Supercritical methanolysis of leather tanning waste biomass: experimental investigation, process simulation, and sustainable biodiesel production
Bibliographic record
Abstract
Abstract Background This study explores a sustainable method for biofuel production from leather tanning waste (LTW) using a novel single-step, catalyst-free supercritical methanolysis (SpCM) process. Traditional biodiesel production methods often require catalysts and involve complex purification steps, whereas this study aims to optimize process parameters to enhance biodiesel yield while reducing energy and material consumption. Methods The research employed a Box–Behnken experimental design integrated with response surface modeling (RSM) to optimize key process variables, including alcohol-to-LTW molar ratio ( r mo ), operating temperature, pressure, and reaction duration. The produced biodiesel was characterized according to EN 14214 standards. Kinetic studies of the transesterification reaction were conducted, and a robust reactor model was developed in Aspen Plus to estimate the kinetic parameters. Results The activation energy ( E a ) and pre-exponential factor ( A ) were determined to be 45.085 kJ/mol and 86.24 s⁻ 1 , respectively, with a rate constant ( k ) of 0.0098 s⁻ 1 at an optimized temperature of 322.938 °C. The optimized conditions using RSM achieved a biodiesel yield of 89.35% at an r mo of 32.35:1, a temperature of 322.938 °C, a pressure of 219.073 bar, and a reaction time of 14.26 min. The simulation model under the same conditions predicted a biodiesel yield of 89.49%, demonstrating excellent agreement with experimental results, with a maximum error of 0.54%. Conclusions This study presents a comprehensive approach to biodiesel production from LTW, demonstrating an efficient and sustainable alternative to conventional methods. The optimized catalyst-free SpCM process minimizes energy input and material usage while achieving high biodiesel yields. These findings contribute to waste valorization in the leather industry and support environmental sustainability efforts. Graphical abstract
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".