Adaptive control for degree of refrigerant superheat in a direct expansion air conditioning system under variable speed operation
Huaxia Yan, Yudong Xia, Shiming Deng
2019
Energy Procedia
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand
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... door temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. Abstract Direct expansion air conditioning systems are widely installed in small-to medium-scale buildings due to their advantages of simple configuration, high energy efficiency and low cost to own and maintain. The two main targets of air conditioning operation include improving the system operational efficiency and energy efficiency. Stable system operational efficiency is directly related with the operation of electronic expansion valves and degree of superheat at the outlet of evaporator. In this paper, an adaptive DS controller is developed to improve the system operational efficiency based the experimental performance of a DX A/C system and reported. The adaptive DS controller is designed to seasonably regulate the degree of superheat when the system is subjected to changes in operating conditions. Experimental controllability test results show that the controller has a very stable behavior, allowing an effective and fast control of the DS setting. Abstract Direct expansion air conditioning systems are widely installed in small-to medium-scale buildings due to their advantages of simple configuration, high energy efficiency and low cost to own and maintain. The two main targets of air conditioning operation include improving the system operational efficiency and energy efficiency. Stable system operational efficiency is directly related with the operation of electronic expansion valves and degree of superheat at the outlet of evaporator. In this paper, an adaptive DS controller is developed to improve the system operational efficiency based the experimental performance of a DX A/C system and reported. The adaptive DS controller is designed to seasonably regulate the degree of superheat when the system is subjected to changes in operating conditions. Experimental controllability test results show that the controller has a very stable behavior, allowing an effective and fast control of the DS setting.
doi:10.1016/j.egypro.2019.01.618
fatcat:7edssnwxszazvmtj5nh6knb5qa