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Abstract

The PT100 temperature sensor, a type RTD (resistance temperature detector), is widely employed in industries, particularly for steam boiler temperature regulation. Precise temperature measurement is vital for operational efficiency and safety maintenance. Utilizing linear regression for calibrating and measuring temperature with the PT100 sensor integrated with Arduino Uno and LCD I2C is proposed to enhance accuracy. Testing involved assembling the PT100 sensor with Arduino Uno and LCD I2C. Test data were employed for linear regression to derive the calibration equation. Measurements were conducted by comparing PT100 sensor readings with a digital thermometer across a temperature range of 30ºC to 75ºC at 5ºC intervals, with each temperature point tested five times. Results exhibit good accuracy in temperature measurement with the PT100 sensor, featuring low error and percentage error. The standard deviation indicates consistent measurement. Integration of the PT100 temperature sensor with Arduino Uno and LCD I2C, coupled with linear regression for calibration, yields a precise temperature measurement system. Testing against a digital thermometer demonstrates highly accurate outcomes. This developed system finds application in high-precision temperature measurement for steam boiler operations.

Keywords

PT100 temperature sensor Temperature calibration Linear regression Arduino Uno

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Copyright (c) 2024 Kartika Kartika, Asran Asran, Mhd Perdiansyah Hasibuan, Misriana Misriana

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biography

Misriana Misriana, Politeknik Negeri Lhokseumawe

Teknik Elektro

References

  1. S. Paul, A. Saikia, V. Majhi, and V. K. Pandey, “Transducers and amplifiers,” in Introduction to Biomedical Instrumentation and Its Applications, 1st ed., S. Paul, A. Saikia, V. Majhi, and V. K. B. T.-I. to B. I. and I. A. Pandey, Eds., Pushpa Gujral: Academic Press, 2022, pp. 87–167. doi: https://doi.org/10.1016/B978-0-12-821674-3.00008-5.
  2. I. G. C. Dryden, “Boiler plant and auxiliaries,” in The Efficient Use of Energy, 2nd ed., I. G. C. B. T.-T. E. U. of E. (Second E. DRYDEN, Ed., Butterworth: Butterworth-Heinemann, 1982, pp. 200–248. doi: https://doi.org/10.1016/B978-0-408-01250-8.50019-2.
  3. F. S. Hidayatulloh, W. Dirgantara, and D. C. Permatasari, “Implementasi Kontrol PID Untuk Optimasi Suhu Boiler Pada Mesin Kopi Espresso,” JEECOM J. Electr. Eng. Comput., vol. 5, no. 2, pp. 106–114, 2023, doi: 10.33650/jeecom.v5i2.6162.
  4. F. Baskoro, B. Suprianto, L. Anifah, Ekohariadi, and A. Nurdiansyah, “Berpikir Kreatif Dalam Pengembangan Plug-And-Play Sistem Dan Database Case Study Sensor Suhu DHT11,” J. Zetroem, vol. 5, no. 1, pp. 19–27, 2023, doi: 10.36526/ztr.v5i1.2585.
  5. T. Agung Priatama, Y. Apriani, and M. Danus, “Sistem Monitoring Solar Cell Menggunakan Mikrokontroller Arduino Uno R3 dan Data Logger Secara Real Time,” SNITT- Politek. Negeri Balikpapan, pp. 250–251, 2020.
  6. S. Baldi, T. Le Quang, O. Holub, and P. Endel, “Real-time monitoring energy efficiency and performance degradation of condensing boilers,” Energy Convers. Manag., vol. 136, pp. 329–339, 2017, doi: https://doi.org/10.1016/j.enconman.2017.01.016.
  7. Y. G. V. Y. Malau and N. Nopriadi, “Perancangan Alat Sistem Kontrol Ketinggian Air Dengan Menggunakan Metode Prototype Berbasis Arduino,” Comput. Sci. Ind. Eng., vol. 9, no. 1, 2023, doi: 10.33884/comasiejournal.v9i1.7532.
  8. M. Badura, P. Batog, A. Drzeniecka-Osiadacz, and P. Modzel, “Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements,” SN Appl. Sci., vol. 1, no. 6, p. 622, 2019, doi: 10.1007/s42452-019-0630-1.
  9. A. V. Rachmawati and M. Yantidewi, “BME280 Sensor Calibration Analysis with Linear Regression Approach for Temperature , Relative Humidity and Dew Point Measurements,” vol. 7, no. 5, pp. 1589–1597, 2024, doi: 10.56338/jks.v7i5.5272.
  10. N. Yulita, D. Setyaningsih, and I. A. Rozaq, “Karakterisasi Sensor LM35 Waterproof Untuk Mengetahui Kualitas Air Sungai Akibat Limbah Industri Berbasis IOT,” Pros. SENDI_U, vol. 1, no. 1, pp. 978–979, 2018.
  11. J. Chen et al., “A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide,” Environ. Int., vol. 130, no. 2, p. 104934, 2019, doi: https://doi.org/10.1016/j.envint.2019.104934.
  12. D. Taler, T. Sobota, M. Jaremkiewicz, and J. Taler, “Control of the temperature in the hot liquid tank by using a digital PID controller considering the random errors of the thermometer indications,” Energy, vol. 239, p. 122771, 2022, doi: https://doi.org/10.1016/j.energy.2021.122771.
  13. M. Putri Anastasia, D. Nur Ilham, and F. Atabiq, “Development of NRF24L01 Communication Module-Based Temperature Monitoring System,” Jrcs, vol. 2, no. 1, pp. 2770–1800, 2022, [Online]. Available: http://journal.station-it.org/index.php/jrcs
  14. B. H. Yusuf, I. M. S. Made, and I. K. Putra, “Pembuatan Alat Ukur Suhu Rendah Berbasis Mikrokontroler ATmega328 Menggunakan Sensor Suhu RTD PT-100 Manufacture of Low Temperature Measuring Instrument Based on ATmega328 Microcontroller Using PT-100 RTD Temperature Sensor,” Bul. Fis., vol. 21, no. 1, pp. 26–32, 2020.
  15. S. W. Jarantow, E. D. Pisors, and M. L. Chiu, “Introduction to the Use of Linear and Nonlinear Regression Analysis in Quantitative Biological Assays,” Curr. Protoc., vol. 3, no. 6, pp. 1–56, 2023, doi: 10.1002/cpz1.801