Industrial site particulate pollution monitoring with an eye-safe and scanning industrial fiber lidar
Bibliographic record
Abstract
12 Over the past few years, INO has developed an Industrial Fiber Lidar (IFL). It enables the particulate pollution monitoring on industrial sites. More particularly, it has been used to take measurements of particulate concentration at Port Facilities of an aluminum plant during boat unloading. It is an eye-safe and portable lidar. It uses a fiber laser also developed at INO emitting 1.7 microJoules at 1534 nm with a pulse repetition frequency of 5 kHz. Given the harsh environment of an industrial site, all the sensitive equipment like the laser source, detector, computer and acquisition electronics are located in a building and connected to the optical module, placed outside, via optical fibers up to 500 m long. The fiber link also offers all the flexibility for placing the optical module at a proper location. The optical module is mounted on a two axis scanning platform, able to perform an azimuth scan of 0 to 355 deg and an elevation scan of +/- 90 deg, which enables the scanning of zones defined by the user. On this industrial site, materials like bauxite, alumina, spathfluor and calcined coke having mass extinction coefficients ranging from 0.53 to 2.7 m<SUP>2</SUP>/g can be detected. Data for different measurement configurations have been obtained. Concentration values have been calculated for measurements in a hopper, along a wharf and over the urban area close to the port facilities. The lidar measurements have been compared to high volume samplers. Based on these comparisons, it has been established that the IFL is able to monitor the relative fluctuations of dust concentrations. It can be integrated to the process control of the industrial site for alarm generation when concentrations are above threshold.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".