A Review of Membrane Technology for Integrated Forest Biorefinery
Bibliographic record
Abstract
More recently, the concept of integrated forest biorefinery (IFBR) has received much attention as a promising solution for the struggling forest industry in North America and Europe to overcome its difficult financial period and competes globally. This new business paradigm offers a broad range of potentially attractive products, from bioenergy to value-added green organic chemicals in addition to traditional pulp and paper products. However, it also implies adoption of different types of appropriate separation technologies. Recent advancements in membrane technologies and their valuable applications have resulted in numerous breakthroughs in IFBR. The review of the implementation of membrane technologies for the separation of the value-added chemicals in the integrated forest biorefinery could contribute to the knowledge required for the large-scale adoption of membrane technologies in the forest industry. This paper aims to present a state-of-the-art review on the applications and the recent advancements of membrane technologies in IFBR, and their capacities to produce value-added chemicals and bioenergy. The emphasis is given to the focus areas of IFBR, particularly: the recovery of value-added chemicals, black liquor concentration, product recovery from Kraft evaporator condensates, tall oil recovery, inorganic and inorganic compounds recovery, fermentation inhibitors removal, enzyme recovery, biobutanol and bioethanol production and recovery. The paper also discusses the challenges and opportunities of this new business paradigm of forest industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".