Real-Time Monitoring and Coordinated Purification Strategy for PM2.5/Particulate Concentration in Cleanroom Air Conditioning Systems
Bibliographic record
Abstract
When addressing rapidly changing indoor PM2.5 and particulate concentrations, real-time monitoring accuracy is insufficient and purification response lags. This paper develops a real-time monitoring and coordinated purification model based on high-precision laser particle sensors and an Internet of Things (IoT) platform to achieve intelligent response and control to excessive PM2.5 concentrations. 1) Laser particle sensors are strategically placed in the cleanroom to collect real-time data at one-minute intervals; 2) Kalman filtering is used to fuse multi-point data, eliminating outliers and improving monitoring reliability; 3) Based on a cloud-based data analysis module, dynamic thresholds are set to trigger a coordinated purification strategy, automatically adjusting air volume and purification unit operating status; 4) Device coordinated responses are achieved through a wireless control system. Experimental results show that the system can reduce the response time to PM2.5 peaks to within 3 minutes, with a monitoring error of ±2 μg/m³. The conclusion shows that the clean air-conditioning system based on real-time monitoring and intelligent linkage significantly improves the indoor particle control capability and provides effective protection for a high-cleanliness environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".