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Figure 2. Recuperated ORC layout and typical T-s diagram for a random pure working fluid can be obtained from these pure substances. Taking a decision when choosing the working fluid for the ORC has a major impact on the cost, the components and cycle performance and on the safety requirements (Macchi and Astolfi, 2017). (Feng et al, 2015) propose the use of mixtures in the ORC, due to the non-isothermal change of phase in the evaporator, which allows to obtain better gliding temperature matches between the heat source and the working fluid. However, the analysis reflects that mixtures are not always giving better cycle performances than pure working fluids. Due to the high complexity that this problem has, the study of different mixtures was not included in this work. The uncertainty fluid properties can influence the reliability of the optimized cycles, and, for this reason J. Frutiger et al. propose in (Frutiger et al, 2016) an analysis of the impact of uncertainty of the fluid properties in the behavior of the ORC, including the Molecular Weight (MW), critical temperature, critical pressure and acentric factor. This gives an idea of how important the working fluid properties are when choosing the working fluid. There are different factors or properties to consider when taking this decision, and we can find, among others (Macchi and Astolfi, 2017): • The molecular complexity. It influences the T-s diagram shape (differencing between dry, isentropic and wet fluids) which is essential for the performance of the turbine, since it determines the amount of liquid that may be formed during the expansion. (Zhai et al, 2014) demonstrate that complex molecules that include double bonds or cycle bonds are much more efficient than molecules with only single bonds, with a lower molecular complexity • The molecular mass. This parameter has a major influence on the design of the turbine. When the molecular weight increases, the enthalpy drop in the turbine is lower, and this is translated into a need of less stages for the expansion, lower peripheral velocities and mechanical stresses, although the heat transfer coefficient diminishes and heat exchangers with larger heat transfer areas are needed (higher costs). • The critical temperature. It is recommended to use working fluids with critical temperatures as closest as possible to the highest temperature of the cycle, since they guarantee an efficient heat exchange process. The impact of the working fluid on the environment is important. Fluids with a high Ozone Depletion Potential (ODP) are undesirable, while organic fluorinated fluids (HFCs) are to be avoided because of their high Global Warming Potential (GWP). From (Zhai et al, 2014) it was found that the GWP is not only important for the environment concern, but also for the efficiency of the cycle (results show that those working fluids with a GWP lower than 1500 can absorb more energy from the heat source, improving the cycle efficiency). The most common working fluid for conventional Rankine cycles is H2O. However, when the heat source temperature is low, H2O shows poor performances. As H2O is a wet working fluid, it condensates during the expansion, and this may damage the turbine. To solve this problem, H2O requires a too high degree of superheating and a low enthalpy drop across the expander, meaning that the power produced is low (low plant efficiencies). It was found to be of interest to investigate how H2O behaves in our case of study even if the performance was expected to be poor. 3 Methodology The Rankine cycle model was implemented in MATLAB and optimized with a Gradient Based Algorithm (Sequential Quadratic Programming, SQP). The advantage of Gradient Based Algorithms against the popular direct search algorithms, such as the Genetic Algorithm, is that the former has a faster convergence rate. The code can simulate and optimize both the simple DOI: 10.3384/ecp17138251 Proceedings of the 58th SIMS 253 September 25th - 27th, Reykjavik, IcelandPDF Image | ORC for Power Generation Low Temperature Geothermal Heat Source
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