A Comprehensive Review on Thermal Coconversion of Biomass, Sludge, Coal, and Their Blends Using Thermogravimetric Analysis
Bibliographic record
Abstract
Lignocellulosic biomass is a vital resource for providing clean future energy with a sustainable environment. Besides lignocellulosic residues, nonlignocellulosic residues such as sewage sludge from industrial and municipal wastes are gained much attention due to its large quantities and ability to produce cheap and clean energy to potentially replace fossil fuels. These cheap and abundantly resources can reduce global warming owing to their less polluting nature. The low-quality biomass and high ash content of sewage sludge-based thermal conversion processes face several disadvantages towards its commercialization. Therefore, it is necessary to utilize these residues in combination with coal for improvement in energy conversion processes. As per author information, no concrete study is available to discuss the synergy and decomposition mechanism of residues blending. The objective of this study is to present the state-of-the-art review based on the thermal coconversion of biomass/sewage sludge, coal/biomass, and coal/sewage sludge blends through thermogravimetric analysis (TGA) to explore the synergistic effects of the composition, thermal conversion, and blending for bioenergy production. This paper will also contribute to detailing the operating conditions (heating rate, temperature, and residence time) of copyrolysis and cocombustion processes, properties, and chemical composition that may affect these processes and will provide a basis to improve the yield of biofuels from biomass/sewage sludge, coal/sewage sludge, and coal/biomass blends in thermal coconversion through thermogravimetric technique. Furthermore, the influencing factors and the possible decomposition mechanism are elaborated and discussed in detail. This study will provide recent development and future prospects for cothermal conversion of biomass, sewage, coal, and their blends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".