Modelling and optimization of biomass to bio-products supply chain
Bibliographic record
Abstract
Supply chain of biomass is one of the major areas that has direct influences towards biomass utilization activities and commercialization progresses. In this paper, an optimization model of biomass to bio-products supply chain was formulated by considering several cost factors such as biomass cost, production cost and transportation cost. A superstructure that has assisted in the model’s formulation provided alternatives in the biomass processing routes which in turn aiming for profit maximization. It has involved a biomass-based manufacturing company in southwestern Ontario which was looking for business expansion and product portfolios’ improvements. Optimal results indicated that an annual profit of $ 22,618,673 was expected to be achieved, and this value was contributed mainly by the sales of bio-filler, bio-ethanol and by-product from the milling plant. The developed model offers flexibilities in biomass resources utilization and technological uses. Even though it was modeled and optimized specifically for the company in Ontario, Canada, the general framework of the optimization model is however could be applicable for different parts of the world including Asia.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".