Environmental and Economics Trade-Offs for the Optimal Design of a Bitumen Upgrading Plant
Bibliographic record
Abstract
This work presents a novel multiobjective optimization model for bitumen upgrading operations. The proposed model considers five basic upgrading stages; which are the base of any bitumen/heavy oil upgrading operation. These stages include: primary distillation, vacuum distillation, cracking, hydrotreating, and blending. The model includes different processing units per upgrading stage. Each unit includes a set of operating modes; which are defined in terms of particular products yield and energy requirements. The proposed model takes into account two competing objective functions that must be minimized: 1) operating energy costs, and 2) associated CO 2 emissions. The optimization approach seeks for the optimal bitumen upgrading configuration by selecting the most suitable upgrading steps based on their corresponding unit’s operating modes. This is done to obtain a particular type of synthetic crude oil (SCO) blend according to composition specifications. The problem was modeled as a mixed-integer nonlinear program (MINLP) using the GAMS modeling system. The model was validated using historical data of the Canadian heavy oil industry. The results show that the proposed model is a practical tool to (1) select and plan the most suitable bitumen upgrading configuration according to product specifications, (2) determine the upgrading energy costs, (3) and mitigate CO 2 emissions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".