# Optimization of an Organic Rankine Cycle System for an LNG-Powered Ship

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

_{x}(sulfur oxides), and NO

_{x}(nitrogen oxides) than any other fossil fuel [1,2,3]. Therefore, natural gas is considered the cleanest fuel among various fossil fuels, such as gasoline, diesel, kerosene, and coal.

_{4}) and propane (C

_{3}H

_{8}) as the WF and adopted a vapor absorption process to enhance the efficiency of the ORC system. Bao et al. investigated seven cycle configurations to recover cold energy from LNG using propane as the WF [14]. They optimized each cycle using three different objective functions and concluded that the combined cycle (direct expansion + ORC system) systems are the most efficient in recovering cold energy from LNG. Bao et al. also proposed a superstructure for the three-stage ORC system for LNG cold energy recovery [15]. They optimized the three-stage condensation ORC system with 12 different WFs and found that the arrangement of the compression process is not important, whereas that of the expansion process has a significant effect on the cycle performance. Tomków and Cholewiński proposed an ORC system coupled with an absorption cycle using a mixture of ethane and krypton as the WF [16]. They concluded that the proposed cycle had the best performance compared to a simple ORC system and a Brayton cycle. Lee studied a cascade Rankine cycle and concluded that the cascade cycle, which uses ethane and propane as the WF, was the optimal cascade cycle for cold energy recovery from LNG [17]. Le et al. also studied an ORC system and found that pressure and thermal energy recovery through a combination of direct expansion and an ORC system using propane as the WF gives the best performance [18].

- ORC systems should be applicable to both high-pressure and medium-pressure dual-fuel engines.
- A configuration of an ORC system should be simple because the available space is small.
- ORC systems should be more economical than a typical LNG fuel supply system. To this end, ORC systems should have high exergy efficiency and net power output.

## 2. Methods

#### 2.1. System Description

#### 2.2. Simulation Model

#### 2.3. Optimization Framework

_{LNG}is the exergy of the LNG feed stream from the LNG storage tank and Ex

_{NG}is the exergy of the NG stream fed to the engine.

_{net}) by the system. To solve the optimization problem, the simulation model was connected to MATLAB using a component object model (COM) interface [28] (pp. 523–544), and the solution was found using the PSO algorithm [29,30,31,32,33,34] in MATLAB. The setting parameters of the PSO algorithm are listed in Table A2. All simulations and optimization are performed on a computer with Intel quad-core processors (4.2 GHz) and 16 GB RAM.

#### 2.4. Cost Estimation

_{1998}= 436 and CEPCI

_{2017}= 672, respectively. $\frac{CEPC{I}_{2017}}{CEPC{I}_{1998}}$ is used to adjust the cost to US dollars in 2017 because the equipment costs in previous research were based on data in 1998. For the sake of convenience, PC

_{LNGpump}was assumed to be the same as PC

_{pump}, and PC

_{vaporizer}was assumed to be the same as PC

_{condenser}.

## 3. Results and Discussion

#### 3.1. Optimization Results

#### 3.2. Cost Estimation Results

#### 3.3. Sensitivity Analysis

#### 3.3.1. Changes in Evaporation Temperatures

#### 3.3.2. Changes in Electricity Generation Costs

#### 3.4. Discussion

_{4}) and propane (C

_{3}H

_{8}) exhibits relatively good performance at low evaporation temperatures in the study of Liu and Guo [13], but this working fluid seems to be inappropriate for an LNG-powered ship because the required ORC system is very complex due to triple condensation levels. Some studies have shown remarkable results with propane as the working fluid for recovering LNG cold energy. However, Bao et al. used the ORC system with triple condensation levels [14], and Le et al. used two dual ORC systems in series [18].

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Nomenclature

AAC | actual annualized cost |

CEPCI | chemical engineering plant cost index |

CFOH | closed-feed organic fluid heater |

ECA | emission control areas |

GB | gigabyte |

GWP | global warming potential |

IMO | International Maritime Organization |

LHV | lower heating value |

LNG | liquefied natural gas |

MITA | minimum internal temperature approach |

NG | natural gas |

ORC | organic Rankine cycle |

PC | purchase cost |

PSO | particle swarm optimization |

RAM | random access memory |

WF | working fluid |

Symbols | |

ψ_{ex} | exergy efficiency |

Ex | exergy potential |

fr_{wf} | working fluid stream fraction ratio |

h_{0} | enthalpy |

h_{x} | enthalpy at 293 K |

m_{wr} | mass flow rate of working fluid (kg/s) |

P_{exp1} | expander discharge pressure at WF condenser (bar) |

P_{exp2} | expander discharge pressure at CFOH (bar) |

P_{LNG} | LNG pump discharge pressure (bar) |

P_{wf} | working fluid pump discharge pressure (bar) |

s_{0} | entropy |

s_{x} | entropy at 293 K |

SAC | simplified annualized cost (US dollars) |

T_{0} | temperature at 293 K |

T_{boil} | boiling temperature (K) |

T_{c} | critical temperature (K) |

T_{sup} | superheating temperature at WF evaporator (K) |

v_{f} | vapor fraction |

V_{cw} | volumetric flow rate of cooling water (m^{3}/h) |

W_{net} | net power output (kW) |

W_{pump} | power consumed by pumps |

W_{turbine} | power generated by turbines |

## Appendix A

**Table A1.**Properties of working fluids [40].

Working Fluid | Chemical Formula | T_{c} (K) | P_{c} (bar) | T_{boil} (K) | GWP (100-yr) |
---|---|---|---|---|---|

Methane (R-50) | CH_{4} | 190.6 | 46.1 | 111 | 25 |

Ethane (R-170) | C_{2}H_{6} | 305.3 | 49.1 | 184.6 | 5.5 |

Ethylene (R-1150) | C_{2}H_{4} | 282.5 | 50.6 | 169 | 3.7 |

Propane (R-1270) | C_{3}H_{8} | 369.9 | 42.5 | 231.1 | 1.8 |

n-Butane (R-600) | C_{4}H_{10} | 425 | 38 | 273 | 4.0 |

Krypton (R-784) | Kr | 209.5 | 55.2 | 119.8 | 0 |

R-152a | C_{2}H_{4}F_{2} | 386.5 | 45.2 | 248.5 | 124 |

R-32 | CH_{2}F_{2} | 351.3 | 57.8 | 221.4 | 675 |

R-41 | CH_{3}F | 317.4 | 58.8 | 195 | 92 |

Parameters | Value |
---|---|

Number of Particles | 14 per decision variable |

Max. Iteration | 150 |

Social-adjustment weight | 1.99 |

Self-adjustment weight | 0.99 |

Hybrid Function | Sequential Quadratic Programming |

Parameters | Value | |
---|---|---|

Vessel engine duty (kW) | 12000 | |

LNG pump duty (kW) | High-pressure engine: | 41.58 |

Medium-pressure engine: | 2.17 | |

Vaporizer area (m^{2}) | High-pressure engine: | 7.05 |

Medium-pressure engine: | 6.51 | |

Cooling water flow (m^{3}/h) | High-pressure engine: | 110.53 |

Medium-pressure engine: | 164.18 |

## References

- Ancona, M.A.; Bianchi, M.; Branchini, L.; De Pascale, A.; Melino, F.; Mormile, M.; Palella, M.; Scarponi, L.B. Investigation on small-scale low pressure LNG production process. Appl. Energy
**2017**, 227, 672–685. [Google Scholar] [CrossRef] - Kim, J.; Seo, Y.; Chang, D. Economic evaluation of a new small-scale LNG supply chain using liquid nitrogen for natural-gas liquefaction. Appl. Energy
**2016**, 182, 154–163. [Google Scholar] [CrossRef] - Lim, W.; Choi, K.; Moon, I. Current Status and Perspectives of Liquefied Natural Gas (LNG) Plant Design. Ind. Eng. Chem. Res.
**2013**, 52, 3065–3088. [Google Scholar] [CrossRef] - Seo, S.; Han, S.; Lee, S.; Chang, D. A pump-free boosting system and its application to liquefied natural gas supply for large ships. Energy
**2016**, 105, 70–79. [Google Scholar] [CrossRef] - Eise Fokkema, J.; Buijs, P.; Vis, I.F.A. An investment appraisal method to compare LNG-fueled and conventional vessels. Transp. Res. Part Transp. Environ.
**2017**, 56, 229–240. [Google Scholar] [CrossRef] - Geertsma, R.D.; Negenborn, R.R.; Visser, K.; Hopman, J.J. Design and control of hybrid power and propulsion systems for smart ships: A review of developments. Appl. Energy
**2017**, 194, 30–54. [Google Scholar] [CrossRef] - Aspelund, A.; Gundersen, T.; Myklebust, J.; Nowak, M.P.; Tomasgard, A. An optimization-simulation model for a simple LNG process. Comput. Chem. Eng.
**2010**, 34, 1606–1617. [Google Scholar] [CrossRef] - Wang, M.; Khalilpour, R.; Abbas, A. Thermodynamic and economic optimization of LNG mixed refrigerant processes. Energy Convers. Manag.
**2014**, 88, 947–961. [Google Scholar] [CrossRef] - Yamamoto, T.; Furuhata, T.; Arai, N.; Mori, K. Design and testing of the Organic Rankine Cycle. Energy
**2001**, 26, 239–251. [Google Scholar] [CrossRef] - Desai, N.B.; Bandyopadhyay, S. Process integration of organic Rankine cycle. Energy
**2009**, 34, 1674–1686. [Google Scholar] [CrossRef] - Kajurek, J.; Rusowicz, A.; Grzebielec, A.; Bujalski, W.; Futyma, K.; Rudowicz, Z. Selection of refrigerants for a modified organic Rankine cycle. Energy
**2019**, 168, 1–8. [Google Scholar] [CrossRef] - Astolfi, M.; Fantolini, A.M.; Valenti, G.; De Rinaldis, S.; Inglese, L.D.; Macchi, E. Cryogenic ORC To Enhance The Efficiency Of LNG Regasification Terminals. Energy Procedia
**2017**, 129, 42–49. [Google Scholar] [CrossRef] - Liu, Y.; Guo, K. A novel cryogenic power cycle for LNG cold energy recovery. Energy
**2011**, 36, 2828–2833. [Google Scholar] [CrossRef] - Bao, J.; Lin, Y.; Zhang, R.; Zhang, N.; He, G. Effects of stage number of condensing process on the power generation systems for LNG cold energy recovery. Appl. Therm. Eng.
**2017**, 126, 566–582. [Google Scholar] [CrossRef] - Bao, J.; Zhang, R.; Lin, Y.; Zhang, N.; Zhang, X.; He, G. Simultaneous optimization of system structure and working fluid for the three-stage condensation Rankine cycle utilizing LNG cold energy. Appl. Therm. Eng.
**2018**, 140, 120–130. [Google Scholar] [CrossRef] - Tomków, L.; Cholewiński, M. Improvement of the LNG (liquid natural gas) regasification efficiency by utilizing the cold exergy with a coupled absorption—ORC (organic Rankine cycle). Energy
**2015**, 87, 645–653. [Google Scholar] [CrossRef] - Lee, S. Multi-parameter optimization of cold energy recovery in cascade Rankine cycle for LNG regasification using genetic algorithm. Energy
**2017**, 118, 776–782. [Google Scholar] [CrossRef] - Le, S.; Lee, J.-Y.; Chen, C.-L. Waste cold energy recovery from liquefied natural gas (LNG) regasification including pressure and thermal energy. Energy
**2018**, 152, 770–787. [Google Scholar] [CrossRef] - LNG Fuel Gas Systems. Available online: https://marine.man-es.com/technology/marine-fuel-gas-systems/lng-fuel-gas-systems (accessed on 6 May 2019).
- Wärtsilä LNGPac—Fuel Gas Supply System. Available online: https://www.wartsila.com/products/marine-oil-gas/gas-solutions/lng-solutions/wartsila-lngpac (accessed on 6 May 2019).
- Rochyana, M.F.; Jinca, M.Y.; Siahaya, J. MDO and LNG as Fuels (Duel Fuel) to Support Sustainable Maritime Transport (A Case Study in KM. Ciremai). Int. Refereed J. Eng. Sci.
**2014**, 3, 32–38. [Google Scholar] - Wang, E.; Zhang, H.; Fan, B.; Wu, Y. Optimized performances comparison of organic Rankine cycles for low grade waste heat recovery. J. Mech. Sci. Technol.
**2012**, 26, 2301–2312. [Google Scholar] [CrossRef] - MAN Energy Solutions. Available online: https://marine.man-es.com/applications/projectguides/2stroke/content/epub/S50ME-C8_2-GI.pdf (accessed on 6 May 2019).
- The Motor Ship. Available online: https://www.motorship.com/__data/assets/pdf_file/0029/619229/X-DF-engines-brochure.pdf (accessed on 8 February 2019).
- Park, K.; Oh, S.-R.; Won, W. Techno-economic optimization of the integration of an organic Rankine cycle into a molten carbonate fuel cell power plant. Korean J. Chem. Eng.
**2019**, 36, 345–355. [Google Scholar] [CrossRef] - Aghahosseini, S.; Dincer, I. Comparative performance analysis of low-temperature Organic Rankine Cycle (ORC) using pure and zeotropic working fluids. Appl. Therm. Eng.
**2013**, 54, 35–42. [Google Scholar] [CrossRef] - Wang, E.H.; Zhang, H.G.; Fan, B.Y.; Ouyang, M.G.; Zhao, Y.; Mu, Q.H. Study of working fluid selection of organic Rankine cycle (ORC) for engine waste heat recovery. Energy
**2011**, 36, 3406–3418. [Google Scholar] [CrossRef] - Sahraei, M.H.; Ricardez-Sandoval, L.A. An Integration Framework for CO
_{2}Capture Processes. In Materials and Process Systems for CO_{2}Capture: Modeling, Design, Control and Integration, 1st ed.; Papadopoulos, A.I., Seferlis, P., Eds.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 523–544. [Google Scholar] - Park, K.; Won, W.; Shin, D. Effects of varying the ambient temperature on the performance of a single mixed refrigerant liquefaction process. J. Nat. Gas Sci. Eng.
**2016**, 34, 958–968. [Google Scholar] [CrossRef] - Tian, D.; Shi, Z. MPSO: Modified particle swarm optimization and its applications. Swarm Evolut. Comput.
**2018**, 41, 49–68. [Google Scholar] [CrossRef] - Xu, G.; Yu, G. On convergence analysis of particle swarm optimization algorithm. J. Comput. Appl. Math.
**2018**, 333, 65–73. [Google Scholar] [CrossRef] - Godio, A.; Santilano, A. On the optimization of electromagnetic geophysical data: Application of the PSO algorithm. J. Appl. Geophys.
**2018**, 148, 163–174. [Google Scholar] [CrossRef] - Chen, Y.; Li, L.; Xiao, J.; Yang, Y.; Liang, J.; Li, T. Particle swarm optimizer with crossover operation. Eng. Appl. Artif. Intell.
**2018**, 70, 159–169. [Google Scholar] [CrossRef] - Li, Y.; Li, Y.; Li, G.; Zhao, D.; Chen, C. Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process. Energy
**2018**, 147, 286–296. [Google Scholar] [CrossRef] - Li, Y.; Wang, J.; Zhao, D.; Li, G.; Chen, C. A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making. Energy
**2018**, 162, 237–254. [Google Scholar] [CrossRef] - Douglas, J.M. Conceptual Design of Chemical Processes, 1st ed.; McGraw-Hill, Inc.: New York, NY, USA, 1988; pp. 37–42. [Google Scholar]
- Lee, U.; Jeon, J.; Han, C.; Lim, Y. Superstructure based techno-economic optimization of the organic rankine cycle using LNG cryogenic energy. Energy
**2017**, 137, 83–94. [Google Scholar] [CrossRef] - Saito, N. The Economic Analysis of Commercial Ships with Hydrogen Fuel Cell through Case Studies. Master’s Thesis, World Maritime University, Malmö, Sweden, 2018. [Google Scholar]
- Boveri, A.; Silvestro, F.; Gualeni, P. Ship electrical load analysis and power generation optimization to reduce operational costs. In Proceedings of the 2016 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference, Toulouse, France, 2–4 November 2016; IEEE: New York, NY, USA; pp. 1–6. [Google Scholar]
- Linstrom, P. NIST Chemistry WebBook, NIST Standard Reference Database 69; Stanford Library: Stanford, CA, USA, 1997. [Google Scholar]

**Figure 1.**Schematics of (

**a**) a typical liquefied natural gas (LNG) fuel supply unit, and (

**b**) an LNG fuel supply unit using engine jacket water.

**Figure 2.**Organic Rankine cycle (ORC) systems for the high-pressure dual fuel engine system [Type (

**a**), (

**b**), and (

**c**)] and ORC systems for the medium-pressure dual fuel engine system [Type (

**d**), (

**e**), and (

**f**)]. Diamond symbols identify stream segments of fluids.

**Figure 3.**Convergence curve for Type (f) when the working fluid is propane. (Convergence time = 146.1 min).

**Figure 7.**Temperature-entropy diagrams. (

**a**) Type (d), (

**b**) low-temperature condensation level in Type (e), (

**c**) high-temperature condensation level in Type (e).

Decision Variables | Units | Types | Lower Bound | Upper Bound |
---|---|---|---|---|

WF mass flow rate (m _{wf}) | kg/s | (A), (B), (C) (D), (E), (F) | 0.028 | 1.67 |

Super heating temperature of the WF evaporator (T _{sup}) | K | (A), (B), (C) (D), (E), (F) | 273 | 443 |

WF pump discharge pressure (P _{wf}) | bar | (A), (B), (C) (D), (E), (F) | 2 | Critical point |

WF expander discharge pressure to the WF condenser (P _{exp1}) | bar | (A), (B), (C) (D), (E), (F) | 1.3 | Critical point |

WF expander discharge pressure to the CFOH (P _{exp2}) | bar | (B), (C) (E), (F) | 1.3 | 30 |

WF mass fraction to the CFOH (fr _{wf}) | - | (B), (C) (E), (F) | 0 | 0.3 |

LNG pump discharge pressure (P _{LNG}) | Bar | (D), (E), (F) | 17 | 80 |

Parameters | Values | |
---|---|---|

Expander isentropic efficiency [14,15] | 0.8 | |

Pump isentropic efficiency [14,15] | 0.8 | |

Ambient temperature (K) | 293 | |

Hot source temperature for the evaporator (K) | 298 | |

Engine jacket water temperature (K) [23] | 353 | |

Minimum approach temperature in heat exchangers (K) | 2 | |

Pressure drop of heat exchangers (bar) | 0.2 | |

Property package | Peng-Robinson | |

Inlet LNG pressure (bar) | 1.01325 | |

Inlet LNG temperature (K) | saturated | |

Inlet LNG mass flow rate (kg/s) | High-pressure engine [23] | 0.487 |

Medium-pressure engine [24] | 0.474 | |

LNG composition (mol. %) [7,25] | N_{2}: 0.37C _{1}: 95.89C _{2}: 2.96C _{3}: 0.72nC _{4}: 0.06 | |

NG send-out pressure (bar) | High-pressure engine [23] | 299.6 |

Medium-pressure engine [24] | 16.6 | |

NG send-out temperature (K) | 293 |

WF | m_{wf}(kg/s) | T_{sup}(K) | P_{wf}(bar) | P_{exp}_{1}(bar) | P_{exp}_{2}(bar) | fr_{wf} | P_{LNG}(bar) | |
---|---|---|---|---|---|---|---|---|

Type a | Propane (R-1270) | 0.415 | 0.000 | 9.251 | 1.300 | - | - | - |

Type b | Propane (R-1270) | 0.489 | 0.020 | 9.426 | 3.745 | 1.301 | 0.152 | - |

Type c | Propane (R-1270) | 0.533 | 23.973 | 19.175 | 5.982 | 1.300 | 0.224 | - |

Type d | Propane (R-1270) | 0.687 | 0.008 | 9.249 | 1.300 | - | - | 64.288 |

Type e | Propane (R-1270) | 0.820 | 0.001 | 9.450 | 3.748 | 1.301 | 0.153 | 61.647 |

Type f | Propane (R-1270) | 0.901 | 23.435 | 19.393 | 5.745 | 1.300 | 0.217 | 57.875 |

Type | Simplified Annualized Costs ^{1}(US dollars/year) | Produced Electricity (kW) | Estimated Cost Saving ^{2}(US dollars/year) | Actual Annualized Costs (US dollars/year) |
---|---|---|---|---|

LNG fuel supply system (High-pressure) | 52,449 | 0 | 0 | 52,449 |

ORC Type (a) (WF = propane) | 84,691 | 24.1 | 21,112 | 63,579 |

ORC Type (b) (WF = propane) | 102,656 | 28.7 | 25,141 | 77,515 |

ORC Type (c) (WF = propane) | 108,267 | 44.4 | 38,894 | 69,373 |

LNG fuel supply system (Medium-pressure) | 42,008 | 0 | 0 | 42,008 |

ORC Type (d) (WF = propane) | 111,597 | 82.4 | 72,182 | 39,415 |

ORC Type (e) (WF = propane) | 132,244 | 90.2 | 79,015 | 53,229 |

ORC Type (f) (WF = propane) | 141,154 | 116.8 | 102,316 | 38,838 |

References | Working Fluid | Exergy Efficiency (%) | Evaporation Temperature (K) | Net Power Output (kW) |
---|---|---|---|---|

This study | Propane | 40.7 | 353 | 116.8 246.4 ^{3} |

[12] | R41 | N/A | 276–303 | 3250–3413 ^{1} |

[13] | CF_{4} + C_{3}(mixture) | 23.5 | 293 | 206.42 ^{3} |

[14] | Propane | N/A | 284 | 3134–8598 104.5–286.6 ^{3} |

[16] | Ethane + Krypton (mixture) | 3.3 | 277 | 9200 57.5 ^{3} |

[17] | Ethane + Propane (cascade) | 11.1 ^{2} | 288 | 96.1 ^{3} |

[18] | Propane | 26 | 303 | 215^{3} |

^{1}Total power output of ORC systems.

^{2}Thermal efficiency.

^{3}Based on 1 kg/s mass flow rate of LNG.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Koo, J.; Oh, S.-R.; Choi, Y.-U.; Jung, J.-H.; Park, K.
Optimization of an Organic Rankine Cycle System for an LNG-Powered Ship. *Energies* **2019**, *12*, 1933.
https://doi.org/10.3390/en12101933

**AMA Style**

Koo J, Oh S-R, Choi Y-U, Jung J-H, Park K.
Optimization of an Organic Rankine Cycle System for an LNG-Powered Ship. *Energies*. 2019; 12(10):1933.
https://doi.org/10.3390/en12101933

**Chicago/Turabian Style**

Koo, Jamin, Soung-Ryong Oh, Yeo-Ul Choi, Jae-Hoon Jung, and Kyungtae Park.
2019. "Optimization of an Organic Rankine Cycle System for an LNG-Powered Ship" *Energies* 12, no. 10: 1933.
https://doi.org/10.3390/en12101933