Modeling and Optimization of Biomass Supply Chain for Energy, Chemicals and Materials Productions
Bibliographic record
Abstract
In the growing concerns towards global environmental qualities and sustainable feedstocks supplies, scientific and technological efforts were intensified to utilize alternative renewable resources. In this regard, biomass appeared to be one of the potential feedstocks because it is generally carbon neutral and essentially renewable. Furthermore, biomass is virtually found in every part of the world in abundance and could provide socio-economic benefits. However, if it is not managed properly, biomass will be less competitive due to several issues that are associated with its supply chain. Typical biomass supply chain has a series of activities such as growing, harvesting, transporting, aggregating, and conversion which systematic and efficient flows of materials from the fields to the users are highly important. Biomass has competing uses, different kinds and origins which are potentially exploitable, poor geographic distributions for retrieving and transporting, and variations in physical and chemical properties. It is difficult to make informed decision for any biomass utilization project without having an optimal supply chain. This presentation addresses these issues and presents models for the biomass supply chain for manufacturing energy, chemicals, and materials based on their respective processing routes. The aim is not only to focus on energy production from biomass but also to include chemicals and materials because of several factors such as an emerging cost competitive energy resource such as shale gas, highly volatile energy prices, and customer’s preparedness and acceptances. Furthermore, it also could leverage biomass plantations on the producers’ sides. The models consider annual profitability of producing products as the performance indicator. Profitability was based on revenues of selling the products and takes costs into consideration as well (e.g. biomass cost, transportation cost, production cost, and emission treatment costs). All processing options are presented in a superstructure of alternatives over which an optimal search was conducted to yield the optimal utilization of biomass for producing materials and energy, while a single ownership was assumed for the whole supply chain’s facilities.
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) | 0.001 | 0.002 |
| 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 | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".