Heating Value Reduction of LNG (Liquefied Natural Gas) by

Subscriber access provided by UNIV OF NEW ENGLAND ARMIDALE

Article

Heating value reduction of LNG (Liquefied Natural Gas) by recovering heavy hydrocarbons: Technoeconomic analyses using simulation-based optimization Arnab Dutta, Iftekhar A Karimi, and Shamsuzzaman Farooq Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b04311 • Publication Date (Web): 20 Feb 2018 Downloaded from http://pubs.acs.org on February 22, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 24 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

Industrial & Engineering Chemistry Research

Heating value reduction of LNG (Liquefied Natural Gas) by recovering heavy hydrocarbons: Technoeconomic analyses using simulation-based optimization 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 is expected to play a major role in global natural gas trade. Implementing a heating value adjustment unit within regasification terminal allows importing LNG from diverse sources around the world, thus increasing the energy security of the importing country. In this study, we focus on heavy hydrocarbons removal (HHR) as a strategy to reduce heating value of LNG to meet desired specifications. We have developed a superstructure for the HHR process, which is optimized using a particle swarm optimization algorithm to maximize profitability of the HHR process. The optimal process configuration not only meets heating value specification, but also utilizes LNG’s cold energy to liquefy the methane-rich stream before pumping to send out pressure, and produces ethane and LPG as co-products. Our technoeconomic analyses show that at the current prices of co-products, the HHR process configuration yields 7 – 10% profit depending on the heating value of the LNG feed. Keywords LNG, Heating value reduction, Heavy hydrocarbons removal, Superstructure, Particle swarm optimization, Profitability

*

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

1 ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 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

Page 2 of 24

1. Introduction Natural gas (NG) is the cleanest among the fossil fuels1. Increased exploitation and shale gas boom in the United States have made NG the fastest growing energy source in the world. With abundant availability, its consumption is projected to increase at a rate of 1.4 – 1.6% per year from 2008 to 20352,3. However, owing to the geographically diverse locations of NG reserves most countries do not have direct access to NG. NG is exported either via pipeline (Piped Natural Gas or PNG), as CNG (Compressed Natural Gas), or as LNG (Liquefied Natural Gas) in cryogenic tankers. For transportation over long distances, typically above 3500 km, LNG has been the preferred method for economical, technical, safety-related, and political reasons2. Both PNG and LNG trades are expected to increase continually in the future. However, the LNG trade is projected to have a higher growth rate than the PNG trade2,3. Based on the data published in 2011 by Kumar et al.3, 26 export or liquefaction terminals are located onshore or offshore in 17 countries, 60 import or regasification terminals are operating in 20 countries, and around 30 liquefaction terminal projects in 15 countries and 50 regasification terminal projects have either been proposed or are under construction. Undoubtedly, LNG will play a major role in the global NG trade. To produce LNG, NG (post extraction and gas processing) is liquefied via a refrigeration cycle. Lim et al.2 and Khan et al4. have presented excellent reviews of prevailing NG liquefaction processes. Liquefying NG reduces its volume by about 600 times, making it suitable for long distance transport via LNG tankers. When NG is imported as a cryogenic liquid, it should first be regasified at the import terminal before it can be sent out to the end consumers. Regasification is an energy intensive process and the most prevalent practice is to regasify LNG using sea water5. Thus, the entire cold energy is completely wasted and dumped into the sea. Gómez et al.6 and Feier et al.7 have presented excellent reviews on utilizing this cold energy for generating electricity. Kanbur et al.8 presented a comprehensive review of processes that can benefit by utilizing the cold energy associated with the LNG stream. As LNG is stored at sub-ambient conditions, heat leak leads to the vaporization of LNG leading to boil-off gas (BOG). Significant research efforts are underway to minimize the BOG and handle it efficiently 9–14. 1.1

Motivation Regasified LNG is sent out through pipelines to meet the end customer demand. In

this context, the send out gas must meet certain specifications15. One such specification is its heating value. LNG is composed of methane along with other heavy hydrocarbons like 2 ACS Paragon Plus Environment

Page 3 of 24 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


ethane, propane, butane, and traces of pentane. If the fraction of these heavy hydrocarbons in the LNG stream is significantly high, then it is conventionally termed as rich LNG. If LNG is composed predominantly of methane, then it is termed as lean LNG. Owing to the presence of these heavy hydrocarbons, the heating value of the send out gas may exceed the desired pipeline specification. Then, it becomes necessary to reduce this heating value within the LNG regasification terminal. A heating value reduction unit will allow the terminal to import LNG from diverse sources around the world, thus increasing the energy security of the importing country. Moreover, the terminal will no longer be constrained by only some fixed (contracted) suppliers, which would enable it to take advantage of reduced LNG spot prices, if and when available. The heating value of LNG can be reduced by adding nitrogen to the LNG stream16. This will not only require N2, but will also increase the percentage of inert in the send out NG. According to pipeline specifications, the percentage of inert in the send out gas should not exceed 3 mole%15,17. We have analyzed LNG from 15 exporting countries with heating values in the range of 39 – 42 MJ/Nm3. Figure 1 represents the nitrogen content in the send out NG after the LNG feed is diluted with nitrogen to match its specified heating value. If N2 is added to attain a specified heating value of 38.5 MJ/Nm3, then as evident from Figure 1, the percentage of inert in the send out NG exceeds the specified limit of 3 mole% in most cases. Readers may refer to Table S1 in the supporting information (SI) for the compositions of the 15 LNG cargos. Another strategy for lowering the heating value of LNG is to blend a rich LNG with a lean LNG15. The blended LNG can then be subjected to N2 dilution to attain the specified heating value. In this context, we have analyzed LNG-4 with a heating value of 42.2 MJ/Nm3. We blended it with a lean LNG stream followed by N2 dilution to reduce its heating value to 38.5 MJ/Nm3. N2 mole% in the send out gas depends on the ratio of available lean LNG to LNG-4 that needs to be processed. From Figure 2, it is evident that there is a cut off limit on the mass ratio of lean LNG to LNG-4 below which N2 dilution cannot be implemented as the inert percentage in the send out gas exceeds the limit of 3 mole%. The third strategy for reducing the heating value involves heavy hydrocarbons removal (HHR) from the LNG feed16–19. This strategy unlike N2 dilution or blending with lean LNG does not depend on the availability of an external agent, i.e. N2 or a lean LNG. Rather, this strategy can be applied to any LNG feed. The separated heavy hydrocarbons can be further separated to obtain value-added products like ethane and LPG (Liquefied



Page 4 of 24

Petroleum Gas). Thus, HHR can produce sellable co-products that will generate additional revenue for the regasification terminal. In some LNG terminals, the heating value may also require to be increased to meet the pipeline specification. In this case, heavy hydrocarbon like ethane or propane may be injected into the LNG feed 15. However, this scenario is not considered in this study.

Figure 1: N2 mole % in send out NG after diluting LNG with N2. Heating values [MJ/Nm3] of LNG streams prior to N2 dilution are given in bracket.


Page 5 of 24 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


Figure 2: N2 mole % in send out NG after blending with lean LNG and diluting with N2.

1.2

Objective It is evident from the previous section that N2 dilution or blending with a lean LNG

may not always suffice to meet the desired pipeline specifications. However, an HHR process can both reduce the heating value of a given LNG feed as well as produce value-added products. Baek et al.16 proposed a configuration to condense the light component of LNG after it is separated from the heavier component. Fahmy et al.17 studied various configurations to separate a stream of heavy hydrocarbons from LNG. However, in both studies the authors did not separate the heavier hydrocarbons stream further to generate sellable products like ethane and LPG. Uwitonze et al.18 and Gao et al.19 considered HHR to produce sellable co-products. However, they did not optimize their proposed process. There is still a gap in the literature on optimization study and technoeconomic analyses for the HHR process. The objective of this study is to obtain a process configuration that not only reduces the heating value of LNG but also uses the LNG’s cold energy to produce revenue-generating products such as ethane and LPG. In line with our objective, we have developed a superstructure for the HHR process. The superstructure is solved using a simulation-based optimization algorithm to obtain a process configuration that maximizes the profit for the terminal. As market prices of the hydrocarbons fluctuate, it is imperative to investigate whether the revenue generated from these co-products can justify the investment for the HHR process. We present three case studies for LNG streams of different heating values, followed 5 ACS Paragon Plus Environment


Page 6 of 24

by sensitivity analyses to highlight the impact of co-products prices on the profitability of the HHR process. What we have not considered here is the co-existence of any other strategies like N2 dilution or blending with lean LNG. The main contribution of this communication is to determine the technoeconomic feasibility of the HHR process as a standalone strategy to reduce the heating value of any given LNG feed within an LNG regasification terminal. The outline of this communication is as follows. We present the process superstructure in section 2, and the solution methodology in sections 3 through 5. A set of case studies and technoeconomic analyses are then presented in section 6. In section 7, we highlight the concluding remarks from this study. 2. Superstructure for the HHR process The HHR process simultaneously reduces the heating value of a given LNG stream and co-produces sellable hydrocarbons namely ethane and LPG. The HHR process involves distillation as the separation technique to separate the heavy hydrocarbons from LNG to reduce its heating value. LNG is stored as liquid at near ambient pressure. However, send out pressure for the natural gas is significantly higher than the ambient pressure. The distillation column needs to be operated at a pressure less than the critical pressure of methane. The LNG stream is first pressurized to a certain pressure and then fed to a demethanizer column, where methane is completely recovered as the top product, and the bottom product is a hydrocarbons stream rich in ethane, propane, and butane. The top product is primarily methane vapor. The operating pressure of the demethanizer column is usually lower than the required send out pressure of natural gas. Thus, the top vapor product from the demethanizer needs to be pressurized to the desired send out pressure. This stream can be pressurized either by directly using a compressor or it can be liquefied using the available cold energy of LNG, and then pressurized using a pump. A multi-stream heat exchanger is implemented to liquefy the vapor top product. In the multi-stream heat exchanger, the pressurized cold LNG stream (which is in subcooled state) acts as a coolant to liquefy the saturated vapor stream exiting the top of the demethanizer. In case the cold LNG stream can only partially liquefy the vapor stream, then the liquefied fraction is pumped, and the remaining vapor is compressed to the send out gas pressure. The cold LNG stream which gains heat in the process passes through a flash drum, the liquid from the flash drum is fed to the demethanizer, and the methane–rich vapor stream is sent back to the multi-stream heat exchanger for liquefaction and then undergoes pressurization. The bottom product of the demethanizer is then sent to the deethanizer column to obtain pure ethane as the top product and LPG as the bottom product. 6 ACS Paragon Plus Environment

Page 7 of 24 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


From a storage viewpoint, it is advantageous to obtain these co-products as liquid and at a low pressure. As a result, the pressure of the bottom stream from the demethanizer column needs to be reduced before being fed to the deethanizer. In this context, the bottom stream from the demethanizer which is liquid can either be throttled or it can be expanded using an expander to generate power that can partially offset the electricity consumption of the HHR process. However, to implement an expander the stream must be superheated using a hot utility. In this study, steam is used for superheating the liquid bottom product of demethanizer before expansion. The reboilers of the demethanizer and the deethanizer use either steam or sea water as the hot utility depending on the operating pressure of the distillation column. The cold energy associated with the LNG stream not only liquefies the top product of the demethanizer but also satisfies the condenser duty of the deethanizer. The LNG stream (with the desired heating value) at the send out pressure is finally vaporized using sea water to meet the required send out gas temperature. In this study, we have adopted a comprehensive approach to incorporate these options within our proposed superstructure for the HHR process, and then it is solved using an optimization algorithm to obtain the process configuration and the operating parameters that maximize the profitability of the process for a given LNG stream. A representation of the proposed superstructure for the HHR process is illustrated in Figure 3.

Figure 3: Superstructure of the HHR process.



Page 8 of 24

3. Superstructure optimization To obtain a process configuration that maximizes the profitability of the HHR process, the developed superstructure for the HHR process (as illustrated in Figure 3) needs to be optimized. The decision variables used for optimizing the superstructure along with their respective lower and upper bound are summarized in Table 1. The number of stages in the distillation column is also used as a decision variable. The strategy implemented for varying the number of stages along with the feed stage location of a distillation column as decision variables is adopted from Yeomans and Grossmann20. It is to be noted that we have considered the number of stages for distillation columns as continuous variables, and rounded them to their nearest integers. The optimization problem is solved with the following process constraints: the minimum temperature approach (MTA) for the multi-stream heat exchanger (MSHE) must be at least 3 0C, the stream exiting the expander (i.e. stream 12 in Figure 3) must be in vapor phase as liquid droplets at the exit of an expander is usually avoided to protect damaging the turbine blades21, and the heating value of the send out natural gas must meet the desired heating value specification. Heating value of pure methane is about 37.4 MJ/Nm3. In this study, we have assumed that the LNG terminal is designed to supply lean LNG, and it can withstand slight deviations in the heating value of the imported LNG feed. Thus, we have fixed the heating value specification to be 38.5 MJ/Nm3. However, it is just a design specification and our methodology can be applied to solve for any desired heating value. All specifications (i.e. constraints) associated with the simulations of distillation columns are specified within the process simulator (HYSYS). However, if the process simulator fails to converge any distillation column, then it is considered as an infeasible solution. These constraints along with the bound constrains on each of the decision variable represent the set of process constraints (g(X)), where X denotes the set of decision variables. The objective is to maximize the profitability of the HHR process. The objective function is given by equation (1). max Profit = (∑ Revenue NG,

X g(X)≤0

Ethane, LPG

− OPEX − AF ∗ CAPEX)

(1)

The capital cost for each equipment is calculated based on its respective size using empirical cost correlations as given in Turton et al.22 The costing methodology implemented in this study along with the cost correlations used to evaluate the capital cost for each equipment, and the overall capital and operating costs of the HHR process are given in section S2 of the 8 ACS Paragon Plus Environment

Page 9 of 24 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


SI. The capital investment is annualized using the annualizing factor (AF) as given by equation 2. AF =

i(1+i)t

(2)

(1+i)t −1

i is the interest rate per year and t is the lifespan of plant equipment; 0.1 and 20 years are used for i and t respectively22. Table 1: Summary of decision variables and respective bounds Decision

Bounds

variable P2 (kPa)

[1000 – 4250]

∆T3-2 (0C)

[10 – 100]

∆T6-5A (0C)

[-100 – 0]

∆T4-3V (0C)

[-100 – 0]

∆P3L-FDeM (kPa)

[0-3270]

∆P7-8 (kPa)

[0-7100]

PFDeE (kPa)

[140 – 1000]

split5A:5B

[0 – 1]

ns_DeM

[10 – 30]

nrs_DeE

[1 – 14]

nss_DeE

[16 – 30]

split10A:10B

[0 – 1]

MTAHEX (0C)

[10 – 30]

degree of

[0 – 85]

superheat11 4. Simulation-based optimization paradigm Here we adopt a simulation-based optimization paradigm where we combine the benefits of a process simulator to seamlessly perform all thermodynamic calculations and an external platform to perform rigorous optimization. In this study, Aspen HYSYS as a process simulator is interfaced with MATLAB v2016b as the optimization platform. A connection between MATLAB and HYSYS can be established through a Component Object Model (COM) in ActiveX, which allows direct two-way communication between HYSYS and 9 ACS Paragon Plus Environment


Page 10 of 24

MATLAB23,24. As shown in Figure 4, stream and process data obtained from HYSYS is sent to MATLAB, which performs the optimization, and the decision variables obtained at each iteration of the optimization are sent back to HYSYS to perform the process simulation. Derivative-based optimization algorithms usually rely on accurate gradient information, which is not readily available from process simulators. Although derivatives for the variables can be indirectly obtained through perturbation, it involves significantly longer computational time because the flowsheet within the process simulator needs to be solved each time a variable is perturbed. Moreover, numerical noise inherent in process simulators affects the accuracy of the computed derivatives25,26. These problems can be avoided by using metaheuristic algorithms like particle swarm optimization (PSO), genetic algorithm, simulated annealing, etc. that are suitable for black-box optimization and do not require any derivative information27–30. For example, PSO has been widely used in the literature to optimize various chemical processes26,31–35. However, none of these metaheuristic algorithms can guarantee a global optimal solution.

Figure 4: Simulation-based optimization paradigm via HYSYS-MATLAB interface.

5. PSO algorithm In this study, PSO algorithm is implemented in MATLAB to optimize the proposed superstructure for the HHR process. Background PSO is a population based algorithm introduced by Eberhart and Kennedy36. In PSO, each potential solution is called a particle, and the set of particles known as swarm (population) moves through the multi-dimensional search space of the optimization problem. 10 ACS Paragon Plus Environment

Page 11 of 24 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


The PSO algorithm involves an element of learning and communication between particles within the population, and the movement of each particle is governed by the respective bestknown position (particle best) as well as the best-known position of the entire population (global best). Based on the interaction scheme between the particles, PSO algorithm can be categorized into two variants: the 𝑙𝑏𝑒𝑠𝑡 type and the 𝑔𝑏𝑒𝑠𝑡 type26. In the 𝑙𝑏𝑒𝑠𝑡 type, each particle is affected by the best performance of its immediate neighbours. In the 𝑔𝑏𝑒𝑠𝑡 type, every particle is attracted to the best solution obtained by any particle within the population, hence this is equivalent to a fully connected social network, and offers a faster rate of convergence26. The PSO algorithm implemented in this study is based on the 𝑔𝑏𝑒𝑠𝑡 type. Parameters PSO effectively handles exploration and exploitation using three parameters: a cognitive component (c1), a social component (c2), and inertia weight (w). In the PSO algorithm implemented in this study, the cognitive component is linearly decreased and the social component is linearly increased with each iteration of PSO,37 and the inertia weight is adapted based on the total number of particles that improve its solution in each iteration as a feedback parameter.38 Constraints handling Lower and upper bounds of the decision variables confine the positions of particles within the multi-dimensional search space. The velocities of particles are also restricted by suitable bounds specified in each dimension. When the position or velocity of a particle violates the upper or lower bound in one dimension, it is fixed at the respective bound. If a particle is at its upper bound with a positive velocity or if the particle is at its lower bound with a negative velocity, then the direction of velocity is reversed to impede the movement of the particle in the wrong direction. To handle process constraints, Deb39 proposed a formulation that compares two solutions according to the following rules: 1. A feasible solution is preferred to an infeasible solution. 2. Among two feasible solutions, one having better objective function value is preferred. 3. Among two infeasible solutions, one having smaller constraint violation is preferred. In this study, constraints are handled based on the above stated rules. Thus, infeasible PSO particles are compared based on the extent of constraint violations, whereas the feasible PSO particles are compared based on their objective function values. 11 ACS Paragon Plus Environment


Page 12 of 24

Implementing mesh adaptive search technique with PSO algorithm The implemented PSO algorithm is combined with NOMAD, a nonlinear optimization algorithm based on mesh adaptive direct search technique40, available from OPTI Toolbox41. Feasible PSO particles are used as initial guesses for NOMAD. Feasible solutions obtained from NOMAD are then used to replace infeasible PSO particles followed by PSO particles with worse objective function values compared to feasible NOMAD solutions. Stopping criteria A distribution based criterion, based on the difference between the best and the worst objective function value for p% of particles is used to terminate our optimization algorithm42. The PSO algorithm implemented in this study to optimize the HHR process superstructure incorporates each of the features as described in section 5. The pseudo code for the PSO algorithm including constraint handling and PSO parameters are presented in section S3 of the SI. 6. Case studies on LNG feeds with different heating values Using the superstructure for the HHR process and the simulation-based optimization paradigm, we address the following key questions: 1. Is it profitable to reduce the heating value of LNG by recovering heavy hydrocarbons? 2. How do fluctuations in the market prices of heavy hydrocarbons (i.e. the co-products) affect the profitability of the HHR process? 3. For given prices of co-products (i.e. ethane and LPG), what will be the maximum LNG acquisition price, and the minimum NG selling price at which the HHR process will break-even? In this context, we have performed three case studies, where LNG feeds of different heating values are used in the HHR process to achieve a desired heating value. Parameters used in HYSYS for process simulation are presented in Table 2. The market prices of sellable products (NG, ethane, and LPG), raw material (LNG), and utilities (electricity and steam) used to evaluate the profitability of the HHR process are given in Table S4 of the SI. The HHR process configuration obtained by solving the superstructure of the HHR process (as shown in Figure 3) is illustrated in Figure 5. Irrespective of the quality of the LNG feed, the same process configuration as shown in Figure 5 is obtained for the HHR process however,


Page 13 of 24 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


the operating conditions do vary with the LNG feed. Results pertaining to each of the case studies are summarized in Table 3. For detailed results, readers may refer to Table S5 of SI. Table 2: Process simulation parameters Fluid Package

Peng-Robinson

Send out NG: Pressure (kPa)

8000

Temperature (0C)

15

Target HHV (MJ/Nm3)

38.5

Flowrate (t/h)

100

Efficiency: Compressor, Pump, Turbine

75%

Pressure drop per stage in distillation

0.7

column (kPa) Pressure drop on each side of heat

20

exchangers (kPa) De-methanizer: Methane recovery

99.99%

De-ethanizer: Ethane & Propane recovery 99.9% Sea water: Inlet temperature (0C)

25

Outlet temperature (0C)‡

22

Inlet pressure (kPa)

500

‡ Temperature difference between inlet and outlet sea water is usually not allowed to exceed 3-5 0C owing to environmental norms

Figure 5: Process configuration for reducing the heating value of LNG by the HHR process.



Page 14 of 24

Table 3: Summary of results pertaining to the three case studies

Power

Case Study 1#:

Case Study 2#:

Case Study 3#:

LNG: 40.1 MJ/Nm3

LNG: 41.3 MJ/Nm3

LNG: 42.1 MJ/Nm3

1480.0

1485.0

1473.0

3.08

3.67

5.25

5425

5441

5225

109.55

115.88

122.00

5.90

6.55

10.65

3.61

9.33

11.31

TAC (million$/y)

339.0

355.6

374.8

Total Revenue

363.5

390.3

410.2

24.5

34.7

35.4

consumption (kWh) Steam consumption (t/h) Seawater consumption (t/h) LNG processed (t/h) Ethane produced (t/h) LPG produced (t/h)

(million$/y) Profit (million$/y)

# Compositions and properties of LNG streams are given in Table S1 in the SI

The amount of LNG processed to maintain a desired send out gas rate depends on the extent of heating value reduction required. The LNG stream in case 1 needs minimal heating value reduction, consequently the amount of LNG processed, and the total amounts of coproducts produced are the least for case 1. The operating pressures of the distillation columns are such that steam, and sea water are used as external hot utilities in the reboilers of demethanizer and deethanizer respectively. It is to be noted that for all the three case studies, the topology corresponding to expansion via throttle valve is always preferred over the use of an expander. Although an expander has the potential to generate power that can offset the power demand of the HHR process resulting in a reduction of operating cost, but this is not sufficient to compensate the additional capital investment. As a result, the use of an expander leads to an increase in the total annualized cost that subsequently reduces the profitability of the process. Irrespective of the operating conditions in each case study, the cold energy of LNG is sufficient to liquefy the various vapor streams in the HHR process, thus no compressors are chosen. This not only reduces the power consumption (thus the operating 14 ACS Paragon Plus Environment

Page 15 of 24 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


cost) but also the capital investment of the HHR process. The methane-rich LNG stream (with the desired heating value) is then pumped to the send out gas pressure, and passes through the multi-stream heat exchanger, where it exchanges its cold energy before it is heated using sea water to meet the send out gas temperature. The temperature of the LNG stream entering the sea water vaporizer is significantly higher compared to the LNG feed temperature, this shows that a portion of the available cold energy of LNG is utilized within the HHR process. The total power consumption of the process (as given in Table 3) includes the power required to pump sea water from the ambient pressure to the inlet pressure. Technoeconomic analyses The revenue of the HHR process depends on the costs of co-products thus, any fluctuations in these costs will impact its profitability. We have implemented five different scenarios, as shown in Table 4, based on fluctuations in costs of co-products. Figure 6 illustrates the impact of variations in costs of co-products on the profitability of the HHR process for different LNG feeds. At current market prices of these co-products the profitability of the HHR process is maximum for the LNG stream corresponding to case 3. It generates a profit of 35.4 million$/y, which can further increase by about 88% if costs of coproducts increase by 40%. However, if there is a reduction in costs of co-products by up to 35% compared to current market prices, the profitability for the LNG stream corresponding to case 2 outperforms both cases 1 and 3. On the other hand, if prices of these co-products fall below 35% of current market prices, then the profitability for the LNG stream corresponding to case 1 outperforms both cases 2 and 3. The profitability of the HHR process reduces to 12.4 million$/y for a 40% decrease in costs of these co-products compared to current market prices. Thus, at the prevailing or at an increased market prices of these co-products it is economically beneficial to purchase the LNG stream that needs maximum heating value reduction whereas if prices of these co-products fall below 35% then it is preferable to purchase the LNG stream that needs minimal heating value reduction. Apart from the profitability of the process, two other parameters of interest are the maximum purchase price of LNG, and the minimum selling price of NG at which the HHR process can break-even. In order to evaluate the maximum LNG purchase price, the selling price of NG is kept fixed at the base price (as given in Table S4). At current market prices of these co-products, the maximum break-even LNG purchase price is approximately 6.6 $/MMBtu, which increases by around 8% for a 40% increase in costs of co-products, and reduces by about 5% for a 40% decrease in co-products costs. The purchase price of LNG is fixed at the base price (as given 15 ACS Paragon Plus Environment


Page 16 of 24

in Table S4) while calculating the minimum selling price of NG. At current market prices of these co-products, the minimum break-even NG selling price is approximately 7.2 $/MMBtu, which reduces by around 11% for a 40% increase in costs of co-products, and increases by about 8% for a 40% decrease in co-products costs. For different LNG feeds, the effect of various scenarios on these two parameters are illustrated in Figure 7 and 8 respectively. We have also performed regression analyses to represent profitability of the HHR process as a function of different cost parameters, for each of the case studies. For a given set of cost parameters, the regression equation can estimate the profitability of the HHR process. The regression equation can also be used to predict the maximum LNG purchase price as well as the minimum NG selling price at which the HHR process will break-even. Readers may refer to Table S6 in the SI for the regression equations. Table 4: Scenarios based on the variations in co-products prices Percent change in

Ethane price

LPG price

price w.r.t current

($/kg)

($/kg)‡

market price 1. -40%

0.17

0.35

2. -20%

0.23

0.46

3. 0%#

0.29

0.58

4. +20%

0.35

0.70

5. +40%

0.41

0.81

‡ LPG price is assumed to be the average of propane and butane price # Current market price


Page 17 of 24 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


Figure 6: Profitability of the HHR process under various scenarios for different LNG feeds.

Figure 7: LNG break-even purchase price under various scenarios for different LNG.



Page 18 of 24

Figure 8: NG break-even selling price under various scenarios for different LNG feeds.

7. Concluding remarks An effective heating value adjustment unit can increase the flexibility of an LNG regasification terminal, as it can process practically any LNG stream thus, increasing the energy security of the LNG importing country. In this communication, we have demonstrated the technoeconomic feasibility of the HHR process within an LNG regasification terminal to reduce the heating value of any given LNG feed to meet its desired specification. We have developed a superstructure for the heavy hydrocarbons removal process, and solved it within a simulation-based optimization paradigm. It is notable that a single process configuration suffices irrespective of the quality of the LNG feed stream. However, the operating parameters do vary with the quality of the LNG feed. The HHR process configuration utilizes the cold energy of LNG thus, resulting in an energetically efficient process that not only reduces the heating value of the LNG feed but also produces value added co-products i.e. ethane and LPG. It is evident from our technoeconomic analyses that market prices of these co-products have a significant impact on the profitability of the HHR process. However, the additional revenue generated by selling these co-products has the potential to offset the supplementary investment required for the HHR process. At current market prices of coproducts, the HHR process yields approximately 7 – 10% profit depending on the LNG feed.


Page 19 of 24 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


This profit increases to around 11 – 18% for a 40% increase in costs of co-products, and reduces to about 1 – 4% for a 40% decrease in co-products costs. The simulation-based optimization paradigm presented in this study can be used to evaluate the technoeconomic feasibility of the HHR process to reduce the heating value of any given LNG feed within a regasification terminal. Acknowledgement This work is funded by the National University of Singapore through a seed grant (R261-508001-646/733) for CENGas (Centre of Excellence for Natural Gas). Nomenclature P

pressure

∆T

temperature difference

∆P

pressure difference

ns_DeM

number of stages in De-Methanizer

nrs_DeE

number of stages in rectification section of DeEthanizer

nss_DeE

number of stages in stripping section of De-Ethanizer

OPEX

operating cost

CAPEX

capital investment

Supporting information Compositions and properties of LNG streams Costing framework Particle Swarm Optimization framework Market prices of raw material, products and utilities Additional results This information is available free of charge via the Internet at http://pubs.acs.org/.



Page 20 of 24

References (1)

International Energy Agency. World Energy Outlook 2011; 2011.

(2)

Lim, W.; Choi, K.; Moon, I. Current Status and Perspectives of Liquefied Natural Gas (LNG) Plant Design. Ind. Eng. Chem. Res. 2013, 52 (9), 3065-3088.

(3)

Kumar, S.; Kwon, H. T.; Choi, K. H.; Hyun Cho, J.; Lim, W.; Moon, I. Current Status and Future Projections of LNG Demand and Supplies: A Global Prospective. Energy Policy 2011, 39 (7), 40974104.

(4)

Khan, M. S.; Karimi, I. A.; Wood, D. A. Retrospective and Future Perspective of Natural Gas Liquefaction and Optimization Technologies Contributing to Efficient LNG Supply: A Review. J. Nat. Gas Sci. Eng. 2017, 45, 165-188.

(5)

Wang, M.; Zhang, J.; Xu, Q. A Novel Conceptual Design by Integrating NGL Recovery and LNG Regasification Processes for Maximum Energy Savings. AIChE J. 2013, 59 (12), 4673-4685.

(6)

Romero Gómez, M.; Ferreiro Garcia, R.; Romero Gómez, J.; Carbia Carril, J. Review of Thermal Cycles Exploiting the Exergy of Liquefied Natural Gas in the Regasification Process. Renew. Sustain. Energy Rev. 2014, 38, 781-795.

(7)

Xue, F.; Chen, Y.; Ju, Y. A Review of Cryogenic Power Generation Cycles with Liquefied Natural Gas Cold Energy Utilization. Front. Energy 2016, 10 (3), 363-374.

(8)

Kanbur, B. B.; Xiang, L.; Dubey, S.; Choo, F. H.; Duan, F. Cold Utilization Systems of LNG: A Review. Renew. Sustain. Energy Rev. 2017, 79 (August 2016), 1171-1188.

(9)

Park, S.; Park, C.; Lee, U.; Jung, I.; Na, J.; Kshetrimayum, K. S.; Han, C. Comparative Study of Process Integration and Retrofit Design of a Liquefied Natural Gas (LNG) Regasification Process Based on Exergy Analyses: A Case Study of an LNG Regasification Process in South Korea. Ind. Eng. Chem. Res. 2014, 53 (37), 14366-14376.

(10)

Park, C.; Song, K.; Lee, S.; Lim, Y.; Han, C. Retrofit Design of a Boil-off Gas Handling Process in Liquefied Natural Gas Receiving Terminals. Energy 2012, 44 (1), 69-78.

(11)

Rao, H. N.; Wong, K. H.; Karimi, I. A. Minimizing Power Consumption Related to BOG Reliquefaction in an LNG Regasification Terminal. Ind. Eng. Chem. Res. 2016, 55 (27), 7431-7445.

(12)

Querol, E.; Gonzalez-Regueral, B.; García-Torrent, J.; García-Martínez, M. J. Boil off Gas (BOG) Management in Spanish Liquid Natural Gas (LNG) Terminals. Appl. Energy 2010, 87 (11), 3384-3392.

(13)

Li, Y.; Li, Y. Dynamic Optimization of the Boil-Off Gas (BOG) Fluctuations at an LNG Receiving Terminal. J. Nat. Gas Sci. Eng. 2016, 30, 322-330.

(14)

Effendy, S.; Khan, M. S.; Farooq, S.; Karimi, I. A. Dynamic Modelling and Optimization of an LNG Storage Tank in a Regasification Terminal with Semi-Analytical Solutions for N2-Free LNG. Comput. Chem. Eng. 2017, 99, 40-50.

(15)

Mokhatab Saeid, Mak John, Valappil Jaleel, Wood, D. A. Handbook of Liquefied Natural Gas, First.; Elsevier, 2014.

(16)

Baek, S.; Hwang, G.; Lee, C.; Jeong, S.; Choi, D. Novel Design of LNG (liquefied Natural Gas) Reliquefaction Process. Energy Convers. Manag. 2011, 52 (8-9), 2807-2814.


Page 21 of 24 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


(17)

Fahmy, M. F. M.; Nabih, H. I.; El-Rasoul, T. A. Optimization and Comparative Analysis of LNG Regasification Processes. Energy 2015, 91, 371-385.

(18)

Uwitonze, H.; Han, S.; Jangryeok, C.; Hwang, K. S. Design Process of LNG Heavy Hydrocarbons Fractionation: Low LNG Temperature Recovery. Chem. Eng. Process. Process Intensif. 2014, 85, 187195.

(19)

Gao, T.; Lin, W.; Gu, A. Improved Processes of Light Hydrocarbon Separation from LNG with Its Cryogenic Energy Utilized. Energy Convers. Manag. 2011, 52 (6), 2401-2404.

(20)

Yeomans, H.; Grossmann, I. E. Optimal Design of Complex Distillation Columns Using Rigorous Trayby-Tray Disjunctive Programming Models. Ind. Eng. Chem. Res. 2000, 39 (11), 4326-4335.

(21)

Choi, I. H.; Lee, S.; Seo, Y.; Chang, D. Analysis and Optimization of Cascade Rankine Cycle for Liquefied Natural Gas Cold Energy Recovery. Energy 2013, 61, 179-195.

(22)

Turton, R.; Bailie, R. C.; Whiting, W. B.; Shaewitz, J. A.; Bhattacharya, D. Analysis Synthesis and Design of Chemical Processes; 2012.

(23)

Navarro-Amorós, M. A.; Ruiz-Femenia, R.; Caballero, J. A. Integration of Modular Process Simulators under the Generalized Disjunctive Programming Framework for the Structural Flowsheet Optimization. Comput. Chem. Eng. 2014, 67, 13-25.

(24)

Khan, M. S.; Husnil, Y. A.; Kwon, Y. S.; Lee, M. Automated Optimization of Process Plant Using Particle Swarm Optimization. In Advanced Control of Industrial Processes (ADCONIP); IEEE, 2011; pp 615–620.

(25)

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 (10), 1606-1617.

(26)

Javaloyes-Antón, J.; Ruiz-Femenia, R.; Caballero, J. A. Rigorous Design of Complex Distillation Columns Using Process Simulators and the Particle Swarm Optimization Algorithm. Ind. Eng. Chem. Res. 2013, 52 (44), 15621-15634.

(27)

Adams, T. A. I.; Seider, W. D. Practical Optimization of Complex Chemical Processes with Tight Constraints. Comput. Chem. Eng. 2008, 32 (9), 2099-2112.

(28)

Martelli, E.; Amaldi, E. PGS-COM: A Hybrid Method for Constrained Non-Smooth Black-Box Optimization Problems. Brief Review, Novel Algorithm and Comparative Evaluation. Comput. Chem. Eng. 2014, 63, 108-139.

(29)

Rios, L. M.; Sahinidis, N. V. Derivative-Free Optimization: A Review of Algorithms and Comparison of Software Implementations. J. Glob. Optim. 2013, 56 (3), 1247-1293.

(30)

Alarifi, A.; Liu, Z.; Erenay, F. S.; Elkamel, A.; Croiset, E. Dynamic Optimization of Lurgi Type Methanol Reactor Using Hybrid GA-GPS Algorithm: The Optimal Shell Temperature Trajectory and Carbon Dioxide Utilization. Ind. Eng. Chem. Res. 2016, 55 (5), 1164-1173.

(31)

Khojasteh Salkuyeh, Y.; Adams, T. A. A Novel Polygeneration Process to Co-Produce Ethylene and Electricity from Shale Gas with Zero CO2 Emissions via Methane Oxidative Coupling. Energy Convers. Manag. 2015, 92, 406-420.

(32)

Khojasteh Salkuyeh, Y.; Adams, T. A. Co-Production of Olefins, Fuels, and Electricity from Conventional Pipeline Gas and Shale Gas with near-Zero CO2 Emissions. Part II: Economic Performance. Energies 2015, 8 (5), 3762-3774.



Page 22 of 24

(33)

Sun, Z.; Xu, F.; Wang, S.; Lai, J.; Lin, K. Comparative Study of Rankine Cycle Configurations Utilizing LNG Cold Energy under Different NG Distribution Pressures. Energy 2017, 139, 380-393.

(34)

Shokrian, M.; High, K. A. An Efficient Multi Criteria Process Optimization Framework: Sustainable Improvement of the Dimethyl Ether Process. Comput. Chem. Eng. 2014, 60, 213-230.

(35)

Khan, M. S.; Lee, M. Design Optimization of Single Mixed Refrigerant Natural Gas Liquefaction Process Using the Particle Swarm Paradigm with Nonlinear Constraints. Energy 2013, 49 (1), 146-155.

(36)

Eberhart, R.; Kennedy, J. A New Optimizer Using Particle Swarm Theory. In MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science; IEEE, 1995; pp 39–43.

(37)

Ratnaweera, A.; Halgamuge, S. K.; Watson, H. C. Self-Organizing Hierarchical Particle Swarm Optimizer with Time Varying Acceleration Coefficients. IEEE Trans. Evol. Comput. 2004, 8 (3), 240255.

(38)

Nickabadi, A.; Ebadzadeh, M. M.; Safabakhsh, R. A Novel Particle Swarm Optimization Algorithm with Adaptive Inertia Weight. Appl. Soft Comput. J. 2011, 11 (4), 3658-3670.

(39)

Deb, K. An Efficient Constraint Handling Method for Genetic Algorithms. Comput. Methods Appl. Mech. Eng. 2000, 186 (2-4), 311-338.

(40)

Le Digabel, S. Algorithm 909. ACM Trans. Math. Softw. 2011, 37 (4), 1-15.

(41)

Currie, J.; Wilson, D. I. OPTI, Lowering the Barrier Between Open Source Optimizers and the Industrial MATLAB User. Found. Comput. Process Oper. 2012, No. January 2012.

(42)

Karin, Z.; Rainer, L. Stopping Criteria for a Constrained Single-Objective Particle Swarm Optimization Algorithm. Informatica 2007, 31, 51-59.


Page 23 of 24 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


List of Figures Figure 1: N2 mole % in send out NG after diluting LNG with N2. Heating values [MJ/Nm3] of LNG streams prior to N2 dilution are given in bracket. Figure 2: N2 mole % in send out NG after blending with lean LNG and diluting with N2. Figure 3: Superstructure of the HHR process. Figure 4: Simulation-based optimization paradigm via HYSYS-MATLAB interface. Figure 5: Process configuration for reducing the heating value of LNG by the HHR process. Figure 6: Profitability of the HHR process under various scenarios for different LNG feeds. Figure 7: LNG break-even purchase price under various scenarios for different LNG feeds. Figure 8: NG break-even selling price under various scenarios for different LNG feeds.



Page 24 of 24

Graphical Abstract


Heating Value Reduction of LNG (Liquefied Natural Gas) by

Recommend Documents