Selected Journal of the Excellence Action Plan for China's Science and Technology Journals (Phase II),Selected Journal of Chinese Applied Core Journals (Extended Edition) / CACJ (Extended),Indexed in the Chinese Core Journals (Selection) Database
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Analysis of Factors Affecting the COP of a Lithium Bromide Flue-Gas-Waste-Heat-Driven Absorption Heat Pump and Development of a Prediction Model Based on Operational Data

Issue Number Issue 07, 2026 [View other articles in this issue]
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Publication Date 2026.07.15
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Author 任举卫1,杨 艳1,杨济宁2
Organization 1.青岛能源集团有限公司
2.泰能天然气有限公司

Abstract

This study takes the lithium bromide flue-gas waste-heat absorption heat pump system installed at a gas-fired heating station as its research object. Based on operational monitoring data from the 2023–2024 and 2024–2025 heating seasons, it systematically evaluates the actual operating performance of the heat pump system. The results indicate that the system demonstrated high overall energy efficiency across both heating seasons. Furthermore, this paper quantitatively analyzes two key operating parameters influencing the coefficient of performance (COP) of the heat pump: the flue-gas temperature difference and the supply-and-return water temperature difference. It is found that the heat pump COP exhibits a positive correlation with the flue-gas temperature difference and a negative correlation with the supply-and-return water temperature difference. Building upon these analyses, a predictive regression model for the heat pump COP has been successfully developed, using the flue-gas temperature difference and the supply-and-return water temperature difference as input variables. The findings of this study hold significant engineering value for optimizing the operational strategies of heating stations, enhancing system energy efficiency, and enabling precise energy-saving control.

Keywords

Gas-fired boiler; lithium bromide flue gas waste heat absorption heat pump; waste heat recovery; coefficient of performance (COP); operating characteristics; regression model; prediction

References

Declaration:

The English version of this article is translated with the assistance of AI.
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