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Figure 8 Operating maps of screw expander (left) and radial turbine (right) with typical working fluids [12] Based on this approach a case study will be performed until the end of the project. 4.4. Methodology for working fluid selection: The following is a rough procedure to find a suitable working fluid • Literature review on existing organic fluids • First selection taking into account the working temperature range (heat source and heat sink temperature) • Second selection with focus on safety and environmental issues (Montreal Protocol) • Comparison of thermodynamic properties and determination of cycle efficiencies • Check availability of expansion machines in terms of reasonable operating range 5. Economic Evaluation The attractiveness of ORC is strongly coupled to economic feasibility, which depends on many factors such as investment, operating and maintenance costs, prices of the electricity etc. The financial feasibility of ORC systems will be assessed in terms of payback period and profitability with the following approach: 1. Estimation of specific investment costs 2. Development of a simple mathematical model covering the specific investment costs of ORC plants as a function of ORC module size 3. Extension of the mathematical model to provide input to a best-case, worst-case and average scenario 4. Compilation of electricity prices in various nations as well as green energy incentives, tax breaks and other stimuli to estimate revenues 5. Assumptions of interest rates, operating, maintenance and insurance costs 6. Economic analysis of ORC systems based on the data gathered above 7. Visualization of the results. Steps 1 to 5 is accomplished to a large extend. Figure 9 (left) shows the specific investment costs as a function of the ORC module size. The sample data is acquired through case studies and as quotes from manufacturers [13]- [21]. It can be seen that the larger the ORC module the lower the specific investment costs. On the other hand the specific investment costs rise very fast as the ORC module becomes smaller. The cost function is mathematically modeled by a fitting curve (black). However, since the sample data are quite scattered and installation costs vary vastly depending on geographic location, heat source and complexity of the desired system, it’s worth defining a lower and upper cost function that will be used for best- and worst case scenarios respectively. The upper and lower cost function is not defined yet but may look similar to ones indicated in the figure.PDF Image | Application of Organic Rankine Cycles (ORC)
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