Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production

Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production

international journal of hydrogen energy xxx (xxxx) xxx Available online at www.sciencedirect.com ScienceDirect journal homepage: www.elsevier.com/l...

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Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production Jing Han a, Xi Wang b,*, Jianjun Xu c,**, Na Yi c, Seyed Saman Ashraf Talesh d a

Electrical and Information College, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, 163319, China Engineering College, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, 163319, China c Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, Heilongjiang, 163318, China d Department of Mechanical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran b

highlights  A novel power and hydrogen generation system is presented.  For efficiency improvement, zeotropic mixtures are used efficiently in ORC unit.  Energy, exergy analysis, and optimization of the proposed system are carried out.  The generator pinch point temperature effect on the efficiency is analyzed.  The energy and exergy efficiencies are obtained 18.9% and 57.39%, respectively.

article info

abstract

Article history:

Organic Rankine cycle (ORC) power generation system is an attractive unit applied widely to

Received 16 November 2019

exploit low-to medium-grade energy sources. This work proposes a thermodynamic anal-

Received in revised form

ysis and optimization of a flash-binary geothermal cycle for power generation and

2 January 2020

hydrogen production aims where the binary cycle used is an organic Rankine cycle in which

Accepted 14 January 2020

different mixtures of zeotropic fluids are used as the working fluid. The integration of ORC

Available online xxx

and zeotropic mixtures presents the considerable capability to combine their superiority and further enhance the system performance considerably. The superiority of the proposed

Keywords:

system which combines the advantages of zeotropic mixtures with ORC positive aspects, is

Flash-binary power plant

revealed through the energy and exergy analysis. Also, the particle swarm optimizer is

ORC

employed to optimize the net output power as the objective function. The results reveal

Zeotropic mixture

some precious facts; for instance, the overall thermal and exergy efficiencies are obtained

PEM electrolyzer

18:9% and 57:39%, respectively for the base case. However, the optimized results demon-

Optimization

strate that the Pentane (0.31)/Butene (0.69) and Pentane (0.41)/Butane (0.59) combinations hold the maximum energetic efficiency of 18:96% and 18:91%, respectively. The net output powers for these combinations are 125:712 kW and 124:923 kW, respectively. Besides, The highest exergy efficiency belongs to Pentane (0.31)/Butene (0.69) and Pentane (0.41)/Butane (0.59) combination whose values are 57:24 % and 57:10 %, respectively. Also, from the

* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (X. Wang), [email protected] (J. Xu). https://doi.org/10.1016/j.ijhydene.2020.01.093 0360-3199/© 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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parametric study, it can be inferred that increasing the generator's pinch point temperature results in a lower power generation rate of the ORC and total system. © 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Introduction Nomenclature Symbols C D E_ E_D h m_ MM P Q_ s T v _ W

thermal conductivity (W:m1 : K1 ) equilibrium phase vapor density exergy rate (kW) exergy destruction rate ðkWÞ specific enthalpy (kJ:kg1 ) mass flow rate (kg:s1 ) molar mass pressure (MPa) heat flow rate (kW) specific entropy (KJ:kg1 :K1 ) temperature (K) dynamic viscosity (Pa:s) power (kW)

Greek Symbols h efficiency (%) ε effectiveness u mass fraction Abbreviations ORC Organic Rankine cycle Particle swarm optimization PSO PEM Proton Exchange Membrane Subscripts and superscripts Cond Condenser C.V. Control volume D Destruction E.V Expansion valve ex exergy F Fuel Gen Generator H.X. Heat exchanger in inlet is isentropic k kth component L loss LMTD logarithmic mean temperature difference Mix mixer pum pump PPT Pinch point temperature q Heat transfer sys System th termal tot total tur turbine W Work 1, 2, … cycle locations 0 Dead state

Recently, the energy consumption rate is increased due to the population increment in addition to the industrial developments [1]. Subsequently, environmental issues such as global warming and damaging the ozone layer forced governments to consider energy policies. Moreover, yearly growth of the electrical power tariff over 12% caused the motivation to utilize the waste heat as well as alternative energy resources [2,3]. Utilization of Organic Rankine Cycles, Kalina Cycles as well as other kinds of low-grade heat-power generation systems is considered as a probable solution. From various cycles, the ORC cycle, due to its simple structure, reliable and flexible behavior, is regarded as an efficient system [4]. Some energy sources, including geothermal, solar, ocean thermal, and waste heat resources, have the potential to be utilized as the heat source of this cycle. The geothermal resource as a low-grade resource is considered in many studies in recent years. Great exergy destruction of the ORC system is its main challenge. Based on Ref. [5], the evaporator is the leading cause of this destruction in ORC systems, which is due to the temperature difference of the resource with the working fluid. The energy transmits to the operating fluid in three phases containing the preheating step, evaporation step, and superheating step. In the evaporation step, the temperature preserves in a fixed value for a pure fluid, which results in a considerable temperature mismatch between the operating fluid and heat resource chiefly in greater quality levels. Accordingly, exergy destruction will increase. Various investigations are performed in the literature for minimizing this temperature difference as well as decreasing the exergy destruction of the system that results in higher performance. Zeotropic mixtures and cycle structure improvement are two potential solutions that are suggested in related works. Zeotropic mixtures are the combination of various pure fluids, which have non-isothermal phase change. The compositions of these mixtures in the vapor and liquid phases are not equal in the vapor-liquid balance condition. By utilization of these mixtures in the power systems, a temperature change occurs in the operating fluid in the phase change procedure resulting in enhancing the temperature match in the evaporator. The application of this technique will reduce the irreversibility of the evaporation step. Impacts of ten types of mixtures over the ORC system efficiency are studied in Ref. [6]. Based on their obtained results, R245fa  R600a with 0.9e0.1 fraction resulted in the best efficiency from various considered operating fluids. In Ref. [7], six different mixtures were suggested for an ORC cycle fed by a low-temperature geothermal resource. They demonstrated that the best exergetic performance (47%) is obtained for R  143a ð0:7Þ=R  124 ð0:3Þ. They also presented a comparison between the mixtures and R  143a, where the system

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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performance is enhanced by about 15% with similar operating circumstances. In Ref. [8], a thermodynamic study for a regenerative ORC system is presented for R245fa=R152a with various mole fractions. Respect to their results, the evaporator irreversibility, as well as the condenser irreversibility, decreases with the reduction of the R245fa ratio. They also demonstrated that the irreversibility is almost steady in R245fa mass fractions of lower than 80%. The exergetic efficiency of a non-superheated subcritical Organic Rankine cycle is investigated in Ref. [9] for various zeotropic operating fluids. Based on their study, the exergetic performance can grow 7:4% to 14:2% in comparison with pure operating fluids in the same cycle. Ref. [10] employed zeotropic mixtures as the working fluids in an Organic Rankine cycle to recover the waste heat of an industrial boiler. Concerning their obtained results, the ORC cycle efficiency is enhanced from the economical aspect by zeotropic mixtures. Also, the operation of the ORC system with zeotropic working fluids for using the geothermal water is studied in Ref. [11]. In this reference, the efficiency of the cycle is assessed for various mixtures, including R422A, R22M, and R407A, as well as R22D from energy and exergy aspects. Based on their results, R22M and R422Acould achieve higher efficiency compared to other fluids in a parallel ORC/CHP plant. A number of the related works to improve the system constructions are presented in the next. The efficiency of different ORC systems with dry operating fluids is analyzed in Ref. [12] from energy and exergy aspects. They demonstrated that the energetic and exergetic performances of the regenerative organic Rankine cycle, including an internal heat exchanger, averagely obtained 30% higher compared to a simple cycle. In Ref. [13], a comparison is performed between a standard ORC, an ORC system with an internal heat exchanger, a regenerative one, as well as a regenerative cycle, including an internal heat exchanger from first and second thermodynamic laws. Based on their obtained results, the best-achieved energy performance is about 7:65 % related to the ORC cycle with an internal heat exchanger and R123 operating fluid. Various constructions of the Organic Flash Cycles are studied in Ref. [14] for improving the power generation. Concerning their results, dividing the expansion procedure into two stages resulted in a performance growth of about 10% in comparison with the optimal simple ORC system. Besides, Kanoglu [15] studied a dual-pressure binary system from the exergy aspect. They found that thermal performances of the suggested cycles were about 5:8% and 8:9%. A new cycle as the dual-loop bottoming organic Rankine cycle is studied in Ref. [16] to recover the waste thermal power in the diesel engine. Regarding their results, utilization of the low-temperature loop results in higher net power compared to high-temperature one. They also depicted that the max thermal performance can be enhanced in the low-load regions up to about 13:15%. Moreover, the efficiency of three various systems containing Trilateral Rankine cycle, ORC, and KC cycles are studied in Ref. [17] from the exergoeconomic aspect, where low-temperature resources are used. Based on their results, the obtained net power from the Trilateral Rankine cycle can be higher compared to ORC and KCS11 cycles. Also, a novel cycle is proposed in Ref. [18] for the reduction of the exergy destruction of the evaporator. Two evaporators were

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used in this reference, and the results were compared with the results of the simple ORC system. The hydrogen can be generated through electrolysis methods. There are three different kinds of electrolysis methods containing alkaline, Proton Exchange Membrane (PEM), and solid oxide [19]. The hydrogen is an energy carrier, and it has high importance to decrease the pollution once green energy resources are used [20]. Various kinds of resources, such as natural gas, geothermal resource, solar, and biomass, can be employed to generate hydrogen [21]. In recent years, green power resources are interested in many works to produce hydrogen [22]. From various methods, the PEM method is demonstrated a promising efficiency as electrolysis. For example, green power sources are utilized in Ref. [23], where energetic/exergetic analyses are conducted over the PEM electrolysis device. They demonstrated that the PEM electrolysis device mostly works in an exothermic state as the heat generation because the over-potential violates the heating demand. They also performed a parametric study, in which the energetic performance is highly relevant to some terms such as the working temperature of the PEM, current density, thickness of the electrolyzer, as well as the electrode catalytic activity. An enhanced type of PEM device is presented in Ref. [24] regarding the liked modular mathematical models for electrolyte, cell voltage, positive and negative electrodes. They could provide a helpful to perform a sensitivity analysis for the polarization profile of a determined electrolysis system in order to enhance the chosen parameters. The anode exchanges a greater current density in the improved PEM electrolyzer, which leads to a considerable enhancement of the performance, chiefly in higher current densities. But still, the derived model contains some problems for obtaining a complete model. An energetic/exergetic analysis are provided in Ref. [25] for a solar-improved PEM electrolysis system based on an OTEC (Ocean Thermal Energy Conversion system) to generate the hydrogen. In this reference, an ORC turbine is utilized to drive the electrolysis system. Based on their achieved results, the first and second law performances of the proposed system can respectively increase up to 3:6% and 22:7%. Furthermore, the generated hydrogen and PEM exergetic performance were respectively computed 1:2kg=h and 56:5%. Also, a PEM water electrolysis device powered by an ORC system is studied in Ref. [26] from first and second thermodynamic laws aspects. Regarding their results, temperatures of the geothermal water and electrolysis have a direct relationship, and they can influence the hydrogen generation. They obtained the energetic and exergetic performances of 11:4% and 45:1%, respectively. The hydrogen generation rate is also achieved by 0:034kg=s. Among various green resources, the geothermal resource has appeared as an attractive resource in recent years. This resource is more proper in comparison with traditional fossil fuel sources, which directly utilize geothermal energy [27]. There are mainly three kinds of power systems, including dry-steam systems, flash-steam, and binary-cycle ones. Among these plants, binary plants and hybridized flashbinary types are almost new structures. The geothermal resource can be utilized for producing power and direct applications like space heating/cooling, industrial applications,

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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and greenhouse heating utilizations. In general, high-grade geothermal energy with temperatures higher than 150  C is utilized to generate electricity. After that, moderate-grade sources with a temperature range of 90  150  C and lowgrade ones (lower than 90 C) are suitable for direct applications [28]. A comparison is presented in Ref. [29] between a singleflash and double-flash systems by exergy tables of them. They obtained the exergy performance of the single and double-flash plants, about 38:7% and 49:0%, respectively. The geothermal water and sink temperatures are considered 250 and 40  C, respectively. A binary system with 12:4MW potential is analyzed in Ref. [15] from the second law aspect using real data for evaluation of the system efficiency as well as the pinpoints of initial exergy destruction. They quantified the exergy destruction of the system and depicted them by a flow curve in comparison with the energetic flow curve. The exergy destruction has mainly resulted from the exergy of the operating fluid loss of the condenser, the exergy of the brine that is re-injected, losses of the turbine pump, and preheater vaporizer. Proportions of these destructions to the entire input exergy are obtained respectively 22:6%, 14:8%, 13:9%, and 13%. They also obtained the exergy performances of 29:1% and 34:2% respect to the geothermal fluid exergy in the vaporizer input and exergy destruction of the brine across the preheater-vaporizer, respectively. Additionally, exergy analysis is presented in Ref. [30] for a binary system powered by low-grade geothermal fluids. Based on their results, binary systems have the ability to work with too high exergy performances even with low-grade and lowexergy fluids. They could obtain 40:0% (or higher) exergy performances in determined systems by fluids with 200kj= kg (or lower) specific exergies. Their main designing advantage that results in better exergy performance relates to the heat exchanger design for minimizing the exergy losses in the heat transfer process. Accessibility of the low-grade cooling water can be considered another principal advantage of their system, which results in better exergy performance and permits a once-through system to reject the waste heat. Also, a binary system is analyzed from the exergy aspect in Ref. [28] by real data for evaluation of the system efficiency and pinpoint sites of initial exergy destruction. They quantified the whole exergy destruction and depicted them by a profile in comparison with the energy profile, where the sites with higher destructions contain brine re-injection, losses of the heat exchanger, and condenser. Exergy performances of main elements of the system are obtained for evaluation of their single efficiencies. Regarding the energy and exergy of the water in the heat exchanger entering, the energetic and exergetic performances were obtained 4:5% and 21:7%. On the other hand, energetic and exergetic performances are 10:2% and 33:5%, regarding the heat and exergy input of the binary Rankine system. In addition, impacts of the turbine input pressure/temperature and the pressure of the condenser over the exergetic and energetic performances, the net obtained power, and brine re-injection temperature is studied and analyzed. Authors in Ref. [31] represented a costefficient optimal design for ORC systems with low-grade geothermal resources, where the proportion of the entire heat exchanger area to the net obtained power is defined as

the cost function for optimizing the remarked cycle. In this regard, they utilized the steepest descent approach. Based on their results, the selection of the operating fluid can considerably influence the system expenditure. An approach to the growth of the output power in a geothermal system is proposed in Ref. [32] regarding an organic operating fluid. It was observed that the output power is increased with the growth of the water flow fed to the evaporator through returning the water stream from the evaporator downstream for a repetitive passage across the heat exchanger. They analyzed the system for a multitude of kinds of organic fluids. Concerning their obtained results, there is an optimal evaporation temperature for the operating fluid, depending on the amount of the recycled water. Capacity of the Clausius Rankine cycle has the greatest value in this optimal temperature. Moreover, some methods are used in different fields for parameter identification, optimization, and prediction. For instance, Noruzi et al. [33] combined two efficient methods known as Monte Carlo (MC) and Stochastic-algebraic (SA) methods for stochastic analyses and probabilistic assessments in electric power systems. Mohammadi et al. [34] proposed a new prediction model based on a new feature selection algorithm and hybrid forecast engine of enhanced version of empirical mode decomposition named sliding window EMD bundled with an intelligent algorithm for short term load forecast (STLF). Ahmadian et al. [35] presented a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). Ghadimi et al. [36] proposed a new hybrid forecast model based on dual-tree complex wavelet transform and multi-stage forecast engine (MSFE) for optimal operation as well as planning in power system. Xu et al. [37] applied quadrature demodulation algorithm for displacement measurement in all-fiber selfmixing sensors. The feasibility of the proposed algorithm is authenticated using a set of experimental data under nonpolarization-maintaining conditions. Han et al. [38] employed MPSO-based Elman neural network (ENN) to assess the steadystate efficiency of the Solid Oxide Electrolyzer Cell under different circumstances. the EGSA-FCM algorithm (enhanced gravitation search-fuzzy c-mean algorithm) used by Zhao et al. [39] in order for false detection in the power systems.

Main contributions Based upon the reviewed literature, a work including a geothermal flash cycle-based hybrid system in which the hot geothermal fluid acts as a source of energy for driving subsystems comprising zeotropic-based ORC plant for power generation aim and PEM electrolyzer for hydrogen production purpose has not yet been carried out. Besides, a significant portion of the proposed works is assigned using different pure fluids and mixtures just for ORC systems. Despite the high number of the presented works, only a few works are offered the utilization of zeotropic combinations in the flash cycle and ORC systems. Therefore, two subsystems are chosen as bottoming units of the geothermal flash cycle. By the integration of geothermal flash cycle, zeotropic-based ORC, and PEM electrolyzer the energy, and exergy performance can be increased considerably. This work concentrated on the

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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integration of ORC and zeotropic mixtures (Pentane, Butane, Butene, Isopentane, Hexane, Isohexane, and R254fa mixtures) and a thorough energy and exergy investigation of the proposed system. The main goals of the present work are summarized as: ❖ A novel integration of the geothermal flash cycle, zeotropic-based ORC unit, and PEM electrolyzer is designed for power and hydrogen production aims. ❖ An investigation is performed over a flash-binary system, and its key factors are adjusted. ❖ Various zeotropic mixtures are applied to the proposed ORC plant as the operating fluid. They are mixtures of different pure fluid comprising Pentane, Butane, Butene, Isopentane, Hexane, Isohexane, and R254fa. ❖ The energy and exergy analysis, as well as optimization of the proposed system, is carried out.

Description of the system and working fluid selection System description Fig. 1 displays a schematic of the hybrid flash-binary cycle and PEM electrolyzer system for power and hydrogen generation, simultaneously. Referring to this figure, the designed hybrid system comprises three different subsystems, including the flash cycle as the central geothermal system, the zeotropic ORC as the binary unit, and the PEM electrolyzer (PEMEC) unit for hydrogen production aim. Besides the power generation aims, The geothermal flash cycle acts as the heat source for zeotropic ORC and PEM electrolyzer units. The PEMEC unit utilizes a particular portion of ORC turbine power and heat of the flash cycle to generate hydrogen. Besides, the zeotropic Rankine unit uses the geothermal heat to generate electricity. In the following subsections, the comprehensive description of different subsystems is given.

Flash cycle Within the geothermal flash cycle, the extracted geothermal fluid from the production well is expanded through the Expansion valve at an isenthalpic process to a low-pressure stream (1 / 2). The low-pressure two-phase flow is then separated into two flows through the separator; one is saturated steam, and the other one is a saturated liquid. The saturated steam is then entering the steam turbine to be expanded within the turbine for electricity production purposes (3 / 5). The turbine's leaving flow cools down to lower temperature flow through the condenser 2. On the other side, the saturated liquid leaving the flash chamber (state 4) goes to the generator and heat exchanger (H.X.) to give up the required energy of the Rankine system and PEM electrolyzer system. Ultimately, the leaving flow of the heat exchanger (state 8) and condenser 2 (state 6) combine and return to the injection well (state 9).

Zeotropic Rankine cycle Based on the schematic diagram of the proposed system, the zeotropic Rankine system is composed of the following

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components: vapor generator (Gen), a turbine (ORC turbine), a condenser (Cond 1), and a pump. As Fig. 1 illustrates, the needed heat for the ORC unit is supplied by the geothermal fluid in the generator. The zeotropic working fluid is pumped toward a higher pressure by the aim of the pump (13 / 10). The high-pressure flow (state 10) is heated up through the generator to the saturated vapor state (state 11). The leaving flow of the generator enters into the ORC turbine in the highpressure and high-temperature stage, which is expanded to the low-pressure state and produces electricity output (11 / 12). The exhaust flow of the ORC turbine cools down through the condenser 1 to the saturated liquid state by the aim of cooling water.

PEM electrolyzer As displayed in Fig. 1, the power and heat needed for the PEM electrolyzer to produce hydrogen is supplied by the ORC turbine and the geothermal fluid in the heat exchanger, respectively. The liquid water in reference circumstances goes into the heat exchanger (state 14), which is heated up to the PEMEC working temperature and then goes into the electrolyzer (state 15). The water then separates into its elements: hydrogen and oxygen. The generated hydrogen in the cathode electrode releases heat to the environment and stores in the hydrogen cylinders (state 16). On the other hand, in the anode electrode, the generated oxygen and unreacted water mixture enter the oxygen separator unit in which oxygen separated from the mixture and stored in the oxygen cylinder (state 17). The unreacted water is circulated to reused in the next hydrogen generation cycle. Regarding all the phenomena occurring in the PEM electrolysis, the generated hydrogen can be stored in cylinders to be used in various applications. The T  s diagram of the ORC cycle is represented in Fig. 2. As it is evident from the figure, there is an apparent temperature glide in the two-phase zones, which is due to the applying zeotropic mixtures.

Working fluids selection For the flash cycle, the geothermal fluid is regarded as the pure water; thus, in the system analysis, the thermodynamic properties of the geothermal fluid are the properties for the pure water. On the other hand, the ORC unit, as the binary system, employs a combination of different organic fluids that are known as the zeotropic mixtures. The selection of proper mixing of fluids is in high importance in the modeling process of the binary cycle. In this regard, some assumptions should be regarded for employing the working fluid [40]:  Energy and exergy efficiency: for a definite value of energy received from the heat source, the maximum output power, and thermodynamic efficiency should be obtained. The thermodynamic efficiency is a function of some mutual-dependent thermodynamic properties of operating zeotropic mixtures such as density, specific heat, critical point, etc.  The positive vapor curve slope (ds dt > 0) must be regarded; since the negative curve results in droplet generation in the turbine blades, which leads to vital damage to turbine blades.

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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Fig. 1 e Schematic view of the combined power and hydrogen generation system.

 A higher vapor density must be regarded. The lower vapor density results in increasing the flow rate; thus, the component size will increase, which leads to higher cost indexes.  A high value for the viscosity and a low value for the conductivity of working fluid must be regarded.  A higher level of safety for the working fluid is a critical point. The flammability and toxicity are the two leading indices for safety.  Other criteria for the working fluid selection are lower ODP (Ozone Depleting Potential) and lower GWP (Global Warming Potential).  Accessibility and cost-effectivity.

Because collecting all the above-mentioned indices together is somehow impossible, a great candidate is the application of zeotropic combinations alternative to pure fluids. Employing the zeotropic mixture proposes some advantages such as better accommodation of working fluid profile and heat source profile (temperature glide), higher performance, and lower irreversibility rate. Ultimately, based on the considerations, different mixtures of Pentane, Butane, Butene, Isopentane, Hexane, Isohexane, and R254fa are selected as the working fluids. The main properties of the candidates of the working fluid are listed in Table 1. Referring to this table, the positive curve (dry fluid) is regarded for the working fluids.

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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studies have been devoted to proposing new systems to improve the performance of the Sabalan power plant. Because of the myriad advantages of the ORC unit, especially the dualpressure ORC unit for exploiting low and medium-grade energies, the integration of geothermal flash cycle and dualpressure ORC is presented. Some main characteristic of the Sabalan geothermal well is listed in Table 2.

Mathematical modeling In this section, for modeling the proposed hybrid system, the energy and exergy relations for evaluation of the plant efficiency is presented. First of all, to simplify the modeling, some assumptions are made for simplification of the plant efficiency modeling [42]:

Fig. 2 e T-s diagram of the ORC system.

Heat source condition In this work, the heat source is the geothermal energy whose circumstances are obtained from Sabalan geothermal power plant. The Sabalan geothermal power plant is established in 1975 in Ardabil province in Iran [41]. This geothermal site is in the high potential to be used for the construction of various plants. The geographical location of Sabalan geothermal power plant is shown in Fig. 3 which is located in Meshkinshahr region of Ardabil Province. The Sabalan geothermal source can be efficiently employed for different aims by the integration of other systems to this site. Thus, numerous

❖ All of the processes in the plant occur in the steady-state conditions. ❖ There are no heat losses; pressure drops in the heat exchanging units. ❖ Changes in the potential and kinetic energies are neglected. ❖ There is no change in the components of the working fluids and mixtures in the operating process. ❖ The dead-state condition is set to T ¼ 293:15K and P ¼ 1atm. ❖ Flow within the throttling valve is considered isenthalpic. ❖ Isentropic efficiency is regarded for all the pumps and turbines operating in the proposed system. Besides the considered assumptions, some thermodynamic factors and conditions are needed to analyze the considered plant which is given in Table. Moreover, MATLAB

Table 1 e The properties of fluids used in this work. Fluid

Tcr KÞ

Pcr ðMPaÞ

MM ðg:mol1 Þ

D ðkg:m3 Þ

Pentane Butane Butene Isopentane Hexane Isohexane R245fa

469.7 425.13 419.29 460.35 507.82 497.70 427.16

3.37 3.80 4.01 3.38 3.03 3.04 3.65

72.15 58.12 56.11 72.15 86.18 86.18 134.05

578.5 520.5 531.07 571.14 616.34 609.97 1217.35

Type

  dT ds

Dry Dry Dry Dry Dry Dry Dry

Fig. 3 e The geographical location of Sabalan geothermal site. Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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Table 2 e Some required input parameters and assumptions for simulation. Parameter

symbol

E_Q ¼

Value

unit

m_ 1

1:

Geothermal fluid enthalpy

h1

1000:

Geothermal fluid pressure Flash chamber pressure Steam turbine pressure ratio

P1 P2 rp

kJ:kg1 1200000000 kPa 550: kPa 120: 

Turbine efficiency Pump efficiency

hT hp

85: 85:

The cooling water inlet temperature Cooling water inlet pressure Pinch point temperature difference of condenser Pinch point temperature difference of the generator Pinch point temperature difference of the H.X.

T18 ; T20 20:

kg:s1

e ¼ h  h0  T0 ðs  s0 Þ

P18 ; P20 1:5: DTPP;cond 10:

% % C

bar C



PEM electrolyzer In the PEM electrolyzing system, the electrical power is needed in addition to the heat source for the electrochemical reactions in order to decompose the water into the hydrogen and oxygen. The entire demanded energy is presented as follows [45,46]:

DTPP;gen 15:



C

DTPP;H:X: 10



C

PO 2

bar A:m2 bar  C

PEM input parameters Oxygen pressure

1

(5)

In which, s denotes the specific entropy. Tables 3 and 4 presents the comprehensive relations for all of the components. It's remarkable, the transferred exergy toward the cold sink in these relations is regarded as the waste. Also, the exergy parameters (fuel exergy, product exergy, exergy destruction, and exergy efficiency) for each component of the system are presented in Table 4.

Zeotropic-based ORC inputs



(4)

where T denotes the temperature in which the transferring happens in it. As well, T0 signifies the dead-state temperature. The exergy flow can be presented as follows [44]:

Geothermal unit inputs Geothermal fluid mass flow rate

 X  T0 Q_ 1  T

DH ¼ DG þ TDS

(6)

Hydrogen pressure PH 2 PEM working temperature TPEM The activation energy in the anode Eact;a

1 80 76

The activation energy in the cathode Water content at the anodemembrane interface Water content at the anodemembrane interface Membrane thickness the pre-exponential factor for anode the pre-exponential factor for cathode Faraday constant

Eact;c

18

(kJ:mol1 )

la

14

U1

herein, DG and TDS are the Gibbs free energy and the needed heat energy, respectively. It's assumed that J is lower than 10; 000 mA2 [23], and concentration loss is ignored. So, the potential of the electrolyzer is computed by:

lc

10

U1

V ¼ V0 þ Vact; a þ Vact; c þ Vohm

D

100 1:7e5

mm

ref

where, V0 is the reversible voltage, and is computed using the Nernst relation in the following form:

Jc

ref

4:6e3

A:m2

V0 ¼ 1:229  8:5E  4½TPEM  298

F

98,486

C:mol1

In addition, Vact; a and Vact; c are the activation losses of the anode and cathode, respectively. Also, Vohm denotes the ohmic voltage loss, which is computed by:

Ja

(kJ:mol1 )

software is used to simulate the considered plant. In this regard, the REFPROP toolbox is employed to obtain the thermodynamic properties of the working fluids [43].

X

m_ in ¼

_ ¼ Q_  W

X X

_ ¼ E_Q  W

m_ out hout 

X

RPEM ¼ 0

dx sPEM ½lðxÞ

m_ out eout 

m_ in hin

X

m_ in ein þ E_D

(10)

In which, sðxÞ is the local ionic conductivity that is presented as follows:

(1) X

(9)

where RPEM is the total ohmic resistance and J denotes the current density, and it is presented as [23,47]: Zd

m_ out

(8)

Vohm ¼ JRPEM

Energy/exergy study Ignoring the variations of the kinetic and potential energies; mass, energy, and exergy balance relations in the steady-state condition can be given as follows [28]:

(7)

 1268



1 1 303T

(2)

sPEM ½lðxÞ ¼ ½0:5139lðxÞ  0:326e

(3)

here, lðxÞ indicates the water content of the membrane in x location that is computed as [48]:

In these relations, m_ denotes the mass flow rate assigned _ are the net inlet heat and with the operating fluid; Q_ and W output work, respectively; h indicates the specific enthalpy, and E_D determines the exergy destruction rate. Also, E_Q the net exergy transfer that can be computed by Ref. [44]:

lðxÞ ¼

la  lc x þ lc L

(11)

(12)

In this relation, L is the thickness of the membrane; la denotes the water contents of the anode-membrane; lc is the interface of the cathode-membrane.

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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Table 3 e Mass, energy, and exergy balance equations for each constituent of the proposed system. Component

Mass balance equation

Energy balance equation

Flash cycle Expansion valve (E.V 1) Flash chamber Steam turbine

m_ 2 ¼ m_ 1 m_ 2 ¼ m_ 3 þ m_ 4 m_ 3 ¼ m_ 5

Condenser 2 (Cond 2)

m_ 5 ¼ m_ 6 , m_ 20 ¼ m_ 21

m_ 2 h2 ¼ m_ 1 h1 m_ 2 h2 ¼ m_ 3 h3 þ m_ 4 h4 _ ST ¼ m_ 3 h3  m_ 5 h5 ¼ ðm_ 3 h3  m_ 5 h5s ÞhST W ¼ m_ 5 h5  m_ 6 h6 ¼ m_ 21 h21  m_ 20 h20 Q_ Cond2

Zeotropic ORC Generator (Gen)

m_ 4 ¼ m_ 7 , m_ 10 ¼ m_ 11

ORC Turbine (ORCtur )

m_ 11 ¼ m_ 12

Condenser 1 (Cond 1)

m_ 12 ¼ m_ 13 , m_ 18 ¼ m_ 19

Pump

m_ 10 ¼ m_ 13

Heat exchanger (H.X.)

m_ 7 ¼ m_ 8 , m_ 14 ¼ m_ 15

_ Pump ¼ m_ 10 h10  m_ 13 h13 ¼ ðm_ 10 h10s  m_ 13 h13 Þ=hp W _ ¼ m_ 7 h7  m_ 8 h8 ¼ m_ 15 h15  m_ 14 h14 Q

PEM Electrolyzer (P.M.)

m_ PEM

_ PEM ¼ JV W in

in

Q_ Gen ¼ m_ 4 h4  m_ 7 h7 ¼ m_ 11 h11  m_ 10 h10 _ ORC tur ¼ m_ 11 h11  m_ 12 h12 ¼ ðm_ 11 h11  m_ 12 h12s ÞhT W Q_ ¼ m_ 12 h12  m_ 13 h13 ¼ m_ 19 h19  m_ 18 h18 Cond1

H:X:

¼ m_ 16 þ m_ 17 þm_ water;re

As well, Vact; a and Vact; c are the metrics of the electrodes’ activity and are presented in the following form [23]:   8 RT J 1 > > V *sinh ¼ act;a > < F 2J0;a   > RT J > 1 > : Vact;c ¼ *sinh F 2J0;c

(13)

In which, j0 indicates the exchange current density that determines the electrode's ability in electrochemical reactions, and it's computed by Ref. [23]:   8 E >  act;a > RT > > < J0;a ¼ Jref a *e   > > > E >  act;c : RT J0;c ¼ Jref c *e

J N_ H2 ¼ 2F

(15)

J N_ O2 ¼ 4F

(16)

where, J and F are the current density and Faraday constant, respectively.

Performance metrics The energetic and exergetic efficacies in the thermal plants are often described by:

(14)

In this equation, Jref signifies the pre-exponential parameter; Eact denotes the activation energy. More details are available in Ref. [48]. Furthermore, the molar mass flow rates of the generated hydrogen and oxygen in the positive and negative electrodes are presented respectively as follows [45,46]:

energy performance ¼

energy of products entire input energy

(17)

exergy performance ¼

exergy of products entire intput exergy

(18)

So, the energetic and exergetic performances of the entire plant can be computed as follows:

Table 4 e Exergy equations for each constituent of the proposed system. Component

Fuel Exergy

product Exergy

Exergy destruction

Exergy efficiency

Flash cycle Expansion valve (E.V 1)

E:V:1 ¼ E_1 E_F

E:V:1 ¼ E_2 E_P

E:V:1 E:V:1 E:V:1 ¼ E_F  E_P E_D

E:V:1 E:V:1 E_P =E_F

Separator

Sep E_F ¼ E_2 ST E_F ¼ E_3  E_5 Cond2 E_F ¼ E_5  E_6

Sep E_P ¼ E_3 þ E_4 ST _ ST E_P ¼ W Cond2 _ EP ¼ E_21 

Sep Sep Sep E_D ¼ E_F  E_P ST ST ST E_D ¼ E_F  E_P Cond2 Cond2 Cond2 E_D ¼ E_F  E_P

Sep Sep E_P /E_F

Generator (Gen)

Gen E_F ¼ E_4  E_7

Gen E_P ¼ E_11  E_10

Gen Gen Gen E_D ¼ E_F  E_P

Gen Gen E_P =E_F

ORC Turbine (ORCtur )

ORC E_F

ORC E_D

Cond1 ¼ E_12  E_13 E_F _ Pump _ Pump ¼W E_F H:X: E_F ¼ E_7  E_8

Cond1 ¼ E_19  E_18 E_P Pump ¼ E_10  E_13 E_P H:X: E_P ¼ E_15  E_14

tur ORC tur ORC tur ¼ E_F  E_P Cond1 Cond1 Cond1 ¼ E_F  E_P E_D Pump Pump Pump ¼ E_F  E_P E_D H:X: H:X: H:X: E_D ¼ E_F  E_P

ORC E_P

Condenser 1 (Cond 1)

P:M: _ PEM þ E_15 E_F ¼ W in

P:M: E_P ¼ E_16 þ E_17

P:M: P:M: P:M: E_D ¼ E_F  E_P

P:M: P:M: E_P =E_F

Steam turbine Condenser 2 (Cond 2)

E_20

ST ST E_P =E_F Cond2 Cond2 E_P =E_F

Zeotropic ORC

Pump Heat exchanger (H.X.)

tur

¼ E_11  E_12

ORC E_P

tur

_ ORC ¼W

tur

tur ORC tur =E_F Cond1 Cond1 =E_F E_P Pump Pump E_P =E_F H:X: H:X: E_P =E_F

PEM electrolyzer PEM Electrolyzer (P.M.)

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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hItot ¼

_ tot þ m_ H :LHVH W 2 2 m_ 1 ðh1  h9 Þ

(19)

hIORC ¼

_ tot þ E_16 _ tot þ E_16 W W ¼ _ _ m Ein 1 ðe1  e9 Þ

(20)

hIIORC ¼

hIItot ¼

In which; _ ST þ W _ ORC  W _ PEM _ tot ¼ W W _ ORC ¼ W _ OT  W _P W

_ ORC _ ORC W W ¼ _ _ m4 h4  m_ 7 h7 Q Ev

(23)

_ ORC W _ m4 e4  m_ 7 e7

(24)

Particle swarm optimizer for optimization aims (21) (22)

Similarly, these performances for the binary ORC cycle can be written in the following form:

Particle swarm optimization algorithm is a population-based algorithm that is applied to various problems. This optimization method explores the domain of a cost function by setting the particles’ trajectories. In this paper, m particles are regarded, and each one of them includes D components. Location and velocity vectors for ith individual in kth iterative step is presented respectively by Ref. [49]: (

  xki ¼ xki1 ; xki2 ; …; xkiD   vki ¼ vki1 ; vki2 ; …; vkiD

(25)

These locations and velocities are updated using below relations [49]: (

  ¼ wvki þ c1 rk1 pki  xki þ c2 rk2 gk  xki vkþ1 i xkþ1 ¼ xki þ vikþ1 i

(26)

and i ¼ 1; 2; …; m

In these equations, w2R denotes the inertia weight; c1 ; c2 are acceleration factors; pki and gk are the personal best and global best locations, respectively. The personal best belongs to the ith particle and global best is the best-found solution by the whole population, and they are as follows [49]: (   pki ¼ pki1 ; pki2 ; …; pkiD (27)   gk ¼ gk1 ; gk2 ; …; gkD Also, rk1 ; rk2 are randomly generated positive values that have a uniform distribution over [0, 1]. In this algorithm, the location and velocity of each particle are constrained as follows [49]: (

Vmax  vkid  Vmax xmin 

xkid

 xmax

(28)

d2f1; 2; ::; Dg

Table 5 e Validation of geothermal flash cycle with Ref. [51]. State Working fluid

1 2 3 4 5 6 8 9

Fig. 4 e Particle swarm optimizer (PSO) algorithm flowchart.

water water water water water water water water

Temperature ( C)

Pressure (bar)

This work

Ref. [51]

This work

Ref. [51]

200 158.83 158.83 158.83 45.9 45.9 76.08 73.44

200 158.8 158.8 158.8 45.8 45.8 76.1 74

15.55 6 6 6 0.1 0.1 6 0.361

15.55 6 6 6 0.1 0.1 6 0.36

Other parameters

This work

Ref. [51]

_ ST ðMWÞ W

4.333

4.334

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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international journal of hydrogen energy xxx (xxxx) xxx

Table 6 e Validation of zeotropic-based ORC with Ref. [52]. State

Temperature ( C)

Working fluid

10 11 12 13

iCyclohexaneð0:7Þ=R123ð0:3Þ iCyclohexaneð0:7Þ=R123ð0:3Þ iCyclohexaneð0:7Þ=R123ð0:3Þ iCyclohexaneð0:7Þ=R123ð0:3Þ

Pressure (kPa)

This work

Ref. [52]

This work

Ref. [52]

63.78 236 154.95 62.5

63.51 236.12 155.14 62.508

120.197 2795.768 2795.768 120.197

120.2 2795.8 2795.8 120.2

Other parameters

This work

Ref. [52]

hth ð%Þ Q_ Ev ðMWÞ

13.72 122.15

13.73 124.96

Inserting the first relation of Eq. (26) into its second relation results in the below non-homogeneous recurrence equation [49]:

2.00

   xikþ1 ¼ xki þ w xki  xik1 þ fk1 pki  xki þ fk2 gk  xki

Cell Voltage (V)

1.75

1.50

(29)

In which:

Modeling Experiment

fki ¼ ci rki ; i ¼ 1; 2

1.25

(30)

The below general form is considered for the optimization problem:

1.00

0.75

0.50 0

1000

2000

3000

4000

5000

6000

Cell density (A/m2)

minf ðxÞ subject to : x2X4RD

(31)

In this formula, f ðxÞ denotes the cost function, and X is a feasible exploration domain. Local and global bests are updated by Ref. [49]:

Fig. 5 e Validation PEM electrolyzer.

Table 7 e Some basic thermodynamic properties of each state as well as performance metrics. Main performance criteria For Pentane (0.3)/Butene (0.7) 1 Parameter

PEM

ORC

Total

_ W(kW) _ D (kW) Ex

21.746

28.995

125.652

9.632

31.016

86.131

hth (%) hex (%)

55.85 55.71

12.47 48.32

18.9 57.39

State parameters State 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

TðKÞ

PðkPaÞ

hðkj =kgÞ

  kj s kg:K

m_ (kg=s)

461.11 428.61 428.61 428.61 304.48 304.48 363.20 363.13 353.61 303.95 381.48 328.14 303.24 293.15 353 353 353

1200 550 550 550 4.58 4.58 550 550 48.11 1520.45 1520.45 283.61 283.61 101 101 101 101

1000 1000 2752.33 655.76 2163.65 131.31 377.61 377.33 336.94 58.91 511.22 452.34 56.45 84.01 334.43 4720.43 321.71

2.653 2.700 6.789 1.897 7.130 0.455 1.193 1.192 1.081 0.270 1.522 1.554 0.269 0.296 1.074 55.816 6.546

1 1 0.1642 0.8358 0.1642 0.1642 0.8358 0.8358 1 0.5140 0.5140 0.5140 0.5140 0.000912 0.000912 0.000102 0.00081

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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0.360 0.3496 0.3457 0.3683 0.3589 0.3514 87.221 87.604 87.336 86.702 86.826 87.043 31.753 32.391 32.078 30.959 31.239 31.618 21.268 20.615 20.366 21.791 21.199 20.726 125.014 124.143 123.811 125.712 124.923 124.292 28.358 27.486 27.154 29.055 28.266 27.635 55.79 55.90 55.94 55.70 55.80 55.88 55.93 56.04 56.08 55.84 55.94 56.02 57.0 56.77 56.80 57.24 57.10 56.95

Efficiencies

hth;tot (%)

0.71 0.47 0.51 0.31 0.41 0.46

In this part of the paper, the results of the simulation for the proposed systems in which the zeotropic-based ORC unit is utilizing different combinations of zeotropic admixtures are presented. As it is described earlier, the recommended power and hydrogen generation system is an integration of three systems; the central unit is the geothermal flash cycle and the zeotropic-based ORC cycle as the binary unit and a PEM electrolyzer for hydrogen production aims. Firstly, to authenticate the outcomes are obtained, it is necessary to evaluate the validity of the models with the previous works. Then, the results of the optimization process using the PSO algorithm are

hth;ORC (%)

Results and discussion

Working fluids characteristics

In these constraints, DTPPGen ; DTPPCond1 denote the pinch point temperature differences; ε signifies efficiencies for the heat exchangers in the phase-change step that is considered higher than 50% here [50]. The entire process of the PSO optimization method is depicted in Fig. 4. According to this flowchart, the optimization is terminated by reaching the final iterative step. The max iteration and population numbers are adjusted for obtaining a reliable convergence of the cost function.

Table 8 e Main results obtained from the optimization.

(35)

47.18 45.90 45.84 48.41 47.50 46.64

_ D;ORC (kW) Ex _ PEM; input (kW) W hth;PEM (%)

And: 8 < 0  mf  1 Tlow < Ttop < Tcr : T0 < Tlow < Ttop

18.84 18.74 18.79 18.96 18.91 18.86

(34)

hex;PEM (%)

8 DTPPGen  15K > > < DTPPCond1  10K ε  50% > > : E_D > 0

_ ORC (kW) W

where, mf denotes the mass fraction for the first component of the mixture. In addition, below constrained should be met in the optimization process:

12.17 11.86 11.88 12.50 12.30 12.09

(33)

Isopentane/Hexane Isopentane/Isohexane Isopentane/R245fa Pentane/Butene Pentane/Butane Pentane/R245fa

 _ ORC ¼ f mf ; Ttop ; Tlow W

_ tot (kW) W

Particle swarm optimization method is applied in the present work to optimize a cost function. The main objective _ tot . of this paper is to maximize the entire output power and W Because doesn't vary by variation of optimization parameters, _ ORC (based on the cost function decreases to maximize only W Eq. (21)). In fact, the main objective is to optimize the top temperature and low temperature in the ORC system (Ttop ; Tlow ) that are respectively determined here by T11 ; T13 . This optimization will result in obtaining the max output power with no violations from the limitations. This objective is presented mathematically by:

_ D;tot (kW) Ex

(32)

hex;tot (%)

; otherwise xkþ1 i > >   0  > > 0 0 > ¼ argmin f x ; f x2 ; …; f x0m g > 1 > >     k > kþ1 > : gkþ1 ¼ argmin f x1kþ1 ; f xkþ1 ; …; f xm ; f g 2

hex;ORC (%)

:

mf

i

p0i ¼ x0i   if f xkþ1  f pki i

Fluid combination

pikþ1 ¼

8 < pk ;

Output powers and H2 and exergy destruction

8 > > > > > > > > > > <

m_ H2 ðkg=hrÞ

international journal of hydrogen energy xxx (xxxx) xxx

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

international journal of hydrogen energy xxx (xxxx) xxx

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Fig. 6 e The trend of variation for different performance parameters with mass fraction change for various working fluids.

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

international journal of hydrogen energy xxx (xxxx) xxx

To testify the attained findings resulted from thermodynamic analysis of the devised system, REFPROP toolbox in MATLAB software is used to expand an appropriate code. Based upon this expanded code, our results are compared with three previous established works. For verification of the outcomes of present work, three pieces of literature are chosen: the study of Yilmaz et al. [51] for the flash cycle, the work of Shu et al. [52] for zeotropic-based ORC, and the work of Ioroi et al. [53] for PEM electrolyzer unit. Applying results reported in these works, the validation of the proposed system is presented in Tables 5 and 6 for the flash cycle and ORC, respectively. Besides, the validation of the electrolyzer is presented in Fig. 5. Referring to these two tables and Fig. 5, it can be seen that the obtained data profusely agree with those of literature.

Optimization results The main thermodynamic flow parameters obtained from the mathematical analysis for the base case (The “base case” term represents the results that are achieved from the base input data and assumptions given by the paper). The key thermodynamic parameters include temperature, pressure, enthalpy, entropy, mass flow rate. Also, some of the main performance criteria which are obtained in the base case condition is listed in Table 7. Base on Table 7, the overall thermal and exergy efficiencies are obtained by 18:9% and 57:39%, respectively. Also, it can be stated that the overall exergy destruction rate and power derived from the simulation are calculated as 86:131 kW and 125:652 kW, respectively. The results of the optimization using the PSO algorithm are presented in Table 8. Table 8 presents that the mixture of the Pentane (0.31)/Butene (0.69) and the Pentane (0.41)/Butane (0.59) hold the highest energy efficiency with 18:96% and 18:91%, respectively. The net output power for these combinations are 125:712 kW and 124:923 kW, respectively; Also, the greatest energetic efficiency of the ORC unit belongs to the Pentane (0.31)/Butene (0.69). From the exergy efficiency viewpoint, the highest exergy efficiency refers to Pentane (0.31)/Butene (0.69) and Pentane (0.41)/Butane (0.59) combination whose values are 57:24 % and 57:10 %, respectively. Besides, the highest power generation rate with the value of 125:712 kW belongs to the mixture of Pentane (0.31)/Butene (0.69). Also, the highest hydrogen production rate refers to the mixture of Pentane (0.31)/Butene (0.69) with 0:3683 kg= hr.

Performance analysis The variation in the first-law efficiency of the zeotropic base ORC in the various mass fraction of various zeotropic admixtures is illustrated in Fig. 6a. What stands from Fig. 6a is that the highest value of the thermal efficiency for the ORC unit refers to Pentane/Butene, Pentane/Butane, and Pentane/ R245fa, respectively. It can be said that the combination of Pentane with other fluids holds the highest thermal efficiency for the ORC unit compared to other working fluids.

Isopentane/Hexane Isopentane/Isohexane Isopentane/R245fa

17.0

Pentane/Butene Pentane/Butane Pentane/R245fa

16.5

ED,Gen

Model validation

Also, the change in the first-law efficiency variation of PEM electrolyzer in different mass fractions and working fluids are presented in Fig. 6b. This figure reveals that with raising the mass fraction, the thermal efficiency first decreases to a minimum value in different working fluids and then increases; the lowest value obtained for the PEM thermal efficiency belongs to Pentane (0.3)/Butene (0.7). However, according to Fig. 6d, the highest amount of generated hydrogen belongs to Pentane/Butane mixture where the highest power is supplied; the reason behind this fact is that the variation of power not only depends on mass fractions but also relies on power used by PEM electrolyzer. Moreover, the energy efficiency of the total system is presented in Fig. 6c. According to the aforementioned mathematical equations, the energetic efficiency of the entire system relies on net output power, the mass flow rate of geothermal fluid, and the temperature of extracted geothermal fluid. Because the mass flow rate and temperature of the geothermal unit are regarded as constant, the change's trend of the thermal efficiency is dominated by the ORC unit. Referring to Fig. 6c, the highest thermal efficiency of the total system belongs to Pentane/Butene and Pentane/Butane mixtures, correspondingly. Besides, the power generation of the ORC unit, power utilized by PEM electrolyzer, and total system power are presented in Fig. 6feh. According to these figures,

16.0 15.5 15.0 14.5

9

8

ED,Cond1

presented. Eventually, the effect of some parameters on the cycle performance is shown.

7

6

ED,Pump ED,ORC Turbine

14

4.5 4.0 3.5 0.2 0.0 0.0

0.2

0.4

0.6

0.8

1.0

mf Fig. 7 e The rate of exergy destruction for each component of the ORC unit with mass fraction variation.

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

international journal of hydrogen energy xxx (xxxx) xxx

the highest power generation and power consumption rate belong to Pentane/Butene and Pentane/Butane mixtures, correspondingly. Besides, Fig. 6e illustrates the temperature variation in the generator output (T7 ). The reason for plotting Fig. 6e is that the maximum obtained work through the ORC unit is achieved where the lowest temperature at the generator outlet occurs. The rate of exergy destruction in each component of the ORC system is plotted in Fig. 7. As can be seen from Fig. 7, the exergy destruction rate of the high-pressure turbine firstly increases, then decreases. Moreover, the exergy destruction

15

rate of the pump drops linearly with an increase of mass fraction. The trend of variation of exergy destruction for generator and condenser is also plotted against mass fraction in Fig. 7, where the exergy destruction rate firstly drops and then rises for condenser. while in the generator, the exergy destruction rate does not follow a particular trend. Summation of the components' exergy destruction rate gives the total rate of exergy destruction of the ORC unit, which is plotted in Fig. 8a. Also, the exergy destruction rate of the overall system is presented in Fig. 8b. Since the ORC unit is dominated over the geothermal flash cycle, the behavior of

Fig. 8 e Total exergy destruction rate and exergy efficiency of ORC, PEM electrolyzer, and total system. Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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both total and ORC exergy destruction rate is almost similar. Also, the second-law efficiency of the ORC and the entire system is plotted in Fig. 8c and d, respectively. Since the second-law efficacy of the whole system relies on the net generated power and geothermal fluid's exergy, and because the geothermal fluid condition is regarded invariable, the change of the net output power and exergy efficiency of the total system is almost similar. Besides, the combinations of Pentane with other fluids hold the highest exergy efficiency. Moreover, exergy efficiency and exergy destruction rate for the PEM electrolyzer are plotted in Fig. 6e and f. Based on these figures, the lowest exergy efficiency appears where the

highest exergy destruction happens which belongs to Pentane (0.3)/Butene (0.7).

Analysis of the generator pinch point temperature (PPTgen ) impact on the net output power The influence of generator pinch point temperature as one of the main operating parameters of the system on the performance of the proposed power and hydrogen production system is analyzed in this part. For this purpose, Fig. 9 is plotted to show the variation of ORC power generation capacity with the change in the mass fraction and generator pinch point

Fig. 9 e The generator's pinch point temperature influence on the net output power. Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

international journal of hydrogen energy xxx (xxxx) xxx

temperature. As the plot illustrates, with augmentation of PPTgen , the ORC power generation rate in all of the working fluids combination decreases. Besides, the working fluids with Pentane present a higher power generation rate. More from the figure is the highest output power which belongs to Pentane/Butene combination in the pinch point temperature of PPTgen ¼ 11, and in mf ¼ 0:3.

Conclusion remarks In the present study, a novel hybrid system comprising a geothermal flash cycle, zeotropic-based ORC plant for power generation aim and PEM electrolyzer for hydrogen production purposes is proposed. Also, in this work integration of ORC and zeotropic mixtures including Pentane, Butane, Butene, Isopentane, Hexane, Isohexane, and R254fa mixtures is considered for performance enhancement of conventional ORC systems. The main contributions of the present work include a. Designing a new integration of geothermal flash cycle, zeotropic-based ORC, and PEM electrolyzer unit b. Employing zeotropic mixtures capabilities for performance enhancement, c. Application of zeotropic working fluid can decrease the irreversibility in the heating and cooling processes in comparison to traditional pure fluids so that a higher energy and exergy efficiencies are obtainable. The most important conclusion remarks of the proposed system and analysis can be presented as:  With increasing the mixture's mass fraction, the main performance criteria comprising net output power, thermal efficiency, and exergy efficiency increase to a maximum value and then decreases.  The working fluid containing Pentane (Pentane/Butane, Pentane/Butane, and Pentane/R245fa) illustrates superior performance.  In all of the zeotropic combinations the greatest powers generation rate as well as higher efficiencies observed in the average values of the mass fraction.  The energetic and exergetic efficiency, as well as the power generation rate of the present system, is higher than that of conventional ORC-based units.  In the base case result, the overall energy and exergy efficiencies are obtained 18:9% and 57:39%, respectively.  The mixture of the Pentane (0.31)/Butene (0.69) and the Pentane (0.41)/Butane (0.59) hold the highest energy efficiency with 18:96% and 18:91%, respectively.  The highest exergy efficiency belongs to Pentane (0.31)/ Butene (0.69) and Pentane (0.41)/Butane (0.59) combination whose values are 57:24 % and 57:10 %, respectively.  An increase in the generator's pinch point temperature results in a lower power generation rate of the ORC system.

Acknowledgements This work was supported by 13th Five-year Plan of National Key Research and Development Project (NO.

17

2016YFD020060802), and the National Natural Science Foundation Project of China (51774088).

references

[1] Ziapour BM, Saadat M, Palideh V, Afzal S. Power generation enhancement in a salinity-gradient solar pond power plant using thermoelectric generator. Energy Convers Manag 2017;136:283e93. [2] Rostamzadeh H, Ebadollahi M, Ghaebi H, Shokri A. Comparative study of two novel micro-CCHP systems based on organic Rankine cycle and Kalina cycle. Energy Convers Manag 2019;183:210e29. [3] Boyaghchi FA, Heidarnejad P. Thermoeconomic assessment and multi objective optimization of a solar micro CCHP based on Organic Rankine Cycle for domestic application. Energy Convers Manag 2015;97:224e34. [4] Bianchi M, De Pascale A. Bottoming cycles for electric energy generation: parametric investigation of available and innovative solutions for the exploitation of low and medium temperature heat sources. Appl Energy 2011;88(5):1500e9. [5] Venkatarathnam G, Mokashi G, Murthy SS. Occurrence of pinch points in condensers and evaporators for zeotropic refrigerant mixtures. Int J Refrig 1996;19(6):361e8. [6] Kang Z, Zhu J, Lu X, Li T, Wu X. Parametric optimization and performance analysis of zeotropic mixtures for an organic Rankine cycle driven by low-medium temperature geothermal fluids. Appl Therm Eng 2015;89:323e31. [7] Radulovic J, Castaneda NIB. On the potential of zeotropic mixtures in supercritical ORC powered by geothermal energy source. Energy Convers Manag 2014;88:365e71. [8] Deethayat T, Kiatsiriroat T, Thawonngamyingsakul C. Performance analysis of an organic Rankine cycle with internal heat exchanger having zeotropic working fluid. Case Stud Therm Eng 2015;6:155e61. [9] Lecompte S, Ameel B, Ziviani D, van den Broek M, De Paepe M. Exergy analysis of zeotropic mixtures as working fluids in Organic Rankine Cycles. Energy Convers Manag 2014;85:727e39. [10] Li Y-R, Du M-T, Wu C-M, Wu S-Y, Liu C. Potential of organic Rankine cycle using zeotropic mixtures as working fluids for waste heat recovery. Energy 2014;77:509e19. [11] Habka M, Ajib S. Evaluation of mixtures performances in Organic Rankine Cycle when utilizing the geothermal water with and without cogeneration. Appl Energy 2015;154:567e76. [12] Yari M. Performance analysis of the different organic Rankine cycles (ORCs) using dry fluids. Int J Exergy 2009;6(3):323e42. [13] Yari M. Exergetic analysis of various types of geothermal power plants. Renew energy 2010;35(1):112e21. [14] Ho T, Mao SS, Greif R. Increased power production through enhancements to the organic flash cycle (OFC). Energy 2012;45(1):686e95. [15] Kanoglu M. Exergy analysis of a dual-level binary geothermal power plant. Geothermics 2002;31(6):709e24. [16] Zhang HG, Wang EH, Fan BY. A performance analysis of a novel system of a dual loop bottoming organic Rankine cycle (ORC) with a light-duty diesel engine. Appl Energy 2013;102:1504e13. [17] Yari M, Mehr AS, Zare V, Mahmoudi SMS, Rosen MA. Exergoeconomic comparison of TLC (trilateral Rankine cycle), ORC (organic Rankine cycle) and Kalina cycle using a low grade heat source. Energy 2015;83:712e22.

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093

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international journal of hydrogen energy xxx (xxxx) xxx

[18] Li T, Zhu J, Hu K, Kang Z, Zhang W. Implementation of PDORC (parallel double-evaporator organic Rankine cycle) to enhance power output in oilfield. Energy 2014;68:680e7. [19] Grigoriev SA, Porembsky VI, Fateev VN. Pure hydrogen production by PEM electrolysis for hydrogen energy. Int J Hydrogen Energy 2006;31(2):171e5. [20] Midilli A, Dincer I. Key strategies of hydrogen energy systems for sustainability. Int J Hydrogen Energy 2007;32(5):511e24. [21] Ghaebi H, Farhang B, Parikhani T, Rostamzadeh H. Energy, exergy and exergoeconomic analysis of a cogeneration system for power and hydrogen production purpose based on TRR method and using low grade geothermal source. Geothermics 2018;71:132e45. [22] Bozoglan E, Midilli A, Hepbasli A. Sustainable assessment of solar hydrogen production techniques. Energy 2012;46(1):85e93. [23] Ni M, Leung MKH, Leung DYC. Energy and exergy analysis of hydrogen production by a proton exchange membrane (PEM) electrolyzer plant. Energy Convers Manag 2008;49(10):2748e56. [24] Abdin Z, Webb CJ, Gray EM. Modelling and simulation of a proton exchange membrane (PEM) electrolyser cell. Int J Hydrogen Energy 2015;40(39):13243e57. [25] Ahmadi P, Dincer I, Rosen MA. Energy and exergy analyses of hydrogen production via solar-boosted ocean thermal energy conversion and PEM electrolysis. Int J Hydrogen Energy 2013;38(4):1795e805. [26] Yilmaz C, Kanoglu M. Thermodynamic evaluation of geothermal energy powered hydrogen production by PEM water electrolysis. Energy 2014;69:592e602. [27] Lund JW, Freeston DH. World-wide direct uses of geothermal energy 2000. Geothermics 2001;30(1):29e68. [28] Kanoglu M, Bolatturk A. Performance and parametric investigation of a binary geothermal power plant by exergy. Renew Energy 2008;33(11):2366e74. [29] Bodvarsson G, Eggers DE. The exergy of thermal water. Geothermics 1972;1(3):93e5. [30] DiPippo R. Second Law assessment of binary plants generating power from low-temperature geothermal fluids. Geothermics 2004;33(5):565e86. [31] Hettiarachchi HDM, Golubovic M, Worek WM, Ikegami Y. Optimum design criteria for an organic Rankine cycle using low-temperature geothermal heat sources. Energy 2007;32(9):1698e706. [32] Borsukiewicz-Gozdur A, Nowak W. Maximising the working fluid flow as a way of increasing power output of geothermal power plant. Appl Therm Eng 2007;27(11e12):2074e8. [33] Noruzi Alireza, et al. A new method for probabilistic assessments in power systems, combining Monte Carlo and stochastic-algebraic methods. Complexity 2015;21(2):100e10. [34] Mohammadi Mohsen, et al. Small-scale building load forecast based on hybrid forecast engine. Neural Process Lett 2018;48(1):329e51. [35] Ahmadian Iraj, Abedinia Oveis, Ghadimi Noradin. Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front Energy 2014;8(4):412e25.

[36] Ghadimi Noradin, et al. A new prediction model based on multi-block forecast engine in smart grid. J Ambient Intell Humanized Comput 2018;9(6):1873e88. [37] Xu Jianjun, et al. All-fiber self-mixing interferometer for displacement measurement based on the quadrature demodulation technique. Opt Rev 2018;25(1):40e5. [38] Han J, et al. Modelling the performance of an SOEC by optimization of neural network with MPSO algorithm. Int J Hydrogen Energy 2019;44(51):27947e57. [39] Zhao Y, Xu J, Wu J. A new method for bad data identification of oilfield system based on enhanced gravitational searchfuzzy C-means algorithm. IEEE Trans Ind Inf 2019;15(11):5963e70. [40] Quoilin S, Van Den Broek M, Declaye S, Dewallef P, Lemort V. Techno-economic survey of organic Rankine cycle (ORC) systems. Renew Sustain Energy Rev 2013;22:168e86. [41] Najafi G, Ghobadian B. Geothermal resources in Iran: the sustainable future. Renew Sustain Energy Rev 2011;15(8):3946e51. [42] Parikhani T, Jannatkhah J, Shokri A, Ghaebi H. Thermodynamic analysis and optimization of a novel power generation system based on modified Kalina and GT-MHR cycles. Energy Convers Manag 2019;196:418e29. [43] Lemmon EW, Huber ML, McLinden MO. In: NIST standard reference database 23, reference fluid thermodynamic and transport properties (REFPROP), version 9.0, vol. 22. National Institute of Standards and Technology; 2010. p. 2010. R1234yf. fld file dated December. [44] C¸engel YA, Boles MA. Thermodynamics: an engineering approach. eight edition.” New York: McGraw Hill Education; 2015. [45] Ahmadi P, Dincer I, Rosen MA. Thermodynamic modeling and multi-objective evolutionary-based optimization of a new multigeneration energy system. Energy Convers Manag 2013;76:282e300. [46] Ahmadi P, Dincer I, Rosen MA. Development and assessment of an integrated biomass-based multi-generation energy system. Energy 2013;56:155e66. [47] Santarelli MG, Torchio MF. Experimental analysis of the effects of the operating variables on the performance of a single PEMFC. Energy Convers Manag 2007;48(1):40e51. [48] Gurau V, Barbir F, Liu H. “An analytical solution of a half-cell Model for PEM fuel cells. J Electrochem Soc 2000;147(7):2468e77. [49] Xu G, Yu G. Reprint of: on convergence analysis of particle swarm optimization algorithm. J Comput Appl Math 2018;340:709e17. [50] Shah RK, Sekulic DP. Fundamentals of heat exchanger design. John Wiley & Sons; 2003. [51] Yilmaz C, Kanoglu M, Abusoglu A. Exergetic cost evaluation of hydrogen production powered by combined flash-binary geothermal power plant. Int J Hydrogen Energy 2015;40(40):14021e30. [52] Shu G, Gao Y, Tian H, Wei H, Liang X. Study of mixtures based on hydrocarbons used in ORC (Organic Rankine Cycle) for engine waste heat recovery. Energy 2014;74:428e38. [53] Ioroi T, Yasuda K, Siroma Z, Fujiwara N, Miyazaki Y. Thin film electrocatalyst layer for unitized regenerative polymer electrolyte fuel cells. J Power Sources 2002;112(2):583e7.

Please cite this article as: Han J et al., Thermodynamic analysis and optimization of an innovative geothermal-based organic Rankine cycle using zeotropic mixtures for power and hydrogen production, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.093