Fuzzy Logic Method to Control Evenly Distributed and Stable Waterbath Temperature with Four Heaters

  • Ripqi Kurniawan Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia
  • Syaifudin Syaifudin Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia https://orcid.org/0000-0002-3922-4429
  • Lamidi Lamidi Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia https://orcid.org/0000-0002-3922-4429
  • Shubhrojit Misra Department of Electronics and Telecomunication Engineering, Jadavpur University, Kolkata, INDIA https://orcid.org/0000-0002-3922-4429
Keywords: Waterbath, Fuzzy Logic, Temperature, DS18B20


Water baths are commonly used in scientific fields to incubate samples at specific temperatures. The temperature of the water bath must be controlled precisely, because even the slightest temperature variation can affect the results of the experiment. It can handle imprecise, uncertain and incomplete information, making it suitable for temperature control in water baths. This research aims to determine the distribution and stability of fuzzy logic control to control the temperature of a water bath with four heaters. Even heat distribution from the four heaters will ensure consistent water bath temperature throughout the bath. This research uses an Arduino microcontroller to process the temperature sensor output from the DS18B20, then the processed temperature value will be displayed on the TFT LCD. The independent variable in this research is the temperature value, while the dependent variable is the DS18B20 temperature sensor. The largest error value from the module measurements is at a temperature setting of 30 ºC on the 2nd temperature sensor with an error value of 1.43%. Meanwhile, the smallest error value is found at a temperature setting of 35 ºC on the 1st and 4th temperature sensors with an error value of 0.01%. Data collection used a digital thermometer comparison tool with 10 repetitions as a temperature sensor reference tool. The results obtained using this sensor are more stable and have a high accuracy value. The results of the research show that the temperature difference between points 1 to 4 when viewed from the error percentage is very small, or it can be said that the temperature distribution is even. The conclusion from these results is that the module has a stable temperature value and the error value is low and is still within the tolerance limit. permitted is ±5%.


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S. Sumardi and B. Untara, “Shaking Water Bath Berbasis Mikrokontroler Atmega 16,” Med. Tek. J. Tek. Elektromedik Indones., vol. 2, no. 1, 2020, doi: 10.18196/mt.020114.

W. Kartika, Sumardi, and N. H. Wijaya, “The Temperature Measurement Using LM35 on Shaking Water Bath,” Proc. 4th Int. Conf. Sustain. Innov. 2020–Technology, Eng. Agric. (ICoSITEA 2020), vol. 199, no. ICoSITEA 2020, pp. 213–217, 2021, doi: 10.2991/aer.k.210204.039.

I. A, O. S.O, A. A.E, and O. C.D, “Temperature Control System Using Fuzzy Logic Technique,” Int. J. Adv. Res. Artif. Intell., vol. 1, no. 3, pp. 27–31, 2012, doi: 10.14569/ijarai.2012.010305.

B. Dai, R. Chen, and R. C. Chen, “Temperature control with fuzzy neural network,” Proc. - 2017 IEEE 8th Int. Conf. Aware. Sci. Technol. iCAST 2017, vol. 2018-Janua, no. iCAST, pp. 452–455, 2017, doi: 10.1109/ICAwST.2017.8256499.

N. Hasim, M. S. M. Aras, M. Z. A. Rashid, A. M. Kassim, and S. S. Abdullah, “Development of fuzzy logic water bath temperature controller using MATLAB,” Proc. - 2012 IEEE Int. Conf. Control Syst. Comput. Eng. ICCSCE 2012, pp. 11–16, 2012, doi: 10.1109/ICCSCE.2012.6487107.

R. A. Koestoer, Y. A. Saleh, I. Roihan, and Harinaldi, “A simple method for calibration of temperature sensor DS18B20 waterproof in oil bath based on Arduino data acquisition system,” AIP Conf. Proc., vol. 2062, 2019, doi: 10.1063/1.5086553.

Z. Zhang and T. L. Guo, “Design of water bath temperature control system based on DS18B20,” Adv. Mater. Res., vol. 462, pp. 753–756, 2012, doi: 10.4028/www.scientific.net/AMR.462.753.

M. Khalid, S. Omatu, and R. Yusof, “Adaptive fuzzy control of a water bath process with neural networks,” Eng. Appl. Artif. Intell., vol. 7, no. 1, pp. 39–52, 1994, doi: 10.1016/0952-1976(94)90041-8.

S. Saini and S. Rani, “Temperature control using intelligent techniques,” Proc. - 2012 2nd Int. Conf. Adv. Comput. Commun. Technol. ACCT 2012, pp. 138–145, 2012, doi: 10.1109/ACCT.2012.110.

M. D. Khairunnas, E. Ariyanto, and S. Prabowo, “Design and implementation of smart bath water heater using Arduino,” 2018 6th Int. Conf. Inf. Commun. Technol. ICoICT 2018, vol. 0, no. c, pp. 184–188, 2018, doi: 10.1109/ICoICT.2018.8528772.

A. K. S. and D. S. Bharat Bhushanl, “Fuzzy & ANFIS based Temperature,” 1s IEEE Int. Conf. Power Electron. Intell. Control Energy Syst., pp. 1–6, 2016.

M. Coban and M. Fidan, “Fuzzy Logic Based Temperature Control,” 3rd Int. Symp. Multidiscip. Stud. Innov. Technol. ISMSIT 2019 - Proc., pp. 1–4, 2019, doi: 10.1109/ISMSIT.2019.8932906.

P. Mi, L. Ma, and J. Zhang, “Integrated optimization study of hot water supply system with multi-heat-source for the public bath based on PVT heat pump and water source heat pump,” Appl. Therm. Eng., vol. 176, no. February, p. 115146, 2020, doi: 10.1016/j.applthermaleng.2020.115146.

T. Odynanto and N. Jamaludin, “Characteristics of limiting devices of electric power use triac based microcontroller,” ARPN J. Eng. Appl. Sci., vol. 10, no. 22, pp. 10578–10583, 2015.

J. S. Madugu and P. G. Vasira, “Modeling and Performance Evaluation of P, PI, PD and PID Temperature Controller for Water Bath,” Technol. Sci. Am. Sci. Res. J. Eng., vol. 47, no. 1, pp. 186–200, 2018, [Online]. Available: http://asrjetsjournal.org/

C. T. Lin, C. F. Juang, and C. P. Li, “Water bath temperature control with a neural fuzzy inference network,” Fuzzy Sets Syst., vol. 111, no. 2, pp. 285–306, 2000, doi: 10.1016/S0165-0114(98)00075-X.

N. Hasim, M. F. Basar, and M. S. Aras, “Design and development of a water bath control system: A virtual laboratory environment,” Proc. - 2011 IEEE Student Conf. Res. Dev. SCOReD 2011, pp. 403–408, 2011, doi: 10.1109/SCOReD.2011.6148773.

X. W. Li and B. Xu, “Research of artificial rumen constant temperature water bath based on fuzzy logic controller,” Appl. Mech. Mater., vol. 602–605, pp. 2487–2490, 2014, doi: 10.4028/www.scientific.net/AMM.602-605.2487.

M. A. Muslim and G. D. Nusantoro, “Fuzzy logic based temperature control of a vacuum distiller,” Int. J. Appl. Eng. Res., vol. 10, no. 17, pp. 38504–38508, 2015.

B. Ozceylan, B. R. Haverkort, M. De Graaf, and M. E. T. Gerards, “Improving temperature prediction accuracy using kalman and particle filtering methods,” 2020 26th Int. Work. Therm. Investig. ICs Syst. THERMINIC 2020 - Proc., 2020, doi: 10.1109/THERMINIC49743.2020.9420535.

D. Rushalina, I. D. G. H. Wisana, P. C. Nugraha, and N. Ragimova, “Analysis of Transmitted and Received ECG Signal Based on Internet of Thing Using Web Browser and Server-Client HTML Protocol,” J. Teknokes, vol. 15, no. 4, pp. 216–222, 2022, doi: 10.35882/teknokes.v15i4.469.

Febri Indiani, Dyah Titisari, and Lamidi, “Waterbath Design equipped With Temperature Distribution Monitor,” J. Electron. Electromed. Eng. Med. Informatics, vol. 1, no. 1, pp. 11–15, 2019, doi: 10.35882/jeeemi.v1i1.3.

R. N. Sonawane, A. S. Ghule, A. P. Bowlekar, and A. H. Zakane, “Design and Development of Temperature and Humidity Monitoring System,” Agric. Sci. Dig., vol. 39, no. 2, pp. 114–118, 2019, doi: 10.18805/ag.D-4893.

M. Salehi, I. Sepahvand, and M. Yarahmadi, “TLCSBFL: A Traffic Lights Control System Based on Fuzzy Logic,” Int. J. u- e-Service, Sci. Technol., vol. 8, no. 3, pp. 27–34, 2014, doi: 10.14257/ijunesst.2014.7.3.03.

M. A. Haidekker, “First Comprehensive Example: The Temperature-Controlled Waterbath,” Linear Feed. Control., pp. 77–87, 2013, doi: 10.1016/b978-0-12-405875-0.00005-x.

Rizkiyatussani, Her Gumiwang Ariswati, and Syaifudin, “Five Channel Temperature Calibrator Using Thermocouple Sensors Equipped With Data Storage,” J. Electron. Electromed. Eng. Med. Informatics, vol. 1, no. 1, pp. 1–5, Jul. 2019, doi: 10.35882/jeeemi.v1i1.1.

J. P. Cao, S. K. Jeong, and Y. M. Jung, “Fuzzy logic controller design with unevenly-distributed membership function for high performance chamber cooling system,” J. Cent. South Univ., vol. 21, no. 7, pp. 2684–2692, 2014, doi: 10.1007/s11771-014-2230-y.

N. Yanti, T. Nur, and R. Randis, “Implementation of Fuzzy Logic in Fish Dryer Design,” Ilk. J. Ilm., vol. 14, no. 1, pp. 39–51, 2022, doi: 10.33096/ilkom.v14i1.1092.39-51.

S. Subramanian, N. Raghothaman, and B. Chellappa, “Fuzzy logic control in high temperature furnace,” Recent Res. Sci. Technol., vol. 2, no. 4, pp. 104–108, 2010.

C. Umam, S. M. Sutan, and Y. Hendrawan, “Fuzzy Logic in Determining The Control Temperature and Humidity in Plant Factory for Cultivation of Pak Choy (Brassica chinensis L.) Hydroponics,” Indones. Green Technol. J., pp. 9–14, 2019, doi: 10.21776/ub.igtj.2019.008.01.02.

How to Cite
R. Kurniawan, S. Syaifudin, L. Lamidi, and S. Misra, “Fuzzy Logic Method to Control Evenly Distributed and Stable Waterbath Temperature with Four Heaters”, Teknokes, vol. 17, no. 1, pp. 48–56, Mar. 2024.
Biomedical Engineering