A conceptual review of sustainable electrical power generation from biogas
Bibliographic record
Abstract
Abstract High‐energy demand with rapid industrialization and mechanization combined with environmental pollution due to the burning of fossil fuels has driven a shift toward renewable energy. Biogas derived from biomass is a potential renewable energy source that can be used in different sectors such as transportation sector, electricity generation, heat production, combined heat and power (CHP) systems, and fuel cells. Moreover, the upgraded biogas can be applied as transportation fuel via an internal combustion chamber (for internal combustion engine (ICE) vehicles), and electricity station (for electric vehicles). In the present work, a conceptual review of biogas‐based electrical power production systems is presented. It is clear that the conventional types of biomass contain a high amount of pollutants and unwanted constituents, which lower the lower heating value (LHV) of biogas fuel. Moreover, the energy and exergy efficiencies of biogas applications are influenced by these components. Consequently, several biogas‐upgrading technologies have been elaborated to increase the LHV of biogas fuel by removing biogas pollutants. So, the energy and exergy analyses of biogas‐driven plants are discussed in this regard. Also, the economic analysis of biogas‐fueled systems is measured through the connection between biogas production, purchased electrical power, and selling of an additional amount of biogas. Biogas represents an important source of renewable energy as shown before, and it helps in waste management and W‐to‐E (waste to energy) conversion, which allows utilizing huge amounts of wastes instead of disposal or landfill procedures. However, handling of biogas from production to utilization has an impact on the environment. Therefore, the assessment of the environmental impacts of biogas plants is presented. In addition, a combination of the biogas energy with other sources, especially renewable energy sources (eg, solar‐biogas, geothermal‐biogas, wind‐biogas, CHP, CCHP, and concentrated photovoltaic‐biogas), and reusing waste energy for other tasks (eg, employing the waste heat from a gas turbine) are examined.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".