Simulating Floating Head Pressure Control With Artificial Intelligence
Bibliographic record
Abstract
In this work, we model with artificial intelligence techniques one of the most used solutions for energy saving in the field of refrigeration. This solution, called floating head pressure control, allows for the optimum pressure condensation depending on the working environment, thus increasing the overall efficiency of the plant. Usually, these mechanisms are controlled by algorithms that are stored in the control memory of the chillers. Under certain environmental conditions, the optimal temperature and condensation pressure fluctuate with the environmental temperature. Thus, the absorbed electricity depends on the working parameters fixed by the control system of the chiller. Results in energy savings in these terms could grow as the external temperature decreases. The problem addressed in this work regards standard floating head pressure control (FHPC) systems that do not combine working environmental factors with the conditions of the machine and historical data because they are implemented adopting static models. We provide evidence about the potential of machine learning models in predicting the velocity of fans in order to adjust their load based on environmental factors and the conditions of the machine. A good setting of fan velocity will result in lower energy consumption. To do that we analyzed and implemented machine learning algorithms to provide instruments that support the operation of the chiller, enabling the floating head pressure control mode in the control system of the machine, during its process. We performed an empirical evaluation on both synthetic and real data to assess the quality of our proposal. Synthetic data are produced by an industrial software that simulates the behavior of chillers, while real-world data is collected from a commercial chiller. The results show that machine learning approaches are able to approximate real data getting errors that are 5 times smaller than the errors committed by the system that is now adopted. All the source code as well as the datasets will be made available online after the review process.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".