On the design and analysis of forest biomass to biofuel and bioenergy supply chains
Bibliographic record
Abstract
The efficient management of a diverse portfolio of resources is vital for sustainable economic growth in the bioenergy and biofuel sector. Considerable complexities and inherent uncertainties in supply and demand, and ever evolving technology for the utilization of biomass necessitate careful design and management of supply chains. Supply chain modelling is commonly implemented to develop “decision support tools” required in the planning of highly integrated, multi-faceted value-adding processes. This thesis demonstrates an object-oriented approach to simulate the supply chain of forest biomass to biofuel and bioenergy in three case studies in British Columbia, Canada. Three main sources of complexity, namely uncertainties, interdependencies, and resource constraints, are considered in system parameterization and model development. After verification and validation, the models are used as a representation of the system to conduct model-based analysis. The supply chain of forest biomass for large-scale power generation is considered in the first case study. Different harvesting systems are considered that are employed based on the limitations on the annual harvest volume, characteristics of the stand, and intended products. Reliability of feedstock supply over the project’s lifespan, and the delivered costs were subject of the analysis. Demand fulfilment at the power plant and the cost of raw materials depend on the realized harvest volume, dictated by the practice of primary wood processing facilities. The delivered cost to the plant shows an ascending trend during the planning horizon, further complicating the investment. The second case study concentrates on the wood pellets production and distribution supply chains; modifications in an existing system are evaluated through simulation, and assessment of integrating torrefaction into the chain is carried out. Torrefaction technology promises an opportunity to reduce the distribution cost of wood pellets in the presented case study, contingent on the market readiness and fluctuating prices. Combined heat and power generation is considered in the third case study where modifications to an existing supply chain are evaluated. Realization of the vast bioenergy and biofuel potentials in BC requires coordinated planning across the forest biomass supply chains, and simulation modeling provides valuable decision support tools to facilitate future investments.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".