Economic Feasibility of Power Generation by Recovering Cold Energy

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Economic Feasibility of Power Generation by Recovering Cold Energy during LNG (Liquefied Natural Gas) Regasification Arnab Dutta, Iftekhar A Karimi, and Shamsuzzaman Farooq ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b02020 • Publication Date (Web): 25 Jun 2018 Downloaded from http://pubs.acs.org on July 1, 2018

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ACS Sustainable Chemistry & Engineering

Economic Feasibility of Power Generation by Recovering Cold Energy during LNG (Liquefied Natural Gas) Regasification Arnab Dutta, Iftekhar A Karimi*, Shamsuzzaman Farooq* Department of Chemical and Biomolecular Engineering National University of Singapore, 4 Engineering Drive 4, Singapore 117585 Abstract LNG has emerged as the leading option for global natural gas trade. Imported LNG must be regasified at the receiving terminal. The practice of using seawater as the heat source for regasification is a sheer waste of the available cold energy in LNG. In this study, power generation from LNG cold energy is investigated to reverse this wastage. We have developed a superstructure for this power generation process (PGP) that includes direct expansion of LNG and Organic Rankine Cycle (ORC) with simultaneous selection of the working fluid components and their compositions. Using a simulation-based optimization paradigm, the economic viability of the PGP is investigated by maximizing its net present value (NPV). Our results show that although a PGP with both direct expansion and ORC has a higher exergy efficiency, its NPV is nearly 64% lower than that of a PGP with ORC only. We present case studies for various combinations of process parameters and found that LNG regasification pressure has the maximum impact on NPV of the PGP followed by LNG feed temperature and composition. The maximum-NPV PGP uses only ORC and produces a net power of about 0.5 – 12.9 kW/t-LNG after satisfying the power demand of the LNG regasification process. The NPV is about 6.87 – 2.45 million$ thus, power generation by recovering cold energy in an LNG regasification terminal is economically viable. We also present a robust process design strategy to handle uncertainties in LNG composition. Keywords LNG cold energy, Organic Rankine Cycle, Power generation, Process optimization, Net present value, Design under uncertainty

*

Corresponding authors: Email: [email protected] (Karimi); [email protected] (Farooq). Tel: +65 6516-6359 (Karimi); +65 6516-6545 (Farooq).

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Introduction A shift towards cleaner energy resources is urgently needed to combat global warming. An immediate solution is to make natural gas (NG), the cleanest fossil fuel, a major energy source worldwide, especially for power generation. Indeed, the shale gas boom in the United States and discovery of new NG reserves in other gas rich countries are already facilitating this paradigm shift. The worldwide consumption of NG is projected to increase from 120 trillion cubic feet (Tcf) in 2012 to 203 Tcf in 2040.1 Just like other fossil fuels, NG reserves are unevenly distributed in geographically diverse locations and NG trading requires long distance transportation. Countries endowed with surplus NG reserves export it as Piped Natural Gas (or PNG) via pipeline, CNG (Compressed Natural Gas) in high-pressure cylinders, or LNG (Liquefied Natural Gas) in cryogenic tankers. Among these alternatives, LNG is the preferred mode for long distance transport (typically above 3500 km) since liquefaction decreases the volume of NG by a factor of about 600.2 The annual growth rate of LNG trade has been nearly twice as of PNG, with LNG accounting for about 10% of global NG consumption and 31% of global NG trade.1 As more countries opt for LNG as the energy source, particularly where PNG is not a feasible option owing to economic or geographical limitations, LNG trade is projected to increase from about 12 Tcf in 2012 to 29 Tcf in 2040.1 Given the number of terminals (export or liquefaction and import or regasification) operating or under construction worldwide, LNG seems destined to dominate the global NG trade. To produce LNG, NG (post extraction and gas processing) is liquefied using a refrigeration cycle.3,4 At the receiving terminal, regasification or vaporization of LNG is normally achieved using seawater.5 Although seawater is a free heat source, large volumes are required, and pumping incurs significant electrical energy. Cryogenic LNG is a rich source of cold energy, which is completely wasted when sea water is used for its regasification.2 Instead of dumping into the


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sea, a sustainable approach should be to recover the available cold energy during the LNG regasification process. Kanbur et al.6 presented a comprehensive review of processes that can benefit from utilizing the LNG cold energy. In this context, power generation from cold energy recovery has gained significant interest. The LNG cold energy can be used within an Organic Rankine Cycle (ORC) to generate power before LNG regasification. Sun et al.7 performed an exergy-based optimization of LNG cold to power process. Bao et al. 8 optimized the power generation process by maximizing its net power output. However, both these studies considered pure working fluids within the ORC. LNG being a mixture of hydrocarbons undergoes nonisothermal evaporation. As a result, multicomponent working fluids reduce exergy losses in an ORC owing to its nonisothermal evaporation compared to pure working fluids.9 Liu and Gao10 proposed a power cycle using binary mixtures coupled with a vapor absorption unit to increase energy efficiency. Ghaebi et al.11 presented a parametric study from an exergoeconomic viewpoint to recover waste heat and waste cold in an integrated process. The authors used ammonia-water mixture as the working fluid. However, they did not explicitly define the source of the waste heat. Lee and Han12 performed an exergy based optimization and proposed a multicomponent working fluid ORC to utilize the LNG cold energy integrated with a post-combustion CO2 capture process. Park et al.13 proposed a process that stores the LNG cold energy within a cryogenic energy storage system during off-peak hours, and releases the stored energy as electricity during peak hours. Direct expansion of LNG is another process to generate power in an LNG regasification terminal. LNG is pressurized to a higher pressure than the desired terminal pressure followed by regasification. The regasified LNG is then expanded to produce power.14 Power can also be generated in a combined cycle that incorporates both direct expansion of LNG and ORC.15 Choi et al.16 presented several process configurations for generating power from LNG cold energy within an ORC and direct expansion of LNG. Xue at al.17 proposed a two-stage ORC to recover the LNG cold energy combined with the low-grade heat of an exhaust flue gas from a combined 3 ACS Paragon Plus Environment


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cycle power plant. Both these studies considered single working fluids. Sun et al.18 optimized four schemes involving multicomponent working fluids and direct expansion to maximize the net power output of the process. Ferreira et al.19 considered ORC, direct expansion, and their combinations, and performed a multi-objective optimization for each scheme to maximize the net power output and minimize the total heat exchanger capacity. The authors modeled LNG as pure methane and allowed only pure components as working fluids within the ORC. Li et al.20 presented process configurations to maximize the recovery of pressure and thermal energy during LNG regasification. The authors used pure methane to represent the thermodynamic properties of LNG and used only propane as the working fluid to recover the LNG cold energy. Lee and Mistos21 proposed an optimization methodology to select multicomponent working fluid for the ORC with an objective of minimizing the area between the hot and cold temperature curves in order to minimize the exergy losses associated with the ORC. Lee et al.22 presented a superstructure to maximize the profitability of a power generation process by recovering LNG cold energy via multi-stage ORC. However, the authors used the same working fluid components and the compositions as that reported in their earlier work.9 The authors assumed the availability of a lowpressure waste steam at 850C as the heat source, which further limits the wider usefulness of the quantitative findings. Gómez et al.23 and Xue et al.24 presented a comprehensive review on different process configurations for power generation in an LNG regasification terminal. Objective It is evident from the previous discussion that most studies in this area have focused on optimizing the process with the aim of maximizing the thermal or exergetic efficiencies without devoting much attention to the economics. The ORC-based power generation processes are known for low efficiencies25 thus, the power generated must justify the additional investment. The ultimate adoption of a power generation configuration will normally depend on its overall economic profitability. 4 ACS Paragon Plus Environment

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In most regasification terminals, a part of the available LNG cold energy is used to recondense the BOG.26,27 This decreases the cold energy available for power generation. However, the existing studies in the open literature have neglected the BOG recondensation and assumed that the entire LNG cold energy is available for power generation. In addition to methane, LNG has other heavier hydrocarbons like ethane, propane, butane, and pentane. If the fraction of these heavy hydrocarbons in the LNG is high, then it is conventionally called rich LNG. On the other hand, if LNG is predominantly methane, then it is called lean LNG. Regasification pressure in a terminal can be either supercritical (i.e. above the critical pressure of LNG) or subcritical. The composition of LNG along with its regasification pressure will dictate its nonisothermal temperature profile within the ORC, and hence the power generation. LNG compositions at the import terminal will vary with source hence, the associated uncertainties must also be addressed during the design of a power generation process (PGP). In view of the above gaps in the existing literature, this paper aims to investigate the economic feasibility of power generation by cold energy recovery in an LNG regasification terminal. The major contribution of this study is to propose a superstructure-based optimization methodology that not only considers direct expansion of LNG, ORC, and their combination as potential options for the PGP but also selects working fluid components and their respective compositions. The superstructure is optimized within a simulation-based optimization paradigm to maximize the net present value (NPV) of the PGP. The proposed methodology is then used to investigate the effect of various process parameters like LNG feed temperature, regasification pressure, and LNG composition on the NPV of the PGP via several case studies. We also present a robust design strategy to deal with uncertainties in LNG composition within the PGP.



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Power Generation Process Consider a typical LNG regasification terminal that recondenses its BOG before regasification using seawater (SW). Figure 1 illustrates our conceptual framework that integrates the PGP between the BOG recondenser and the SW vaporizer. In an LNG regasification terminal, power can be produced by recovering the LNG cold energy in an ORC, direct expansion of LNG, or in a combined cycle that incorporates both ORC and direct expansion of LNG. We assume that the LNG feed into the PGP is at the terminal pressure with a known temperature. The LNG feed temperature can be determined from the terminal’s BOG recondensation process as discussed in section S1 of the Supporting Information (SI). We assume that the terminal sells/buys the net power to/from a local grid at a price of 𝐶𝑂𝐸 ($/kWh).

Figure 1: Schematic overview [Bold lines represent the conventional LNG regasification process; Dotted lines represent the PGP integrated with the LNG regasification process].

Figure 2 shows our proposed superstructure, which has the provision for selecting either ORC employing single or multicomponent working fluid (WF), direct expansion, or combined cycle for the PGP. In a combined cycle, depending on the direct expansion pressure, the LNG feed is pumped to a higher pressure than the desired terminal pressure. The high-pressure LNG stream then acts as the cold source to completely condense the WF vapor within the ORC. The resulting LNG stream is then regasified using sea water and expanded to the terminal pressure to produce power. The WF exiting the condenser is pressurized with a pump followed by vaporization using sea water as the hot source. The high-pressure WF vapor is then expanded using an expander to


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generate power. The low-pressure WF vapor exiting the expander is then returned to the condenser, thus completing the cycle.

Figure 2. Superstructure for the PGP.

Process optimization We optimize the superstructure in Figure 2 to find a PGP with maximum NPV. A major decision in this optimization is the selection of a WF for the ORC. This entails the selection of both components and their respective compositions in the WF. Since most common ORCs employ WFs with at most four components,21 we allow WF to have maximum four components from a set of 𝑁𝑐 known hydrocarbons. Let 𝑦𝑖 = 1, if WF employs component 𝑖 (𝑖 = 1, 2, … , 𝑁𝑐 ), and 𝑦𝑖 = 0 otherwise. Let 𝑐𝑖 denote the normalized mole fraction of component 𝑖 (𝑖 = 1, 2, … , 𝑁𝑐 ) in WF. Then, we can write, 𝑦1 + 𝑦2 + ⋯ + 𝑦𝑁𝑐 ≤ 4

𝑦𝑖 is binary

(1)

0 ≤ 𝑐𝑖 ≤ 𝑦𝑖

𝑐𝑖 is continuous

(2)

The remaining decisions for PGP are: 𝑃𝑅𝐿𝑁𝐺−𝐸𝑋𝑃 = 𝑃𝐿4 /𝑃𝐿5 = Pressure ratio for direct expansion in LNG-EXP (LNG Expander) 𝑀𝑇𝐴𝐶𝑉 = Minimum temperature approach in CV (Condenser-Vaporizer) 7 ACS Paragon Plus Environment


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𝑀𝑇𝐴𝑊𝐹𝑉 = Minimum temperature approach in WFV (Working Fluid Vaporizer) 𝑀𝑇𝐴𝐿𝑁𝐺𝑉 = Minimum temperature approach in LNGV (LNG Vaporizer) 𝑃𝑊𝐹4 = Pressure of WF from WF-EXP (Working Fluid Expander) The above variables allow us to compute the NPV of the PGP as follows. Let 𝑃𝐿 denote the project life in years for PGP, 𝐶𝐵𝑀 denote the bare module cost of an equipment, 𝐶𝐴𝑃𝐸𝑋 ($) denote the total capital expenditure, 𝑂𝑃𝐸𝑋 ($/y) denote the operating cost of the PGP, 𝑂𝑇 (h) denote the terminal’s annual operating hours, 𝑁𝑒𝑡𝑃𝑜𝑤𝑒𝑟 denote the net positive power (kWh) generated by the PGP, 𝑅𝑒𝑣 ($) denote its annual revenue, 𝐷𝐸𝑃 denotes the depreciation per year, 𝑟 is the annual interest rate, and 𝑡𝑎𝑥 denote the fractional tax rate. The PGP does not involve any external utility and the working fluid mixture is recycled in a closed loop within the ORC. Therefore, we neglect utility and raw material cost associated with the PGP. The implication of this assumption is that the OPEX of the PGP as represented by equation 4 is only a function of CAPEX, accounting for the maintenance cost, which covers the cost of periodic replenishment of the working fluid components. Depreciation is calculated based on the six years MACRS (Modified accelerated cash recovery system) method. 𝐶𝐸𝑃𝐶𝐼

𝐶𝐴𝑃𝐸𝑋 = 𝐶𝐸𝑃𝐶𝐼2016 × 1.18 × (∑𝐸𝑒=1 𝐶𝐵𝑀,𝑒 )

(3)

𝑂𝑃𝐸𝑋 = 0.18 × 𝐶𝐴𝑃𝐸𝑋

(4)

𝑁𝑒𝑡𝑃𝑜𝑤𝑒𝑟 = 𝑊𝐿𝑁𝐺−𝐸𝑋𝑃 + 𝑊𝑊𝐹−𝐸𝑋𝑃 − 𝑊𝐿𝑁𝐺−𝑃𝑈𝑀𝑃 − 𝑊𝑊𝐹−𝑃𝑈𝑀𝑃 − Δ𝑊𝑆𝑊−𝑃𝑈𝑀𝑃

(5)

𝑅𝑒𝑣 = 𝑁𝑒𝑡𝑃𝑜𝑤𝑒𝑟 × 𝑂𝑇 × 𝐶𝑂𝐸

(6)

𝐵𝑎𝑠𝑒

𝑁𝑃𝑉 = −𝐶𝐴𝑃𝐸𝑋 + ∑𝑃𝐿 𝑡=1

(𝑅𝑒𝑣 − 𝑂𝑃𝐸𝑋 − 𝐷𝐸𝑃 )×(1−𝑡𝑎𝑥)+𝐷𝐸𝑃 (1+𝑟)𝑡

(7)

where, Δ𝑊𝑆𝑊−𝑃𝑈𝑀𝑃 is the change in the power consumption of the SW-PUMP due to the addition of PGP. SW-PUMP is common to both PGP and the existing process (i.e. without PGP) thus, we consider its incremental capital cost only. We demand that the streams entering and exiting the two expanders (WF-EXP and LNG-EXP) be completely vapor, as liquid droplets can be 8 ACS Paragon Plus Environment

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detrimental to the turbine blades.16 The capital cost for each equipment is obtained using the empirical cost correlations given in Turton et al.28 The costing methodology along with equations for evaluating the capital cost, operating cost, and depreciation are given in section S2 of SI. Solution methodology We treat the PGP optimization as a bilevel problem.29 The outer level decides WF components (yi ), WF composition (ci ), 𝑃𝑅𝐿𝑁𝐺−𝐸𝑋𝑃 , and MTAs (𝑀𝑇𝐴𝐶𝑉 , 𝑀𝑇𝐴𝑊𝐹𝑉 , 𝑀𝑇𝐴𝐿𝑁𝐺𝑉 ) of three heat exchangers. The bounds on these decision variables are given in Table 1. The inner level decides the best 𝑃𝑊𝐹4 for a given set of outer level decision variables. The bounds on 𝑃𝑊𝐹4 depend on WF components and their respective compositions, 𝑀𝑇𝐴𝐶𝑉 , and 𝑀𝑇𝐴𝑊𝐹𝑉 as given by equations (8) and (9). The inner level optimization is executed only if WF has at least one component and 𝑚𝑎𝑥𝑃𝑊𝐹4 > 𝑚𝑖𝑛𝑃𝑊𝐹4 . The inlet pressure (𝑃𝑊𝐹3 ) of WF to WF-EXP for a given exit pressure (𝑃𝑊𝐹4 ) is the maximum pressure that will avoid any liquid droplets in the exit vapor from the WF-EXP. Equation (10) represents the two-level optimization problem with an objective of maximizing the NPV of the PGP in an LNG regasification terminal. The NPV for this process is evaluated using equations (3) – (7). 𝑚𝑖𝑛𝑃𝑊𝐹4

𝑇𝑏 = 𝑇𝐿𝑁𝐺 + 𝑀𝑇𝐴𝐶𝑉 + 3 𝑇𝐿2 , 𝑃𝑅𝐿𝑁𝐺−𝐸𝑋𝑃 > 1 = 𝑚𝑎𝑥(𝑏𝑝𝑡𝑃𝑇𝑏 , 𝑚𝑖𝑛𝑃𝐴𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 ) + ∆𝑃 | 𝑇𝐿𝑁𝐺 = { 𝑇𝐿𝑁𝐺𝐹𝐸𝐸𝐷 , 𝑃𝑅𝐿𝑁𝐺−𝐸𝑋𝑃 = 1

𝑚𝑎𝑥𝑃𝑊𝐹4 = {

𝑚𝑖𝑛(𝑑𝑝𝑡𝑃𝑇𝑑 , 𝑚𝑎𝑥𝑃𝐴𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 ) |𝑇𝑑 = 𝑇𝑆𝑊 − 𝑀𝑇𝐴𝑊𝐹𝑉 − 3, 𝑇𝑑 < 𝑇𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝑚𝑎𝑥𝑃𝐴𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 , 𝑇𝑑 ≥ 𝑇𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙

(8)

(9)

𝑚𝑎𝑥 [ 𝑚𝑎𝑥 𝑓(𝑋𝑂 , 𝑋𝐼 )]

𝑋𝑂 𝑋𝐼 𝑔(𝑋𝑂 ) ℎ(𝑋𝑂 ,𝑋𝐼 )

𝑋𝑂 : Outer level decision variables 𝑔(𝑋𝑂 ): Outer level constraints 𝑋𝐼 : Inner level decision variables ℎ(𝑋𝑂 , 𝑋𝐼 ): Inner level constraints 9 ACS Paragon Plus Environment


𝑓(𝑋𝑂 , 𝑋𝐼 ): Objective function

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(10)

For rigorous optimization based on accurate thermophysical properties obtained from a process simulator, we use a simulation-based optimization paradigm within MATLAB. Aspen HYSYS® is interfaced with MATLAB v2016b through a Component Object Model (COM) in ActiveX, which allows direct two-way communication.30,31 Derivative-based optimization usually relies on accurate gradient information, which is not readily available from a process simulator. Although the derivatives can be computed numerically, this is expensive using a process simulator. Moreover, numerical noise inherent in process simulators affects the accuracy of the computed derivatives.31–33 These problems can be avoided by using metaheuristic algorithms like particle swarm optimization, genetic algorithm, simulated annealing, etc. that are suitable for black-box optimization and do not require any derivative information.7,30,34–36 However, none of these metaheuristic algorithms can guarantee a global optimal solution. Genetic algorithm (ga) has been successfully used for various chemical processes.8,19,21,37,38 In this study, we use the ga solver39 for the outer level and fminbnd solver40 for the inner level. A sampling strategy based on LHS (Latin hypercube sampling) method is used to initialize the population for ga. If WF is infeasible, then NPV is set to a penalty value of −1 × 108 . Otherwise, the PGP is simulated inside Aspen HYSYS to compute the NPV. After ga converges (i.e. meets the stopping criteria), all the outer level decision variables are fixed at their best values, and WF composition is fine-tuned using the patternsearch solver.41,42 Figure 3 shows our solution methodology. Readers may refer to section S3 of SI for the details on the sampling strategy and various solver parameters. The parameters used for simulating the PGP are given in Table 2. Working fluid components considered in this study are presented in Table 3.


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Table 1. Bounds on decision variables

Decision

Lower

Upper

variable

bound

bound

𝑀𝑇𝐴𝐶𝑉

5

10

𝑀𝑇𝐴𝑊𝐹𝑉

5

10

𝑀𝑇𝐴𝐿𝑁𝐺𝑉

5

10

𝑃𝑅𝐿𝑁𝐺−𝐸𝑋𝑃

1

𝑚𝑎𝑥𝑃𝐴𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 𝑃𝑅𝑒𝑔𝑎𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛

Table 2. Process simulation parameters

Fluid Package

Peng-Robinson

Send out NG: Temperature (0C)

15

Flowrate (t/h)

100

Efficiency (%): Pump & Turbine

75

Degree of subcooling (0C)

3

Minimum degree of superheat (0C)

3

∆P on each side of heat exchangers (bar)

0.6

Sea water (SW): Inlet temperature (0C)

25

Outlet temperature (0C) ‡

22

Inlet pressure (bar)

5

𝑚𝑎𝑥𝑃𝐴𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 (bar)#

120

𝑚𝑖𝑛𝑃𝐴𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 (bar)*

1.10

𝑟 (%)

10

𝑃𝐿 (years)

20

𝑡𝑎𝑥 (%)

30

𝑂𝑇 (h/y)

8000

𝐶𝑂𝐸 ($/kWh)43

0.15

‡ Temperature difference between inlet and outlet sea water must be 3-5 0C. # Permissible pressure limit for equipment.28 * Operating above the ambient pressure to avoid any leakage of air, as this may pose a risk of fire and explosion with hydrocarbons as the working fluid.16



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Table 3. List of working fluid components Working

Critical

Critical

Normal

fluid

temperature

pressure

boiling

component

(0C)

(bar)

point (0C)

Ethane

32.17

48.72

-88.82

96.74

42.51

-42.11

9.20

50.12

-103.77

91.06

45.55

-47.62

151.98

38.00

-0.55

-82.55

46.00

-161.45

196.55

34.00

36.05

44.13

58.97

-78.13

78.10

57.82

-51.65

113.26

45.17

-24.02

26.14

48.32

-82.09

72.71

37.61

-47.27

101.06

40.59

-26.07

66.02

36.18

-48.09

[R170] Propane [R290] Ethylene [R1150] Propylene [R1270] n-butane [R600] Methane [R50] n-pentane [R601] R41 [CH3F] R32 [CH2F2] R152a [C2H4F2] R23 [CHF3] R143a [C2H3F3] R134a [C2H2F4] R125 [C2HF5]


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R116

19.88

30.48

-78.09

71.87

26.4

-36.79

-45.65

37.00

-128.05

154.05

36.00

15.35

134.65

36.00

-11.75

187.25

34.00

27.85

236.85

61.00

39.75

[C2F6] R218 [C3F8] R14 [CF4] R245fa [C3H3F5] i-butane [R600a] i-pentane [R601a] R30 [CH2Cl2]

Figure 3. Simulation-based optimization paradigm.

Case Studies Most studies in the open literature have aimed to maximize the thermal or exergetic efficiency of the LNG cold to power process. In this context, exergetic efficiency is considered to 13 ACS Paragon Plus Environment


Page 14 of 27

be a better metric as compared to the thermal efficiency.21 However, it is useful to evaluate this metric from the yardstick of NPV. Therefore, first we optimized the superstructure in Figure 2 with maximum exergy efficiency as the objective function and computed the corresponding NPV for the PGP. Next, keeping all process parameters identical we optimized the superstructure with maximum NPV as the objective function. Interestingly, the NPV of the maximum-exergy PGP (case study 1) is nearly 64% lower than the maximum-NPV PGP (case study 2). The maximumexergy PGP uses both direct expansion of LNG and ORC to generate power. However, the extra investment required for the direct expansion is not compensated by the additional power generated hence, the NPV reduces. This demonstrates the futility of using exergy as an objective for designing a PGP for LNG cold energy recovery, because decisions in real life will be based on NPV rather than exergy. Thus, for the remaining case studies (as listed in Table 4) we have used maximum NPV as the objective function. Now let us consider LNG terminals across the world with varying set of process parameters like LNG composition, feed temperature, and regasification pressure. Using combinations of these process parameters, we perform several case studies to obtain a PGP configuration with maximum NPV. Table 5 lists the maximum NPV value for each of the case study along with the corresponding optimal process parameters. It is evident from the results that LNG regasification (or terminal) pressure has the maximum impact on the NPV of the PGP followed by LNG feed temperature and LNG composition. However, the NPV is always positive, which suggests that the power generation by recovering the LNG cold energy in a regasification process is economically viable. In fact, the power generated from the cold energy exceeds the power demand of the regasification process, resulting in a surplus power. Interestingly, the optimal PGP structure is the same (Figure 4) for all the combinations of process parameters (as listed in Table 4), and no power is generated via direct expansion. In other words, generating power from ORC is found to be more economical than that from only direct expansion or the combined cycle 14 ACS Paragon Plus Environment

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(i.e. combination of both direct expansion of LNG and ORC). The extra investment required for the direct expansion within a combined cycle is not justified. We have also evaluated the breakeven COE (𝑁𝑃𝑉 = 0), and are found to be 9.7, 11.0, 12.4, and 10.5 (¢/kWh) for case studies 2, 3, 4, and 5 respectively. Table 4. Description of case studies

Case

LNG

BOG

study

composition# handled‡

pressure

function

1.

Lean

Subcritical

Exergy

5%

Regasification Objective

efficiency 2.

Lean

5%

Subcritical

NPV

3.

Lean

15%

Subcritical

NPV

4.

Lean

5%

Supercritical

NPV

5.

Rich

5%

Subcritical

NPV

# LNG compositions are given in Table S1 of SI. ‡ Readers may refer to section S1of SI for detailed discussion.

Figure 4. Process configuration for power generation by recovering the LNG cold energy.



Page 16 of 27

Table 5. Summary of results pertaining to case studies#

Case study 1

Case study 2

Case study 3

Case study 4

Case study 5

TLNGFeed (0C)

-147.33

-147.33

-123.12

-144.91

-148.17

PRegasification (bar)

40.6

40.6

40.6

75.6

40.6

Working fluid

Ethane:40.88

Ethane:87.15

Ethane:67.38

Ethane:49.87

Ethane:81.40

compositions

R134a:16.79

R218:12.85

Propane:18.78 Propane:14.65 R218:15.80

(mole%)

R116:42.33

R116:13.85

R134a:13.70

i-butane:2.80

R116:21.78 MTACV (0C)

5

5

5

5

5

MTAWFV (0C)

5

5

5

5

5

PR LNG−EXP

1.91

1

1

1

1

MTALNGV (0C)

5

-

-

-

-

PWF3 (bar)

18.58

19.38

18.28

14.87

17.00

PWF4 (bar)

1.7

1.7

1.7

1.7

1.7

MassWF (tons)

220.19

167.32

132.80

171.43

174.38

NetPower (kWh) 3206.23

2700.58

2226.64

2016.10

2488.01

Excess power‡

1800.62

1294.97

48.50

335.82

1131.19

ηex (%)

18.65

14.63

15.11

11.83

14.09

NPV (million$)

2.45

6.87

4.27

2.58

5.41

(kWh)

# Readers may refer to section S4 of SI for stream data. ‡ Excess power = NetPower – Power demand. Power demand = Power consumed by LNG pumps, BOG compressors & seawater pump.

Uncertainties in LNG Compositions LNG composition varies with its supply source. A regasification terminal may not necessarily limit itself to a particular LNG composition, and hence LNG composition may vary during the terminal’s project life, or it is uncertain. A PGP designed for a specific LNG composition may not even be feasible (or optimal) for other LNG qualities. Therefore, it is important to design a robust and flexible PGP that works for any LNG composition that the terminal may accept. We now present a heuristic strategy to handle uncertainties in LNG compositions while designing the PGP for a given terminal. 16 ACS Paragon Plus Environment

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Let us assume that the terminal may receive LNG cargos of 𝐼 compositions (𝑖 𝑜𝑟 𝑗 = 1, 2, … , 𝐼) with equal probability. We use our simulation-based optimization procedure to design the best PGP (𝑃𝐺𝑃𝑖 ) for each composition 𝑖 and obtain its best process parameters and 𝑁𝑃𝑉𝑖∗ . We now consider the process design of each 𝑃𝐺𝑃𝑖 individually, and let it process the LNG of composition 𝑗 (1 ≤ 𝑗 ≠ 𝑖 ≤ 𝐼). For each LNG composition 𝑗 processed in 𝑃𝐺𝑃𝑖 , we perform process simulations to obtain 𝑁𝑃𝑉𝑖𝑗 . Note that 𝑁𝑃𝑉𝑖𝑖 = 𝑁𝑃𝑉𝑖∗ . Keeping all the equipment sizes fixed, we only adjust the mass flowrate of WF, while ensuring the following process constraints: 1. The minimum temperature approach for each heat exchanger must not be less than 1. 2. WF at the inlet of each pump has at least 3 degrees of subcooling. 3. WF at the inlet of each expander has at least 3 degrees of superheat. 4. No liquid droplet is present at the exit of any expander. If any of the above is violated for at least one LNG composition 𝑗, then we discard 𝑃𝐺𝑃𝑖 as a potential robust process design. Then, for all PGPs that survive this initial screening, or for all 𝑃𝐺𝑃𝑖 that successfully process all compositions (𝑗 = 1, 2, … , 𝐼) we compute, ̅̅̅̅̅̅ 𝑁𝑃𝑉𝑖 = (∑𝐼𝑗=1 𝑁𝑃𝑉𝑖𝑗 )/𝐼

(11)

̅̅̅̅̅̅𝑖 is selected as the most economical robust process for the The 𝑃𝐺𝑃𝑖 with the maximum 𝑁𝑃𝑉 terminal. Our proposed strategy to handle uncertainties in LNG composition within a PGP is schematically shown in Figure 5. It can be revised easily to relax our assumption that each LNG composition (𝑗) is equally likely in the terminal.



Page 18 of 27

Figure 5. Strategy to handle uncertainties in LNG composition for the PGP.

For evaluating the performance of our robust design procedure, let us consider the strategy proposed by Gong et al.44 to design a process under uncertainties in shale gas composition. The authors designed a process for each shale gas composition separately. Then, they assembled a process in which each equipment was the largest obtained for any of the feed compositions. For comparison, we use their procedure to evaluate the NPV of the PGP for 10 LNG compositions (as given in Table S10 of SI). Our results show that the average NPV for the PGP obtained using the strategy of Gong et al. 44 is about 34% lower than the NPV from our algorithm. We have presented all data pertaining to the uncertainty-based process design in section S5 of SI.

Concluding Remarks In this communication, we have demonstrated the economic viability of the PGP by cold energy recovery in an LNG regasification process. We have developed a superstructure, which has the provision of selecting ORC and/or direct expansion of pressurized LNG for the PGP. The superstructure is optimized using a simulation-based optimization paradigm. It is shown that


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maximizing the exergetic efficiency of the PGP results in about 19% increase in power, but NPV of the maximum-exergy PGP is about 64% lower compared to the maximum-NPV PGP. Thus, the extra power obtained by maximizing the exergy efficiency cannot offset the additional investment required by the PGP. We then maximize the NPV of the PGP for various combinations of process parameters like LNG feed temperature, regasification pressure, and composition of LNG, and found that LNG regasification pressure has a significant impact on the NPV of the PGP followed by LNG feed temperature and LNG composition. Irrespective of the combination, the same configuration for the PGP is obtained, which only uses an ORC without any direct expansion of pressurized LNG. Depending on the process parameters in an LNG regasification terminal, the cold energy recovery using an ORC produces about 20 – 27 kW of power per tonne of LNG. This is not only sufficient to offset the power demand of the LNG regasification process but also results in an excess power of about 0.5 – 12.9 kW/t-LNG thus, enhancing the sustainability of the LNG value chain. The NPV of the PGP is about 6.87 – 2.45 million$ thus, it is evident that power generation by recovering LNG cold energy is an economically viable option. In this study, we also present a robust design strategy for the PGP with uncertainties in LNG composition. Our strategy gives a set of equipment sizes and operating conditions that maximize the average NPV of the PGP for an equally probable set of LNG compositions. The simulation-based optimization paradigm presented in this study provides a comprehensive tool for process design and economic analyses of power generation by recovering the cold energy in an LNG regasification terminal. Acknowledgement Arnab Dutta acknowledges financial support from the National University of Singapore under a research scholarship. The work was partly funded by the National University of Singapore via a seed grant R261-508-001-646/733 for CENGas (Center of Excellence for Natural Gas). It was also partly funded under the Energy Innovation Research Programme (EIRP) Award No. NRF2014EWTEIRP003-008, administrated by the Energy Market Authority (EMA). The EIRP is 19 ACS Paragon Plus Environment


Page 20 of 27

a competitive grant call initiative driven by the Energy Innovation Programme Office and funded by the National Research Foundation (NRF) of Singapore. We acknowledge AspenTech Inc. for allowing the use of Hysys® under an academic license provided to the National University of Singapore. We would also like to acknowledge MathWorks for providing an academic license of MATLAB to the National University of Singapore. Nomenclature LNG

liquefied natural gas

NG

natural gas

PNG

piped natural gas

BOG

boil-off gas

PGP

power generation process

ORC

Organic Rankine Cycle

WF

working fluid

P

pressure

T

temperature

W

power

𝑁𝑐

number of working fluid components

𝑀𝑇𝐴

minimum temperature approach

𝑚𝑎𝑥𝑃

maximum pressure

𝑚𝑖𝑛𝑃

minimum pressure

𝑏𝑝𝑡𝑃

bubble point pressure

𝑇𝑏

bubble point temperature

𝑑𝑝𝑡𝑃

dew point pressure

𝑇𝑑

dew point temperature

𝑇𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙

critical temperature

𝑃𝑅

pressure ratio

COE

cost of electricity

R

interest rate

Tax

tax rate fraction


Page 21 of 27 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 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


PL

plant life

OPEX

operating cost

𝐶𝐵𝑀

bare module cost of an equipment

CAPEX

capital investment

𝐷𝐸𝑃

depreciation cost

NPV

net present value

𝑅𝑒𝑣

revenue generated

𝑂𝑇

operating hours

𝐼

number of LNG compositions

̅̅̅̅̅̅ 𝑁𝑃𝑉

average NPV

e, t, i, j

counters

∆

difference

𝜂𝑒𝑥

exergy efficiency

SW

seawater

CV

condenser-vaporizer

WFV

working fluid vaporizer

LNGV

LNG vaporizer

WF-EXP

working fluid expander

LNG-EXP

LNG expander

Supporting Information Compositions of LNG streams Costing methodology and cost correlations Sampling strategy Parameters for optimization solvers Stream data Data pertaining to uncertainty-based process design This information is available free of charge via the Internet at http://pubs.acs.org/.



Page 22 of 27

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List of Figures: Figure 1: Schematic overview [Bold lines represent the conventional LNG regasification process; Dotted lines represent the PGP integrated with the LNG regasification process]. Figure 2: Superstructure for the PGP. Figure 3: Simulation-based optimization paradigm. Figure 4: Process configuration for power generation by recovering the LNG cold energy. Figure 5: Strategy to handle uncertainties in LNG composition for the PGP.



Page 26 of 27

List of Tables: Table 1. Bounds on decision variables Table 2. Process simulation parameters Table 3. List of working fluid components Table 4. Description of case studies Table 5. Summary of results pertaining to case studies


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Graphical Abstract

Synopsis The economic viability of power generation by recovering the cold energy available in an LNG regasification process is demonstrated thus, emphasizing the practicality of cold energy utilization.


Economic Feasibility of Power Generation by Recovering Cold Energy

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