ASME ORC 2015

tag DESIGN AND ANALYSIS OF AN ORGANIC RANKINE CYCLE SYSTEM FOR COGENERATION
Peter Collings, Zhibin Yu

Session: Poster session
Session starts: Tuesday 13 October, 13:30

Peter Collings (University of Glasgow)
Zhibin Yu (University of Glasgow)

Abstract:
This paper covers the design and construction of a small scale prototype of Organic Rankine Cycle (ORC) system for domestic-scale cogeneration application, using a variety of working fluids. The efficiency of such systems is limited by the low temperature range available to them. Researches indicate a first law efficiency of up to 11% for such ORC system for a heat source temperature less 250 degree Celsius, meaning that a large proportion of the heat used to drive them is rejected to the environment. This makes them ideal candidates for micro-scale Combined Heat and Power projects producing up to 10kW of electricity. This research aims to investigate the effects of varying several cycle parameters, as the thermal efficiency of a cycle operating in such a limited temperature range is quite low, and even small absolute gains can represent large percentage increases in efficiency, and correspondingly, small decreases in efficiency due to conflicting optimisation demands can be very costly. In particular, the effect that the selection of the working fluid has on the performance of the cycle is investigated, but also the operating pressures of the system, the temperatures at each point in the cycle, the presence or lack of a regenerator, and the flow rates of the working fluid, heating, and cooling water. The ultimate goal is to develop a system configuration that produces the most electrical power while still providing an acceptable final temperature of coolant to be used for space heating. Initially, simulation work was carried out using MATLAB, linked to the REFPROP 9.1 fluid properties program. The results of this program were used to design a lab-scale prototype ORC system, built around a 1kW scroll expander from Airsquared, which will be used to validate the model, and gather experimental data to complement the theoretical predictions.