Expansion of the Petroleum Refinery Life Cycle Inventory Model to Support Characterization of a Full Suite of Commonly Tracked Impact Potentials
Bibliographic record
Abstract
This study updates the Petroleum Refinery Life Cycle Inventory Model (PRELIM) to provide a more complete gate-to-gate life cycle inventory and to allow for the calculation of a full suite of impact potentials commonly used in life cycle assessment (LCA) studies. Prior to this update, PRELIM provided results for energy use and greenhouse gas emissions from petroleum refineries with a level of detail suitable for most LCA studies in support of policy decisions. We updated the model to add criteria air pollutants, hazardous air pollutants, releases to water, releases to land, and managed wastes reflecting 2014 reported releases and waste management practices using data from the U.S. Environmental Protection Agency Greenhouse Gas Reporting Program, National Emissions Inventory, Discharge Monitoring Reports, and Toxic Release Inventory together with process unit capacities and fuel consumption data from the U.S. Energy Information Administration (U.S. EIA). The variability of refinery subprocess release factors is characterized using log-normal distributions with parameters set based on the distribution of release factors across facilities. The U.S. EPA Tool for the Reduction and Assessment of Chemical and Environmental Impacts life cycle impact assessment (LCIA) method is used together with the updated inventory data to provide impact potentials in the PRELIM dashboard interface. Release inventories at the subprocess level enable greater responsiveness to variable selection within PRELIM, such as refinery configuration, and allocation to specific refinery products. The updated version also provides a template to allow users to import PRELIM inventory results into the openLCA software tool as unit process data sets. Here we document and validate the model updates. Impact potentials from the national crude mix in 2014 are compared to impacts from the 2005 mix to demonstrate the impact of assay and configuration on the refining sector over time. The expanded version of PRELIM offers users a reliable, transparent, and streamlined tool for estimating the effect of changes in petroleum refineries on LCIA results in the context of policy analysis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".