Development of an integrated tactical and operational planning model for supply of feedstock to a commercial‐scale bioethanol plant
Bibliographic record
Abstract
Abstract In this paper, a new modeling approach is proposed to integrate the tactical and operational planning levels in the biomass supply chain. The proposed approach includes an optimization model and a simulation model. The integration is made between these models (i) to assure the fulfillment of the daily biomass demand year‐round for a commercial‐scale cellulosic ethanol plant and (ii) to reduce biomass delivery costs. The optimization model prescribes the design of the supply area in a way that the annual biomass demand is met at a minimum delivery cost for a five‐year planning horizon. Given the design of the supply area, the simulation model schedules the flow of multi‐biomass in the supply chain to meet the daily biomass demand of the ethanol plant subject to the dynamics and uncertainties in the supply chain. If the daily demand cannot be met, the outputs of the simulation model are used to adjust the design in the optimization model to assure the fulfillment of the daily demand. The application of the integrated model to a proposed commercial‐sized bioethanol plant shows the efficiency of the integrated approach to design the supply area in a way that the daily biomass demand is met at the minimum delivery cost possible. The results of the sensitivity analysis reveal that the most influential parameter on the design is biomass yield. In addition, bale bulk density, in‐farm and road transportation operations, and farmer participation rates have the highest impact on delivery cost compared to other input parameters. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.000 | 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) | 0.004 | 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".