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Development and Evaluation of an Automated Virtual Refrigerant Charge Sensor Training Kit

机译:自动化虚拟制冷剂充电传感器训练套件的开发与评价

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Virtual sensors have previously been developed and demonstrated that can provide a low cost and relatively accurate estimation of the refrigerant charge contained in packaged (rooftop) air conditioners. One particular virtual refrigerant charge sensor approach uses four surface-mounted temperature measurements to determine suction superheat, liquid-line subcooling and evaporator inlet quality that are inputs to an empirical model for charge. The empirical parameters of the model are determined using linear regression applied to laboratory data collected from the system. In previous studies, extensive psychrometric chamber testing was required at different refrigerant charge levels and ambient conditions to obtain sufficient data for the regression. This testing is expensive for equipment manufacturers and it can be difficult to find available test facilities. The current work describes the development of an automated open lab training kit for calibrating a virtual refrigerant charge level sensor in an open laboratory space. The developed automated training kit algorithm has the ability to modulate the condenser and evaporator fans to simulate the effects of different ambient conditions and automatically add different amounts of refrigerant. The charge level is automatically adjusted and monitored using solenoid valves and a digital weighing scale. This approach reduces the human involvement to a great extent and eliminates the need for psychrometric chambers. An optimal set of test conditions has been determined using optimal experimental design techniques and implemented as a Python application. An Arduino microcontroller is used to continuously send data from the sensors to a personal computer which is used to supervise the process, including determining when the system has reached steady-state. The training kit has been applied to several different rooftop units in an open lab space. A comparison of the virtual refrigerant charge sensor accuracy and time/cost for calibration determined using the automated system and using psychrometric chamber test facilities has been presented.
机译:先前已经开发并证明了虚拟传感器,其可以提供封装(屋顶)空调中包含的制冷剂电荷的低成本和相对准确的估计。一个特定的虚拟制冷剂电荷传感器方法使用四个表面上安装的温度测量来确定抽吸过热,液体管线过冷和蒸发器入口质量,该蒸发器入口质量是对电荷的经验模型的输入。使用应用于从系统收集的实验室数据的线性回归来确定模型的经验参数。在先前的研究中,在不同的制冷剂电荷水平和环境条件下需要广泛的心理测量室测试,以获得回归的足够数据。该测试对于设备制造商来说是昂贵的,并且很难找到可用的测试设施。目前的工作描述了一种用于在开放实验室空间中校准虚拟制冷剂充电水平传感器的自动开放实验室培训套件的开发。开发的自动培训套件算法能够调制冷凝器和蒸发器风扇以模拟不同环境条件的影响,并自动增加不同量的制冷剂。使用电磁阀和数字称重尺度自动调节和监控电荷水平。这种方法在很大程度上减少了人类的参与,并消除了对心理测量室的需求。已经使用最佳实验设计技术确定了最佳的测试条件集并实现为Python应用程序。 Arduino微控制器用于将来自传感器的数据连续发送到用于监督该过程的个人计算机,包括确定系统何时达到稳态。培训套件已应用于开放式实验室空间中的几个不同的屋顶单位。已经介绍了虚拟制冷剂充电传感器精度和时间/成本的比较,使用自动化系统确定并使用心理测量腔试验设施确定。

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