Review on Biodiesel Production from Various Feedstocks Using 12-Tungstophosphoric Acid (TPA) as a Solid Acid Catalyst Precursor
Bibliographic record
Abstract
Solid acid catalysts are an important class of catalysts because of their applications in various organic reactions. A 12-tungstophosphoric acid (TPA) is a member of heteropoly acid (HPA) compounds, which grabbed attention because of its low volatility, low corrosivity, higher activity, and acidity compared to sulfuric acid. However, the major problems of using TPA are its solubility in polar media, and its lower surface area. Therefore, various techniques are applied to use it as heterogeneous catalysts. Biodiesel is a diesel substitute renewable fuel, which is produced from various renewable feedstocks through transesterification or esterification reactions. Acid catalysts can catalyze both transesterification and esterification reactions. For this reason, research has been conducted to study the catalytic activity of various TPA precursory solid acid catalysts for biodiesel production. In this Review, a data mining technique has been applied to extract valuable information from the previously published literature. For this purpose, an artificial neural network (ANN) model has been developed based on the published research data to capture the general trends or to make predictions. Both catalyst properties and reaction conditions are trended and predicted using the network model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".