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Advanced Study: Improving the Quality of Cooling Water Towers’ Conductivity Using a Fuzzy PID Control Model

Author

Listed:
  • You-Shyang Chen

    (College of Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Ying-Hsun Hung

    (Department of Finance, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Mike Yau-Jung Lee

    (Department of Business Administration, China University of Technology, Taipei City 116, Taiwan)

  • Jieh-Ren Chang

    (Department of Electronic Engineering, National Ilan University, I-Lan 26047, Taiwan)

  • Chien-Ku Lin

    (Department of Business Management, Hsiuping University of Science and Technology, Taichung City 412406, Taiwan)

  • Tai-Wen Wang

    (Department of Electronic Engineering, National Ilan University, I-Lan 26047, Taiwan)

Abstract

Cooling water towers are commonly used in industrial and commercial applications. Industrial sites frequently have harsh environments, with certain characteristics such as poor air quality, close proximity to the ocean, large quantities of dust, or water supplies with a high mineral content. In such environments, the quality of electrical conductivity in the cooling water towers can be significantly negatively affected. Once minerals (e.g., calcium and magnesium) form in the water, conductivity becomes too high, and cooling water towers can become easily clogged in a short time; this leads to a situation in which the cooling water host cannot be cooled, causing it to crash. This is a serious situation because manufacturing processes are then completely shut down, and production yield is thus severely reduced. To solve these problems, in this study, we develop a practical designation for a photovoltaic industry company called Company-L. Three control methods are proposed: the motor control method, the PID control method, and the fuzzy PID control method. These approaches are proposed as solutions for successfully controlling the forced replenishment and drainage of cooling water towers and controlling the opening of proportional control valves for water release; this will further dilute the electrical conductivity and control it, bringing it to 300 µS/cm. In the experimental processes, we first used practical data from Company-L for our case study. Second, from the experimental results of the proposed model for the motor control method, we can see that if electrical conductivity is out of control and the conductivity value exceeds 1000 µS/cm, the communication software LINE v8.5.0 (accessible via smartphone) displays a notification that the water quality of the cooling water towers requires attention. Third, although the PID control method is shown to have errors within an acceptable range, the proportional (P) controller must be precisely controlled; this control method has not yet reached this precise control in the present study. Finally, the fuzzy PID control method was found to have the greatest effect, with the lowest level of errors and the most accurate control. In conclusion, the present study proposes solutions to reduce the risk of ice-water host machines crashing; the solutions use fuzzy logic and can be used to ensure the smooth operation of manufacturing processes in industries. Practically, this study contributes an applicable technical innovation: the use of the fuzzy PID control model to control cooling water towers in industrial applications. Concurrently, we present a three-tier monitoring checkpoint that contributes to the PID control method.

Suggested Citation

  • You-Shyang Chen & Ying-Hsun Hung & Mike Yau-Jung Lee & Jieh-Ren Chang & Chien-Ku Lin & Tai-Wen Wang, 2024. "Advanced Study: Improving the Quality of Cooling Water Towers’ Conductivity Using a Fuzzy PID Control Model," Mathematics, MDPI, vol. 12(20), pages 1-27, October.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3296-:d:1502912
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    References listed on IDEAS

    as
    1. Liu, Hua & Wu, Zhiyong & Zhang, Bingjian & Chen, Qinglin & Pan, Ming & Ren, Jingzheng & He, Chang, 2023. "A large-scale stochastic simulation-based thermodynamic optimization for the hybrid closed circuit cooling tower system with parallel computing," Energy, Elsevier, vol. 283(C).
    2. Xiangling Zhao & Wenheng Xiao, 2024. "Uncertainty Analysis of Aircraft Center of Gravity Deviation and Passenger Seat Allocation Optimization," Mathematics, MDPI, vol. 12(10), pages 1-18, May.
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