A Review of the State of the Art of Biomethane Production: Recent Advancements and Integration of Renewable Energies
"> Figure 1
<p>Number of studies published per year with the terms “biomethane” or “biogas” contained in article title, abstract or keywords (data from <a href="https://www.scopus.com/" target="_blank">https://www.scopus.com/</a>, 5 August 2021).</p> "> Figure 2
<p>Total biogas production for power generation for each country at the end of 2018.</p> "> Figure 3
<p>Yearly production of biomethane per geographic area in TWh (2018).</p> "> Figure 4
<p>Sequential steps usually considered in AD models.</p> "> Figure 5
<p>Scheme of biogas gathering: 1—ODC, 2—gas sampler, 3—pump, 4—differential pressure meter, 5—biogas meter [<a href="#B44-energies-14-04895" class="html-bibr">44</a>].</p> "> Figure 6
<p>Three-stage UASB reactor [<a href="#B47-energies-14-04895" class="html-bibr">47</a>].</p> "> Figure 7
<p>Schematic representation of the ECSB reactor coupled with an external circulation tank [<a href="#B50-energies-14-04895" class="html-bibr">50</a>].</p> "> Figure 8
<p>Configurations of AnMBR: (<b>a</b>) Biogas-particle sparging; (<b>b</b>) Liquid recirculation particle-sparging; (<b>c</b>) Anaerobic crossflow-particle sparging MBR; (<b>d</b>) Anaerobic rotating MBR (<b>e</b>) Anaerobic electrochemical membrane bioreactor; (<b>f</b>) Individual fluidized MBER; (<b>g</b>) Hybrid MFC-MBER system [<a href="#B55-energies-14-04895" class="html-bibr">55</a>].</p> "> Figure 9
<p>Scheme of an IC reactor [<a href="#B62-energies-14-04895" class="html-bibr">62</a>].</p> "> Figure 10
<p>Scheme of the water scrubbing process [<a href="#B93-energies-14-04895" class="html-bibr">93</a>].</p> "> Figure 11
<p>Scheme of the membrane separation process [<a href="#B93-energies-14-04895" class="html-bibr">93</a>].</p> "> Figure 12
<p>Scheme of the PSA process [<a href="#B93-energies-14-04895" class="html-bibr">93</a>].</p> "> Figure 13
<p>Flow diagram of the cryogenic process [<a href="#B93-energies-14-04895" class="html-bibr">93</a>].</p> "> Figure 14
<p>Biogas and syngas roads for biomethane production [<a href="#B110-energies-14-04895" class="html-bibr">110</a>].</p> "> Figure 15
<p>Biogas–geothermal hybrid renewable energy system [<a href="#B172-energies-14-04895" class="html-bibr">172</a>].</p> "> Figure 16
<p>Multi-renewables energy system [<a href="#B197-energies-14-04895" class="html-bibr">197</a>].</p> ">
Abstract
:1. Introduction
Aim of the Paper
2. Method
- Plant distribution: the current diffusion of biogas plants around the world is presented, along with their main characteristics; then, the potential diffusion in the next years is evaluated, based on the actual international policies.
- Technology advancements: recent developments of biomethane production show a trend in using wastes and mainly wastewater; domestic and industrial wastewater treatment are gaining a large diffusion, also due to recent developments of sludge bed reactors [27].
- Mathematical modeling: many different dynamic models have been developed, regarding different technologies, the type of biomass to be treated, the external conditions and the possible integration with other renewables.
- Hybrid solution for biogas and biomethane production: lignocellulosic biomass gasification and hydrogen are emerging as interesting enhancers of the methane production in AD processes [28]; the use of other renewable sources, such as solar and wind energy, is also becoming the subject of many works [29].
3. Biogas and Biomethane in the World
4. AD Technologies
4.1. Open Digestion Chamber Reactor
4.2. Sludge Bed Reactor
4.3. AnMBR Reactor
4.4. Internal Circulation Reactor
4.5. Biomass Pre-Treatment
5. Biomethane Production
6. Upgrading Technologies
- Physical Absorption
- Chemical Absorption
- Membrane Separation
- Pressure Swing Adsorption
- Cryogenic Separation
7. Biomethane Impurities
7.1. Lignocellulosic Biomass Gasification
7.2. Hydrogen to Biomethane
8. Mathematical Modeling of the Anaerobic Digestion Process
8.1. Simulation of Biochemical and Physical-Chemical Processes
8.2. Model for a High Total Solid Content
- Two-particle: this model considers “seed” particles and “waste” particles inside the solid mass of SS-AD; the “seed” particles present low biodegradability and high methanogenic activity, while the “waste” particles represent substrates with high biodegradability and low methanogenic activity [133]. Both particles influence the diffusion of VFAs according to the Fick’s Law:
- Reaction front: similar to the previous model, a spatial separation of acidogenic and methanogenic zones is adopted. However, in this case, the “seed” particle is a “reaction front” with multiple layers [134]. Considering some simplifying assumptions and a cubic seeding pattern, it is possible to assume that the methane production is proportional to the surface area (cm2) of the “reaction front”, which decreases according to the following equation:
- Spatial temporal: this model considers heterogeneous mass distribution; it is based on the assumptions of both the reaction front model and distributed model. Using a 3D approach, it was demonstrated that the initial substrate and inoculum spatial distribution determine how methanogenic centers expand inside the reactor [139]. Here, the shifting of the reaction fronts is combined with the leachate downward flow and the subsequent VFAs concentration gradients.
- Diffusion limitation: this model simulates the effects of TS percentages in AD processes due to the different physical and chemical characteristics of the input biomass [140]. More specifically, combining the principles of the two-particle and reaction front models, a better model is achieved for dry, semidry and wet processes [141]. Some models were validated with experimental data. Liotta et al. [142] developed a modified ADM1 to evaluate the different content of Total Solids in complex organic matter. Xu et al. [143] developed a model that predicts the inversion of methane production rate at around a certain threshold value of TS percentage inside cellulosic biomass.
- Logistic model: this model was developed based on the following logistic equation:
- General kinetic model: this model is based on the simplified biochemical reaction rate law for the dissolved organic carbon (DOC) in the solid substrate, which is then derived, leading to the general kinetic equation:
8.3. Thermal Models of Anaerobic Digesters
8.4. Models including Both Biological Processes and Heat Exchange Phenomena
9. Hybrid Biomethane Renewable Plants
9.1. Renewable Penetration for Biogas and Biomethane Plant Energy Supply
9.1.1. Solar Thermal Energy
9.1.2. Solar Thermal and Photovoltaic
9.1.3. Solar Thermal and Wind Energy
9.1.4. Solar Thermal and SOFC Energy
9.1.5. Geothermal Energy
9.2. Multi-Renewable Energy Systems
9.3. Energy-Cost Analysis of the Existing Biomethane-Solar Projects
10. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
GHG | Greenhouse Gases |
EBA | European Biogas Association |
FOG | Fat Oil and Grease |
LCFA | Long Chain of Fatty Acids |
OFMSW | Organic Fraction of Municipal Solid Waste |
AD | Anaerobic Digestion |
CSTR | Continuously Stirred Tank Reactor |
PFR | Plug Flow Reactor |
UASB | Upflow Anaerobic Sludge Blanket |
PSA | Pressure Swing Adsorption |
DU | Degassing Unit |
COD | Chemical Oxygen Demand |
CNG | Compressed Natural Gas |
LNG | Liquefied Natural Gas |
PV | Photovoltaic |
SOFC | Solid Oxide Fuel Cell |
CCS | Carbon Capture and Storage |
ADM1 | Anaerobic Digestion Model No.1 |
OLR | Organic Loading Rate |
TVFA | Total Volatile Fatty Acids |
ANN | Artificial Neural Network |
SPB | Simple Pay Back |
NPV | Net Present Value |
FPC | Flat Plate Collector |
PTC | Parabolic Through Collector |
LCA | Life Cycle Assessment |
CPC | Compound Parabolic Collector |
PVT | Photovoltaic Thermal |
CPVT | Concentrating Photovoltaic Thermal |
CHP | Combined Heat and Power |
PES | Primary Energy Saving |
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Geographical Area | Main Sources [14] | Biogas Production (GWh/Year) [30] | Biomethane Production (GWh/Year) [14] |
---|---|---|---|
EU | Energy crops, animal manure, OFMSW | 61,807 | 22,678 |
North America | Energy crops, landfill gases, OFMSW | 14,491 | 7792 |
Latin America | Sugarcane, vinasse, cassava | 1077 | 4303 |
East Asia | Animal manure, OFMSW | 6757 | 2442 |
Australia | Bagasse | 1556 | 0 |
Rest of the world | Various | 2690 | 0 |
Technology | Waste | Operating Conditions | Scale | Ref. |
---|---|---|---|---|
ODC | Wastewater | Psychrophilic | Both | [44] |
UASB | Co-digestion | Mesophilic | Lab | [46] |
UASB | Ethanol wastewater | Mesophilic | Lab | [47] |
EGSB | Synthetic wastewater | Mesophilic | Lab | [49] |
ECSB | Cheesy wastewater | Mesophilic | Full | [50] |
UASB/EGSB | Wastewater | Mesophilic | Lab | [51] |
FAF-R | Sugar industry effluent | Mesophilic | Semi-industrial | [52] |
UASB | Wastewater | Sub-mesophilic | Lab | [53] |
AnMBRs | Synthetic wastewater/Food Waste | Mesophilic | Lab | [55] |
AGS-MBR | Wastewater | Mesophilic | Both | [56] |
AnMBR | Domestic wastewater | Mesophilic | Lab | [57] |
MBBR | Coal gasification wastewater | Sub-mesophilic | Lab | [58] |
IC | Brewery wastewater | Mesophilic | Full | [62] |
Technology | Max. Efficiency | Purity | Total Costs (€/kWh) | Percentage of Use | Ref. |
---|---|---|---|---|---|
Physical absorption | 95.5% | 99% | 370 | 36% | [95] |
Chemical absorption | 97% | 98% | 390 | 19% | [96] |
Membrane separation | 98% | 95% | 400 | 27% | [97] |
PSA | 93.6% | 98% | 413 | 15% | [88] |
Cryogenic separation | 99% | 98% | 1052 | 2% | [98] |
Authors | Model | Experimental Validation | Software | Waste |
---|---|---|---|---|
Fatolahi [121] | ADM1 | √ | MatLab/Simulink | OFMSW |
Shang [122] | ADM1 | √ | Excel/Visual Basic | Wastewater |
Rathnasiri [123] | ADM1 | √ | AQUASIM | Food waste |
Derbal [124] | ADM1 | √ | - | Wastewater |
Esposito [126] | Modified ADM1 | X | MatLab | OFMSW/sewage |
Pastor-Poquet [127] | Modified ADM1 | √ | MatLab | High TS OFMSW |
Fezzani [128] | Modified ADM1 | √ | MatLab | Olive mill waste |
Parker [129] | Modified ADM1 | X | AQUASIM | High TS sludge |
Bai [130] | Modified ADM1 | √ | AQUASIM | Sewage sludge |
Blumensaat [131] | Modified ADM1 | √ | MatLab/Simulink | Sewage sludge |
Reference | Software | Thermodynamic Analysis | Environmental Analysis | Economic Analysis | Optimization | Country |
---|---|---|---|---|---|---|
[167] | TRNSYS | √ | X | X | X | Morocco |
[168] | TRNSYS | √ | √ | √ | √ | China |
[169] | MatLab/Simulink | √ | X | √ | X | China |
[170] | TRNSYS | √ | X | √ | X | China |
[171,172] | EES | √ | √ | √ | √ | - |
Reference | Technology Integrated | Thermodynamic Analysis | Environmental Analysis | Economic Analysis | Optimization | Country |
---|---|---|---|---|---|---|
[184] | PVT-CPC | √ | X | X | √ | India |
[185] | PV | √ | √ | √ | X | Egypt |
[188] | Wind | √ | X | X | √ | - |
[189] | SOFC | √ | √ | √ | X | Italy |
[190] | Geothermal | X | X | √ | X | Greece |
[168] | PTC | √ | √ | √ | X | China |
[191] | FPC/ETC | √ | √ | √ | X | Italy |
[192] | CPVT | √ | √ | √ | X | China |
[167] | CPVT | √ | √ | √ | X | EU |
[193] | CPVT | √ | √ | √ | X | Italy |
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Calise, F.; Cappiello, F.L.; Cimmino, L.; d’Accadia, M.D.; Vicidomini, M. A Review of the State of the Art of Biomethane Production: Recent Advancements and Integration of Renewable Energies. Energies 2021, 14, 4895. https://doi.org/10.3390/en14164895
Calise F, Cappiello FL, Cimmino L, d’Accadia MD, Vicidomini M. A Review of the State of the Art of Biomethane Production: Recent Advancements and Integration of Renewable Energies. Energies. 2021; 14(16):4895. https://doi.org/10.3390/en14164895
Chicago/Turabian StyleCalise, Francesco, Francesco Liberato Cappiello, Luca Cimmino, Massimo Dentice d’Accadia, and Maria Vicidomini. 2021. "A Review of the State of the Art of Biomethane Production: Recent Advancements and Integration of Renewable Energies" Energies 14, no. 16: 4895. https://doi.org/10.3390/en14164895