Guest editorial: Biomass conversion for energy: Process intensification and economics
Bibliographic record
Abstract
Abstract The symposium Biomass Conversion for Energy: Process Intensification and Economics held at the Canadian Chemical Engineering Conference 2022 corresponds to several United Nations Sustainable Development Goals (SDG): SDG7—produce more affordable and cleaner energy; SDG9—build small‐scale industrial enterprises and domestic technologies; SDG12—improve the management and efficient use of natural resources; SDG13—integrate climate change measures into national strategies. Cooking fuel remains scarce in many regions around the world as three billion people rely on polluting systems while 800 million people have no electricity. The energy density of renewable resources is low, compared to petroleum, which makes transporting bio‐feedstocks to centralized stations prohibitively expensive. Process intensification (PI) and distributed manufacturing are strategies that reduce costs and serve these markets equitably. PI reduces the size of equipment while maintaining productivity by maximizing thermal, electrical, mechanical, and catalytic driving forces. These technologies must be coupled with mass manufacturing (numbering up manufacturing rather than scaling up) to achieve economic parity with centralized power facilities. They apply to converting biomass to electricity, thermo‐processing of lignocellulosics, and producing liquid fuels, butane, and propane from flared and vented natural gas, landfill gas, and biogas, via gas‐to‐liquids technology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".