Biorefinery done right
Bibliographic record
Abstract
Following the COP21 conference in Paris, most of the world's industrialized countries, as well as emerging markets, pledged to reduce or stabilize their greenhouse gas (GHG) emissions in light of increasing concerns regarding climate change [1].The necessity to decrease GHG emissions will have implications on the consumption patterns of different types of energies around the world.Apart from the obvious need to replace part of the increasing fossil fuel consumption in transportation (including road, rail, air and sea), there is a growing demand in other sectors as well, such as for electricity production, heating and cooling.Many opportunities are being investigated to address some of the issues related to this green energy transition, including the increased harnessing of alternative energy sources such as wind, solar, hydro, geothermal and biomass.Despite varying potential for each of the mentioned energy sources to help replace or supplement fossil fuels, only biomass currently has the potential to address most of these needs without requiring significant changes to existing energy distribution networks.For example, biomass can be burnt to generate combined heat and power, but it can also be used as a source of carbon to produce biofuels.In the latter case, biofuels such as ethanol could be blended into the existing fuel pool as well as distributed and utilized in engines without requiring significant modifications to the existing chain of distribution.This adaptiveness is not necessarily the case when considering electric vehicles (EV), although they are also of crucial importance towards collectively reducing GHG emissions.This manuscript will review the Biorefinery Done Right-concept, developed by the company RéSolve Énergie in close collaboration with the Biomass Technology Laboratory.This simple feedstock-agnostic technology allows conversion of any type of residual biomass (including but not limited to softwood bark) to three-types of biofuels.The first objective is to take advantage of the carbohydrate content in the biomass through hydrolysis of the constitutive hemicellulose and cellulose.The fermentable sugars are then converted to ethanol, achieved without any constraints, since the RéSolve process generates a hydrolysate with very low inhibitor levels.The lignin recovered from the process is essentially unmodified lignin and after washing, it is pelletized.Pellets, containing the most energetic components of the lignocellulosic biomass, can provide up to 26 GJ/tonne.Finally, the non-fermentable sugars (C5), as well as the lignin that does not comply with Grade A lignin characteristics, are predigested for utilization in a classical biomethanation system.Hence, through this approach, 100% of the carbon from the biomass is converted into commercial products, which at this point are all related to the energy market.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.195 | 0.116 |
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".