A Review of Thermo-chemical Energy Conversion Process of Non-edible Seed Cakes
Bibliographic record
Abstract
In India, efforts were being made for using non-edible oils for production of bio-diesel on account of country’s potentiality in non-edible oil tree born seeds. Jatropha curcas (Jatropha) and Pongamia pinnata (Karanja) crops had been selected as major source of non-edible oils for production of biodiesel. Considering the future scenario of non edible oil seed’s utilization for biodiesel production, there is a need for efficient utilization of their cakes. The main focus of this review is about the options of energy conversions, for production of suitable fuel. The brief overview of energy conversion option on seed cake is presented by means of general background information available in literatures. From the solvent extracted jatropha seed cake and mechanically de oiled jatropha seed cake the quantity of biogas obtained by biomethanation process was 0.5 m 3 /kg and 0.6 m 3 /kg respectively with CH 4 content of 50% to 70%. By using pongaima seed cake as feed material the yield of average specific biogas over a 30-day retention time was 0.703 m 3 •day -1 •kg -1 TS with 62.5% of CH 4 content. Faster conversion rate and using all the components of biomass includes cellulose, hemicelluloses and lignin were the advantages of thermo chemical conversion process over biological conversion process. Energy production through gasification conversion route is suitable as the process of synfuels from biomass will lower the energy cost, waste management improvement and reduction of harmful emission. Experiments on non edible oil seed cake by gasification conversion route and analyzing for its characteristics are more essential for useful energy recovery and its use for thermal application and power generation.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".