Process Optimization for Enhancing Yield and Quality of Bio-Oil from the Pyrolysis of Cow Hooves
Bibliographic record
Abstract
To reduce the environmental footprints of fossil fuels, there is a need to source more biomasses to increase the renewable energy supply. However, a critical study of the energy-conversion process of biomass must be carried out to make the process economical. In this research, the optimization of the bio-oil production from the thermal conversion of novel biomass- cow hoof was carried out. Three independent variables- temperature of pyrolysis, heating rate and CaO catalyst mass were studied at 3 levels based on the rotatable central composite design (CCD) of the response surface methodology (RSM) to ascertain their influence on two responses- bio-oil yield and its HHV. The quadratic model was more suitable to fit the experimental data. At optimum values of the process variables, bio-oil yield of 50.64% and HHV of 23.86 MJ/kg were obtained. From the analysis of variance carried out, the model R2 values were 0.9949 and 0.9802 respectively for the bio-oil yield and HHV models which showed the models’ ability to predict the bio-oil yield and its HHV in the pyrolysis process is high. The characterization of the bio-oil revealed it has better fuel properties compared with most bio-oils from some biomasses hence it is a viable renewable energy source.
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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 | 0.000 |
| 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".