Real Time Optimization: Classification and Assessment
Bibliographic record
Abstract
Summary The Real-Time Optimization (RTO) Technical Interest Group (TIG) has endeavored to clarify the value of real-time optimization projects. RTO projects involve three critical components: People, Process, and Technology. Understanding these components will help establish a framework for determining the value of RTO projects. In this paper, the Technology component is closely examined and categorized. Levels within each Technology category are illustrated by use of spider diagrams, which help decision makers understand the current status of operations and the future RTO status. The perception of uncertain value has been one of the critical issues in adopting RTO systems in our industry. Therefore, case histories are reviewed to demonstrate the impact of RTO projects. To assist RTO project promotion further, we list lessons learned, suggest a justification process, and present a simple example of an economic-evaluation process. Introduction Industry case histories demonstrate many types of benefits from RTO such as production-volume increase; better return on investment (ROI); higher decision quality; health, safety, and environment (HSE) improvements; and operational expenditures (OPEX) reduction. However, they have lacked systematic project-evaluation processes for justification. Today, promoting RTO is, in essence, a competition for capital within a company. The project teams that recognize this fact and then clearly outline the purpose, benefits, costs (direct or indirect), and strategic business alignment of their proposals will be in an advantageous position to secure funding. Because RTO is still an emerging discipline, classifying projects of this nature is still dependent on an individual's point of view. This paper provides classification of RTO to help provide a common vocabulary to address a multitude of issues.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".