Pretreatment Using Auto/Acid-Catalyzed Steam Explosion and Water Leaching to Upgrade the Fuel Properties of Wheat Straw for Pellet Production
Bibliographic record
Abstract
Lignocellulosic biomass wastes are renewable carbon resources that can be available for conversion into biofuels. There is a growing interest in utilizing a broader range of alternative biomass feedstocks such as agri-crop residues aside from the traditional forest-origin wood residues for fuel pellet production. However, crop residues typically have low and inconsistent fuel quality. This paper investigated the effectiveness of the combined steam explosion and water leaching pretreatment techniques to upgrade the fuel properties of wheat straw. The experimental treatments involved auto-catalyzed steam explosion and acid-catalyzed steam with and without subsequent water leaching. Using steam explosion catalyzed by dilute H2SO4 at a low concentration of 0.5 wt%, results showed the highest ash, Si, and Ca removal efficiencies of 82.2%, 91.1%, and 74.3%, respectively. Moreover, there was significant improvement in fuel quality in terms of fuel ratio (0.34) and calorific value HHV (21.9 MJ/kg), as well as a pronounced increase in the comprehensive combustibility index at the devolatization stage, indicating better combustion characteristics. Overall, the results demonstrate that with adequate pretreatment, the quality of agri-pellets derived from wheat straw could potentially be on par with wood pellets that are utilized for heat and power generation and residential heating. To mitigate the dry matter loss due to steam explosion, future studies shall consider using the process effluent to produce biofuel.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".