Study of the internal environment quality monitoring system for a laboratory model of a mining separator at key sensitive points of operation and process control using artificial intelligence
Bibliographic record
Abstract
Modeling the quality of the indoor environment in buildings using neural networks, as an element supporting automatic process control, has become extremely popular nowadays. By analogy, attempts are being made to use the experience gained in construction and implement it in industry. The publication proposes a method of modeling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of the given quality criterion. This network was implemented in the control system of the mining separator operation as part of pilot studies. The research included testing a laboratory model of the separator placed in a sea container modified for the separator function, in which modern automation technologies and monitoring of environmental parameters were integrated. Among others, time, outside temperature, set temperature, temperature error and controller output were measured. The measurements were taken at the points of installation of devices sensitive to the working environment - controllers, I/O modules, X-ray (XRT-DE) and optical analysis (VIS-NIR), enabling precise examination of the composition and quality of mineral resources. The internal environmental conditions in the housings of the above-mentioned sensitive elements and in the server room were the basis for the analysis. The aim was to develop a performance model enabling effective improvement of the working environment of all electrical and mechanical devices affecting energy efficiency and the internal environment. Separators operate in a very diverse environment, such as: tropical forests, Canadian Tundra, or desert areas in Africa, as well as EU countries, the USA and Australia. These devices are used in both open pit and underground mines. The use of modern technologies and mobile solutions in the mining industry contributes to increased efficiency, operational safety and, consequently, minimizing the negative impact on the environment. The research results confirmed that precise monitoring and control to ensure environmental conditions at selected separator points is crucial to ensuring the continuity and quality of the separation 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".