Biomass refineries: relationships between feedstock and conversion approach
Bibliographic record
Abstract
For conversion purposes, biomass can be classified into three main categories: homogeneous biomass (ex.: corn grains), quasi-homogeneous biomass (ex.: forest residues, straws and plantations in marginal lands) and non-homogeneous biomass (ex.: mixed forest residues, MSW, etc.). In North America homogeneous biomass costs (FOB plant) are over 100 $US/tonne in 2009 (anhydrous basis; 1 tonne = 18 GJ), therefore its conversion to biofuels requires subsidies. As well, alternate and added value uses (food and fibre) compete for such category of feedstock. Quasi-homogeneous biomass, whose cost (FOB plant) 30 60 $US/tonne in 2009 (anhydrous basis) for forest residues and straws and estimated between 80 and 100 $US/tonne for plantation biomass (i.e. willows, switchgrass, etc.), is suitable for bio-refineries aiming at co-products biofuels, green chemicals and fibres. However, the availability of large quantities of quasi-homogeneous residual biomass is strongly linked to the existing biomass industrial sector since mills processing sugar cane, corn, wheat and wood also have access to such biomass category whose competing use is the generation of bioenergy (process heat and, in some cases, power). It is however possible to integrate a pre-treatment (a better term is “fractionation”) of the quasi-homogeneous biomass to produce useful fractions for biofuels, green chemicals and fibres while using the residual fractions for bioenergy. Nonhomogeneous biomass is available in all urban centers of the planet and constitutes a major opportunity for biofuels and green chemicals since its cost is negative (i.e. it is a disposal cost paid by municipalities to landfills or incineration facilities) and a sustainable society ought to aim at zero residues. Non-homogeneous biomass can be prepared and converted into a homogeneous and clean syngas intermediate. The latter contains typically two thirds of the carbon in the feedstock and technologies to convert it into biofuels and green chemicals are being developed.
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 | 0.000 | 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".