Facultat de Física, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain*.
Abstract: An **Organic** **Rankine** **Cycle** is an ideal model that describes power cycles processes that have a low temperature heat source such as biomass, solar energy or waste heat from conventional power plants. This kind of cycles can be modelled and simulated numerically using simple programming languages and specific libraries that contain essential information about phase transition and different properties for a great variety of substances. The aim of this project is to simulate numerically an ORC using Python 2.7 and the library Coolprop, find out how the power produced and the efficiency vary depending on different variables, and finally try to optimize it with some environmental conditions fixed.

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The thermodynamic and thermoeconomic analyses and **optimization** of a solar domestic CHP **cycle** with 2.7 kW electric output and 11kW heating output are conducted. The daily radiation is taken as 2.21 kWh/m 2 [21]. Thermodynamic and thermoeconomic modeling of the system has been conducted based on **simulation** code in EES software [22]. The system thermal and exergy efficiency is determined to be 48.45% and 13.76%.

Schuster, Karellas, Kakaras, Spliethoff studied about energetic and economic investigation of **Organic** **Rankine** **Cycle** applications. In this study the state of the art of ORC applications such as biomass combustion and geothermal plant is presented and also innovative systems that include solar desalination, waste heat recovery and micro CHP are investigated. Innovation systems are simulated a process **simulation** environment using experimental data. The results of the **simulation** like efficiencies, water production rates or achievable electricity production cost are discussed [25]. Wei, Lu X., Lu Z. and Gu made dynamic modeling and **simulation** of an ORC system for waste heat recovery. This study proposes two alternative approaches for the design of a dynamic model for an **Organic** **Rankine** **Cycle** (ORC) to be used for the design of control and diagnostics systems. The model has been developed in Modelica language and simulated with Dymola. The two modeling approaches, based on moving boundary and discretization techniques, are compared in terms of accuracy, complexity and **simulation** speed [26]

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Received: 1 February 2016; Accepted: 26 April 2016; Published: 4 May 2016
Abstract: In this paper, the optimal operation of a stationary sub-critical 11 kW el **organic** **Rankine** **cycle** (ORC) unit for waste heat recovery (WHR) applications is investigated, both in terms of energy production and safety conditions. **Simulation** results of a validated dynamic model of the ORC power unit are used to derive a correlation for the evaporating temperature, which maximizes the power generation for a range of operating conditions. This idea is further extended using a perturbation-based extremum seeking (ES) algorithm to identify online the optimal evaporating temperature. Regarding safety conditions, we propose the use of the extended prediction self-adaptive control (EPSAC) approach to constrained model predictive control (MPC). Since it uses input/output models for prediction, it avoids the need for state estimators, making it a suitable tool for industrial applications. The performance of the proposed control strategy is compared to PID-like schemes. Results show that EPSAC-MPC is a more effective control strategy, as it allows a safer and more efficient operation of the ORC unit, as it can handle constraints in a natural way, operating close to the boundary conditions where power generation is maximized.

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Abstract— The demand for **organic** **Rankine** **cycle** (ORC) systems to be efficient and economically competitive drives the need for a reliable and robust modeling approach that is suitable for **optimization**. However existing commercial **simulation** software is not typically tailored for **optimization** and they generally cannot guarantee global optimum. This paper proposes a modeling approach to approximate a rigorous **simulation** model that is suitable for global **optimization**. This involves a combination of regression and thermodynamic analysis, in addition to integer programming techniques. Three different solvers, COBYLA, SCIP, and BARON are used to optimize the ORC model and are compared against each other to demonstrate the prospect of achieving the global optimum using this approach. In addition, this paper also presents a technique to improve the model accuracy by using a piecewise fit to approximate the output characteristic of the ORC unit operations.

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In this present study, the thermal energy storage system of small scale solar powered **organic** **Rankine** **cycle** system is modelled from governing equations of thermodynamical theories. This model can accurately simulate and predict the charging and discharging processes of thermal energy storage system for the small scale ORC system. The dynamic characteristics of the thermal energy storage system were analyzed by Matlab Simulink which helps for the design, operating, and control strategy of small scale solar ORC system. Furthermore, in this study the solar **organic** **Rankine** **cycle** power output based on solar collector was also analyzed by means of a mathematical model for system components which can evaluate system performances on a monthly basis.

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Table 15 gives the result obtained for the turbine bleeding configuration. The fluids are listed in order of their critical temperature, with the first fluid, R601 having the lowest critical temperature. This order was chosen because critical temperature of the working fluid has correlation with the **cycle** efficiency. It can be observed that the mass flow rate of the working fluid, turbine inlet pressure, and pump power are generally increasing from the top to the bottom of the column with some exceptions. The exceptions observed could be a result of the way the working fluids are ordered based on the critical temperature. The trend may change based on the order the working fluids are arranged. As well, the working fluids are comprised of wet, dry and isentropic fluids, and do not follow any specific order and may also affect the trend observed in Figure 32. The net power and hence efficiency decreases from the top to the bottom of the column. Highest and lowest net power is observed for R601 and R134a respectively. They also display the highest and lowest efficiency. This is because the efficiency depends on the net power produced by the system. Figure 32 shows the variation of the parameters as the function of critical temperature of fluid.

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An exemplary projection of a feasible region for the basic ORC is given in Fig. 3. In order to illustrate the feasible region in a two-dimensional plot, the mass flow of the cooling water and thus the lower pressure is fixed. It can be seen that both objective functions improve with higher evaporating pressures. For the illustrated case, the operating conditions for the optimal net power do not coincide with those for the minimal LCOE. For reasons of comparison, the full model is formulated in an equation-oriented approach in the **optimization** framework GAMS [32] to be solved with state-of-the-art solvers. To demonstrate the value of global **optimization** and the sequential-modular formulation, we also solve the equation-oriented method with different local solvers (CONOPT, IPOPT, SNOPT, KNITRO, MI- NOS). With the exception of CONOPT, which always finds the global minimum, these fail more often than not. More specifically, the solvers converge to substantially suboptimal solutions (up to 20 %) or even fail to find feasible points. The use of the state-of-the-art global NLP solver BARON [14], in contrast, can validate global optimality of the so- lutions found by the developed framework. All results could be validated within a given accuracy, guaranteeing the global optimum for a relative gap between LBD and UBD of 10 −3 , being sufficient with respect to the model accuracy. The results for the **optimization** for the three different scenarios of attainable cooling water temperature are given in Tab. 1 for the basic ORC and in Tab. 2 for the recuperated case. For comparison, the CPU times of the MC++ framework and BARON are given. It can be seen that the computational times for the LCOE **optimization** are signifi- cantly higher compared to the net power cases because of the increased complexity of the model due to the component sizing and investment cost calculation. This effect can be especially seen for the recuperated case, as the CPU time is increased by a factor 100 for the MC++ framework. The maximal producible net power decreases with increas- ing ambient and therefore cooling water temperatures, as the condensing pressure increases. The same holds for the minimization of the LCOE. But, as shown in Fig. 3, the optimal working conditions do not necessarily coincide and lower LCOE can be attained by not producing the maximum power output. Comparing these results with the recu- perated case, it can be seen that the recuperator can increase the optimal net power output up to 4 %, depending on the scenario. But, in contrast, for the LCOE minimization, the heat exchanger area is always set to its lower bound, indicating that for the given boundary conditions, a recuperator can not improve the economics of the ORC and the results are the same as in the lower half of Tab. 1. The higher CPU times for both solvers result from the increased number of degrees of freedom and equality constraints.

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* Corresponding Author: Byung-Sik Park
ABSTRACT
The selection of the working fluid is an important part of design and **optimization** of ORC system as it effects the systems efficiency, design of ORC components, stability, safety and environmental impact. Present study aims to investigate the performance of ORC system using pure working fluids and zeotropic mixtures for low temperature geothermal heat source on the basis of thermodynamic and economic parameters of ORC system. Evaporator, expander, condenser and feed pump models are developed in MATLAB. The control volume approach is adopted for evaporator and condenser model with appropriate database of heat transfer and pressure drop correlations. For comparison, pure working fluids are taken as the base case. The ORC system with pure working fluid and zeotropic mixture under same heat and sink source conditions are optimized using multi objective genetic algorithm for maximum exergy efficiency and minimum specific investment cost. The exergy efficiency of ORC system with zeotropic mixture is improved by 14.33% compared to pure working fluid. The exergy destruction in evaporator and condenser is reduced by 24~30%. The fraction of more volatile component in zeotropic mixture effected the thermal and economic performance of ORC system, for current study the mass fraction of 40% of R245fa corresponds to optimum exergy efficiency and specific investment cost. For same condensing pressure and expander power, area of evaporator for pure working fluids and zeotropic mixture is also calculated. The required heat transfer area for zeotropic mixture is approximately 13% less than required for pure working fluid.

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When using the proposed OHST approach to solve such an **optimization** problem, the computational time can be greatly reduced by introducing a “pre-screening” process to the working fluid selection and additionally by narrowing the dimension of the initial parameter space. In the beginning of **optimization**, OHST is calculated for each candidate fluid by taking into account a wide range of operation parameters. The calculations are simultaneously done, since OHST can be correlated directly with state parameters (Liu et al., 2015). A pre-screening process is followed, in which fluids with OHSTs closer to the available heat source temperature are selected. By adjusting OHST, optimal operation conditions are estimated, leading to a narrowed parameter space. The dimension of the narrowed parameter space depends strongly on the estimation accuracy. If the estimation could be accuracy enough, **cycle** **optimization** can be even neglected. At the end, a set of optimized objectives are resulted, according to which the optimal fluid and the optimal parameters are determined.

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It can therefore be concluded that the thermodynamic efﬁ- ciency alone cannot be considered as the sole criterion for the selection of the working ﬂuid. More holistic selection methods should be considered. However, very few studies include addi- tional parameters taking into account the practical design of the ORC system, mainly because of the difﬁculty to deﬁne a proper function for a multi-objective **optimization** of the **cycle**. Examples of such studies are provided in [58 – 62 ], where a ﬂuid selection taking into account the required heat exchange area, turbine size, cost of the system, risk, etc. are provided. These studies reveal that taking the economics into account can lead to the selection of very different optimal operating conditions and working ﬂuids. Those methods should therefore be preferred to the simplistic thermodynamic benchmarking of candidate working ﬂuids.

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Papers exploring **cycle** **optimization** also comment on the impact of **cycle** parameters on the outcome of the design. Roy [35], Dai [49], Wei [50], Franco [51], and Frick [43] highlight some of the most controlling parameters when modelling an ORC . The amount of superheat on a subcritical ORC is explored by Roy [35]and Dai [49]. These researchers both concluded that superheat is necessary for the system to avoid any droplets during the expansion process; however, increasing the amount of superheat reduces the system performance for both isentropic and dry fluids which are the preferred fluids for an ORC. Wei [50] and Franco [51] both look at impact of condensing conditions in an ORC. The condensing temperature has a greater impact on the ORC performance compared to the evaporation temperature. Wei [50] suggested that avoiding excessive sub cooling will improve system performance; however, it is also important to have a small amount of sub cooling to safely operate the pump; the suggested sub cooling amount was 0.5-0.6K. Franco [51] recommends that the design condenser temperature is one of the critical aspects for a successful ORC design.

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Maizza et al., (2001) [3] reported that the fluid thermodynamic characteristics give rise to thermodynamic limitations to the amount of energy that can be extracted from the heat source. Some refrigerants satisfy the above mentioned criteria more than the others. One such refrigerant is HFCs-245fa. Liu et al, (2002) [4] reported performed a **simulation** on an ORC with various working fluids for a hot temperature of 150 o C and a cold temperature of 30 o C. It turned out in this **simulation** that HCFCs-123 had a slightly better efficiency than iso- pentane. HFCs-245fa and n-pentane were not taken into account in that study. Badr et. al., [5] was developed ORC machine including of working fluid and amplifier device. From the research of variables design of Lee et., al., [6] concluded that saturated vapor temperature in evaporator, condense temperature at condenser, and superheated vapor temperature from high heat vapor generator are important to the economics of energy recovery system. Hung et. al., [7] is studied dry working fluid for ORC which use waste heat recovery and the efficiency of system. This **cycle** was uses low temperature working substance such as benzene, ammonia, CFCs-11, CFCs-12, HFCs-134a and CFCs-113. Furthermore, there are the other researches which use **Rankine** **organic** **cycle** with other **cycle** such as solar energy using, space energy **cycle**, geothermal energy, other efficiency energy using.

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An important parameter in the design of ORC’s, is the pinch point temperature. The pinch point temperature is defined as the temperature difference between the heat source and the point of boiling inception in the evaporator. This temperature difference is shown with the double-headed arrow I in figure 22 and plays a significant role in the heat transfer performance (Wang et al., 2012). A detailed model to predict the location of the pinch point in the evaporator has been presented by (Guo et al., 2014) for cases where an optimal heat exchanger design is required. The **simulation** in chapter 5 varies the pinch point temperature and then obtains the pinch point with the best effect on overall system performance. By defining a control volume over each component in the **cycle**, the conservation of energy and mass principles can be applied to each component at steady state conditions. After combining these individual models, a complete **cycle** model is achieved. Conducting energy and mass balances over each component, the analysis follow below.

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ORC with internal heat exchanger. Instead of adopting only one working fluid for an ORC system, a mixture of several different working fluids has been accepted in recent years. Aghahosseini et al. [8] conducted a theoretical study of six types of pure and zeotropic mixture refrigerants: R123, R245fa, R600, R134a, R407c and R404a in an ORC system with low-temperature heat source, and found the mixed working fluids are more suitable for the system due to the nonisothermal phase change. Based on the **simulation** results, Declaye et al. [9] concluded R134a is a good choice for an ORC system with a smaller size expander. Additionally, Tchanche et al. [10] considered that R134a is the most suitable working fluid for small-scale solar applications in terms of thermodynamic and environmental properties.

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𝜂 𝑐𝑜𝑙 = 𝑚̇ 𝑤𝑓 ×[(𝐶 𝑝 (𝑇 𝑒𝑣𝑎𝑝 𝑆×𝐴 −𝑇 𝑐𝑜𝑙,𝑖𝑛 )+(ℎ 𝑔 −ℎ 𝑙 ))]
𝑐𝑜𝑙 (45)
447
The flow chart of the **simulation** model is represented in Figure 8. 448

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# Contact: rdickes@ulg.ac.be
Abstract:
As for many other technologies, modelling and **simulation** of **organic** **Rankine** cycles (ORCs) are crucial for design, **optimization** and control purposes. However, model development is often time consuming and the scientific community lacks of open-access tools to study ORC systems. For these reasons, researchers from the universities of Liège and Ghent in Belgium gathered their knowledge and created “ORC modelling Kit” (ORCmKit), an open-source library dedicated to the steady-state **simulation** and analysis of **organic** **Rankine** cycles. Both component-level and **cycle**-level models are provided and different ORC architectures can be simulated. For each of the main component of ORC systems, different models are available with increasing complexity which allows a wide range of modelling possibilities. In order to remain general and accessible to as many people as possible, three widely used programming languages are covered within ORCmKit, i.e. Matlab, Python and EES (Engineering Equation Solver). Besides source codes, ORCmKit also includes calibration tools for empirical and semi-empirical models as well as a complete documentation for ease of use.

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The primary objective of this research project was to provide a tool, in the form of a computer **simulation** and modelling capability, to be utilised towards optimising the performance of a solar ORC plant in the medium output basis. The secondary objective of the research was to possibly incorporate all components of an ORC solar-thermal power plant system into one numerical model that can be used to analyse the whole system of energy conversion processes from a solar resource to a power output for the generation of electricity.

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This method focused on the concentration of solar energy into a particular area to produce a large amount of heat, this heat will further utilized in the generation of electric power. This sun’s energy concentrated at a particular area using so many highly reflective mirrors called “heliostat”. Heliostats install in such a way that it concentrates a large area of sunlight (or solar energy) onto a small area (called receiver) at a nominal price compared to photovoltaic cells/plates. There is some other beneficial advantage of Concentrated Solar Power (CSP) system as we compare with other conventional sources such as nuclear, coal etc. The main advantages are it is cheap, reliable, flexible (depend upon design) and easy to install. If Concentrate Solar Power (CSP) system will couple with the traditional thermal **Rankine** **cycle** then this will so helpful in reducing coal consumption and only simple **Rankine** **cycle** can easily operate at night also without any obstacle. This can be achieved economically [1, 2] by implementing new technology, which enhances the efficiency of solar-to- electricity and also by proper **optimization** of operation and maintenance. There is another option to produce **Rankine** steam/gas **cycle**, in which steam can generate directly inside the tube with the help of parabolic trough solar collectors, on another hand; a good option uses high dense gas particles, containing some tiny particle that can easily fluidized at low speed of gas. The fraction of particles within the suspension is high i.e. around 40% by volume [3] resulting in a fluid with a high density (above 1000kg/m 3 ), due to the use of high density fluid the heat

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