The relative exergy‐destroyed array: A new tool for control structure design
Bibliographic record
Abstract
Abstract Due to increasing energy demands, energy crises and strict environmental regulations, the eco‐efficiency of all industrial processes and plants has become vitally important. Control loop configuration or control system structure determination is a major and vitally important activity in the complex task of process control because a poorly structured control strategy can lose much energy from the process or plant when implemented. To save this loss of energy due to a poorly structured control strategy, engineers need to find a way to integrate control loop configuration and measurements of eco‐efficiency. In this paper, we present the relative exergy‐destroyed array (REDA), a new tool to measure the relative eco‐efficiency of a process. The REDA is a means to compare the eco‐efficiency of multi‐input multi‐output processes for different combinations of control structures. Based on steady state information, it is a simple tool for comparing eco‐efficiency. The results obtained from the REDA are interpreted and explained with the help of case studies involving a whole monochlorobenzene (MCB) plant and a heat exchanger network (HEN). The REDA may help guide the process designer to quickly find a control design with low operating costs and high eco‐efficiency.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".