A quadruple power generation system for very high efficiency and its performance optimization using an artificial intelligence method

Journal Pre-proofs A quadruple power generation system for very high efficiency and its performance optimization using an artificial intelligence method Ji Ho Ahn, Min Jae Kim, Yeon Woo Cho, Tong Seop Kim PII: DOI: Reference:

S1359-4311(19)36601-3 https://doi.org/10.1016/j.applthermaleng.2019.114861 ATE 114861

To appear in:

Applied Thermal Engineering

Received Date: Revised Date: Accepted Date:

23 September 2019 1 December 2019 27 December 2019

Please cite this article as: J.H. Ahn, M.J. Kim, Y.W. Cho, T.S. Kim, A quadruple power generation system for very high efficiency and its performance optimization using an artificial intelligence method, Applied Thermal Engineering (2019), doi: https://doi.org/10.1016/j.applthermaleng.2019.114861

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© 2019 Published by Elsevier Ltd.

A quadruple power generation system for very high efficiency and its performance optimization using an artificial intelligence method

Ji Ho Ahna, Min Jae Kima, Yeon Woo Cho a, and Tong Seop Kimb,*

aGraduate bDept.

School, Inha University, Incheon 22212, Korea

of Mechanical Engineering, Inha University, Incheon 22212, Korea

All work in this paper is original and is not published or intended to be published by any other means or in other languages

* Corresponding author. Tel.: +82-32-860-7307; Fax: +82-32-868-1716 E-mail address: [email protected] (T. S. Kim)

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Abstract The demand for highly efficient power generation systems is ever escalating to reduce carbon dioxide emissions. This study proposes a novel quadruple power generation system, investigates its performance characteristics, and optimizes its efficiency using an artificial intelligence method based on the results of a thermodynamic simulation. The quadruple system integrates four power blocks consisting of a solid oxide fuel cell (SOFC), a molten carbonate fuel cell (MCFC), a gas turbine, and an organic Rankine cycle to achieve high efficiency. A parametric analysis of the main design parameters was conducted. An artificial intelligence method combining an artificial neural network and a genetic algorithm was devised to avoid the slow speed of the calculation for the thermodynamically optimal solution and used to find the optimized design parameters that produce the maximum efficiency. The results of the thermodynamic parametric analysis were used to train the artificial neural network. The results of the optimization method were verified through comparison with the results of a thermodynamic analysis. A very high maximum efficiency of 78.1% was predicted. The performance of the optimized quadruple system was examined by comparing it with that of other hybrid power systems.

Keywords: Solid Oxide Fuel Cell (SOFC), Molten Carbonate Fuel Cell (MCFC), Gas Turbine (GT), Organic Rankine Cycle (ORC), Quadruple power generation system, Artificial intelligence method

Nomenclature A

Total active area

a

Coefficient of a balanced equation

AC

Alternating current

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ANN

Artificial neural network

ARR

Anode off-gas recirculation ratio

AI

Artificial intelligence

b

Coefficient of a balanced equation

CDT

Compressor discharged temperature

CSR

Cathode off-gas supply ratio

DC

Direct current

Η

Efficiency

F

Faraday constant

FC

Fuel cell

FSR

Pre-reformed fuel supply ratio

G

Gibbs free energy [kJ/kmol]

GA

Genetic algorithm

GT

Gas turbine

GTCC

Gas turbine combined cycle

HX1

First heat exchanger

h

Molar specific enthalpy [kJ/kmol]

I

Current [A]

J

Current density [A/m2]

LHV

Lower heating value [kJ/kg]

m

Mass flow rate [kg/s]

MCFC

Molten carbonate fuel cell

MGT

Micro gas turbine

MLFF

Multi-layer feed forward

n

Number of electrons

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n

Molar flow rate [kmol/s]

ORC

Organic Rankine cycle

p

Pressure [kPa]

PR

Pressure ratio

Q

Heat transfer rate [kW]

R

Cell resistance [Ω∙m2]

R

Universal gas constant [kJ/kmol∙K]

SCR

Steam to carbon ratio

SOFC

Solid oxide fuel cell

T

Temperature [K]

TET

Turbine exhaust temperature

U

Utilization

V

Voltage [V]

W

Power [kW]

Greeks α

Activity

ρ

Overpotentials [V]

σ

electronic conductivity [Ω-1∙m-1]

τ

Thickness [m]

Subscripts act

Activation loss

an

Anode

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amb.

Ambient

B

Blower

C

Compressor

ca

Cathode

conc

Concentration loss

conv

Conversion

el

Electrolyte

f

Fuel

gen

Generator

i

Index of compositions

mech

Mechanical

mo

Motor

NG

Natural gas

ohm

Ohmic loss

P

Pump

T

Turbine

1. Introduction Global warming and its serious effects require a worldwide reduction in greenhouse gases, especially carbon dioxide [1]. Fossil fuels supply 82% of primary energy requirements, and 70% of the global CO2 emissions come from industrial electricity and heat production [2]. Thus, industry should use clean energy sources and reduce the carbon dioxide emissions [3]. The power industry is making various efforts, such as steadily increasing the use of renewable energies. However, renewable energy sources, such as wind and solar power, are influenced by local weather conditions and have low power output per unit installation area. This makes

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it difficult to meet the majority of worldwide electricity demand [4]. In the future, the use of renewable energies is expected to increase, but at present, it is important to pursue the efficient use of fossil fuels. Coal and natural gas are representative fossil fuels in the power industry. Coal is an inexpensive and low-grade raw material that discharges a relatively large amount of carbon dioxide and fine dust during the combustion process [5], whereas natural gas is known to be a much cleaner energy source. Therefore, many efforts have been made to use natural gas for power generation more efficiently. The gas turbine combined cycle (GTCC) is currently the most efficient power generation system, and various efforts have been made to improve its performance [6, 7]. Recently, various studies have been conducted to use fuel cells in power generation. A fuel cell generates power directly by an electrochemical reaction. Therefore, undesirable byproducts of combustion such as NOx, SOx, and fine dust can theoretically be avoided. Furthermore, the direct reaction makes it a highly efficient system in comparison with conventional power generation systems, leading to less emission of carbon dioxide, which is beneficial for the prevention of global warming. Moreover, a high-temperature fuel cell system has the advantage of using natural gas without an additional production process or the transportation of hydrogen because of the internal reformation of hydrocarbon to hydrogen. Thus, producers, integrators, and research institutes have become involved in high-temperature fuel cells and are already operating megawatt-class fuel cell systems [8, 9]. Several studies have been conducted on improving system efficiency by the integration of a high-temperature fuel cell with another power generation unit. High-temperature fuel cells, especially solid oxide fuel cells (SOFCs), can be combined with a gas turbine (GT) because the fuel cell’s operating temperature is very high. However, the size of fuel cells being developed is not yet sufficiently large, so the combination of a micro gas turbine (MGT) and

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an SOFC has been investigated. In the early 2000s, Siemens Westinghouse demonstrated a pressurized pressure SOFC-CHP power system that generated 217 kW of power and achieved over 57% efficiency [10]. RollsRoyce Fuel Cell Systems installed a test rig consisting of 120 cell stack elements to develop a 1-MW hybrid system [11]. However, the systems were not commercialized successfully, so many researchers have been steadily conducting various studies on SOFC/MGT hybrid systems to investigate their performance characteristics. Yang et al. [12] analyzed the limitation of the temperature difference in the fuel cell stack and compared the performance of hybrid systems applying internal and external reformers. Perna et al. [13] performed numerical modeling and an experimental investigation of an SOFC/MGT hybrid system using syngas from a biomass downdraft gasifier. Studies have been conducted on waste heat recovery to improve system efficiency and the utilization of auxiliary components to reduce carbon emissions. Zhang et al. [14, 15] proposed an efficient method of waste heat recovery from solid oxide fuel cells using a cascading or twostage thermoelectric generator. Ebrahimi and Moradpoor [16] proposed an SOFC/MGT hybrid system and achieved a system efficiency of 65.8% by adding an organic Rankine cycle (ORC). Duan et al. [17] suggested an SOFC/MGT hybrid system without the emission of carbon dioxide using a ceramic proton membrane. Recently, Mitsubishi Hitachi Power Systems (MHPS) commercialized a 250-kW SOFC/MGT hybrid system called Hybrid-FC [18, 19]. Studies for coupling a GTCC with SOFCs are being conducted to enhance the efficiency. Choi et al. [20] proposed a triple power generation cycle by combining a J-class GTCC with an SOFC and reached approximately 70% system efficiency. Sarmah and Gogoi [21] comparatively analyzed triple generation systems using several bottoming cycles to improve the system efficiency. Yi et al. [22, 23] analyzed the system performance for different design parameters and the application of a recuperator. Choi et al. [24] studied the performance of a

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triple generation system using oxy-combustion carbon capture technology. MHPS also aims to develop a triple power generation system by combining a 100-MW SOFC with a GTCC [25]. Molten carbonate fuel cells (MCFCs) have been commercialized earlier than SOFCs. One example is a 2.8-MW unit [26]. A large-scale power generation complex with many MCFC units has been in commercial operation [9]. Studies have also been carried out on the combination of an MCFC with a GT to enhance the power generation efficiency. Oh and Kim [27] analyzed the performance of various system layouts for an atmospheric-pressure MCFC/MGT hybrid system. Sciacovelli and Verda [28] conducted a sensitivity analysis for the multi-objective optimization of an MCFC-based hybrid plant. MCFCs have also been used for carbon capture for a GTCC [29, 30] and an integrated gasification combined cycle [31, 32]. The anode off-gas consists of unreacted fuel, CO2, and water vapor. McLarty and Brouwer [33] proposed a co-production system that generates electricity, hydrogen, heat, and liquefied CO2 simultaneously. Duan et al. [34] suggested an ion transfer membrane that was integrated into an MCFC hybrid system with carbon dioxide capture. Ahn et al. used oxy-combustion capture technology on an MCFC/MGT hybrid system [35] and proposed a method for improving the efficiency of the hybrid system by using an offgas recirculation technique [36]. Fuel cells are also being coupled. Rinaldi et al. [37] connected three MCFCs in series in a co-production system using biogas. An SOFC-MCFC combined system was suggested using coal [38] and natural gas [39] as fuel. Mastropasqua et al. [39] suggested a polygeneration SOFC-MCFC plant with an efficiency of around 68%. In addition, studies have been conducted to predict and optimize the system performance using an artificial neural network (ANN) and a genetic algorithm (GA). Wang et al. [40] and Suresh et al. [41] used thermodynamic calculation results to train an ANN and optimized the system efficiency by using the GA. Yang et al. [42] also used an ANN to make a simulation model of the ORC based on experimental results and optimized the cycle performance using

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the GA. Various studies on improving efficiency have been conducted by system integration to reduce carbon emissions. Although the studies have focused on the integration of two or three systems, further integration still has the potential to improve the system efficiency. This study proposes a novel quadruple power generation system that combines an SOFC, an MCFC, a GT, and an ORC to achieve more than 70% efficiency. A system that combines more than four units including a fuel cell has never been proposed. The way of combining the SOFC and MCFC is similar to that used by Mastropasqua et al. [39], but a gas turbine and an ORC system are added to produce a new quadruple power generation system. Although there are many ways to integrate a gas turbine and ORC, only the proposed configuration satisfies the target efficiency. In addition, the SOFC operates at high pressure in the proposed system, while the reference system operates at ambient pressure. Due to the pressurized operation of the SOFC, some heat exchangers can be eliminated, which makes the overall system more compact. The power generation efficiency is predicted to be more than 70%. A parametric analysis was carried out to investigate the effects on the system efficiency of the main design parameters. An artificial intelligence (AI) method combining an ANN and a GA was devised and used for optimization to determine the design point with the best efficiency. The system efficiency was enhanced to more than 70%.

2. System configuration The configuration of the quadruple system is shown in Fig. 1. The system uses natural gas as fuel, and power is generated by four sub-systems: the SOFC, MCFC, GT, and ORC. The method of coupling the SOFC and MCFC in the quadruple system was referenced from a study on a polygeneration SOFC-MCFC plant [39]. The SOFC has the highest power fraction in this system. However, we designed the overall power output of the entire system based on the

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MCFC because it was developed earlier, and the SOFC is relatively underdeveloped, especially in terms of the power capacity of the commercial units. A 2.8-MW-class commercial MCFC system [26] was referenced, and the power outputs were determined accordingly. This means that the cathode inlet flow rate and the active cell area of the MCFC are always maintained at a fixed value during the parametric analysis of the various design parameters. It is assumed that the cell voltage of the SOFC is kept at the design point through a redesign of the SOFC module’s active area when the operating conditions vary. Pressurized air (line 2) from the compressor is supplied to the SOFC’s cathode side after being heated at the heat exchanger with the MCFC cathode off-gas (line 9) and the combustion gas of the fuel cell (FC) burner (line 7). The SOFC cathode off-gas (line 5) is supplied to the FC burner and the GT combustor. The FC burner combusts a part of the anode off-gas of the SOFC, including unreacted fuel, to supply the carbon dioxide required for the cell reaction of the MCFC. The GT combustor burns the remaining unreacted fuel for the expansion of the working fluid in the turbine. The combustion gas in the FC burner (line 7) is supplied to the cathode of the MCFC after maintaining the inlet temperature through heat exchange with the pressurized air (line 3). The cathode off-gas (line 10) enters the GT combustor after exchanging heat with the pressurized air (line 2) once again. The combustion gas (line 12) is expanded in the turbine, and the heat of the exhaust gas (line 13) is recovered in the regenerative ORC. The sulfur in the fuel (line 16) is removed through a low-temperature desulfurization reactor. The desulfurized fuel is supplied to the primary gas nozzle (high-pressure side) of the ejector after exchanging heat with the unreacted gas that is recirculated to the anode of the MCFC. Part of the SOFC anode off-gas (line 22) is supplied to the secondary gas nozzle (low-pressure side) of the ejector, and the pressure of the ejector outlet gas (line 18) is increased. The outlet gas is a mix of gas with fuel and off-gas. The pressure drops of the off-gas (line 22) passing through the SOFC are assumed to be compensated by the ejector. Several studies have been

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conducted to increase the reduced pressure using an ejector for the cathode or the anode offgas recirculation [11, 43, 44]. Research has been conducted to use an ejector at both low and high temperatures and pressures [45]. The mixed gas (line 18) is reformed in the pre-reformer, and the reformed gas (line 19) is supplied to the anode of the SOFC and MCFC. Part of the SOFC’s unreacted gas (line 22) is recirculated to the ejector for fuel reforming, and the remainder (line 23) is supplied to the FC burner. The pre-reformed gas (line 24) heading to the MCFC’s anode side is mixed with part of the MCFC’s unreacted gas (line 29), which has exchanged heat with the desulfurized fuel, and then it is supplied. The remaining unreacted gas (line 30) enters the GT combustor.

3. System modeling Aspen HYSYS [46] was used to model and simulate the quadruple system. The PengRobinson equation of state was used to predict the working fluid properties. The conditions of the intake of air (O2: 0.2073, N2: 0.7729, Ar: 0.0092, CO2: 0.0003, H2O: 0.0103) to the system are 15°C and 101.325 kPa. Natural gas (CH4: 0.89, C2H6: 0.07, C3H8: 0.01, i-C4H10:0.0011, N2: 0.0089, CO2: 0.02) with a low heating value of 46497.7 kJ/kg was used as a fuel, and it was assumed to be supplied at 15°C and 2000 kPa.

3.1 Fuel cell model All the equilibrium reactions of the fuel cells were calculated by the Gibbs free energy minimization principle according to the temperature and pressure conditions of the components supplied to the cell. Direct internal reforming is assumed to be used in the fuel cell modules. The pre-reformer and fuel cell modules involve steam reforming and water gas shift reactions:

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Steam reforming:





Ca H b + aH 2 O  aCO + a+ 1 b H 2 2 Water-gas shift reaction: CO + H 2 O  CO 2 + H 2

(1)

The water vapor required for the steam reforming reaction is supplied by recirculating the SOFC anode off-gas. The amount of steam is decided by the steam to carbon ratio (SCR), which is calculated as follows:

nH2O  SCR  a  nCaHb

(2)

The overall reactions of the SOFC and MCFC are as follows: SOFC: H 2 + 1 O2  H 2O 2 CO + 1 O 2  CO 2 2

(3)

MCFC: H 2 + 1 O 2 + CO 2  H 2 O + CO 2 2 CO + 1 O 2 + CO 2  2CO 2 2

(4)

The fuel utilization factor is used as a design parameter and defined as follows:

Uf 

 n  n

H2

 

 nCO

 H2  nCO

reacted

(5)

supplied

The following energy balance equation was applied to the entire cell stack:

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 n h   n h  W i i

reactant

i i

DC

Where

WDC  I V

(6)

product

In some SOFC systems, an indirect internal reformer is in contact with the cell stack [17, 20, 24], and heat is transferred from the cell stack to the reformer. However, most of the fuel reforming is achieved in the pre-reformer in our system, and direct internal reforming of the remaining hydrocarbons is assumed to occur inside the cell stack [16, 39]. Therefore, no indirect internal reformer is needed, so heat transfer from the cell stack is not needed either. To meet the target cell temperature, the air supply rate to the cathode is modulated. The current of the fuel cells is determined by the molar flow of reacted hydrogen and carbon monoxide. The current density is calculated by dividing the current by the total active area (A), which is the product of the area per unit cell and the number of cells. The current and the current density are described by Eq. (7).



I  2F nH2  nCO



(7)

reacted

J I/A

The Nernst voltage is a function of the fuel cell temperature and the activities of the component gases. The temperature and activities used in the equation are inlet values [39]. The fuel cell’s operating temperature is usually the temperature at either the inlet or outlet side, and the cathode outlet temperature was used in this study. The Nernst voltages of the SOFC and the MCFC are as follows:

SOFC:

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12 RT H2 , an O2 , ca  ln  Vnernst, SOFC  V0   nF  H2O, an 

p where V0  1.253  2.4516E-004  T , i  i pamb

(8)

MCFC: 12 G0 RT  H2 , an O2 , ca  CO2 , ca  ln  Vnernst, MCFC     nF nF  H2O, an   CO , an  2 

p where G0  2.42E+005  45.8  T , i  i pamb

(9)

The reaction of carbon monoxide oxidation is slower than that of the water-gas shift [47, 48], and its influence on the voltage computation is negligible [49]. Thus, only the reaction of hydrogen oxidation was considered to calculate the Nernst voltage. The actual cell voltage was calculated using correlations for voltage losses (over-potentials). The SOFC voltage considering the activation loss, concentration loss, and ohmic loss is described by the following equation.

VSOFC Vnernst, SOFC  act  conc  ohm 

(10)

The activation loss consists of two parts because it occurs during the transfer of electrons at the surface of the anode and the cathode. The transfer coefficient, β, is assumed to be 0.5, so the cathode activation loss is simply obtained by the hyperbolic definitions. In contrast, the anode activation loss is adjusted to match the current density through iterative calculations. The activation loss is expressed as follows:

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act  act, an  act, ca

(11)

 pH , TPB  2F  J  J 0, an  2  act, an  exp     RT   pH2 

pH2O, TPB pH2O

2F   exp   1      act, an   RT   

(12)

2F   2F    J  J 0, ca exp     act, ca   exp   1      act, ca   RT  RT      E  RT where J 0, el   kel  exp   act, el  , el  an, ca 2F  RT 

The concentration loss is the sum of the losses generated at the anode and cathode:

conc  conc, an  conc, ca 

 pH O, TPB  pH2  RT  pO2  RT  ln  2  ln    2F  pH2O  pH2 , TPB  4 F  pO2 , TPB 

(13)

The fuel cell modeling uses kinetics based on the triple-phase boundaries (TPB). The partial pressures of the main composition at TPB are:

pH2 , TPB  pH2 

RT an J 2 FDeff, an

pH2O, TPB  pH2O 

(14)

RT an J 2 FDeff, an

pO2 , TPB  p  ( p  pO2 )

(15)

RT ca J 4 FDeff, ca p

(16)

Ohmic loss is a function of the resistances caused by the flow of ions in the electrolyte and

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the flow of electrons in two the electrodes. The loss is calculated by adding the thicknesses of the two electrodes and electrolytes, dividing them by the respective electrical conductivity, and multiplying them by the current density:

  an

ohm  

  an



 el  ca    J el ca 

where  an 

4.2E  007  1.2E  003   exp   T T  

 1.03E  004   T   9.5E  007  1.15E  003   exp  ca   T T  

el  3.34E  004  exp 

(17)

The MCFC voltage, including the anode loss, cathode loss, and ohmic loss, is calculated using correlations [29]:

VMCFC  Vnernst, MCFC   Relectrode  Rohm   J

(18)

The electrode loss and the ohmic loss are expressed as follows:

Relectrode  Ran  Rca  53,500  0.42 1.0 0.17 where Ran  2.27E-009  exp     H2   H2O   CO2  RT   77, 229  0.43 0.09 Rca  7.505E-010  exp     O2   CO2  RT 





 Rohm  1.1E-004  exp 3,016  1  1 T 923.15  

- 16 -

(19)

(20)

The SOFC [50] and MCFC [29] voltage loss equations were referenced from the literature. The parameters used in the equations are shown in Table 1. Based on the current and voltage calculated from Eqs. (7)-(20), the fuel cell power is calculated as follows in consideration of the DC to AC conversion loss.

WFC  I VFC conv

(21)

where FC SOFC, MCFC

Some of the pre-reformed fuel is supplied to the SOFC, and the rest is fed to the MCFC. The SOFC cathode off-gas is supplied to the FC burner and GT combustor. The pre-reformed fuel supply ratio (FSR) and the SOFC cathode off-gas supply ratio (CSR) were used as variables for the parametric analysis and performance optimization. FSR for the SOFC and CSR for the FC burner are defined as follows:

m   FSR   supply to SOFC   m    generated reformed fuel

(22)

 m supply to FC burner  CSR     m exhausted cathode off-gas

(23)

In addition, a part of the MCFC anode off-gas is recirculated to the MCFC inlet, and the remainder is supplied to the GT combustor. The anode off-gas recirculation ratio (ARR) is also used as a variable for analysis and optimization.

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 m  ARR   recirculated to the anode of MCFC  m exhausted  anode off-gas

(24)

The model of the SOFC module was verified by the voltage-current density relationship with the experimental results of Zhao and Virkar [51] and the model of Patcharavorachot et al. [52]. The cases from these studies use hydrogen and pre-reformed gas as fuel at the same operating conditions as the references. Fig. 2 shows comparisons of our modeling results with the references. There is close agreement in the performance curves, which confirms the reliability of the SOFC model used in this study. The simulation of the MCFC system (the reference system) was also verified by comparing the simulation results with published data from the manufacturer, as shown in Table 2. The values of the unknown parameters, such as the cell voltage and fuel utilization factor, were chosen to match the system performance with the provided data. The simulated performance of the MCFC stand-alone system is quite similar to that of the commercial system [26], meaning that the modeling in this study is valid. We simulated a polygeneration plant with both an SOFC and an MCFC to validate the fuel cell models, including the cell stack modeling and the SOFC/MCFC coupling [39]. The main operating parameters, such as the fuel cell temperature and fuel utilization factor, are slightly different from those used in our quadruple system, and reported parameters [39] were used as in the validation simulation. The validation results are presented in Table 3. The close agreement in all the system performance outputs confirms the reliability of the fuel cell models.

3.2 Turbine-based power components and the entire system The GT power output is expressed by considering the mechanical and generator efficiencies as follows:

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WGT  (WT WC /mech ) gen

(25)

The waste heat of the exhaust gas is recovered by a regenerative ORC at the end of the system. There is a wide range of working fluid candidates for the ORC, which mostly depend on the temperature of the heat source. For example, hydrocarbons such as refrigerants are generally used in applications with a relatively low heat source temperature around 130oC or less [53, 54, 55, 56]. In high-temperature applications, non-refrigerants are preferred, such as toluene, cyclohexane, and acetone, which have a high critical temperature [53, 57, 58]. Toluene was selected in this study because the turbine exhaust temperature of the system is around 300°C. In an ORC system, maximizing the power output using the available waste heat should be the design target. For this purpose, two design features are introduced. First, the ORC turbine inlet is assumed to be in a saturated vapor state. Second, a regenerative heat exchanger is used between the turbine exit and the pump exit. Both design options increase the flow rate of the working fluid, leading to a large power output [56, 57]. The condensing temperature is set at 40°C [56]. Then, an optimal turbine inlet pressure (i.e., saturation pressure) is found to maximize the net power output. The net ORC power output is calculated using the equation below.

WORC WORC, T gen WORC, P /mo

(26)

The net performance of the entire quadruple power generation system considering the power consumption of the MCFC anode off-gas blower is defined as follows:

- 19 -

Wnet  WSOFC  WMCFC  WGT  WORC  WB

net 

(27)

Wnet m  LHVNG

(28)

Table 4 lists the design parameters. Those indicated by an asterisk are the major design parameters in the performance analysis. The values are reference values around which variations are made in the parametric analysis. The cell voltage of the SOFC is varied in the parametric analysis by varying the current density, which affects the voltage losses. The fuel supply rate to the cell stack is determined from the energy and mass flow balances around the cell stack once the cell operating temperature is obtained. Therefore, varying the current density means variation in the effective cell area.

3.3 Methodology for performance optimization Efficiency optimization of the proposed quadruple generation system is required because there are many design parameters due to the complexity of the system. A traditional optimization technique based on a simple repetitive thermodynamic calculation requires an excessive amount of calculation time and causes frequent calculation failures because of the complicated modeling of the quadruple power system. Therefore, a more effective tool is needed to secure the reliability of the optimization results, and we used an AI method combining an ANN and a GA. An ANN resembles a biological neuron system and obtains new results from given information. An ANN predicts new output values according to the variation of inlet values by training with both of them even without knowing the operation mechanism of the modeled system. The ANN consists of many units called neurons. The artificial neuron architecture in Fig. 3 includes the input value, weight value, transfer function, and output value. The input

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value is multiplied and added to the weight value, and then the output value is obtained through the transfer function. The weight value is controlled to minimize the difference between the actual and expected output, and this process is called training. The input values are transmitted to the neural network, and the output values are obtained from the network. Both values are passed on a unit layer basis. The multi-layer feed forward (MLFF) neural network is the most widely used network. The MLFF is composed of an input layer that receives the input values, an output layer, and one or more hidden layers in between. The hidden layers contribute directly to providing the output value. However, it is not necessary to use more than one layer because only one hidden layer is enough to approximate a continuous function [59]. The number of neurons in the hidden layer directly affects the accuracy of the output values, so it is very important to find out a proper number. The size of the input and output layers (the number of neurons) is a guideline for determining the number of hidden neurons, but this number is empirically determined because the guideline is uncertain. A GA is based on the concept of Darwin's theory of evolution and imitates natural biological evolution to approximate an optimal solution. The GA expresses the solutions for a given problem in a specific form of data structure, and then the solutions are approximated to the optimal point by a gradual variation through the process of evolution. Thus, this algorithm is different from traditional optimization techniques that use numerical analysis. Evolution is an iteration process that usually begins with a randomly generated population of individuals, and the repeated population is called a generation. The solutions in each generation are evaluated by a fitness function. Fitness is a value of the objective function in the optimization problem and should be defined negatively in the form of a minimum value. Solutions with increased fitness in the current generation are stochastically selected and

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recombined with mutations to build a new generation. The new generation is used for the next iteration. In most cases, the algorithm is terminated when either the maximum number of generations or a satisfactory fitness level has been reached. The ANN of Deep Learning Toolbox 12.0 and the GA of Optimization Toolbox 8.2 provided by MATLAB [60] were used to obtain the optimal parameters to reach the highest system efficiency. Fig. 4 shows the sequence of the efficiency optimization. In Step 1, several input variables with a strong influence on the system performance are selected. Output parameters are calculated according to the variation of the input parameters using HYSYS and collected for use in the next step. In Step 2, the collected parameters create a new system model through machine learning using the ANN. In Step 3, the GA predicts the set of input and output parameters shown in Step 1, when the system reaches the maximum efficiency. In Step 4, the output parameters are recalculated by the predicted input parameters using HYSYS. The simulation results of the AI method are validated by comparing the predicted and calculated output parameters. The current optimization method combining the thermodynamic calculation and the ANN is not a very general tool to optimize the component arrangement. Instead, it is a specific tool to optimize the design parameters of the specific system layout suggested in this study. Nevertheless, the proposed method is meaningful in that it illustrates the usefulness of the ANN in the optimization of the design parameters of a very complex system. Thus, it could be a step stone for developing a more general optimization tool in the future.

4. Results and discussion 4.1 Effects of design parameters on system performance The effect of the main design parameters of the proposed system on the system efficiency was analyzed. The selected parameters are the (1) pressure ratio (PR) of the GT compressor,

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(2) SCR, (3) FSR of the SOFC, (4) operating temperature of the SOFC module, (5) cell voltage of the SOFC, (6) fuel utilization of the SOFC, (7) CSR of the SOFC to the GT combustor, and (8) ARR of the MCFC. The values in Table 4 with an asterisk are the reference values used as initial values in the optimization. Other parameters such as component efficiencies are assumed to be constant. Except for the value of PR, the other values were selected by referring to the literature [39].

4.1.1 Pressure ratio The relation between the PR of the GT compressor and the system efficiency is shown in Fig. 5. The temperatures at the compressor discharge (line 2) and the cold-side outlet of the first heat exchanger (HX1, line 3) are also shown. The SOFC operating temperature is assumed to be constant regardless of the variation in PR, so the cold-side outlet temperature of HX1, which highly depends on this operating temperature, is almost constant. This means that the variation in PR has little effect on the cold-side outlet temperature. However, the compressor discharge temperature (CDT) increases with PR and becomes higher than the cold-side outlet temperature of HX1 when PR is greater than 18. Thus, a higher operating temperature is required for the SOFC thereafter. Lower PR requires a heat exchanger with high effectiveness. Specifically, the effectiveness is over 1.0 when PR is less than 5. A pressurized fuel cell system provides higher performance than an ambient-pressure system due to the increases in cell voltage caused by the higher cell operating pressure [61]. However, the power output of the SOFC in this specific simulation of the effect of the pressure ratio is almost constant despite the alteration in PR. This occurs because we assumed that the cell voltage remains constant as a result of varying the active area of the cell stack except for the case when we varied the cell voltage to see its effect on the performance separately. The operating pressure rises as the PR increases, which enhances the MCFC power output.

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The GT power output is improved as the PR increases, but the decrease in the turbine exhaust temperature (TET) degrades the ORC power output. The SOFC power output remains almost constant. The final result is that the net system power output is also almost constant, and the efficiency variation is also marginal. When the PR changes from 6 to 18, the efficiency variation increases by only 1.18%p (from 75.52% to 76.70%).

4.1.2 Steam carbon ratio Fig. 6 shows the relationship between the SCR and system efficiency. The figure also shows the cold-side inlet/outlet temperature of HX1 (lines 2 and 3) and the CO2 utilization factor of the MCFC. The steam for the reforming reaction is supplied by the recirculation of the SOFC anode off-gas. Therefore, the amount of recirculated anode off-gas increases as the SCR increases, and the fuel supply decreases to maintain the SOFC operating temperature. Higher SCR reduces the amount of anode off-gas supplied to the FC burner and increases the air supply with the amount of reduced off-gas to maintain the MCFC cathode inlet flow. Thus, the MCFC power output is reduced due to the decrease in the CO2 fraction of the MCFC cathode inlet flow (increasing the CO2 utilization factor). A decrease in the SOFC anode off-gas reduces the combustion gas temperature of the FC burner. Therefore, the temperature of the cold-side outlet stream of the HX1 should be high enough to achieve the desired SOFC cathode inlet temperature, which requires an increase in the effectiveness of HX1. On the other hand, high HX1 effectiveness decreases the turbine inlet temperature, leading to reduced power outputs of the GT and ORC. The MCFC cannot be operated with an SCR greater than 2.7 because of the lack of CO2 required for the MCFC reaction. In addition, HX1 is not required at SCR less than 1.95 due to the increase in the CDT. Consequently, the design range for SCR is 1.95 to 2.7, where the highest efficiency is 76.74%, and the efficiency difference variation is within 1.0% p.

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4.1.3 Reformed fuel supply ratio The relationship between FSR and the system efficiency is shown in Fig. 7. The cold-side inlet and outlet temperatures of HX1 (lines 2 and 3) are shown as well. An increase in the FSR to the SOFC increases the unreacted fuel flow at the cell stack, leading to a rise in the combustion gas temperature at the FC burner. Thus, to maintain the cathode inlet temperature of the fuel cell, the cold-side outlet temperature of HX1 should be kept low. An increase in the combustion gas flow decreases the intake of air because the MCFC cathode inlet flow is designed to be kept constant. A decrease in the intake air supply reduces the SOFC cathode inlet flow and thus requires less fuel supply, which reduces the SOFC power output. A decrease in the FSR to the SOFC increases the pre-reformed fuel flow to the anode of the MCFC, leading to an increase in the CO2 required for the MCFC cell reaction. Consequently, HX1 is not needed at FSR greater than 0.89 because of the decrease in the coldside outlet temperature of HX1. In addition, system design is not possible at FSR under 0.855 because the CO2 utilization factor of the MCFC should not exceed 1.0. As the FSR to the SOFC decreases, the power output of all components decreases due to the reduced intake air supply. However, the variation in system efficiency is only 0.37%p.

4.1.4 Operation temperature and cell voltage The effect of the operating temperature and cell voltage of the SOFC on the system efficiency was also analyzed. In the MCFC, the cathode inlet flow rate and the active cell area of the MCFC are always maintained during the parametric analysis because we simulated a commercial MCFC. As a result, the variation in the MCFC voltage is marginal. Therefore, the effect of the MCFC voltage variation is not included in our parametric analysis. The results of the analysis are shown in Fig. 8 and Fig. 9. An increase in the operating

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temperature and voltage of the fuel cell improves the system efficiency significantly, which is already well known. An increase in the SOFC operating temperature increases the MCFC operating temperature (line 9) and turbine inlet temperature (line 12), leading to an improvement in the system efficiency. However, the maximum operating temperature of the SOFC is set at 850°C because the high operating temperature poses a major problem for the cost and stability of SOFC components [62]. The minimum operating temperature of the SOFC is set at 750°C because of the reduction in system efficiency. Increases in the SOFC voltage require more fuel supply to maintain the SOFC operating temperature. Thus, the combustion gas temperature of the FC burner is increased due to the increase in the unreacted fuel supply. A low voltage makes it difficult to increase the CDT because it reduces the combustion gas temperature. The system efficiency varies with the SOFC operating temperature and cell voltage by 1.85%p and 5.96%p, respectively.

4.1.5 Fuel utilization Fig. 10 shows the influence of the SOFC fuel utilization factor on system efficiency. The cold-side inlet/outlet (lines 2 and 3) and hot side inlet/outlet (lines 9 and 10) temperatures of HX1 are shown as well. The increase in the fuel utilization factor requires a reduction in the fuel supply to maintain the SOFC operating temperature. A decrease in the fuel supply reduces the unreacted fuel flowing into the FC burner, which leads to an increase in the air supply for maintaining the MCFC cathode inlet flow. Therefore, more heat is required to increase the temperature of the air supply. The temperature of the hot-side outlet (line 10) should not decrease below that of the coldside inlet (line 2), so it is impossible to install HX1 when the SOFC fuel utilization factor is over 0.76. The combustion gas temperature of the FC burner (line 7) increases due to the increase in the unreacted fuel supply as the fuel utilization factor decreases. The increase in the

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combustion gas temperature reduces the cold-side outlet temperature of HX1 (line 3). Thus, HX1 cannot be installed when the fuel utilization factor is less than 0.66. A decrease in the fuel supply due to an increase in the fuel utilization factor reduces the MCFC operating temperature (line 9) and turbine inlet temperature (line 12), which leads to a reduction in the power of each component. Therefore, an increase in the fuel utilization factor reduces the net power output and system efficiency.

4.1.6 Cathode off-gas supply ratio The variation in the system efficiency with respect to CSR is shown in Fig. 11. An increase in the CSR decreases the combustion gas temperature because of the reduction in air supply to preserve the MCFC inlet flow and the decrease in fuel supply to maintain the SOFC operating temperature. However, unlike the previous case, a decrease in the combustion gas temperature reduces the cold-side outlet temperature of HX1 (line 3). Therefore, HX1 cannot be installed at CSR above 0.56. A decrease in CSR increases the amount of air and fuel supply, which also increases the MCFC operating temperature (line 9). The MCFC operating temperature is beyond 653oC at a CSR of 0.34 or less. The CSR affects the system power output depending on the fuel supply, but the efficiency difference is only 0.19%p.

4.1.7 Anode off-gas recirculation ratio Lastly, Fig. 12 shows the effect of the ARR and the MCFC operating temperature (line 9) on the system efficiency. The MCFC operating temperature is the lowest when the MCFC anode off-gas recirculation ratio is 0.5. The increase in the ARR increases the reacted fuel flow, which increases the MCFC operating temperature at ARR greater than 0.5. The decrease in the ARR also increases the MCFC operating temperature when ARR becomes less than 0.5 due to the decreases in the fraction of CO2 and H2O in the anode inlet stream. In addition, the increase

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in the ARR increases the reacted fuel supply to the MCFC anode. Thus, the MCFC power output is improved, but the GT and ORC power decrease due to the decrease in the turbine inlet temperature. The decrease in the ARR shows a reverse tendency. Consequently, the quadruple system has a maximum efficiency of 76.69% at an ARR of 0.65.

4.2. Performance optimization 4.2.1 Result of performance optimization The performance optimization was conducted by using AI method with eight neurons in the input layer. This is the same as the number of major design parameters used for the parametric analysis. The output layer has 12 neurons, which represent the outcomes of the design performance analysis: the MCFC operating temperature, turbine inlet temperature, effectiveness of HX1, air supply mass flow, fuel supply mass flow, power outputs of the four components, power consumption, net power output, and system efficiency. These outputs are used for comparison with the thermodynamic simulation results for validation. The number of hidden neurons is determined through repetitive training of the network. The number of hidden neurons was varied from the sum of the number of input and output neurons to obtain the best agreement between the original thermodynamic calculation results and those of the ANN. As a result, 24 hidden neurons were selected. The function trained by the ANN was used as the fitness function of the GA. The fitness improves as the system efficiency increases. The ranges of each variable are the same as that of the parametric study in Section 4.1 and are summarized in Table 5. Table 6 presents the results of the system with the highest efficiency. The first column presents the results predicted by the AI method, and the second column presents the outcome of the thermodynamic calculation using the original HYSYS model with the design parameters obtained by the AI method. In Table 6, the design parameters in the upper nine rows until the

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MCFC operating temperature have values at which the system operates steadily, and after that, the performance is shown. The best efficiency predicted by the AI method is 78.01%, and the net power is 15.52 MW, while that of the thermodynamic calculation is 78.10% at 15.58 MW. The very good agreement of the final performance parameters between the two methods indicates the validity of the AI method. The properties at various locations in the quadruple system are illustrated in Table 7. The highest temperature in the quadruple system is only 846.2oC, which is the SOFC operating temperature. This moderate maximum temperature is an important advantage over recently proposed systems that operate at higher temperatures. For example, the highest temperature of a commercial H-class GTCC with a system efficiency of about 64% is over 1600oC, which occurs at the turbine inlet [63, 64]. A recently proposed triple power generation system has a system efficiency of 76.0% with a reduced turbine inlet temperature of 952.4oC [24], which is still higher than that of our system. The relatively high temperatures may cause material damage, and various efforts are needed to cool the materials to keep them stable. Accordingly, the stability of the quadruple system is expected to be higher because it operates at a relatively low temperature despite achieving a higher efficiency compared with reference systems.

4.2.2 Examination of the optimized quadruple system The performance of the optimized quadruple system was examined by comparison with other power systems. The integration of several sub-systems is configured differently according to the respective sub-systems and the way to combine them. Thus, the resulting performance is inevitably different. The power split of the quadruple system was compared with those of various integrated power systems that have been proposed recently, as shown in Fig. 13.

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The triple system [24] consists of an SOFC, GT, and ST. It uses an F-class GT, but the turbine inlet temperature of the GT needs to be much less than that of the original F-class GT for the efficiency optimization [24]. The polygeneration plant [43] includes an SOFC and an MCFC. The combination of an SOFC and a micro GT is called a hybrid power generation system [12]. The NGCC [20] is a conventional combined cycle system that combines an Fclass GT and an ST. The triple power generation [24] system and NGCC [20] are rated for hundreds of megawatts while the others are multi-megawatt systems. A few patterns were found from the comparison. The efficiency of a single SOFC is about 56%, and that of an MCFC is between 44 and 48%. The efficiency of the GT is 25% (MGT) to 44% (H-class). The efficiency of the ST for the combined cycle is around 19%. These are already well known. It is generally accepted that a higher power fraction of high-efficiency components results in higher system efficiency. This trend can also be found in Fig. 13, which compares the NGCC without the SOFC and the others with the SOFC. However, simply increasing the power split of the most efficient sub-system (the SOFC in this case) is not the absolute answer for achieving very high efficiency. The hybrid system and the polygeneration plant have an SOFC power fraction of more than 80% but are less efficient than the triple and quadruple power generation system, which has an SOFC power split of 70%. Another important factor in achieving very high efficiency is the maximization of energy utilization by introducing extra sub-systems (i.e., by increasing the number of sub-systems). For example, the efficiency of the triple combined system is at least 10.4% higher than those of dual combined cycles, such as the NGCC, the hybrid system, and the polygeneration plant, where two power systems are combined. Of course, it is still true that a decrease in the power fraction of the less efficient sub-system improves efficiency. One example is the comparison of the 20% GT power split in the triple system and the result of 7.2% for the MGT power split in the quadruple system. As a result,

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the quadruple power generation system achieves impressively high efficiency by combining many components organically and having an appropriate (i.e., optimal) SOFC power split.

5. Conclusion This study has proposed a highly efficient quadruple power generation system that combines a solid oxide fuel cell, a molten carbonate fuel cell, a gas turbine, and an organic Rankine cycle. The impact of the design parameters on the performance was investigated. An artificial neural network and a genetic algorithm were used to obtain the highest system efficiency. 1. The parametric analysis revealed the design parameters that affect the system efficiency. The operating temperature, cell voltage, and fuel utilization factor of the solid oxide fuel cell have a significant influence on the system efficiency. The variation of the system efficiency with the cell voltage of the solid oxide fuel cell was up to 5.96%p. The variation in some parameters has a substantial influence on the inlet/outlet temperature of the first heat exchanger. 2. An artificial intelligence method combining an artificial neural network and a genetic algorithm significantly reduces the time needed to optimize the system efficiency. The highest efficiency of 78.0% was achieved by the artificial intelligence method. The optimization model was verified, and the result was only 0.12% different from the efficiency predicted by the thermodynamic analysis. 3. The performance characteristics of the quadruple system were analyzed by comparison with several high-efficiency systems. The results emphasize that the highest temperature in the quadruple system is only 846.2oC, and the energy utilization is maximized by increasing the number of sub-systems. The most significant finding from our study is that the performance optimization method combining an artificial neural network and a genetic algorithm dramatically decreases the

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calculation time to optimize the system performance, and a very high system efficiency of 78.1% can be achieved by the proposed system.

Acknowledgments This work was supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (No. 20194030202340) and also by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B4006859).

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Cycles (ORCs), Renewable Energy 36(2) (2011) 659-670. [54] J.Y. Lee, J.H. Lee, T.S. Kim, Thermo-economic analysis of using an organic Rankine cycle for heat recovery from both the cell stack and reformer in a PEMFC for power generation, International Journal of Hydrogen Energy 44(7) (2019) 3876-3890. [55] I.S. Kim, T.S. Kim, J.J. Lee, Off-design performance analysis of organic Rankine cycle using real operation data from a heat source plant, Energy conversion and management 133 (2017) 284-291. [56] I. Vankeirsbilck, B. Vanslambrouck, S. Gusev, M. De Paepe, Organic Rankine cycle as efficient alternative to steam cycle for small scale power generation, HEFAT (2011) 785792. [57] S. Wiśniewski, A. Borsukiewicz-Gozdur, The influence of vapor superheating on the level of heat regeneration in a subcritical ORC coupled with gas power plant, Archives of Thermodynamics 31(3) (2010) 185-199. [58] S. Quoilin, M. Van Den Broek, S. Declaye, P. Dewallef, V. Lemort, Techno-economic survey of Organic Rankine Cycle (ORC) systems, Renewable and Sustainable Energy Reviews 22 (2013) 168-186. [59] J. Heaton, Introduction to neural networks with Java, Heaton Research, Inc. (2005). [60] Mathworks, MATLAB R2018b. [61] S.K. Park, T.S. Kim, Comparison between pressurized design and ambient pressure design of hybrid solid oxide fuel cell–gas turbine systems, Journal of Power Sources 163(1) (2006) 490-499. [62] T.M. Gür, Comprehensive review of methane conversion in solid oxide fuel cells: prospects for efficient electricity generation from natural gas, Progress in Energy and Combustion Science 54 (2016) 1-64. [63] GE

power.

Available

from:

- 38 -

https://www.ge.com/content/dam/gepower-

pgdp/global/en_US/documents/product/gas%20turbines/Fact%20Sheet/2017-prodspecs/9ha-power-plants.pdf [Accessed 14. Nov. 2019]. [64] SIEMENS. Available from: https://new.siemens.com/global/en/products/energy/powergeneration/gas-turbines/sgt6-9000hl.html [Accessed 14. Nov. 2019].

- 39 -

List of Tables Table 1

Design parameters of the SOFC module

Table 2

Performance of the MCFC system

Table 3

Validation of the fuel cell modeling by simulating a polygeneration plant

Table 4

Design parameters of the quadruple power generation system

Table 5

Upper and lower bound of input parameters for optimization

Table 6

Performance of the quadruple power generation system

Table 7

Properties at various locations in the quadruple power generation system

- 40 -

Table 1

Design parameters of the SOFC module Parameters Anode transfer coefficient, β Cell length [m], L Cell width [m], W Pre-exponential factor of exchange current 6.54e+011 density [A/m2], k Activation energy of exchange current 1.40e+005 density [J/mol], Ea 3.66e-005 Effective diffusivity coefficient [m2/s], Deff 5.0e-004 Thickness [m], τ

Table 2

Performance of the MCFC system Parameters Ref.[26] Model System inlet water flow rate [kg/s] 0.5572 System inlet water pressure [kPa] 392.3 System inlet fuel flow rate [kg/s] 0.1179 Cathode inlet air flow rate [kg/s] 5.893 Cathode inlet temperature [oC] 593.8 o Anode and cathode outlet temp. [ C] 652.4 Cell voltage, E [V] 0.7273 Activate area, A [m2] 2.330 Fuel utilization, Uf [-] 0.7 System exhaust gas temperature [oC] 358±25 308.4 System exhaust flow rate [kg/s] 4.685 4.865 Pressure losses [%] 2.0 97.0 Inverter efficiency, ηconv [%] MCFC Power, ẆMCFC [kW] 2,741 System net power, Ẇnet [kW] 2,650±150 2,653 47.0±2 45.76 System efficiency, ηsysem [%] - 41 -

Electrolyte 0.5 0.4 0.1

Cathode

-

2.35e+011

-

1.37e+005

2.0e-005

1.37e-005 5.0e-005

Validation of the fuel cell modeling by simulating the polygeneration plant [39] Parameters Ref. Model Fuel inlet (LHV) [MW] 13,419 13,419 Cathode inlet temperature [oC] 735.0 o Anode and cathode outlet temp. [ C] 800.0 Cell voltage, E [V] 0.86 Fuel utilization, Uf, SOFC [-] 0.672 Cathode inlet temperature [oC] 575.0 Cell voltage, E [V] 0.799 Fuel utilization, Uf, MCFC [-] 0.7 SOFC net AC power [MW] 7.944 7.942 MCFC net AC power [MW] 1.401 1.401 Auxiliaries electric power [MW] -0.21 -0.20 Net electric power [MW] 9.13 9.14 H2 production [kg/day] 254.98 253.3 Net electric efficiency [%] 68.05 68.14

MCFC

SOFC

Table 3

Table 4

Design parameters of a quadruple power generation system Parameters Values Pressure ratio of the gas turbine compressor [-]* 17.0 Steam carbon ratio [-]* 2.0 Pre-reformed fuel supply ratio to the SOFC [-]* 0.87 Operating temperature of the SOFC (ΔT=65) [oC]* 800.0 366.1 Current density of the SOFC, 𝐽 [A/m2] * 0.86 Cell voltage of the SOFC, 𝐸 [V]* Fuel utilization factor of the SOFC, Uf, SOFC [-]* 0.672 SOFC cathode off-gas supply ratio to the FC burn. [-]* 0.5 MCFC anode off-gas recirculation ratio [-]* 0.6 Compressor polytropic efficiency [%] 88.0 Turbine polytropic efficiency [%] 88.0 ORC turbine isentropic efficiency [%] 75.0 o ORC pinch point temperature difference [ C] 10 o ORC condensing temperature [ C] 40.0 Heat loss [%] 1.0 Pressure loss [%] 1.5 ~ 4.0 Mechanical efficiency [%] 94.0 ORC motor efficiency [%] 95.0 Generator efficiency [%] 98.5 *Initial value for parametric analysis - 42 -

Table 5 Upper and lower bound of input parameters for optimization Bounds Parameters Lower Upper Pressure ratio of the gas turbine compressor [-] 6.0 17.0 Steam carbon ratio [-] 1.95 2.70 Pre-reformed fuel supply ratio to the SOFC [-] 0.855 0.890 Operating temperature of the SOFC [oC] 750 850 Cell voltage of the SOFC [V] 0.825 0.864 Fuel utilization factor of the SOFC [-] 0.66 0.76 SOFC cathode off-gas supply ratio to the FC 0.34 0.56 burner [-] MCFC anode off-gas recirculation ratio [-] 0.37 0.75 Operating temperature of the MCFC [oC] 600.0 655.0 HX1 effectiveness [-] 0.05 0.9

Table 6 Performance of the quadruple power generation systems Parameters AI Model Pressure ratio of the gas turbine compressor [-] 17.20 Steam carbon ratio [-] 2.022 Pre-reformed fuel supply ratio to the SOFC [-] 0.862 Operating temperature of the SOFC [oC] 846.2 Cell voltage of the SOFC [V] 0.863 Fuel utilization factor of the SOFC [-] 0.668 SOFC cathode off-gas supply ratio to the FC burner [-] 0.500 MCFC anode off-gas recirculation ratio [-] 0.606 Operating temperature of the MCFC [oC] 654.6 657.6 Fuel supply [kg/s] 0.428 0.429 Air supply [kg/s] 10.80 10.78 SOFC power [MW] 11.69 11.73 MCFC power [MW] 2.207 2.228 GT power [MW] 1.129 1.122 ORC power [MW] 0.579 0.582 Fuel recirculation power consumption [MW] 0.085 0.086 System net power [MW] 15.52 15.58 System electric efficiency [%] 78.01 78.10 - 43 -

Table 7 Properties at various locations in the quadruple power generation system #

 [kg/s] T [oC] P [kPa] m

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

15.0 429.2 434.6 781.2 846.2 846.2 1,150 593.8 657.6 647.4 740.4 776.8 324.8 89.51 846.2 15.0 370.4 743.2 651.8 651.8 846.2 846.2 846.2 651.8 588.3 670.0 670.0 539.2 567.5 670.0 205.8 117.2 60.17 40.00 40.17 69.93

101.3 1,720 1,694 1,618 1,594 1,594 1,530 1,507 1,477 1,455 1,455 1,397 103.4 101.3 1,594 2,000 1,911 1,800 1,746 1,746 1,694 1,694 1,694 1,746 1,746 1,720 1,720 1,619 1,878 1,720 833.4 8.483 8.399 8.316 850.2 841.7

10.78 10.78 10.78 10.78 9.619 4.811 5.893 5.893 4.995 4.995 9.804 11.21 11.21 11.21 4.808 0.429 0.429 3.667 3.667 3.159 4.321 3.238 1.083 0.508 2.672 3.570 2.164 2.164 2.164 1.406 4.808 4.808 4.808 4.808 4.808 4.808

Mole fraction Ar 0.0092 0.0092 0.0092 0.0092 0.0102 0.0102 0.0082 0.0082 00092 00092 0.0097 0.0086 0.0086 0.0086 0.0102 -

CH4

CnHm

CO

CO2

H2

H2O

N2

0.0003 0.0103 0.7729 0.0003 0.0103 0.7729 0.0003 0.0103 0.7729 0.0003 0.0103 0.7729 0.0003 0.0114 0.8560 0.0003 0.0114 0.8560 0.0753 0.1500 0.6898 0.0753 0.1500 0.6898 0.0044 0.1680 0.7725 0.0044 0.1680 0.7725 0.0025 0.0938 0.8121 0.0651 0.1312 0.7251 0.0651 0.1312 0.7251 0.0651 0.1312 0.7251 0.0003 0.0114 0.8560 0.8900 0.0811 0.0200 0.0089 0.8900 0.0811 0.0200 0.0089 0.1330 0.0121 0.0615 0.2363 0.1078 0.4456 0.0037 0.1245 0.0614 0.2469 0.1905 0.3730 0.0036 0.1245 0.0614 0.2469 0.1905 0.3730 0.0036 0.0002 0.0722 0.2743 0.1267 0.5238 0.0028 0.0002 0.0722 0.2743 0.1267 0.5238 0.0028 0.0002 0.0722 0.2743 0.1267 0.5238 0.0028 0.1245 0.0614 0.2469 0.1905 0.3730 0.0036 0.0319 0.0336 0.4683 0.0731 0.3908 0.0023 0.0001 0.0240 0.5444 0.0328 0.3969 0.0019 0.0001 0.0240 0.5444 0.0328 0.3969 0.0019 0.0001 0.0240 0.5444 0.0328 0.3969 0.0019 0.0001 0.0240 0.5444 0.0328 0.3969 0.0019 0.0001 0.0240 0.5444 0.0328 0.3969 0.0019 -

- 44 -

O2

Toluene

0.2073 0.2073 0.2073 0.2073 0.1220 0.1220 0.0766 0.0766 0.0459 0.0459 0.0820 0.0700 0.0700 0.0700 0.1220 -

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

List of Figures Fig. 1

Configuration of the quadruple power generation system

Fig. 2

Comparison between the modeled SOFC and reference models

Fig. 3

Artificial neuron model

Fig. 4

Sequence of the optimization to find the maximum system efficiency

Fig. 5

Variation in the system efficiency, compressor discharge temperature, and coldside outlet temperature of HX1 with pressure ratio

Fig. 6

Variation in the system efficiency, compressor discharge temperature, cold-side outlet temperature of HX1, and CO2 utilization factor of the MCFC with the steamcarbon ratio

Fig. 7

Variation in the system efficiency, compressor discharge temperature, cold-side outlet temperature of HX1, and CO2 utilization factor of the MCFC with the prereformed fuel supply ratio

Fig. 8

Variation in the system efficiency, MCFC operating temperature, and turbine inlet temperature with the SOFC operating temperature

Fig. 9

Variation in the system efficiency, compressor discharge temperature, and coldside outlet temperature of HX1 with the SOFC voltage

Fig. 10

Variation in the system efficiency, compressor discharge temperature, cold/hot-side outlet temperature of HX1, MCFC operating temperature, and turbine inlet temperature with the SOFC fuel utilization factor

Fig. 11

Variation in the system efficiency, compressor discharge temperature, cold-side outlet temperature of HX1, and the MCFC operating temperature with the SOFC cathode off-gas supply ratio

Fig. 12

Variation in the system efficiency and MCFC operating temperature with the MCFC anode off-gas recirculation ratio - 45 -

Fig. 13

Comparison of the power split of several systems

- 46 -

Fig.

1 Configuration of the quadruple power generation system

1.2 Hydrogen(Experiment [25]) Hydrogen (Modeling) Reformed fuel (Simulation [26]) Reformed fuel (Modeling)

1.0

Voltage [V]

0.8 0.6 0.4 0.2

o

SOFC operating temp. = 800 C 0.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

2

Current density [A/cm ]

Fig.

2 Comparison between the modeled SOFC and reference models

- 47 -

x1 Transfer function x2 ∑xiwi

 

yi

f

Outputs

Weights

xn Inputs

Fig.

Step 1: Parametric study (1) PRC (2) SCR (3) FSRSOFC (4) SOFCT (5) VSOFC (6) Uf (7) CSRSOFC (8) ARRMCFC

Aspen HYSYS

Input parameter set

3 Artificial neuron model

(1) MCFCT (2) TIT (3) ε HX1 (4) m fuel (5) m  air  SOFC (6) W  MCFC (7) W  GT (8) W  ORC (9) W  Aux. (10) W  net (11) W (12)  ηsys

Step 2: Machine learning (Artificial neural network) Hidden layer

Input layer

Output layer

1

1

Input parameter set

2







n-1

8

12 n

New model by ANN

Neurons

Output parameter set

Step 4: Validation (Model-based vs AI-based) Initial parameters

ANN

Input parameters HYSYS

Fig.

Output parameters vs

Output parameter set

1 2

2

Step 3: Optimization (Genetic algorithm) Initial parameters

Initialize population or Evaluation

Fitness function (ANN model)

Optimized parameters (input and output parameter set)

Output parameters

Mutation

Crossover

Selection

4 Sequence of the optimization to find the maximum system efficiency

- 48 -

480

76.8 sys

= 76.70 %

76.6

440

76.4

400

76.2

360

76.0

320

o

Temperature [ C]

Efficiency [%]



75.8 75.6 

75.4

280

System efficiency Temperature (line 2) Temperature (line 3) sys

240

= 75.52 %

5

10

15

20

200

Pressure ratio [-]

Fig.

5 Variation in the system efficiency, compressor discharge temperature, and cold-side outlet temperature of HX1 with pressure ratio

520

100 Temperature (line 2) Temperature (line 3) System efficiency CO utilization factor

95.0 90.0

= 98.0%

CO2

480

85.0 80.0

500

sys

= 76.74 %



sys

= 75.74 %

460

75.0

o



Temperature [ C]

Efficiency [%]

U

2

440 70.0 65.0

1.9

2.0

2.1

2.2

2.3

2.4

2.5

2.6

2.7

420

Steam-carbon ratio [-]

Fig. 6 Variation in the system efficiency, compressor discharge temperature, cold-side outlet temperature of HX1, and CO2 utilization factor of the MCFC with the steam-carbon ratio

- 49 -

455

100 99.9%

95.0

450 445

85.0 80.0



sys

= 76.82%

440

o

75.0

Temperature [ C]

Efficiency [%]

90.0



System efficiency CO utilization factor 2 Temperature (line 2) Temperature (line 3)

70.0 65.0

sys

= 76.45%

435 430

425 60.0 0.850 0.855 0.860 0.865 0.870 0.875 0.880 0.885 0.890 0.895 Pre-reformed fuel supply ratio to the SOFC [-]

Fig. 7 Variation in the system efficiency, compressor discharge temperature, cold-side outlet temperature of HX1, and CO2 utilization factor of the MCFC with the pre-reformed fuel supply ratio

800

78.0 sys

= 77.53 %

77.0

750

76.0

700

sys

= 75.68 %

650

75.0

74.0 740

o



System efficiency Temperature (line 9) Temperature (line 12)

Temperature [ C]

Efficiency [%]



760

780

800

820

840

600 860

o

SOFC operating temperature [ C]

Fig.

8 Variation in the system efficiency, MCFC operating temperature, and turbine inlet temperature with the SOFC operating temperature

- 50 -

520

78.0 sys

= 77.27 %

76.0

500

74.0

480 

= 71.31 %

460 System efficiency Temperature (line 2) Temperature (line 3)

70.0

68.0 0.820

0.840

0.830

0.850

o

72.0

sys

Temperature [ C]

Efficiency [%]



440

420 0.870

0.860

SOFC voltage [V]

Fig.

9 Variation in the system efficiency, compressor discharge temperature, and cold-side outlet temperature of HX1 with the SOFC voltage



sys

= 76.95 %

800

77.0

700

76.0

600

75.0

500

74.0

0.66

0.68

0.70

0.72

0.74

0.76

o

Efficiency [%]

78.0

900

Temperature [ C]

Temperature (line 2) Temperature (line 3) Temperature (line 10) Temperature (line 9) Temperature (line 12) System efficiency

79.0

400

SOFC fuel utilization factor [-]

Fig. 10 Variation in the system efficiency, compressor discharge temperature, cold/hot-side outlet temperature of HX1, MCFC operating temperature, and turbine inlet temperature with the SOFC fuel utilization factor

- 51 -

700

78.5

650

78.0 77.5



sys

550

o

77.0

600

System efficiency Temperature (line 2) Temperature (line 3) Temperature (line 9)

Temperature [ C]

Efficiency [%]

79.0



= 76.52%

sys

= 76.71%

500 450

76.5 76.0 0.30

0.35

0.40

0.45

0.50

0.55

400 0.60

SOFC cathode off-gas supply ratio to the fuel cell burner [-]

Fig. 11 Variation in the system efficiency, compressor discharge temperature, cold-side outlet temperature of HX1, and the MCFC operating temperature with the SOFC cathode offgas supply ratio

655

77.0 System efficiency 76.8

650

Temperature (line 9)

76.6



sys

= 76.69 % 

640 sys

= 76.62 %

635

76.4

o

630

76.2

Temperature [ C]

Efficiency [%]

645

625 76.0

620 

75.8 0.30

sys

0.40

= 75.87 %

0.50

0.60

0.70

615 0.80

MCFC anode off-gas recirculation ratio [-]

Fig.

12 Variation in the system efficiency and MCFC operating temperature with the MCFC anode off-gas recirculation ratio

- 52 -

1.4 1.2

Power split [-]

1.0 0.8 0.6

System efficiency: 76.0% 78.1% 0.037 0.072 0.142 0.749

68.1%

0.065

ORC ST GT 67.5%

0.150

0.130

0.850

0.870

MCFC SOFC 57.1% 0.358

0.200

0.735 0.642

0.4 0.2 0.0

Fig.

13

Quadruple system

Triple [22]

Polygen. [37]

MGT Hybrid F-class [12] NGCC [18]

Comparison of the power split of several systems

- 53 -

Research highlights 

A novel quadruple power system consisting of fuel cells and turbines is proposed.



Performance optimization to achieve efficiency much higher than 70% was conducted.



Thermodynamic calculation results are used to train an artificial neural network.



An AI method was used to drastically reduce the calculation time for optimization.

- 54 -

Author Contribution Statement  Ji Ho Ahn :

Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing

 Min Jae Kim :

Conceptualization, Methodology

 Yeon Woo Cho:

Software, Validation

 Tong Seop Kim : Conceptualization, Resources, Writing - Original Draft, Writing Review & Editing, Supervision, Project administration, Funding acquisition

- 55 -

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

- 56 -