Performance evaluation of a CO2 scroll expander for work recovery using artificial neural network

Abstract

CO2 trans-critical refrigeration systems operate in sub-critical zone for major part of the up-time even for warm climate regions. Recovery of expansion work from CO2 refrigeration systems is viewed as a workable solution to tide over the challenge of typical low coefficient of performance of such systems. Some of the barriers for wide spread implementation of expanders are; relatively low work recovery and high initial investment. In the present study, the functioning of a scroll work recovery expander under sub-critical condition is investigated in an open-loop setup using CO2 as working fluid. The scroll expander itself is obtained through conversion from a scroll compressor with minimal additional investment. Influence of the various operating parameters like mass flow rate, suction pressure, pressure ratio, and rotational speed on the overall performance of the system are examined. An artificial neural network is then trained in Statistical Package for the Social Sciences (SPSS) platform with part of the experimental data and the same is validated with remaining data. It is observed that, the deviation between the shaft speed and shaft work for the model based prediction and experimental results are within ±7.5% and ±11.1%, respectively. The developed artificial neural network will be useful for predicting performance of work recovery scroll expander in closed loop operation with CO2 refrigeration system in sub-critical zone.