Methodology for Identification of Suitable ORC-Cycle and Working-Fluid using Integration with Multiple Objectives

In recent times the interest in electricity production with Organic Rankine Cycles (ORC) has increased. A look into recent publications shows that the identification of suitable working fluids is in general done by a look at a more or less small number of fluids and a predefined type of cycle (single-stage, two stage, transcritical etc.). In this publication we describe a methodology that is capable of choosing the design-point, a suitable working fluid and a type of cycle. This is done while in parallel integrating the cycle with any process, using composite curves and pinch analysis. The methodology proposes a multitude of decision criteria for the optimal cycle which are either thermodynamic criteria like exergy efficiency, energy efficiency, maximum or minimum pressures and temperatures, or economic criteria. Additionally it is possible to include qualitative and quantitative criteria to make a pre-selection e.g. Toxicity, Global Warming Potential (GWP) and Ozone Depletion Potential (ODP). We propose a new methodology, as shown in Figure 1, using a genetic algorithm for the multi objective optimisation (Master-Problem) of suitable ORCs with available heat sources and MILP (Slave-Problem) for fitting of the cycles. The crucial point of this methodology is the choice of the cycle within the MILP-Slave Problem. This means that all possible fluids are tested for a set of thermodynamic parameters and the best one regarding the chosen selection objective is send back to the Master-Problem for evaluation. This has the advantage of testing all fluids within a limited time frame.

Varbanov, Ps
Klemes, Jj
Liew, Py
Yong, Jy
Stehlik, P
Published in:
Pres 2014, 17Th Conference On Process Integration, Modelling And Optimisation For Energy Saving And Pollution Reduction, Pts 1-3, 39, 1141-1146
Presented at:
17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES 2014)', 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES 2014)
Milano, Aidic Servizi Srl

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 Record created 2015-02-20, last modified 2018-03-18

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