Biodiesel production from by extraction of carp fish (Cyprinidae) oil and transesterification using CaO derived from limestone and eggshells as heterogeneous catalysts
Bibliographic record
Abstract
The fish market produces a huge amount of fish waste and worsens the ecosystem’s condition when dumping into the environment while the treatment to create ecologically and economically acceptable biodiesel is one possible answer. So, this study evaluates the efficiency of three extraction techniques: Wet rendering, Soxhlet, and enzyme hydrolysis in extracting oil from carp fish. The highest yields of oil extraction were 38.61, 36.25, and 22.38% in the Soxhlet extraction, enzyme hydrolysis extraction, and wet rendering respectively. Raw fish oil’s physical and chemical characteristics were investigated, and GC-MS was used to determine its free fatty acid profile. This investigation also inquired about the preparation of calcium oxide (CaO) that is efficient in producing biodiesel from waste eggshells and limestone which were calcined at 950 °C for 2.5 hr, producing stable and high-purity CaO. XRD and FTIR were used to characterize the prepared catalyst while FESEM analysis of the catalyst’s surface structure and EDX spectra analysis of the catalyst’s elemental composition were both performed. The highest yields were 91.74 and 89.21% obtained using CaO derived from limestone and eggshells respectively. The optimum transesterification reaction conditions were a methanol to oil molar ratio of 9:1, 3.5 wt% CaO catalyst, 60 °C, and 90 min.
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 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".