Comparison of different ORC typologies for heavy-duty trucks by means of a thermo-economic optimization

This study focuses on the design phase of ORC systems recovering the heat wasted from two of the sources available on a Heavy-Duty Truck (HDT): the exhaust and recirculated gases. From these heat sources and their combinations, 5 possible architectures are considered. The main components (i.e. the heat exchangers, the pump and the expander) of the WHR systems are investigated and modeled. Plate type heat exchangers are considered for both the hot and cold sides of the system. Regarding the expansion devices, 5 positive displacement machine technologies, the scroll, screw, piston, vane and roots expanders, are considered and modeled while, among the turbo-expanders, the radial-inflow turbine is taken into consideration. A semi-empirical model is proposed to simulate a volumetric pump. The models of components are first confronted with experimental data. The validated models are then used as references for the design of the new components, which is achieved following similitude rules. This ultimately leads to 30 typologies that will be used with 6 of the various investigated working fluids. In order to identify the most promising system(s), a 3-step optimization tool is developed. First, the most suitable conditions are identified for the design of the ORC systems using a simplified model of an expansion machine. In the second step, the design phase, using more detailed models for the expanders and a proposed economic model for the overall system, a thermo-economic optimization is performed. In the third step, the output power for each of the obtained system models is maximized, optimizing the evaporating pressure and the superheating degree for various off-design conditions. The average power weighted using the frequency distribution of the gas operating conditions is computed and used to compare the 180 systems. Finally, because power is not the only criterion to select the most suitable system topology, additional criteria are taken into consideration. (C) 2019 Elsevier Ltd. All rights reserved.