources, Sinks, and Mitigation Strategies. Methane 2026, 5, 7. [Google Scholar] [CrossRef]
  126. Kapustová, Z.; Boháčiková, A.; Lajda, J. The economic viability of the energy production from biomass via anaerobic digestion. In Knowledge Based Sustainable Development; Udruženje Ekonomista i Menadžera Balkana: Belgrade, Serbia, 2020; p. 41. Available online: https://www.ceeol.com/search/chapter-detail?id=1232711 (accessed on 6 February 2026).
  127. Dach, J.; Mazurkiewicz, J.; Janczak, D.; Pulka, J.; Pochwatka, P.; Kowalczyk-Juśko, A. Cow Manure Anaerobic Digestion or Composting–Energetic and Economic Analysis. In 2020 4th International Conference on Green Energy and Applications (ICGEA); IEEE: New York, NY, USA, 2020; pp. 143–147. [Google Scholar] [CrossRef]
  128. Tolessa, A.; Louw, T.M.; Goosen, N.J. Probabilistic techno-economic assessment of anaerobic digestion predicts economic benefits to smallholder farmers with quantifiable certainty. Waste Manag. 2022, 138, 8–18. [Google Scholar] [CrossRef]
  129. Wu, Z.; Tang, M.; Qiu, R.; Hou, S.; Chen, X.; Ma, J. Economic Benefit Analysis of Anaerobic Digestion System. E3S Web Conf. 2023, 409, 02017. [Google Scholar] [CrossRef]
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Figure 1. Conceptual overview of the AD process showing hydrolysis, acidogenesis, acetogenesis, and methanogenesis as sequential but tightly coupled stages, with hydrolysis highlighted as the dominant kinetic bottleneck for complex feedstocks. The figure identifies the major intervention points synthesized in this review—pretreatment, additives, reactor staging, monitoring, and upgrading—and frames AD as an integrated process train.
Figure 1. Conceptual overview of the AD process showing hydrolysis, acidogenesis, acetogenesis, and methanogenesis as sequential but tightly coupled stages, with hydrolysis highlighted as the dominant kinetic bottleneck for complex feedstocks. The figure identifies the major intervention points synthesized in this review—pretreatment, additives, reactor staging, monitoring, and upgrading—and frames AD as an integrated process train.
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Figure 2. Process intensification framework for enhanced methane production. The integrated schematic diagram shows how pretreatment, co-digestion, and advanced reactor configurations act through different mechanisms (hydrolysis acceleration, nutrient balancing, phase separation, biomass retention, electron-transfer enhancement) to improve methane yield and process stability. The figure emphasizes that intensification outcomes are moderated by energy demand, CAPEX/OPEX, and site-specific operating constraints.
Figure 2. Process intensification framework for enhanced methane production. The integrated schematic diagram shows how pretreatment, co-digestion, and advanced reactor configurations act through different mechanisms (hydrolysis acceleration, nutrient balancing, phase separation, biomass retention, electron-transfer enhancement) to improve methane yield and process stability. The figure emphasizes that intensification outcomes are moderated by energy demand, CAPEX/OPEX, and site-specific operating constraints.
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Figure 3. Additive-mediated mechanisms enhancing methane production in anaerobic digestion. Mechanistic schematic linking conductive materials, enzyme additives, and buffering/trace-element systems to their primary process effects (DIET promotion, hydrolysis acceleration, and pH/nutrient stabilization, respectively). The figure also highlights that methane gains are conditional on dose, substrate composition, and reactor operating conditions.
Figure 3. Additive-mediated mechanisms enhancing methane production in anaerobic digestion. Mechanistic schematic linking conductive materials, enzyme additives, and buffering/trace-element systems to their primary process effects (DIET promotion, hydrolysis acceleration, and pH/nutrient stabilization, respectively). The figure also highlights that methane gains are conditional on dose, substrate composition, and reactor operating conditions.
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Figure 4. Biogas upgrading pathways from raw biogas to biomethane. Comparative process schematic showing pretreatment, physicochemical upgrading routes (membrane, cryogenic, water scrubbing, chemical absorption), and biological routes (in situ/ex situ methanation and microalgae), converging on biomethane production and CO2 handling options. The figure emphasizes selection criteria such as plant scale, energy demand, impurity tolerance, methane slip, and CO2 valorization potential.
Figure 4. Biogas upgrading pathways from raw biogas to biomethane. Comparative process schematic showing pretreatment, physicochemical upgrading routes (membrane, cryogenic, water scrubbing, chemical absorption), and biological routes (in situ/ex situ methanation and microalgae), converging on biomethane production and CO2 handling options. The figure emphasizes selection criteria such as plant scale, energy demand, impurity tolerance, methane slip, and CO2 valorization potential.
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Figure 5. Microbial community interactions and process control in anaerobic digestion. Ecological-process schematic showing coupling among hydrolytic, acidogenic, acetogenic, and methanogenic guilds, with methanogenesis differentiated into acetoclastic, hydrogenotrophic, and methylotrophic pathways. The figure also situates additives, inhibitors, and monitoring/AI control as regulators of community function and methane kinetics.
Figure 5. Microbial community interactions and process control in anaerobic digestion. Ecological-process schematic showing coupling among hydrolytic, acidogenic, acetogenic, and methanogenic guilds, with methanogenesis differentiated into acetoclastic, hydrogenotrophic, and methylotrophic pathways. The figure also situates additives, inhibitors, and monitoring/AI control as regulators of community function and methane kinetics.
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Figure 6. Economic value pathways in anaerobic digestion and biomethane systems. Systems-level diagram linking major cost centers (pretreatment, reactor infrastructure, upgrading, monitoring, logistics) with revenue and value pathways (biomethane, heat/electricity, digestate, CO2 utilization, tipping fees, carbon credits). The figure emphasizes value stacking and policy context as primary determinants of project feasibility.
Figure 6. Economic value pathways in anaerobic digestion and biomethane systems. Systems-level diagram linking major cost centers (pretreatment, reactor infrastructure, upgrading, monitoring, logistics) with revenue and value pathways (biomethane, heat/electricity, digestate, CO2 utilization, tipping fees, carbon credits). The figure emphasizes value stacking and policy context as primary determinants of project feasibility.
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Table 1. Process intensification methods in anaerobic digestion.
Table 1. Process intensification methods in anaerobic digestion.
Method CategoryRepresentative TechniquesPrimary MechanismMain Performance EffectsKey Limitations/Implementation NotesReferences
PhysicalMilling, grinding; thermal hydrolysis; hydrothermal subcritical water; ultrasoundIncreases surface area, cell disruption, solubilizationFaster hydrolysis, improved biodegradability, higher methane yield/rate Energy demand can offset gains, especially for ultrasound; thermal systems require heat integration and solids management[20,21,22,29]
ChemicalAlkali/acid pretreatment (e.g., NaOH); ozonation; KMnO4Lignocellulose deconstruction, floc disintegration, oxidation of recalcitrant fractionsImproved substrate accessibility and hydrolysis; increased methane production in sludge and lignocellulosic systems Reagent cost, corrosion, residual chemical handling, and process compatibility at full scale[23,24]
BiologicalEnzyme additives (cellulase, amylase); bioaugmentationEnzymatic depolymerization and targeted microbial function enhancementHydrolysis acceleration and improved methane yield, especially for carbohydrate-rich substrates Feedstock-specific effectiveness; enzyme cost and dosing optimization required[25,27]
HybridCombined physical–chemical–biological approachesMulti-mechanism substrate conditioningCan maximize degradability and yield while improving process robustness Requires careful techno-economic optimization; operational complexity increases[28]
Table 2. Co-digestion feedstock combinations and outcomes.
Table 2. Co-digestion feedstock combinations and outcomes.
Feedstock CombinationTypical RationaleReported OutcomesBoundary Conditions/NotesReferences
Food waste + animal manureBalance C:N ratio, improve buffering, dilute inhibitorsHigher methane yield, improved vs. removal, more stable operation Highly dependent on mixing ratio and loading rate; target C:N is typically 20–30:1[32,33,34,40]
Microalgae + organic wastes (e.g., potato waste, glycerol)Correct unfavorable microalgal C:N and improve biodegradabilitySpecific methane yield improvements reported (53–128%) and improved economicsMicroalgal digestibility and pretreatment requirements remain important[35]
Lignocellulosic residues + manureAdd buffering/nutrients to fibrous substrates; improve inoculation and dilutionMethane yield increases, especially when residues are pretreated Pretreatment is often necessary for consistent gains[39]
Slaughterhouse/poultry-related streams + sludgeReduce instability and manage high-strength wastesHigher methane production and improved stability at optimized ratios Ammonia risk and protein-derived inhibition require close control[11]
Food waste + crude glycerolAdd concentrated carbon source; improve gas yieldEnhanced biogas production potential Glycerol dosing must be controlled to avoid acidification[36]
Sewage sludge fractions + fruit wasteUse carbohydrate-rich co-substrate to improve VFA conversion and pH behaviorLarge biogas increases reported in optimized systems pH control and alkalinity management remain critical[37]
Table 3. Reactor designs and performance implications in anaerobic digestion.
Table 3. Reactor designs and performance implications in anaerobic digestion.
Reactor DesignCore Design PrincipleTypical BenefitsMain Constraints/Deployment ConsiderationsReferences
Two-stage ADSeparates acidogenesis and methanogenesis into distinct reactorsImproved stability, better phase-specific control, higher biogas quality/yield potential, possible H2 + CH4 integration Higher capital and control complexity; requires stable inter-stage management[1,24,40,41]
TPADThermophilic front-end followed by mesophilic methanogenic stageEnhanced hydrolysis, pathogen reduction, improved solids destruction, more stable methanogenesis than fully thermophilic systems Thermal integration and energy demand are critical[22,42]
Fixed-bed/media-supported reactorsPacking media improve biomass retention and local microbial densityHigher COD removal, improved stability, greater loading tolerance Media fouling/clogging and replacement cost[43]
HS-ADOperates at elevated solids to reduce water use and reactor volumeLower water demand, compact footprint, strong performance for dry residues Mixing and mass-transfer constraints; local inhibition risk[22,46,47]
Microbial electrochemical integrationElectrochemical stimulation of microbial pathways and electron transferFaster methane production, improved microbial enrichment, possible CO2-to-CH4 support Electrical infrastructure and scale-up uncertainty[44,45,48,49]
Table 4. Additives and their effects in anaerobic digestion.
Table 4. Additives and their effects in anaerobic digestion.
Additive ClassExamplesDominant MechanismTypical BenefitsKey CaveatsReferences
Conductive nanoparticles/carbonaceous materialsBiochar, graphene, Fe3O4 nanoparticles, nano-magnetite, graphite powderDIET promotion, microbial aggregation, electron-transfer facilitationHigher methane rate/yield, improved VFA conversion, enhanced microbial/enzyme activity Dose-sensitive; excessive loading can inhibit activity or reduce diversity [14,15,31,52,54,56]
Enzyme additivesCellulase, amylaseHydrolysis acceleration via polymer depolymerizationImproved biodegradability and methane yield in carbohydrate-rich substrates Performance depends on substrate composition, pretreatment status, and operating conditions[25,26]
pH and nutrient regulatorsAsh, biochar, K2HPO4/KH2PO4, trace elements (Fe, Co, Ni)Buffering, micronutrient supply, inhibitory compound adsorptionStabilized pH, lower VFA stress, improved methanogenic activity; possible H2S reduction Requires site-specific dosing; excess trace metals may cause toxicity or precipitation[24,61,62,63]
Table 5. Biogas upgrading technologies summarized as a multi-criteria screening matrix for methane purity, scale fit, impurity tolerance, methane slip or energy burden, and CO2 valorization potential.
Table 5. Biogas upgrading technologies summarized as a multi-criteria screening matrix for methane purity, scale fit, impurity tolerance, methane slip or energy burden, and CO2 valorization potential.
Technology ClassMethodOperating PrincipleTypical Methane PurityKey StrengthsMain LimitationsReferences
PhysicochemicalMembrane separationSelective permeation of CO2 and other gases through membranesUp to >95% Modular, scalable, continuous operation, strong CO2 removal Sensitive to impurities; fouling- and pressure-related energy demand [13,65,66,67]
PhysicochemicalCryogenic separationCooling to liquefy/solidify CO2 and impuritiesVery high purity (>95%) High biomethane purity, concentrated CO2 stream, low methane slip High CAPEX/OPEX and process complexity)[70,71]
PhysicochemicalWater scrubbingDissolution of CO2 and H2S in water under pressure~95%Robust, relatively simple, suitable for small/medium plants Water use, regeneration energy, methane dissolution losses [72,73,74]
PhysicochemicalChemical absorption (amines)Selective solvent absorption and thermal regenerationUp to >99% High purity and effective CO2/H2S removalSolvent regeneration energy, corrosion, solvent management [75,76]
BiologicalIn situ methanationH2 injected into digester; methanogens convert CO2 to CH4Up to ~90% in some cases CO2 conversion within digester, potentially lower equipment cost Gas transfer and pH control challenges; difficult co-optimization[7,77]
BiologicalEx situ methanationSeparate methanation reactor with H2 and biogas/CO2~80–90% Better control and stable conversion performance Additional reactor and H2 infrastructure increase complexity/cost[79,80]
BiologicalMicroalgae photobioreactorsPhotosynthetic CO2 uptake with biomass productionEnhanced gas quality CO2 removal with biomass co-product; potential H2S co-removal Land/lighting requirements, climate sensitivity, harvesting cost[8,81]
Hybrid/IntegratedPVT-coupled or multi-method trainsCombines upgrading methods and renewable heat/electricity supplyOften >95% Improved energy efficiency, reduced carbon footprint, system flexibility Higher upfront cost and control-system complexity[84,85,86]
Table 6. Economic analysis of anaerobic digestion innovations.
Table 6. Economic analysis of anaerobic digestion innovations.
Economic DomainKey Findings from LiteratureMain Risk FactorsImprovement StrategiesReferences
Capital costs (CAPEX)Advanced pretreatment, upgrading, and staged/specialized reactors increase upfront cost; farm-scale plants can require high initial investment Long payback periods, financing barriers, small-scale economicsModular designs, phased deployment, policy support (subsidies/grants), and design simplification [65,110,111,113,121,126]
Operating costs (OPEX)Energy-intensive pretreatments and upgrading increase OPEX; skilled labor and maintenance are major contributors Energy price volatility, operator skill gaps, downtimeAI-assisted optimization and predictive maintenance; renewable-energy integration [16,29,85,110]
Biomethane/biogas revenueRevenue depends on gas yield, methane purity, and market route Methane slip, gas quality compliance, market accessCo-optimize intensification and upgrading; route-specific gas polishing and methane-recovery monitoring[123,129]
Digestate valorizationDigestate can provide substantial fertilizer value and additional income Variable product quality, market acceptance, logisticsPost-treatment, nutrient recovery, quality standardization, and local market development[10,124]
CO2 conversion/utilizationBiological methanation and CO2 use can increase carbon efficiency and value H2 availability/cost and process integration complexityIntegrate renewable-H2 pathways and target local CO2 use cases[7]
Policy and overall feasibilityIncentives and regulation often determine bankability Policy volatility, weak carbon pricing, permitting barriersTEA-LCA-informed policy design and bundled value models [110,118,121,126]
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Manono, B.O.; Ming’ate, F.L.M. Recent Advances and Prospects in Methane Production from Anaerobic Digestion: Process Intensification, Additives, and Biogas Upgrading. Methane 2026, 5, 13. https://doi.org/10.3390/methane5020013

AMA Style

Manono BO, Ming’ate FLM. Recent Advances and Prospects in Methane Production from Anaerobic Digestion: Process Intensification, Additives, and Biogas Upgrading. Methane. 2026; 5(2):13. https://doi.org/10.3390/methane5020013

Chicago/Turabian Style

Manono, Bonface O., and Felix Lamech Mogambi Ming’ate. 2026. "Recent Advances and Prospects in Methane Production from Anaerobic Digestion: Process Intensification, Additives, and Biogas Upgrading" Methane 5, no. 2: 13. https://doi.org/10.3390/methane5020013

APA Style

Manono, B. O., & Ming’ate, F. L. M. (2026). Recent Advances and Prospects in Methane Production from Anaerobic Digestion: Process Intensification, Additives, and Biogas Upgrading. Methane, 5(2), 13. https://doi.org/10.3390/methane5020013

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