Optimization of Pyrolysis Operating Condition for Deriving Corn Starch Heterogeneous Acid Catalyst for Biodiesel Production
Bibliographic record
Abstract
Biodiesel can be produced from various oils and fats. Due to possibility of diversion of edible oils from feedstocks to raw materials for biodiesel production, which may lead to food crisis, it is preferable to choosenon-edible oils as raw material for biodiesel production. As a country rich in natural resources, Indonesia has avast amount and variety of non-edible fatty-oil production plants. However, non-edible oils usually have highfree fatty acid (FFA) contents. Oils with high FFA contents cannot be converted directly to biodiesel using aconventional alkaline catalyzed process due to saponification problem. To avoid this problem, the high FFAcontents in the oils must be reduced via esterification process using acid catalyst. The use of homogeneous acidcatalyst in this process can be very corrosive and not environmentally friendly while the use of commerciallyavailable heterogeneous acid catalyst can be very expensive. In this research, a heterogeneous acid catalystsuitable for biodiesel production will be derived from corn starch through pyrolysis followed by sulphonationprocesses. The purpose of this research is to study the effects of pyrolysis temperature and time to the aciddensity of the catalyst and the activity of the catalyst in the esterification of oleic acid using a 22 factorial designwith 3 center points experimental design. It is found that the catalyst obtained from pyrolysis at 400°C for 15hours has the optimum–HSO3 content of 5.9% which corresponds to the highest average conversion of theesterification of oleic acid of 97.45%.
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 | 0.001 | 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) | 0.000 | 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".