Dry Anaerobic Digestion of Selectively Collected Biowaste: Technological Advances, Process Optimization and Energy Recovery Perspectives
Abstract
1. Introduction
2. Characterization and Pretreatment of Organic Waste for Dry Anaerobic Digestion
2.1. Quantitative Aspects of Selective Biowaste Collection in Municipal Waste Management
2.2. Biochemical Characteristics of Organic Waste Streams and Their Potential for Anaerobic Digestion
2.3. Pre-Treatment Methods for Organic Waste Prior to Anaerobic Digestion
3. Key Technological Aspects of Dry Anaerobic Digestion
3.1. Overview of Used Digesters and Feeding/Unloading Techniques
3.2. Comparison of Dry and Wet Fermentation
3.3. Process Parameters Overview
3.4. Analysis of Factors Limiting Process Performance
3.5. Selected Intensification Techniques
4. Energy Efficiency and Economic Aspects of Dry Anaerobic Digestion
4.1. Biogas Plant Energy Balance
4.2. Cost and Revenue Analysis
4.3. The Importance of Biogas in the Energy Transformation
5. Environmental Impact and Legal Regulations
5.1. Comparison of the Carbon Footprint of Dry Anaerobic Digestion and Other Organic Waste Treatment Methods (Composting, Landfilling, Wet Anaerobic Digestion)
5.2. Legal Framework for Waste Management in the European Union and Globally
5.3. The Role of Dry Anaerobic Digestion in the Circular Economy: Soil Carbon Sequestration and Greenhouse Gas Emission Reduction
- Methane emissions reduction from organic waste: Diverting biowaste from landfilling to DAD prevents uncontrolled anaerobic decomposition, which would otherwise generate significant amounts of methane (CH4) [117].
- Replacement of synthetic fertilizers: The use of digestate reduces reliance on mineral fertilizers, the production of which is associated with high carbon dioxide (CO2) emissions [161].
- Production of green energy: Biogas generated through DAD can be converted into electricity, heat, or upgraded to biomethane, thereby reducing dependence on fossil fuels.
6. Priority Research Directions and Policy and Strategic Planning Recommendations for Municipal Integration of DAD Systems
6.1. Overview of Mature DAD Technologies and Emerging Hybrid Optimization Methods
6.2. Holistic Process Optimization in DAD: Addressing Limitations Through Synergistic Engineering
6.3. From Waste to Value: Economic and Governance Pathways for Circular DAD Integration
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AD | Anaerobic Digestion |
BCM | Billion Cubic Meters |
CCS | Carbon Capture and Storage |
CCU | Carbon Capture and Utilization |
CE | Circular Economy |
C/N | Carbon-to-Nitrogen Ratio |
CSTR | Continuous Stirred-Tank Reactor |
DAD | Dry Anaerobic Digestion |
EfW | Energy from Waste |
EU27 | European Union Countries |
F/T | Freezing/Thawing |
GHG | Greenhouse Gas |
HRT | Hydraulic Retention Time |
LCA | Life Cycle Assessment |
KPGO | Polish National Waste Management Plan |
MW | Megawatt |
OFMSW | Organic Fraction of Municipal Solid Waste |
OLR | Organic Loading Rate |
PFR | Plug-Flow Reactor |
PLN | Polish Zloty (currency) |
PP MP | Polypropylene Microplastics |
PPP | public-private partnership |
ROS | Reactive Oxygen Species |
S/I | Substrate To Inoculum Ratio |
SOC | Soil Organic Carbon |
SRB | Sulphate-Reducing Bacteria |
SRT | Solid Retention Time |
TPA | Tons per Annum |
TS | Total Solids |
TWh | Terawatt-hour |
VFA | Volatile Fatty Acids |
VS | Volatile Solids |
WAD | Wet Anaerobic Digestion |
WtE | Waste to Energy |
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Year | Total Mass of Collected Municipal Waste (Thousand Tons) | Amount of Municipal Waste Generated per Capita (kg/a) | The Mass of Separately Collected Biodegradable Waste (Thousand Tons) | Relative Share of Separately Collected Biodegradable Waste to the Total Municipal Waste Stream (%) | The Amount of Biodegradable Waste Collected per Capita (kg/a) | Proportion of Separately Collected Biowaste in Relation to the Total Biodegradable Waste Generated (%) |
---|---|---|---|---|---|---|
2015 | 10,863.5 | 283 | 657.0 | - | - | 25.9 |
2020 | 13,116.9 | 344 | 1610.0 | 12.3 | 42 | 32.4 |
2021 | 13,673.6 | 360 | 1843.0 | 13.5 | 49 | 33.9 |
2022 | 13,420.3 | 355 | 1913.6 | 14.3 | 51 | 35.7 |
2023 | 13,447.9 | 357 | 2042.6 | 15.2 | 54 | 37.4 |
Waste Type | Biogas Yield (mL/g VS) | Methane Yield (mL/g VS) | Fermentation Time (days) | C/N Ratio | pH | Experimental Parameters | Source |
---|---|---|---|---|---|---|---|
Kitchen waste | |||||||
Cooked rice | 2821 | ~1770 | 28 | 30.9 | 7.2 | Lab-scale batch BMP (1 L serum bottles), mesophilic 37 °C, TS ≈ 8%, S/I 1:1 (w/w) | |
Cooked beans | 983 | ~590 | 11 | - | - | [33] | |
Cabbage | 800 | ~480 | 10 | 13.9 | 6.2 | ||
Green waste | |||||||
Fresh grass | 639.7 | ~384 | - | - | - | Lab BMP, mesophilic 37 °C; freshly harvested; TS ≈ 20% | |
Dried grass | 565.9 | ~339 | - | - | - | Lab BMP, mesophilic 37 °C; sun-dried; TS ≈ 90% | [34] |
Ensiled grass | 573.4 | ~344 | - | - | - | Lab BMP, mesophilic 37 °C; ensiled 60 d; TS ≈ 30% | |
Agrofood industry | |||||||
Fruit-vegetable mix | 720 | ~436 | - | - | - | Batch co-digestion test with dairy sludge; mesophilic 35 °C; TS ≈ 12% | [38] |
Corn straw | 330 | ~250 | 8 | - | 7.5 | Batch BMP, mesophilic 37 °C; alkaline-adjusted; TS ≈ 25% | [46] |
Waste Type | Dry Matter Content (%) | Optimal Temperature (°C) | Retention Time (Days) | Advantages | Challenges | Experimental Parameters | Source |
---|---|---|---|---|---|---|---|
Kitchen waste | 8–33 | 35–40 | 15–30 | High biodegradability, easy availability | Low carbon content, possible acidification | Mesophilic 35–37 °C; TS adjusted to ≥15% | [33] |
Green waste | 20–40 | 35–40 | 20–35 | Stable structure, good biodegradability | Seasonal availability | mesophilic 37 °C; batch mode | [34] |
Food industry | 10–30 | 35–40 | 15–25 | Diverse organic composition | Variable chemical composition | mesophilic 35 °C; codigestion | [37] |
Lignocellulose | 85–95 | 35–55 | 25–45 | Low water content, easy storage | High biodegradation resistance | mesophilic 37 °C; pH adjusted 7.5; TS 25–30% | [46] |
Method | Mechanism | Description | Source |
---|---|---|---|
Mechanical shredding | Increased specific surface area, rupture of cell walls, shorter diffusion path for enzymes, substrate homogenization, removal of contaminants (glass, metals) | Shredding to <10 mm, often using hammer or knife mills. | [47,56,57,58,59] |
Alkaline treatment | Deacetylation and loosening of lignin fibers, increase in soluble sugars. | Biomass treated with alkaline solution (e.g., NaOH, Ca(OH)2). | [60,61,62] |
Hydrothermal hydrolysis | Decomposition of hemicellulose into simple sugars, partial degradation or redeposition of lignin. | Biomass decomposition in hot pressurized water without chemical additives. | [63,64,65] |
Parameter | Dry Anaerobic Digestion (DAD) | Wet Anaerobic Digestion (WAD) |
---|---|---|
Dry matter content (TS) | High solids content: 20–40% TS | Low solids content: <10–20% TS |
Water and energy demand | Low—pre-treatment of feedstocks does not require mechanical particle size reduction or dilution | High—preparation of substrates requires dilutions, energy demand during mixing |
Methane yield | Typically higher methane yield | Typically lower methane yield |
Foaming problem | Not applicable | Occurs |
Space requirements | Reactors with smaller working volumes | Reactors are usually larger than in DAD due to the dilute feedstocks |
Process stability | Often lower process stability resulting from, among others, the risk of acidification | More stable process |
Percolate recycling rate | High | Low or lack |
Investment and operating costs | Possible higher costs of mechanical processes, lower energy costs—no mixing equipment required | Higher water and energy costs |
Parameter | 0.5 MW | 1.0 MW |
---|---|---|
Capital expenditure (CAPEX) | PLN 6.5–7 million | PLN 13–14 million |
Annual operating costs (OPEX) | PLN ~2 million | PLN ~4 million |
Revenue from the sale of electricity | PLN ~1.8 million/year | PLN ~3.6 million/year |
Revenue from the sale of heat | PLN ~1.2 million/year | PLN ~2.4 million/year |
Revenue from digestate | PLN ~0.2 million/year | PLN ~0.4 million/year |
Available subsidies | 45–65% of CAPEX | up to 65% of investment costs |
Country | Estimated Number of Biogas Installations | Biogas Production (TWh/Year) |
---|---|---|
Germany | >10,000 | 87 |
France | ~1600 | 25 |
Italy | - | 22 |
Denmark | - | 7 |
Strengths | Weaknesses | Opportunities | Threats |
---|---|---|---|
High energy efficiency and potential for negative carbon footprint | Higher initial investment compared to composting | Alignment with circular economy and climate neutrality targets | Policy or market instability affecting renewable energy support |
Reduced leachate and condensate compared to wet AD | Limited applicability for very high-moisture feedstocks | Development of biomethane markets and energy system integration | Competition from other waste treatment technologies (e.g., incineration, composting) |
Production of digestate usable as organic fertilizer | Technical complexity requiring skilled operation | Access to EU and global green funding instruments | Public acceptance issues related to odor or local siting |
Closed system minimizes fugitive GHG emissions | Lower experience base compared to wet AD in some regions | Potential role in energy security and regional bioeconomy strategies | Risks related to feedstock availability and contamination |
Technology | Reactor Type | Operating Temp. | Mixing Strategy | Advantages | Limitations | Reference |
---|---|---|---|---|---|---|
Dranco | vertical plug-flow | thermophilic (~50 °C) | none (gravity) | simple design, low OPEX, good for high TS | no mixing may cause channeling; limited flexibility | [164] |
Valorga | vertical with gas injection | mesophilic/thermophilic | gas injection | handles heterogeneous waste, stable performance | mixing energy costs; risk of sedimentation | [165] |
Kompogas | horizontal plug-flow | thermophilic (~55 °C) | slow internal paddles | effective mixing, high reliability | maintenance of mechanical parts | [68] |
Bekon | batch (garage-type) | mesophilic (~37 °C) | none | low-tech, suitable for decentralized use | lower throughput; risk of local inhibition | [166] |
Aikan | integrated dry digestion + composting | thermophilic + aerobic stage | combined stages | maximizes recovery, integrates composting | high CAPEX; system complexity | [68] |
Linde BRV | horizontal plug-flow with percolate recirculation | thermophilic | percolate recirculation | good efficiency, moderate energy needs | clogging risk in recirculation | [68] |
BioPercolat | leach-bed | mesophilic | percolate irrigation | low water demand, modular design | uneven percolate flow; inert buildup | [167] |
Iska | two-stage (dry + wet) | mesophilic + wet methanogenesis | separate dry/wet | improved hydrolysis and methane yield | more space and complexity needed | [168] |
Step No. | Technical Optimization Framework |
---|---|
1 | Pretreatment strategy |
Goal: improve feedstock properties before entering the reactor | |
| |
Effect: improves substrate rheology, reduces energy needs for mixing and heating. | |
2 | Reactor design optimization |
Goal: Adapt reactor to handle high-solids content and improve heat/mass transfer. | |
| |
Effect: ensures homogeneity, stabilizes temperature, reduces dead zones. | |
3 | Smart monitoring & control |
Goal: enable real-time process optimization and fault detection. | |
| |
Effect: Increases reliability, reduces downtime, supports automation. | |
4 | Integration & feedback loop |
All three areas are interconnected: | |
| |
Effect: | |
Enhanced process synergy: integration ensures that modifications in one area (e.g., pretreatment) immediately benefit the others (e.g., reactor efficiency, monitoring accuracy). Real-time adaptability: smart monitoring provides continuous data, enabling dynamic adjustments to pretreatment intensity or reactor settings in response to operational changes. Improved system stability: stable temperature and consistent mixing achieved through adaptive feedback reduce microbial stress and increase biogas yield. Resource efficiency: optimized energy use across pretreatment and reactor operations; reduced water input due to better rheology; overall lowered environmental footprint. Learning-based optimization: AI-driven control systems learn from historical performance, leading to predictive maintenance and optimal scheduling. Closed-loop circularity: continuous refinement of parameters leads to higher substrate conversion, lower residue generation, and improved recovery of energy and nutrients. |
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Bień, B.; Grobelak, A.; Bień, J.; Sławczyk, D.; Kozłowski, K.; Wysokowska, K.; Rak, M. Dry Anaerobic Digestion of Selectively Collected Biowaste: Technological Advances, Process Optimization and Energy Recovery Perspectives. Energies 2025, 18, 4475. https://doi.org/10.3390/en18174475
Bień B, Grobelak A, Bień J, Sławczyk D, Kozłowski K, Wysokowska K, Rak M. Dry Anaerobic Digestion of Selectively Collected Biowaste: Technological Advances, Process Optimization and Energy Recovery Perspectives. Energies. 2025; 18(17):4475. https://doi.org/10.3390/en18174475
Chicago/Turabian StyleBień, Beata, Anna Grobelak, Jurand Bień, Daria Sławczyk, Kamil Kozłowski, Klaudia Wysokowska, and Mateusz Rak. 2025. "Dry Anaerobic Digestion of Selectively Collected Biowaste: Technological Advances, Process Optimization and Energy Recovery Perspectives" Energies 18, no. 17: 4475. https://doi.org/10.3390/en18174475
APA StyleBień, B., Grobelak, A., Bień, J., Sławczyk, D., Kozłowski, K., Wysokowska, K., & Rak, M. (2025). Dry Anaerobic Digestion of Selectively Collected Biowaste: Technological Advances, Process Optimization and Energy Recovery Perspectives. Energies, 18(17), 4475. https://doi.org/10.3390/en18174475