An overview of biomass solid fuels: Biomass sources, processing methods, and morphological and microstructural properties
Bibliographic record
Abstract
Biomass solid fuel (BSF) has emerged as a promising renewable energy source, but its morphological and microstructural properties are crucial in determining their physical, mechanical, and chemical characteristics. This paper provides an overview of recent research on BSF. The focus is on biomass sources, BSF processing methods, and morphological and microstructural properties, with a special emphasis on energy-related studies. Specific inclusion and exclusion criteria were established for the study to ensure relevance. The inclusion criteria encompassed studies about BSFs and studies investigating the influence of biomass sources and processing methods on the morphological and microstructural properties of solid fuels within the past five years. Various technologies for converting biomass into usable energy were discussed, including gasification, torrefaction, carbonization, hydrothermal carbonization (HTC), and pyrolysis. Each has advantages and disadvantages in energy performance, techno-economics, and climate impact. Gasification is efficient but requires high investment. Pyrolysis produces bio-oil, char, and gases based on feedstock availability. Carbonization generates low-cost biochar for solid fuels and carbon sequestration applications. Torrefaction increases energy density for co-firing with coal. HTC processes wet biomass efficiently with lower energy input. Thermal treatment affects BSF durability and strength, often leading to less durability due to voids and gaps between particles. Hydrothermal carbonization alters surface morphology, creating cavities, pores, and distinctive shapes. Slow pyrolysis generates biochar with better morphological properties, while fast pyrolysis yields biochar with lower porosity and surface area. Wood constitutes 67% of the biomass sources utilized for bioenergy generation, followed by wood residues (5%), agro-residues (4%), municipal solid wastes (3%), energy crops (3%), livestock wastes (3%), and forest residues (1%). Each source has advantages and drawbacks, such as availability, cost, environmental impact, and suitability for specific regions and energy requirements. This review is valuable for energy professionals, researchers, and policymakers interested in biomass solid fuel.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".