Supply chain planning for bioenergy and biofuel production from forest-based residues in interior British Columbia : a simulation study
Bibliographic record
Abstract
This thesis analyzes a forest-based biomass supply chain network considering uncertainties and variations. It is based on the Williams Lake Timber Supply Area (TSA) located in British Columbia, Canada. The network includes: five conversion facilities distributed in three locations, two types of forest-based biomass, sourced from 337 cutblocks, and two types of sawmill residues sourced from three local sawmills. The main objective of this research is to evaluate the supply chain of forest-based residues for bioenergy and biofuel production considering uncertainties and variations. The specific objectives of this research are to: 1) Develop a simulation model to evaluate a forest-based biomass supply chain for bioenergy and biofuel production considering uncertainties and variations; and 2) apply the simulation model to a case study. To achieve the objectives, a discrete-event simulation model is developed using the commercial software Anylogic 7® (Anylogic 7, 2000). Evaluating a network with various supply and demand points, with various biomass types, and a hybrid push-pull biomass flow management distinguishes this work from previous research. The results show the demand is fulfilled to at least 95%, requiring 23 to 24 trucks during the peak season. Furthermore, the cost and CO₂ equivalent emissions vary per location, from $56.52 to $87.36 and from 19.66 to 72.61 (kg/odt), respectively. Long transportation distances and transportation cycle times greatly affected the number of required resources, and consequently the final cost per oven dry tonne. This results in higher costs than similar studies performed in less remote areas. Finally, a sensitivity analysis is performed to evaluate the effect of changes in moisture content and in supply and demand. Extreme changes in biomass supply and demand affected dramatically the demand fulfillment. By increasing the biomass demand 20% while simultaneously decreasing the biomass supply 20%, reduced the demand fulfillment by 23.18%. Finally, this model can be improved in several ways, one of them being by including the possibility of routing between different cutblocks to consolidate biomass pick-ups, therefore increasing the demand fulfillment of the supply chain and possibly reducing costs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".