Statistical Modeling and Parameter Optimization of Electric-Powered Rotary Screw Air Power Compressor

In this study, a statistical correlation was established among the input parameters, namely, ambient temperature (AT), oil injection orifice (OIO) size, and cooling fan speed with free air delivery (FAD), input power (IP), and discharge oil temperature (DOT) of an electric-powered twin screw air compressor. Experiments were designed based on a central composite design (CCD). A response optimizer is used to identify the combination of input operating parameter settings that optimizes responses independently and collectively. A model considering all responses together with equal priorities provides the maximum FAD of 254.71 cfm and minimum IP of 44.16 kW by setting the compressor with an AT of 44°C, OIO size of 4.0 mm, and a cooling fan speed of 1220 rpm. Higher ambient conditions are achieved for experimental purposes by designing a hot chamber wherein hot air from the cooling fan exhaust is mixed with the ambient air. Confirmatory tests are conducted to validate the statistical model proposed in this study. The mean percentage (%) error observed for FAD, IP, and DOT are 0.29%, 0.48%, and 1.85%, respectively. The results show that the proposed statistical models are robust and can be used to obtain the performance characteristics of screw compressors.