Implementation of particle swarm optimization strategy in Venlo‐type greenhouse climate to make energy‐efficient process
Bibliographic record
Abstract
Abstract This study appears to be the first to use a MATLAB simulator to illustrate Particle Swarm Optimization with multiple input–output restrictions. This proposed study's overarching objective was to make the entire process energy efficient, which provides improved performance with high accuracy and minimizes the operating cost by incorporating energy, ventilation, and CO2. Further, to reduce the complexity of the system, the optimization technique was divided into control and controlled variables. Meanwhile, to define state constraints for variables used in the objective function was to make the overall process cost‐effective, composing energy, CO2 supply, and ventilation cost. The chosen technique effectively decreased operating costs while maintaining the appropriate ranges for temperature (14–26°C), relative humidity (0–90%), and CO2 concentration (400–2000 ppm), according to simulation results. Off‐peak, standard, and peak energy cost levels were R1080.26, R748.56, and R7078.4, respectively. On the other hand, it was found through comparative analysis that the standard and off‐peak energy consumption figures decreased by 65.4 and 8.1%, respectively, as compared to the peak tariff (2279.9 kWh). The suggested PSO technique is implied to be a viable means of increasing greenhouse energy efficiency and achieving sustainable, cleaner manufacturing.
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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 | 0.000 |
| 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) | 0.001 | 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".