Energy optimization using metaheuristic bat algorithm assisted controller tuning for industrial and residential applications
Why this work is in the frame
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Bibliographic record
Abstract
The advent of model based control provides optimization and constrained control capabilities that can be tailored to specific goals. Metaheuristic algorithms are being researched in various fields owing to their efficiency in providing global optimization. In this paper, both model regression and energy efficiency based controller tuning are attempted using the chaotic bat algorithm (CBA). Two sets of plant data, one from an industrial precalciner temperature loop (PTL) and another from a domestic heating ventilation and air conditioning system (HVAC) are considered. Explicit model predictive control is designed. Minimizing the energy consumption (HVAC) and coal feed rate (PTL) by tuning the controllers is attempted. Results indicate that CBA can be successfully deployed in both regression and achieving control objectives as observed from the case studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it