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Energies 2020, 13, 370 12 of 31 2.2. Modeling and Thermodynamic Optimization The s-CO2 power cycles were modeled in the Engineering Equation Solver (EES) environment using a consistent set of assumptions for a fair comparison. A thermodynamic optimization was performed to find the optimum parameters of each s-CO2 power cycle layout for utilization of a waste heat source with an inlet temperature of 600 ◦C. The steady-state mass and energy balances for each plant component are not shown for brevity. 2.2.1. Objective Function and Other Performance Metrics The aim of the thermodynamic optimization set up in this study was the maximization of the . “total heat recovery efficiency” (ηTOT), which is the ratio between the net power output (Wnet) and . the heat available from the waste heat source (Qhs,av) from the inlet temperature (Tin) to the ambient temperature (Tamb): .. η=Wnet= Wnet . (1) TOT . m.·c·(T−T) Qhs,av hs p in amb In this study, the net power output of all layouts was set to equal 1 MW and the mass flow rate of the heat source (m. from a given waste heat source translates to the minimization of the mass flow rate of the heat source required to generate such a fixed amount of power. The total heat recovery efficiency can be interpreted as the product of two more elementary performance metrics, namely the “thermal efficiency” of the power cycle (ηth) and the “heat recovery effectiveness” (φ) in the heat transfer process between the heat source and the power cycle: ηTOT = ηth · φ. (2) Indeed, ηth is the ratio between the net power and the heat transferred from the heat source to the . power cycle (Qhs,av): .. η = Wnet = Wnet th. m.·c·(T−T) Qhs,in hs p in out hs ) was calculated accordingly. Thus, the maximization of the net power output whereas φ is the ratio between the heat transferred to the power cycle and the heat available from the heat source from the inlet temperature to the ambient temperature: . φ= Qhs,in = (Tin−Tout). (4) Q. hs,av (Tin −Tamb) , (3) From Equation (2), it is clearly apparent that both elementary performance metrics have the same weight in determining the final value of ηTOT. Thus, in the search of the maximum power output from a given heat source, it is important to maximize both of them. The improvement in ηth is obtained by increasing the average temperature of the heat input from the heat source and decreasing the average temperature of the heat rejection to the environment. Instead, the improvement in φ is obtained by cooling down the heat source to the highest extent in the heat transfer process to the power cycle. These two requirements are generally antithetic because the cooling of the heat source in the heat transfer process implies heat input at lower temperatures. The target of the thermodynamic optimization is to identify those conditions (i.e., the values of the optimum decision variables), which represent a balance between high ηth and high φ that yields the highest ηTOT. While this approach based on the decomposition of the overall performance metric (ηTOT) into two elementary sub-metrics (i.e., ηth, φ) has been widely applied in the field of organic Rankine cycles (see, e.g., [37]), it has been seldom used in the field of s-CO2 power cycles and almost exclusively for the optimum point (see, e.g., Wright et al. [22]). Thus, one of the goals of this work was to apply thisPDF Image | Novel Supercritical CO2 Power Cycles for Waste Heat Recovery
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