A Comprehensive Review on Thermal Coconversion of Biomass, Sludge, Coal, and Their Blends Using Thermogravimetric Analysis
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it