Hydrothermal Conversion of Neutral Sulfite Semi-Chemical Red Liquor into Hydrochar
Bibliographic record
Abstract
Hydrochar was produced from neutral sulfite semi-chemical (NSSC) red liquor as a possible bio-based solid fuel for use in power generation facilities. Hydrothermal conversion (HTC) experiments were conducted using a fixed liquor-to-water volume ratio of 1:8 and reaction time of 3 h. Solutions were processed using different chemical additives, pH and temperature conditions to determine the optimum conditions required for producing a high energy content solid fuel. The hydrochar samples produced were analyzed by ultimate, thermogravimetric (TGA) and Fourier transform infrared spectroscopy (FTIR) analyses to determine physicochemical properties that are important for utilization as a fuel. The residual process liquids were also analyzed to better understand the effect of HTC process conditions on their properties. It was determined that the optimum conditions for producing a solid fuel was at a reaction temperature of 250 °C, in the presence of acetic acid at pH 3. The maximum energy content (HHV) of the hydrochar produced from red liquor at this condition was 29.87 MJ/kg, and its ash content was 1.12 wt.%. This result reflects the effect of increasing reaction temperature on the physicochemical characteristics of the hydrochar. The increase of HTC temperature significantly reduces the ash content of the hydrochar, leads to a significant increase in the carbon content of the hydrochar, and a reduction in both the oxygen and hydrogen content. These effects suggests an increase in the degree of condensation of the hydrochar products, and consequently the formation of a high energy content material. Based on TGA and FTIR analyses, hydrochars prepared at high HTC temperature showed lower adsorbed moisture, hemicellulose and cellulose contents, with enrichment in content of higher temperature volatiles, such as lignin.
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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".