工業用冷卻水塔智慧節能技術

Smart Energy-saving Technology for Industrial Cooling Towers

偕睿仁 ； 楊凱智 ； 吳鴻材

《機械工業雜誌》 494期 (2024/05) Pp. 20-25

摘要

隨著全球工業化程度的不斷提高，對冷卻水塔的需求也在不斷增加，在COVID-19後改變的商務環境中，全球冷卻水塔市場在2022年已達37億美元，預計在2030年前達到54億美元的規模，並將以4.9%的年複合成長比率增加，而半導體廠務用電（冷卻水塔、冰機、空調箱及空壓系統）占半導體產業用電47%，其中冷卻水塔又占了11.1%的廠務用電。在全球節能減碳的趨勢下，本文的三大功能包含，冷卻水塔健康狀態的監測、風扇傳動預兆診斷系統及智慧節能最佳化預測，有助於降低能源成本，並減少產業的碳排放。

關鍵字

健康狀態 ； 預兆診斷系統 ； 智慧節能最佳化預測

並列摘要

With the continuous advancement of global industrialization, the demand for cooling towers is steadily increasing. In the evolving business environment post-COVID-19, the global cooling tower market reached $3.7 billion in 2022 and is projected to reach $5.4 billion by 2030, with an expected annual compound growth rate of 4.9 %. The power consumption of semiconductor facility operations (including cooling towers, chiller, air handling units, and compressed air systems) accounts for 47% of the electricity usage in the semiconductor industry. Among these, cooling towers contribute to 11.1% of the facility's electricity consumption. Aligned with the global trend of energy conservation and carbon reduction, this article encompasses three key functions: monitoring the health and efficiency of cooling towers, prognosis monitoring system of the fan drive system, and intelligent optimization prediction. These features contribute to reducing energy costs and mitigating carbon emissions within the industry.

並列關鍵字

Health and efficiency ； Prognosis monitoring system ； Intelligent optimization prediction

參考文獻

李侃峰,盧江溪(2022).真空製程設備節能技術.機械工業雜誌.474,19-23.

王俊傑(2007).旋轉機械故障診斷方法之探討(博士論文).中原大學機械工程學系.

Ke, G.(2017).LightGBM: A highly efficient gradient boosting decision tree.31st Conference on Neural Information Processing Systems (NIPS 2017).(31st Conference on Neural Information Processing Systems (NIPS 2017)).

Chen, C.(2019).LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion.Chemometrics and Intelligent Laboratory Systems.191,54-64.

Wang, D.,Zhang, Y.,Zhao, Y.(2017).LightGBM: an effective miRNA classification method in breast cancer patients.Proceedings of the 2017 international conference on computational biology and bioinformatics.(Proceedings of the 2017 international conference on computational biology and bioinformatics).