Miniature Furnace Temperature Monitoring System Using Wireless-Based Resistance Temperature Detector Sensor
Bibliographic record
Abstract
PT. GCNS in Morowali, which produces NPI (Nickel Pig Iron), has a furnace area of the Ferronickel Department for melting raw ore materials into NPI.In this area, there are many sensors, one of which is the RTD sensor, which is used to measure temperature in various processes in the furnace system.Through the observation, problems were found related to replacing damaged sensors because these sensors still use cables in pipes with complicated and long paths.So that during the sensor maintenance process, it is necessary to check and dismantle the complicated cable paths in the pipes, resulting in the production process stopping in the furnace for a long time.This study implemented a sensor system that uses NodeMCU ESP8266 as a wireless device that functions as a data sender and receiver module from the RTD sensor, which is integrated with the PWM to voltage and voltage to current converter module so that the sensor reading data can be integrated with the 4-20 mA analog input on the PLC.So, wireless sensors can eliminate complicated wiring systems and save more time during maintenance.The test results on Sensors 1, 2, and 3 have an average difference value of 0.36, 0.53, and 0.59, respectively.The percentage of errors produced respectively are 0.68%, 1.162%, and 1.21%; the occurrence of error values in the testing process is due to the accuracy of the NodeMCU used of 10 bits, or 4 mV for every 1 decimal change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".