Abstract:
The combination of computer-aided molecular design
(CAMD) with an organic Rankine cycle (ORC) power-system
model presents a powerful methodology that facilitates an integrated
approach to simultaneous working-fluid design and
power-system thermodynamic or thermoeconomic optimisation.
Existing CAMD-ORC models have been focussed on simple
subcritical, non-recuperated ORC systems. The current work
introduces partially evaporated or trilateral cycles, recuperated
cycles and working-fluid mixtures into the ORC power-system
model, which to the best knowledge of the authors has not been
previously attempted. A necessary feature of a CAMD-ORC
model is the use of a mixed-integer non-linear programming
(MINLP) optimiser to simultaneously optimise integer workingfluid
variables and continuous thermodynamic cycle and economic
variables. In this paper, this feature is exploited by introducing
binary optimisation variables to describe the cycle layout,
thus enabling the cycle architecture to be optimised alongside
the working fluid and system conditions. After describing
the models for the alternative cycles, the optimisation problem
is completed for a defined heat source, considering hydrocarbon
working fluids. Two specific case studies are considered,
in which the power output from the ORC system is maximised.
These differ in the treatment of the minimum heat-source outlet
temperature, which is unconstrained in the first case study, but
constrained in the second. This is done to replicate scenarios
such as a combined heat and power (CHP) plant, or applications
where condensation of the waste-heat stream must be avoided.
In both cases it is found that a working-fluid mixture can perform
better than a pure working fluid. Furthermore, it is found
that partially-evaporated and recuperated cycles are optimal for
the unconstrained and constrained case studies respectively
Description:
Papers presented at the 13th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Portoroz, Slovenia on 17-19 July 2017 .