A High-Precision Constant Temperature Control System for Microenvironments in Industrial HVAC Systems

Xiyuan Zhang; Menghan Pan; Lingxin Hu; Tengteng Ma

doi:10.54691/87e1yc26

Authors

Xiyuan Zhang
Menghan Pan
Lingxin Hu
Tengteng Ma

DOI:

https://doi.org/10.54691/87e1yc26

Keywords:

Industrial HVAC; Temperature Control; PID Algorithm; Semiconductor Cooling.

Abstract

In industrial HVAC (Heating, Ventilation, and Air Conditioning) systems, precise temperature control is critical for ensuring process stability, product quality, and equipment reliability. To address the demand for high-precision temperature regulation in localized microenvironments within industrial settings, a high-precision constant temperature control system was developed for industrial HVAC applications. This system utilizes an Arduino Uno microcontroller, integrated with DS18B20 digital temperature sensors and NTC thermistors, and employs a PID algorithm combined with relay feedback tuning to achieve adaptive, high-precision temperature control. Extensive operational testing demonstrates that the system can rapidly respond to and accurately regulate microenvironment temperatures in complex and variable industrial conditions, achieving temperature control accuracies of ±0.2°C for cooling and ±0.4°C for heating. This solution provides an efficient and stable temperature management approach for industrial HVAC systems.

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References

[1] Wang Bing. Design of a Semiconductor Cooling Box Based on Arduino Microcontroller [D]. Southwest Jiaotong University, 2020.

[2] Zhang Lulu. Design and Implementation of a Microcontroller-Based Temperature Measurement and Control System [D]. Jilin University, 2014.

[3] Peng Mengdi. Research and Implementation of a High-Precision Temperature Measurement and Control System [D]. Taiyuan University of Technology, 2021.

[4] Qiao Guan. Research on an Intelligent Coffee Roasting System Based on Adaptive Temperature Control [D]. Hangzhou Dianzi University, 2021.

[5] Qin Shixuan. Research on an Adaptive PID Temperature Control System [D]. Changchun University of Science and Technology, 2018.

[6] Kundu S, Sinitsyn N, Backhaus S, et al. Modeling and control of thermostatically controlled loads[J]. arXiv preprint arXiv:1101.2157, 2011.

[7] Singhala P, Shah D, Patel B. Temperature control using fuzzy logic[J]. arXiv preprint arXiv: 1402. 3654, 2014.

[8] Liang H, Sang Z K, Wu Y Z, et al. High precision temperature control performance of a PID neural network-controlled heater under complex outdoor conditions[J]. Applied Thermal Engineering, 2021, 195: 117234.

[9] Możaryn J, Petryszyn J, Ozana S. PLC based fractional-order PID temperature control in pipeline: design procedure and experimental evaluation[J]. Meccanica, 2021, 56: 855-871.

[10] Kawasaki Y, Yoneda Y. Local temperature control in greenhouse vegetable production[J]. The Horticulture Journal, 2019, 88(3): 305-314.

[11] Villegas-Uribe C A, Alcántara-Avila J R, Medina-Herrera N, et al. Temperature control of a Kaibel, Agrawal and Sargent dividing-wall distillation columns[J]. Chemical Engineering and Processing-Process Intensification, 2021, 159: 108248.

[12] Khalid M, Omatu S. A neural network controller for a temperature control system[J]. IEEE control systems magazine, 1992, 12(3): 58-64.

[13] Jeroish Z E, Bhuvaneshwari K S, Samsuri F, et al. Microheater: material, design, fabrication, temperature control, and applications—a role in COVID-19[J]. Biomedical microdevices, 2022, 24: 1-49.

[14] Ruelens F, Claessens B J, Vandael S, et al. Residential demand response of thermostatically controlled loads using batch reinforcement learning[J]. IEEE Transactions on Smart Grid, 2016, 8(5): 2149-2159.

[15] Nazir M S, Hiskens I A. Noise and parameter heterogeneity in aggregate models of thermostatically controlled loads[J]. IFAC-PapersOnLine, 2017, 50(1): 8888-8894.

[16] Brandi S, Piscitelli M S, Martellacci M, et al. Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings[J]. Energy and Buildings, 2020, 224: 110225.