Assessment of Efficient Thermal Conversion Technologies and HHV from Compositional Characteristics of Cassava Peelings, Plantain Peelings and Corn Cobs
Bibliographic record
Abstract
The understanding of compositional characteristics permits to predict the efficient thermal conversion technologies and Higher Heating Value (HHV).Although HHV can be determined directly, many models have been proposed for HHV prediction.They are based on proximate, ultimate, and structural analysis and require much data collected.The present work assesses to predict efficient thermal conversion technologies and HHV of the most abundant agricultural biomass from Cameroon, namely cassava peelings, plantain peelings, and corn cobs, by using the existing models and exploring the calculation of HHV from the formula.The results show that investigated biomasses can be efficiently used in thermochemical conversion to produce bio-oil/syngas, in the biochemical process to produce bioethanol/biogas, and in the physical process to densify feedstock into fuel briquette.Flue gas reveals a value less than the toxic values fixed by the European standard for household waste incineration and can therefore be used as an environmentally friendly bioenergy source.Nevertheless, the levels of S and N could be taken into account in the design of a gasification plant to control the emission of NO2 and SOx-derived pollutants as their value is more than the limit fixed.Amongst the existing models, the model based on ultimate analysis gives the best correlation.The Average Absolute Error (AAE) is ranging from 2.47 to 10.71%.The calculation of HHV from combustion enthalpy and the formulae of cassava peelings, plantain peelings, and corn cob are C5H4O2, C5H8O4, and C3H4O2 respectively.The HHV derived from them is 16.13, 16.27, and 20.02 MJ/kg for cassava peelings, plantain peelings, and corn cob respectively.The AAE lies within 2.52 and 8.08%.These values are lower than those obtained from the literature models.These AAE varies between 4 and 10%.The AAE of the twenty biomasses from the literature ranges from 0.16 to 10.96%.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".