E-Green − A Robust Risk-Based Environmental Assessment Tool for Process Industries
Bibliographic record
Abstract
This paper proposes a risk-based environmental assessment approach (E-Green) for evaluating different process options at an early design or a retrofit stage. The approach splits the cradle-to-gate life cycle into two domains: the raw materials production and supply domain and the process domain or gate-to-gate domain. It allows an analyst to investigate adverse impacts of the process activity on each domain separately and results in a more manageable assessment of process design alternatives. It is a risk-based approach contrary to the existing hazard-based approaches. E-Green replaces the conventional normalization step of the impact assessment phase of a life cycle assessment (LCA) with a ranking step, which compares the effect scores of all the impact categories for different options and gives a relative score to each option. This eliminates the complexity and bias of the conventional normalization step in the evaluation phase and enables the analyst to perform the effective evaluation easily. The applicability of the E-Green has been illustrated in the assessment of two solvent options in an acrylic acid manufacturing plant. E-Green methodology is implemented by combining an Aspen-HYSYS process simulator and a quantitative exposure assessment tool (E-Fast).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".