ORC for Power Generation Low Temperature Geothermal Heat Source

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and the recuperated Rankine cycles and it computes all the thermodynamic states of the cycle, using the REFPROP library. 3.1 Fluid screening selection Most of the manufacturers opt to work with organic fluids that have already been tried, to reduce the uncertainty and simplify the fluid selection process. However, this does not necessarily mean that they are the best choice. In fact, (Colonna et al, 2015) insist on the need of carrying out research on new substances. In this work, some of the most used working fluids were analyzed, although others fluids that are not so well known were also included. After a literature search, we found that the most commonly used organic working fluids for ORC applications are: R134a, R245fa, n- pentane, octamethultrisiloxane, toluene (Maraver et al, 2014), and, among the hydrocarbons (HCs), butane (R600), iso-butane (R600a), pentane (R601), iso- pentane (R601a) and hexane (Liu et al, 2013). To select the working fluid, we took as a starting point a list of more than 130 different pure substances. Since not all of these pure substances were suitable for the given conditions, a first screening selection based on the need of meeting four main requirements was carried out to discard unsuitable working fluids. The requirements were: 1. The ODP had to be zero or close to zero 2. The GWP must be lower than 2000 3. The fluid critical temperature needed to be higher than the ambient temperature to make condensation possible 4. The saturation pressure at ambient temperature had to be higher than 1 kPa to limit vacuum in the condenser From these requirements, a revised list of 44 working fluids was obtained. 39 working fluids were simulated and optimized by means of the Gradient Based Algorithm in MATLAB. 3.2 Assumptions and boundary conditions The main assumptions and boundary conditions can be found in Table 1. Some data such as the pump and turbine efficiencies were assumed based on the information found during the literature review process, although the impact of these parameters was studied by means of a sensitivity analysis. Values for the pump polytropic efficiency between 10% and 100% were simulated for the most optimal working fluids under subcritical and transcritical conditions. For the expander, the correlation from (Astolfi and Macchi, 2015) for axial turbines of one, two, and three stages to compute the polytropic efficiency as a function of the volume ratio and the size parameter was also analyzed for some of the best working fluids. This correlation states: = ๐‘“(๐‘‰ , ๐‘†๐‘ƒ) (1) ๐‘Ÿ ๐œŒ๐‘–๐‘› ๐‘Ÿ (2) ๐‘†๐‘ƒ = (๐‘šฬ‡ ๐‘œ๐‘ข๐‘ก/๐œŒ๐‘œ๐‘ข๐‘ก,๐‘ )1/2 (3) (ฮ”h๐‘ )1/4 Table 1. Assumptions and boundary conditions. Hot source ๐œ‚ ๐‘ก๐‘ข๐‘Ÿ๐‘ ๐‘‰ = ๐œŒ๐‘œ๐‘ข๐‘ก ๐‘‡h,๐‘–๐‘› ๐‘‡h๐‘š๐‘–๐‘›,๐‘œ๐‘ข๐‘ก ๐‘‡ h๐‘š๐‘Ž๐‘ฅ,๐‘œ๐‘ข๐‘ก ๐‘šฬ‡ ๐‘ƒ ๐‘œ๐‘ข๐‘ก Working fluid Cold source ๐‘‡ ๐‘–๐‘› [oC] 120 [oC] 75 [oC] 85 [kg/s] 13.23 [bar] 3 [-] H2O [oC] 10 Working fluid โˆ†๐‘‡ [oC] 10 [-] H2O โˆ†๐‘‡ [oC] 5 ๐‘,๐‘š๐‘Ž๐‘ฅ ๐‘,๐‘š๐‘–๐‘› Ambient conditions ๐‘ƒ๐‘Ž๐‘š๐‘ ๐‘‡๐‘Ž๐‘š๐‘ Pinch points โˆ†๐‘‡ ๐‘š๐‘Ž๐‘ฅ โˆ†๐‘‡๐‘š๐‘–๐‘› Pressure drops โˆ†๐‘ƒ (evap,cond,rec) Pump Polytropic efficiency Turbine Polytropic efficiency [bar] 1.013 [oC] 15 [oC] 8 [oC] 5 [bar] 0.01ยท๐‘ƒ ๐‘–๐‘› [%] 70 [%] 80 DOI: 10.3384/ecp17138251 Proceedings of the 58th SIMS 254 September 25th - 27th, Reykjavik, Iceland Regarding the minimum outlet temperature of the hot source, the limitation was given to allow for use of the remaining heat in a low-temperature district heating system (this system is not considered in this work). 3.3 Objective function Defining the objective function is crucial when it comes to optimizing the cycle. For this work, the variable to be optimized was the second law efficiency of the plant. A natural choice would have been optimizing the net power output or the first law efficiency (these three objective functions would give the same thermodynamic optimum), but optimizing the second law efficiency gives insight about how much potential for improvement is left. The parameter to be optimized was the second law efficiency of the whole power plant, considering the recovery efficiency in the main heat exchanger. This is because not all the heat which was available from the heat source could be used (due to the limitation of the

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