Technoeconomic Feasibility of Bioenergy Production from Wood Sawdust
Bibliographic record
Abstract
In this study, the technoeconomic feasibility of bioenergy production from sawdust under four different case scenarios is simulated and compared. These scenarios include: (1) heat and electricity generation from raw sawdust; (2) pellet production from sawdust; (3) and (4) integrated biorefinery approach for the simultaneous manufacturing of multiple products (steam-exploded and torrefied pellets) and co-products (furfural, hydroxy methyl furfural (HMF), acetic acid), along with heat and electricity generation. Economic assessments such as cost analysis, payback time (PBT), net present value (NPV) and internal rate of return (IRR) were determined for these scenarios. The results showed that the approach of producing torrefied pellets, furfural, and acetic acid, along with co-generated heat and electricity, in terms of multiproducts and profitability (NPV (at 7%): USD 38.29 M) was preferable over other alternatives. In terms of simplified technology and other economic indices (PBT: 2.49 year, IRR: 51.33%, and return on investment (ROI): 40.1%), the scenario for producing pellets from wood sawdust was more promising than others. If plant capacity was not a limiting factor, the optimal size for the combined heat and power (CHP) plant was between 250–300 kt for the main product. Additionally, untreated and treated pellet plants equipped with CHP had an optimal size of 150–200 kt of wood pellets per year.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".