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Cite This: Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Integrating Simulation in Optimal Synthesis and Design of Natural Gas Upstream Processing Networks Saad A. Al-Sobhi,†,‡ Munawar A. Shaik,⊥,§ Ali Elkamel,*,†,§ and Fatih S. Erenay*,∥ †

Department of Chemical Engineering, University of Waterloo, Waterloo, ON N3L 3G1, Canada Department of Chemical Engineering, Qatar University, Doha, Qatar § Department of Chemical Engineering, The Petroleum Institute, Khalifa University of Science & Technology, Abu Dhabi, UAE ∥ Department of Management Sciences, University of Waterloo, Waterloo, ON N3L 3G1, Canada ⊥ Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi− 110016, India ‡

S Supporting Information *

ABSTRACT: A natural gas upstream processing network consists of several main processing units. Many process configurations are available for selection, and the choice of technologies can be vast. There is no single technology or process configuration that is superior in all aspects. Thus, there is a need for a mathematical model that considers different flowsheet configurations and operating mode options and selects optimally among them. In this paper, a comprehensive design and operational mixed integer programming model is presented for superstructure optimization to optimally select the most cost-effective pathway in natural gas upstream processing networks. The key processing units of the considered processing network include stabilization, acid gas removal, dehydration, sulfur recovery, natural gas liquid (NGL) recovery, and NGL fractionation. The developed optimization model considers a superstructure with all available technologies for each processing step as well as mode of operation, such as variations in temperature and pressure which impacts the product yields. These units have been simulated using ASPEN Plus to determine the yields of different units for each design alternative under different operating modes. The bilinear terms in the resulting mixed integer nonlinear programming (MINLP) model are linearized based on either input or output streams, whichever are less in number. The model has been applied to design and operate optimally the natural gas upstream processing network. Two illustrative case studies are presented to show the applicability of the overall framework and formulated models.

1. INTRODUCTION The main role of a natural gas upstream processing facility/ network is to process both associated and nonassociated gas to produce high-quality natural gas and liquid hydrocarbon products. Such a facility/network typically consists of several major processing units including stabilization, acid gas removal, dehydration, sulfur recovery, natural gas liquid (NGL) recovery, and NGL fractionation. For each processing unit, there is no single technology or process configuration that is superior in all aspects. In addition, process unit design and selection of the corresponding processing modes should be made in an integrated manner. Therefore, a vast number of natural gas facility/network configurations (i.e., all possible combinations of design alternatives and operating modes) need to be evaluated for proper selection of processing unit designs and their efficient integration as a whole system which significantly impacts the network performance. The main objective of this work is to provide a systematic framework that enables consideration and evaluation of several possible flowsheet alternatives for developing the superstructure of a given natural gas upstream processing network with the aid of optimization theory. In particular, mixed integer © XXXX American Chemical Society

optimization is extensively used in several process industry applications such as production planning, process design, and network design.1 In this context, a comprehensive mixed integer linear programming model is proposed in this paper. In general, natural gas processing networks are designed to maximize the profit while other metrics including plant capex, efficiency, power consumption, product throughput, and CO2 emission can also be considered. The model helps decision makers in the natural gas industry to optimally select the most appropriate processing network configuration. Because several studies have addressed offshore processing and limited attention has been given to onshore processing, the present work focuses on modeling and optimization of onshore processing. Note that this work significantly differs from our previous work with respect to the network representation where the network topology was fixed.2 In other words, a Special Issue: PSE Advances in Natural Gas Value Chain Received: June 27, 2017 Revised: November 5, 2017 Accepted: November 16, 2017

A

DOI: 10.1021/acs.iecr.7b02624 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research

production, product delivery, and inventory management with different levels of rigorousness. The objective function was set to maximize the total profit; the difference between sales revenue and the total operating cost plus any penalty for not meeting demands and inventory targets. Quaglia et al.5 presented an MINLP formulation to simultaneously solve both the business and engineering aspects of soybean resource allocation problems. Floudas et al.6 and Wood et al.7 presented reviews of alternate processes for conversion of natural gas to multiple liquid fuels (GTL). Baliban et al.8 simulated seven process alternatives based on the H2Car process with detailed economic analysis for hybrid biomass, coal, and natural gas to liquid transportation fuels. Baliban et al.9 presented an optimization based synthesis model for GTL based on different natural gas conversion technologies including steam reforming, autothermal reforming, partial oxidation to methanol, and oxidative coupling to olefins, along with simultaneous heat, power, and water integration. Onel et al.10 presented an MINLP model for process synthesis superstructure including multiple natural gas conversion pathways, methanol conversion technologies, and olefin purification section for production of C2−C4 olefins from natural gas via methanol intermediate. Similarly, optimization based synthesis models were proposed for conversion of biomass and natural gas to liquid fuels11 and olefins.12 Gao and You 13 presented a review of design and optimization approaches for shale gas energy systems. He and You14 presented techno-economic analysis for three novel process designs for integrating shale gas processing with ethylene production. He and You15 presented ASPEN simulation-based NSGA-II optimization for producing chemicals through oxidative coupling of methane and cocracking technologies from shale gas and bioethanol. He and You16 presented techno-economic and environmental analysis for shale gas to olefins. Yang and You17 presented technoeconomic and life cycle analysis for production of ethylene and propylene from shale gas. Gong and You18 presented superstructure and product distribution optimization for integrated shale gas processing and chemical manufacturing processes. Garcia and You19 presented an MINLP model for design and synthesis of a bioconversion network with 193 technologies and 129 materials based on economic and environmental criteria. Granjo and Oliveira20 presented techno-economic analysis based on ASPEN simulation for producing sodium methoxide, a catalyst used in biodiesel production. Gong and You21 presented an MINLP model for design and synthesis of an algae processing network with 7800 processing routes based on economic and environmental criteria. In this context, the unique feature of the presented paper is proposing a comprehensive MILP model to derive the optimal superstructure of a natural gas upstream processing network by considering different design alternatives of the key processing units (e.g., different acid gas removal technologies, different sulfur recovery configurations, etc.) in an integrated manner with selection of their optimal operating conditions/modes. This is achieved by sequentially using ASPEN Plus simulation platform to evaluate the performance of different processing unit flowsheets under various operating conditions. Another unique feature is that this is the first paper that focuses on the superstructure of natural gas onshore/upstream processing network which is an important component of the overall natural gas supply chain. In conclusion, we can mention that

prespecified technology and operating mode were utilized for the major processing units. In this paper, we extend our earlier work2 by considering a superstructure of the natural gas processing network with various design alternatives for simulation and optimization as shown in Figure 1.

Figure 1. Representation of the material flow for a natural gas upstream processing network.

The natural gas stream (Fnatgas) enters the processing network at a stabilization unit, Unit A, producing a C5+ product whose quality depends on the operating mode of selected technology. Then, the resulting residual gas (Fresgas) enters the acid gas removal unit, Unit B, for H2S and CO2 capturing. The resulting acid gas stream (Facidgas) enters the sulfur recovery unit, Unit C, to produce sulfur and tail gas. The resulting sweet-gas stream (Fsweetgas) from Unit B with the required H2S and CO2 concentration level enters the dehydration unit, Unit D, for water removal to reduce pipeline corrosion and prevent line blockage caused by hydrate formation. The dehydrated stream (Fdehgas) enters the NGL recovery unit, Unit E, to recover NGL products from the stream. The resulting NGL stream (FNGL) is finally fractionated into ethane, LPG, and plant C5+ products in Unit F. Although modeling, simulation, and optimization design studies for other fuel types and products have been addressed previously, to the best of our knowledge, the design and operation of enterprise-wide natural gas upstream processing network has not been addressed in the literature to this extent. For example, Liu et al.3 presented a mixed integer nonlinear programming (MINLP) model for the optimal design of poly generation energy systems of a coal-based plant producing electricity and methanol. A suitable superstructure was proposed by partitioning a general poly generation energy system into four major blocks, where for each block alternative technologies and types of equipment were considered. Schulz et al. 4 addressed the supply chain optimization of a petrochemical complex consisting of NGL, ethylene, chlorine, vinyl chloride monomer (VCM), polyvinyl chloride (PVC), polyethylene, ammonia, and urea plants. They formulated two multiperiod finite-horizon MINLP models coordinating B


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form a weak acid when dissolved in water. The acid gas removal is conducted to meet specifications and prevent corrosion and plugging problems. Currently, there are four possible contexts for natural gas purification through acid gas removal:26 (i) CO2 removal from a gas that contains no H2S, (ii) H2S removal from a gas that contains no CO2, (iii) simultaneous removal of both CO2 and H2S, and (iv) selective removal of H2S from a gas that contains both CO2 and H2S. Since the acid gas concentration and the final product specification varies based on region and consumers, many different purification processes are available for selection. Liquid-phase absorption processes are the most common sweetening technologies and classified into three categories:22 chemical solvent, physical solvent, and hybrid solvent. In the chemical solvent process, absorption of acid gases is achieved mainly by the use of alkanolamines or alkaline salt of various weak acids such as sodium and potassium salts of carbonate. Physical solvent processes use an organic solvent without any chemical reaction, i.e., acid gas removal depends on physical absorption. Hybrid solvent processes use a mixture of a chemical and physical solvent. In all liquid absorption processes, the two major cost factors are the solvent circulation rate (affecting both equipment size and operating costs) and the energy requirement for solvent regeneration.22 In this study we focus on chemical solvent method based on diethanolamine (DEA) and methyldiethanolamine (MDEA) amines, in different operating modes based on concentrations ranging from approximately 10 to 65 wt % amines. An additional option based on two absorbers in sequence (e.g., first MDEA and then DEA) is also considered. A detailed discussion may be found in the works of Campbell,23 GPSA,24 and Kohl and Nielson.27 2.3. Sulfur Recovery Unit C. Currently, two options are available for dealing with gases with significant H2S component: (i) disposal of the gas by injection into underground formations and (ii) conversion of the H2S into usable elemental sulfur. The main purpose of the sulfur recovery unit is converting the H2S component in the acid gas to elemental sulfur which is commonly used for sulfuric acid production. This conversion is done industrially using Claus process or one of its modifications. The two modified Claus processes are straightthrough and split-flow. The straight-through process is preferred when the feedstock has a high H2S concentration, e.g., up to 55 mol % H2S. The split-flow configuration can process feedstock that contains lower concentrations, e.g., 5−30 mol % H2S. The straight-through process provides the highest sulfur-recovery efficiency.24 All Claus units involve a two-step process of initial combustion in a furnace followed by processing of combustion products through a series of catalytic converters, to produce elemental sulfur. A tailgas cleanup unit is often employed to eliminate the last sulfur compounds to meet environmental regulations. The most commonly used Claus processes are Shell Claus offgas treating, SUPERCLAUS, and cold-bed adsorption.24 In this study we focus on straightthrough and split-flow Claus processes with operating modes based on different temperatures. 2.4. Dehydration Unit D. The water removal processes considered in the industry are mainly based on absorption and adsorption. The water level in sweet gas can be reduced to 10 ppmv range via a physical absorption process in which the gas is contacted with a liquid that absorbs the water vapor.26 In practice, different glycols such as ethylene glycol, diethylene glycol, triethylene glycol, tetraethylene glycol, and propylene glycol are commonly used as absorbents. In this study we focus

our main contribution lies in integration of simulation within a mathematical programming modeling framework to realistically represent the effect of the selected processing unit designs and operating modes on product yields. Unlike many existing models that are based on constant yields, we are able to use different yield values for each combination of processing unit design and operating mode. These yield values are obtained by developing and calibrating simulation models in ASPEN Plus representing the processing units with possible design alternatives. These yields for different designs and operating conditions were then integrated within a mathematical programming model through the use of binary variables for different choices of operating modes. The remainder of the paper is organized as follows: The description of key processing units, problem statement, and development of superstructure are presented in sections 2 and 3, respectively. The base model formulation is presented in section 4, and a reformulated model is presented in section 5. The results of two illustrative case studies are reported in section 6 to show the applicability of the proposed approach. The paper ends with some concluding remarks in section 7.

2. PROCESS DESCRIPTION OF MAJOR PROCESSING UNITS In this section, the main processes, available technologies, other design alternatives, and possible operating modes for each major processing unit of the natural gas network in Figure 1 are described. 2.1. Stabilization Unit A. The primary purpose of the stabilization unit is to recover the intermediate and heavy C5+ byproducts early from the natural gas feedstock. The contribution of revenue from these liquid byproducts to the profit can be maximized by adjusting the fraction of byproduct recovery while meeting the specifications of the gas feedstock (e.g., residual gas) fed to the next major processing units. The stabilized liquid is characterized by its vapor pressure and hydrogen sulfide content. The stabilization process can be performed in industry through either flash vaporization or fractionation. The flash vaporization is a simple operation where the feed is flashed through two or three flash tanks. The separation between the vapor and condensate phases occurs due to equilibrium principles. However, flash vaporization is an old technology and is not currently used in modern gas plants.22 On the other hand, stabilization by fractionation is a modern and widely accepted technology in the natural gas industry. The stabilization is typically carried out in an absorber with a reboiler and internal trays. Moreover, a refluxed distillation tower is used for better separation. The C5+ condensate product is sold based on a specified Reid vapor pressure (RVP) defined by the customer. The RVP is controlled by manipulating the bottom reboiler temperature. Different product qualities (with varying RVP) can be produced by changing the operating conditions (particularly the column pressure) of stabilization column. A detailed discussion can be found in the works of Campbell23 and GPSA.24 2.2. Acid Gas Removal Unit B. The primary purpose of the acid gas removal unit is to reduce the concentration of the acid gases, carbon dioxide (CO2) and hydrogen sulfide (H2S), in the residual gas from Unit A to very low levels. Natural gas with H2S or other sulfur compounds is called sour gas, whereas gas with only CO2 is called sweet gas.22,25 Furthermore, H2S and CO2 are referred to as acid gas components because they C


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Industrial & Engineering Chemistry Research Table 1. Different Possible Technologies and Operating Modes Considered in the Superstructure major processing unit (i) condensate stabilization (A)

process/technology considered here (Ji)

possible processes/technologies 1. flash vaporization

operating pressure

direct conversion (liquid phase)

solvent type solvent concentration absorber arrangements, etc.

gas phase Claus

Claus straight at different temperatures Claus split at different temperatures glycol concentration and circulation rate

sulfur recovery (C)

stabilization by fractionation indirect conversion direct conversion, (i.e., dry-bed or liquid phase) separation technologies (i.e., membrane or cryogenic fractionation) 1. Gas Phase Claus type

dehydration (D)

2. liquid phase 1. liquid desiccant (glycol)

glycol

NGL recovery (E)

2. solid desiccant 3. cooling the gas 1. refrigeration process

refrigeration process

demethanizer pressure in expander plant

2. 3. 4. 5. 1. 2.

direct sequence indirect sequence

molar recovery

acid gas removal (B)

NGL fractionation (F)

2. 1. 2. 3.

operating modes (Mi)

stabilization by fractionation

lean oil absorption solid-bed adsorption membrane separation twister supersonic direct sequence indirect sequence

on use of triethylene glycol being the most common24 in industry. 2.5. NGL Separation Unit E. The primary purpose of NGL separation unit is to separate the feedstock gas from liquids to reach a specific heating value because pipeline quality gas specification requires a high heating value of around 950−1150 Btu/scf (35 400−42 800 kJ/Sm3) as well as limiting the sulfur and water content.25 The process elements for hydrocarbon recovery vary based on the desired products, gas volume being processed, and inlet gas composition/pressure. Broadly, the commercially available technology options for NGL separation are (i) refrigeration processes, (ii) lean oil absorption, (iii) solid bed adsorption, (iv) membrane separation, and (v) twister supersonic separation. A detailed discussion can be found in the works of Campbell23 and GPSA.24 In this study we focus on use of refrigeration process, being most common in industry, with different operating modes based on demethanizer pressures in an expander plant. 2.6. Fractionation Unit F. The primary purpose of this unit is to fractionate the NGL stream into its various components (ethane, propane, isobutene, normal butane, and gasoline) which have a higher market value as pure products.22 The bottom liquid from Unit F is fractionated by heating and passing it through a series of distillation columns. There are many design options related to the sequence of distillation columns for product recovery. The number of possible sequences can be calculated28 based on the number of components (K), as [2(K − 1)!/K!(K − 1)!]. In this study we focus on two process schemes: direct (conventional) and indirect (nonconventional) configurations with different product recoveries. In the direct configuration, de-ethanizer, depropanizer, and debutanizer are placed in sequence to separate NGL into different products; whereas in the nonconventional configuration, the depropanizer, de-ethanizer, and debutanizer are placed in sequence. The different process alternatives considered for each processing unit in the overall superstructure of a natural gas upstream processing network are summarized in Table 1.

3. PROBLEM STATEMENT Consider a superstructure with different design alternatives and operating modes for each key processing unit, such as shown in Table 1. It is desired to determine the optimal natural gas processing network configuration that maximizes production (e.g., product yields), minimizes the capital investment and operating costs, and meets the product specifications (e.g., RVP value) along with environmental constraints (e.g., CO2 and H2S concentration levels), using a simulation−optimization framework. Figure 2 illustrates the general solution strategy for using

Figure 2. Sequential use of simulation and optimization methods to optimize natural gas network design and operation. D


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among the available options is selected, and eq 2 is the overall mass balance around Unit A. Equations 3 and 4 specify the feedstock rate of C5+ and residual gas generated by Unit A based on the product yields associated with the selected technology and operating mode. Note that equations 3 and 4 are nonlinear as the binary variable Xij,m is multiplied with continuous variable FNatgas. They can be linearized to solve the problem efficiently. Lastly, constraints 5 and 6 ensure compliance with operational upper limits in residual gas (FUresgas) and field condensate (FUfieldC5+) productions, respectively.

ASPEN Plus simulation platform and mixed integer programming approaches sequentially to derive an optimal solution for this problem. Prior to the selection of optimal technology, the set of conditions for each key processing unit should be specified such as flow rate, composition, pressure, and temperature of feedstock, specification levels for product purity and quality, and capital and operation costs for each process. We start with a specific natural gas flow rate, composition, and operating conditions (model boundary) for the simulation. The yields for different technologies and operating modes are determined after running ASPEN simulation for each unit, along with estimation of annual capital and operating costs using ICARUS software. Then we employ this information as input to the formulation and solution of MINLP model. Before we solve the model for maximization of profit, we consider model reformulation and linearization based on either input or output streams, whichever is less in number, as explained later in section 5. The output from the solution is compared to the set of conditions specified for running the ASPEN simulation to begin with. The simulation is carried out with updated operating conditions and revised yield data is obtained for input to optimization and the procedure is continued until convergence.

∑ ∑ XjA,m = 1 (1)

FNatgas = Fresgas + FfieldC5 +

(2)

FfieldC5 + =

∑ ∑ XjA,myAfieldC5 +FNatgas j,m

m ∈ MA j ∈ JA

(3)

∑ ∑ XjA, myAresgas FNatgas

Fresgas =

j,m

m ∈ MA j ∈ JA

(4)

U FfieldC5 + ≤ FfieldC5 +

(5)

U Fresgas ≤ Fresgas

4. MATHEMATICAL FORMULATION The proposed mixed integer programming model includes maximization of a profit function subject to the model constraints for balancing material flow between the major processing units, satisfying end-product demands as well as complying with CO2 and H2S quality specifications and operational restrictions. The following constraints in the model mathematically describe the operational and material flow in each block of the superstructure in terms of overall mass balance, yield, quality, use of different technologies, and capacity constraints. The details of ASPEN simulation flowsheets for each unit under different design alternatives are provided in the Supporting Information. 4.1. Overall Mass Balance and Yield Model. Constraints 1−58 represent the selection of design alternatives and processing modes, overall mass balance, and yield specifications for units A−F. In this model formulation, FNatgas denotes the continuous decision variable of natural gas feedstock rate. Moreover, Xij,m is the binary decision variable specifying the design alternative (e.g., technology) and operating mode selection for unit i ∈ I where Ji and Mi refer to sets of design alternatives and operating modes, respectively. That is X ji , m

m ∈ MA j ∈ JA

(6)

yfieldC5+ Aj,m

yresgas Aj,m

The C5+ and residual gas yield values and for selecting technology j and operating mode m are obtained from ASPEN Plus steady state simulator, and are used to estimate the field condensate and residual gas flow rates (FfieldC5+ and Fresgas, respectively) in eqs 3 and 4 for a given natural gas flow rate. It is worth mentioning here that the natural gas feedstock flow rate is variable in the optimization model stage. However, for the simulation, we consider a specific feed flow rate value and find the yields. To solve the model effectively, these equalities are linearized and the model is solved as an MILP. Equations 3 and 4 can be linearized by defining additional continuous variables FNatgasj,m, Fresgasj,m, and FfieldC5+j,m and by replacing constraints 3−6 with eqs 3a−6a.

∑ ∑ FNatgas

j,m

=

m ∈ MA j ∈ JA

∑ ∑ FfieldC5 +

j,m

m ∈ MA j ∈ JA

+

∑ ∑ Fresgas

j,m

m ∈ MA j ∈ JA

FfieldC5 +j ,m = yAfieldC5 + FNatgasj ,m j,m

⎧1, if technology j with operating mode m is ⎪ =⎨ selected for unit i ⎪ ⎩ 0, otherwise

∀ j ∈ JA , m ∈ MA

(4a)

A U FfieldC5 +j ,m ≤ Xjm FfieldC5 +

∀ j ∈ JA , m ∈ MA

(5a)

A U Fresgasj ,m ≤ Xjm Fresgas

m ∈ {1, 2, ..., |Mi|}

(3a)

yAresgas FNatgasj ,m j,m

Fresgasj ,m =

∀ i ∈ {I : A , B , ..., F }, j ∈ {1, 2, ..., |Ji |},

∀ j ∈ JA , m ∈ MA

(2a)

∀ j ∈ JA , m ∈ MA

(6a)

For subsequent units, we directly present the linearized equations. Constraints for Unit B. The next set of constraints addresses the removal of H2S- and CO2-rich acid gas stream from residual gas. Constraints 7−12 present the formulation for Unit B where Fsweetgas and Facidgas refer to the sweet and acid gas mass flow rate. Equations 7 and 8 manage selection of acid gas removal technology with an appropriate operating mode, and the overall

Constraints for Unit A. The purpose of the stabilization process in Unit A is the recovery of C5+ from natural gas feedstock. This C5+ product is called field condensate since it is recovered early from natural gas field feedstock with a mass flow rate FfieldC5+· Constraints 1−6 present the formulation for Unit A where Fresgas refers to the mass flow rate of residual gas. Equation 1 ensures that just one stabilization technology E


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mass balance, respectively. Equations 9 and 10 specify the feedstock rate of acid gas and sweet gas generated in Unit B based on product yields associated with the selected technology and operating mode. The yield values ysweetgas and yacidgas given Bj,m Bj,m in eqs 9 and 10 are obtained from ASPEN Plus steady state simulator. Lastly, constraints 11 and 12 ensure compliance with operational upper limits (FUsweetgas) in acid and sweet gas (FUacidgas) productions, respectively.

CO2 A CO2 Fresgas ≤ Xjm Fresgas jm

U

CO2 A CO2 FfieldC5 +jm ≤ XjmFfieldC5 +

(7)

Fresgas = Fsweetgas + Facidgas

(8)

∑ ∑ yBsweetgas Fresgas

Fsweetgas =

m

j,m

j

∑ ∑ yBacidgas Fresgas

Facidgas =

m

j,m

j

B U Fsweetgasjm ≤ Xjm Fsweetgas

jm

(9)

(23)

CO2 CO2 CO2 Fresgas = Facidgas + Fsweetgas

(24)

(10)

H2S ∑ ∑ yBH2Sacidgas Fresgas m

H2S Fsweetgas =

(11)

Facidgasjm ≤

∀ j ∈ JB , m ∈ MB

(12)

CO2 Facidgas =

H2S and CO2 Quality Specifications in Units A and B. The H2S and CO2 in the sweet gas need to satisfy certain specifications for LNG and GTL production, (e.g., H2S concentration in sweet gas need to be less than 4 ppmv for GTL application and CO2 concentration need to be either 50 ppmv for LNG or possibly up to 1 or 2%v for GTL application). Therefore, we need to have similar constraints and equations on H2S and CO2 flow to manage their mass balance around Units A and B. Constraints 13−18 specify the H2S and CO2 related mass flow around Unit A based on selected technology and operating mode. The yield values yH2Sresgas , yH2SfieldC5+ , yCO2resgas , and Aj,m Aj,m Aj,m CO2fieldC5+ yAj,m are obtained from the simulator. Equations 19−22 ensure compliance with the aforementioned H2S and CO2 concentration specifications as production upper limit constraints as the final produced methane gas is assumed to be sent to LNG and GTL plants. H2S H2S H2S FNatgas = Fresgas + FfieldC5 +

(13)

CO2 CO2 CO2 FNatgas = Fresgas + FfieldC5 +

(14)

H2S Fresgas =

H2S ∑ ∑ yAH2Sresgas FNatgas m

H2S FfieldC5 +

=

∑∑ m

CO2 Fresgas

=

=

j

≤

H2S yAH2SfieldC5 + FNatgas jm j,m

j

H2S yACO2fieldC5 + FNatgas jm j,m

A H2SU Xjm Fresgas

∀ j ∈ JA , m ∈ MA U

H2S A H2S FfieldC5 +jm ≤ XjmFfieldC5 +

∀ j ∈ JA , m ∈ MA

jm

j,m

j

H2S ∑ ∑ yBCO2acidgas Fresgas

CO2 Fsweetgas =

(25)

H2S ∑ ∑ yBH2Ssweetgas Fresgas

m

j,m

j

jm

j,m

j U

CO2 B CO2 Facidgas ≤ Xjm Facidgas jm

(26)

jm

H2S ∑ ∑ yBCO2sweetgas Fresgas m

(27) jm

(28)

∀ j ∈ JB , m ∈ MB

(29)

CO2 B CO2 Fsweetgas ≤ Xjm Fsweetgas ∀ j ∈ JB , m ∈ MB jm

(30)

U

U

H2S B H2S Facidgas ≤ Xjm Facidgas jm

∀ j ∈ JB , m ∈ MB

U

H2S B H2S Fsweetgas ≤ Xjm Fsweetgas jm

∀ j ∈ JB , m ∈ MB

(31) (32)

Constraints for Unit C. The H2S-rich acid gas stream with feedstock rate Facidgas is fed to Unit C to recover elemental sulfur. The mass balance equations around Unit C are given by constraints 33−38 where Fsulfur and Ftailgas refer to the mass flow rates of the sulfur and tail gas produced in Unit C, ytailgas Cj,m and denote the tail gas and sulfur yields for the selected design ysulfur Cj,m alternative and the operating mode calculated using the simulator, and FUtailgas and FUsulfur represent the operational upper limits for tail gas and sulfur production.

jm

H2S yACO2resgas FNatgas jm j,m

∑∑ m

H2S Fresgas jm

j

∑∑ m

CO2 FfieldC5 +

j,m

j

j,m

j

m

B U Xjm Facidgas

(22)

H2S H2S H2S Fresgas = Facidgas + Fsweetgas

H2S Facidgas =

jm

∀ j ∈ JB , m ∈ MB

∀ j ∈ JA , m ∈ MA

(21)

Similarly, eqs 23−32 specify H2S and CO2 related mass flow around Unit B based on selected technology and operating mode. The yield values yH2Sacidgas , yH2Ssweetgas , yCO2acidgas , and Bj,m Bj,m Bj,m CO2sweetgas yBj,m are obtained from the ASPEN Plus simulator. Since CO2 and H2S concentrations will be at the appropriate parts per million levels after acid gas removal, their specific material balance is not considered in the formulation beyond Unit B.

∑ ∑ XjmB = 1 m ∈ MB j ∈ JB

∀ j ∈ JA , m ∈ MA

(15)

∑ ∑ X Cjm = 1

(16)

m ∈ MC j ∈ JC

(33)

Facidgas = Ftailgas + Fsulfur

(34)

Ftailgas =

(17)

∑ ∑ yCtailgas Facidgas m

Fsulfur =

(18)

j,m

j

∑ ∑ yCsulfur Facidgas m

j

j,m

(19)

U Ftailgasjm ≤ X CjmFtailgas

(20)

U Fsulfurjm ≤ X CjmFsulfur

F

jm

(35)

jm

(36)

∀ j ∈ JC , m ∈ MC ∀ j ∈ JC , m ∈ MC

(37) (38)


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Industrial & Engineering Chemistry Research Constraints for Unit D. The sweet gas stream Fsweetgas from Unit B is sent to the dehydration unit for water removal. Water mass balance around Unit D is given in constraints 39−44, where Fdehgas and FwaterD refer to the mass flow rates of the dehydrated gas and removed water in Unit D, ydehgas and ywater Dj,m Dj,m denote the associated yields from ASPEN Plus simulator, and FUdehgas and FUwater represent the operational upper limits for tail gas and sulfur production. D Xjm

∑ ∑

(39)

Fsweetgas = FwaterD + Fdehgas

(40)

∑ ∑ yDwater Fsweetgas j,m

m ∈ MD j ∈ JD

D U Xjm Fwater

D U Fdehgasjm ≤ Xjm Fdehgas

∑ ∑

(60)

(42)

DplantC5 + ≤ FplantC5 +

(61)

∀ j ∈ JD , m ∈ MD

(43)

Dsulfur ≤ Fsulfur

(62)

∀ j ∈ JD , m ∈ MD

(44)

DC2 ≤ FC2

(63)

DLPG ≤ FLPG

(64)

DCH4 ≤ FCH4

(65)

A FNatgasjm ≤ Q jAXjm

Fdehgas = FC1 + FNGL

(46)

Facidgasjm ≤ Q jCX Cjm

∑ ∑ yEC1 Fdehgas j,m

m ∈ ME j ∈ JE

FNGLjm ≤

E U FC1jm ≤ Xjm FC1

D Fsweetgasjm ≤ Q jDXjm

jm

(47)

yENGL Fdehgasjm j,m

E U Xjm FNGL

∀ j ∈ JE , m ∈ ME

∀ j ∈ JE , m ∈ ME

E Fdehgasjm ≤ Q jEXjm

m ∈ MF j ∈ JF

j,m

(70)

(50)

H2S H2S Fsweetgas /Fsweetgas ≤ qsweetgas

(73)

All the variables are non‐negative

(74)

(52)

∀ j ∈ JF , m ∈ MF

(71)

Constraint 59 guarantees that natural gas feedstock is set to a UB value between the lower (FLB Natgas) and upper (FNatgas) supply limit of the market. Constraints 60−65 ensure that enough end products will be produced to cover the demand for field C5+ (DfieldC5+), plant C5+ (DplantC5+), sulfur (Dsulfur), C2 (DC2), LPG (DLPG), and methane (DCH4). Capacity constraints of major processing units of the network are given by constraints 66−71, where Qij, i ∈ {A, ..., F} denotes the upper capacity limit for unit i with technology j. Some additional quality constraints on the allowable limits of CO2 and H2S fraction in sweet gas from Unit B are given in constraints 72 and 73. 4.3. Objective Function. The proposed mathematical programming model optimizes the network design and operation to maximize the annual profit in eq 75. The total annual profit is represented as the difference between the overall sales revenue and total annual cost which includes

jm

∑ ∑ yFLPG FNGL

∀ j ∈ JE , m ∈ ME

(69)

(72)

FNGL = FC2 + FLPG + FplantC5 +

FLPG =

∀ j ∈ JD , m ∈ MD

(68)

CO2 CO2 Fsweetgas /Fsweetgas ≤ qsweetgas

(51)

j,m

∀ j ∈ JC , m ∈ MC

(67)

(49)

m ∈ MF j ∈ JF

m ∈ MF j ∈ JF

∀ j ∈ JB , m ∈ MB

(66)

F FNGLjm ≤ Q jFXjm

∑ ∑ XjmF = 1

∑ ∑ yFC2 FNGL

∀ j ∈ JA , m ∈ MA

(48)

Constraints for Unit F. Finally, the recovered NGL stream in Unit D is sent to the fractionation unit, Unit F, for C2, LPG, and plant C5+ production with rates FC2, FLPG, and FplantC5+, respectively. The overall mass flow around Unit F is given according to constraints 51−57. Although the field C5+ and plant C5+ streams have similar compositions, it is assumed that plant C5+ has a higher selling value.

FC2 =

(58)

DfieldC5 + ≤ FfieldC5 +

jm

B Fresgasjm ≤ Q jBXjm

∑ ∑

(57)

(59)

(45)

FNGL =

∀ j ∈ JF , m ∈ MF

(56)

LB UB FNatgas ≤ FNatgas ≤ FNatgas

=1

m ∈ ME j ∈ JE

(55)

4.2. Other Constraints. The proposed MILP model also incorporates other operational constraints related to raw material supply, demand satisfaction, capacity limits, and product quality restrictions into the formulation. All these additional constraints are as follows:

m ∈ ME j ∈ JE

FC1 =

jm

∀ j ∈ JF , m ∈ MF

F U FplantC5 +jm ≤ Xjm FplantC5 +

Constraints for Unit E. The methane-rich dehydrated gas stream enters Unit E which produces methane and NGL with mass flow rates FC1 and FNGL. The overall mass flow around NGL unit is given in constraints 45−50. E Xjm

j,m

∀ j ∈ JF , m ∈ MF

F U FLPGjm ≤ Xjm FLPG

(41)

j,m

m ∈ MD j ∈ JD

FwaterDjm ≤

F U FC2jm ≤ Xjm FC2

jm

∑ ∑ yDdehgas Fsweetgas

Fdehgas =

∑ ∑ yFplantC5 +FNGL m ∈ MF j ∈ JF

=1

m ∈ MD j ∈ JD

FwaterD =

FplantC5 + =

(53) jm

(54) G


Article

Industrial & Engineering Chemistry Research annualized capital cost, variable annual operating costs, fixed annual operating costs of major units, and the annual cost of natural gas usage. It is assumed that capital costs are amortized over the project lifetime of 20 years with 10% compound interest rate. Thus, the overall model can be represented as follows: max

based on yields is an equality constraint given in eq 79. It can be eliminated along with the outflow variable, Fsout, by substituting it in the objective function and relevant constraints. max ∑ spp Fsout − np s∈P

−

∑ spp Fp − npFnatgas − ∑ ∑ ∑ X jmi p∈P

(ACCijm + AOCijm)

in ∑ ∑ Fnatgas, j,m m ∈ Mi j ∈ Ji

∑ ∑ ∑ X jmi(ACCijm + AOCijm) i ∈ I m ∈ Mi j ∈ Ji

i ∈ I m ∈ Mi j ∈ Ji

∑ ∑ X ji,m = 1

(75)

(76)

∀i∈I (77)

m ∈ Mi j ∈ Ji

s.t.

in i i i ,UB F ji ,,LB m X j , m ≤ Fs , j , m ≤ X j , mF j , m

Constraints 1−74, where P  {fieldC5+, S, C1, C2, LPG, plantC5+} denotes the set of commercial endproducts spp is selling price of each product np is natural gas feedstock price Fp is product mass flow rate Fnatgas is natural gas mass flow rate ACCijm is amortized capital cost of unit i for technology j in operating mode m AOCijm is annual operating cost of unit i for technology j in operating mode m

∀ i ∈ I , j ∈ Ji , m ∈ Mi , s ∈ Siin Fsout =

i ∑ ∑ ∑ Y sjm ∑ i ∈ Isout

m ∈ Mi j ∈ Ji

Fsin′ , j , m

(78)

∀ s ∈ S out

s ′∈ Siin

(79)

In the above model, so far there is no connection across different units. Therefore, we propose to use the following plant topology based constraint, which ensures adherence to the sequence of units as per the specified plant topology.

∑

Fsout =

i ∈ Iout s

5. MODEL REFORMULATION AND GENERALIZATION It can be observed that the base model presented above has some extra dependent variables and equality constraints. As a first step, these are eliminated to make the problem compact. More importantly, it can be observed that the linearization of bilinear terms was done primarily based on output streams of each unit. Since the number of output streams is generally more than the number of input streams for any unit, we consider model reformulation using linearization of bilinear terms based on input streams for each unit, as the second step toward reformulation. In general, we can consider linearization based on either input or output streams for each unit of other networks, whichever are less in number, as mentioned in section 3. For instance, for Unit A, rather than defining three bilinear terms FNatgasj,m, Fresgasj,m, and FfieldC5+j,m we define only one term based on input stream (natural gas), FNatgasj,m. The output streams are simply calculated from input streams using yield values. Similarly for Unit B the linearization is done based on input stream, i.e. residual gas, and so on. To generalize the base model presented in section 4, the following additional nomenclature is introduced. Index s or s′ is defined for all streams; s ∈ Sini = {natgas, resgas, acidgas, sweetgas, dehgas, NGL) refers to input streams entering unit i out ∈ Iins ; s ∈ Sout i refers to output streams leaving unit i ∈ Is ; s ∈ Sint = {resgas, acidgas, sweetgas, dehgas, NGL} refers to connecting/intermediate streams between units, specifying plant topology and unit sequence/connectivity. We define Fins,j,m the flow rate of incoming stream s ∈ Sini with selected technology j in operating mode m. Fout is the flow rate of s outgoing stream s ∈ Sout for each unit i. i 5.1. Reformulated Model. The following is the reformulated model. The generalized objective function is given in eq 76. The selection of technology and mode for each unit is given in eq 77. The activation of flow rates based on input streams is given in eq 78 for selection of technology j and mode m. The same equation can be used for capacity constraints in each unit with updated bounds. The calculation of output flow rates

∑ ∑ ∑ Fsin,j ,m

∀ s ∈ S int

i ∈ Isin m ∈ Mi j ∈ Ji

(80)

Equation 80 states that the total outgoing flow rate from the units, which produce each connecting/intermediate stream, should be equal to the total incoming flow rate entering the next unit in sequence, which consumes this stream. The above plant topology constraint also replaces the conventional material balances used across units, i.e., instead of using eqs 2, 2a, 8, 34, 40, 46, and 52, we just use eq 80. The demand constraint is given in eq 81 for all products. Fsout ≥ Ds

∀s∈P

(81)

The mole fraction of component k (CO2 and H2S) in sweet gas from Unit B for technology j in mode m should be within the acceptable limit as given in eq 82, where the parameter, MFk,sweetgas , is obtained from ASPEN simulations. B,j,m k MFkB,sweetgas ≤ XjB, mqsweetgas ,j,m

∀ k , j, m

(82)

Thus, in the reformulated MILP model, all the earlier 75 equations of the base model are captured in 7 blocks of equations, 76−82.

6. ILLUSTRATIVE CASE STUDIES Two illustrative case studies are presented to show the applicability of the overall modeling framework formulated in sections 3−5. In the first case, the design and operation of a single processing unit is optimized; whereas, in the second case, a generic natural gas upstream processing network with the setting shown in Figure 1 and Table 1 is optimized as a whole system. To generate realistic case instances, we derived the steady state operation characteristics (e.g., yields) of each major processing units using ASPEN Plus V7.329 [details given in the Supporting Information]. A typical natural gas composition (mol %) used in our analysis was adopted from an earlier study2 and is shown in Table 2. The sale prices of end-products and cost of raw-materials2 are shown in Table 3. The plantrelated cost data (i.e., capital and operating costs) are estimated based on ICARUS software.29,30 H


Article


values of field condensate are equal to one minus the residual gas yield values shown in Figure S2; thus, they are omitted from the paper for brevity. The total capital cost, annuity of capital, and total operating costs for all condensate qualities under different operating conditions such as stabilizer pressure are given in Table 4. Based on the different yields obtained for condensate and residual gas (both are considered as products in case study 1), the formulated model was applied on the stabilization unit (Unit A) by representing the condensate qualities with the second index of the MILP formulation (e.g., j = 1 for 12 RVP, j = 2 for 16 RVP, and j = 3 for 26 RVP) and the operating pressure with the third index (e.g., m = 1 for 180 psia, m = 2 for 200 psia, and so on). The corresponding base MILP model has 78 variables (18 of which are binary) and 80 constraints. It was solved in LINGO 14.0.31 The optimal flow rates for field condensate and residual gas products are 68 815.35 and 1 481 184.65 kg/h, respectively. The optimal stabilization unit configuration corresponds to 16 RVP condensate quality at 200 psia pressure. The optimal annual profit is found as MM $785.27. 6.2. Case Study 2. In the second case study, we optimized the technology configuration and operating modes for all major units of the natural gas processing network. Many design alternatives are available for selection as shown in Table 1. For this case study, three operating modes were considered for stabilization Unit A for 16 RVP field condensate at column pressures of 180, 200, and 220 psia. Three configurations were considered for acid gas removal Unit B: (i) using MDEA only, (ii) using a mixed amine solution of MDEA and DEA, (iii) using two absorbers in sequence, first MDEA and then DEA. Two sulfur recovery technologies were considered for Unit C: Claus straight vs split flow with different operating conditions (at 5 bar and temperatures of 200, 300, and 400 °C). For Units D and E, one operating mode was considered. For fractionation Unit F, two process schemes are considered: the direct (conventional) and indirect (nonconventional) configurations, as discussed in section 2.6. In both sequences, at least 90% ethane mole recovery, 80% propane mole recovery, and 80% butane mole recovery are achieved. The corresponding operating conditions around each major unit are tabulated in Tables S1−S6 (in the Supporting Information). After running the steady state simulation using ASPEN Plus V7.3 for each key processing unit under various configurations and operating modes, the necessary material balances are obtained. The details of ASPEN simulation flowsheets for each unit under different design alternatives are provided in the Supporting Information. The total capital, annuity of capital, and total operating cost of each processing unit are shown in Table 5. The data in

Table 2. Composition and Operating Conditions of Natural Gas Feedstocka flow rate temperature pressure composition H2S CO2 N2 H2O CH4 C2H6 C3H8 i-C4H10 n-C4H10 i-C5H12 n-C5H12 n-C6H14 total a

1.50−1.55 × 106 kg/h 20 °C 70 bar mol % 1 2 4 0.05 83 5 1.8 0.4 0.7 0.3 0.3 1.45 100

Al-Sobhi and Elkamel.2

Table 3. Base Case Sale Prices and Material Costs natural gas feedstock residual gas (product for case study 1) field condensate (fieldC5+) sulfur (S) methane (C1) ethane (C2) LPG plant condensate (plantC5+)

$4.4 per MMBtu $5.28 per MMBtu $12 per MMBtu $200 per ton $19.25 per MMBtu $10 per MMBtu $2.5 per gallon $15 per MMBtu

For the condensate products, there is a bit of composition variation between the two products. The field condensate is recovered early in the stabilization unit (Unit A). The remaining amount is recovered later from the NGL unit, and we assumed a higher selling price as the plant C5+ is a more refined product. 6.1. Case Study 1. Given the natural gas feedstock described in Table 2, we optimally designed a stabilization unit, (Unit A) with the ASPEN Plus flowsheet shown in Figure S1 (in the Supporting Information). Different product qualities with varying Reid vapor pressure (RVP) can be produced by changing the stabilization column’s operating conditions (particularly the column pressure) as reported by Campbell.23 Three condensate qualities, i.e., 12, 16, and 26 RVP, were selected for comparison by manipulating the column pressure. Figure S2 (in the Supporting Information) shows the residual gas yield values for all considered condensate qualities and all stabilizer column pressure values. The corresponding yield

Table 4. Total Capital, Annuity of Capital, and Total Operating Costs for 12, 16, 26 RVP Products annuity of capital cost, ACCijm (MM$/y)

total capital cost (MM$)

annual operating cost, AOCijm (MM$/y)

stabilization unit pressure (psia)

12 RVP

16 RVP

26 RVP

12 RVP

16 RVP

26 RVP

12 RVP

16 RVP

26 RVP

180 200 220 240 260 280

12.8 11.8 12.0 12.0 12.5 11.7

12.2 11.4 11.9 11.4 11.5 11.9

11.0 11.0 10.9 11.0 11.0 11.0

1.56 1.44 1.46 1.46 1.52 1.42

1.48 1.39 1.45 1.39 1.40 1.45

1.34 1.34 1.33 1.34 1.34 1.34

3900 6.40 6.58 6.70 6.84 8.08

2810 5.32 5.44 6.13 6.25 6.40

5840 5610 5400 5230 5070 4930

I


Article

Industrial & Engineering Chemistry Research Table 5. Total Capital, Annuity of Capital, and Total Operating Cost of Each Processing Unit processing unit Condensate Stabilization (A)

Acid Gas Removal (B) (1) MDEA 35 wt % (2) MDEA 30 wt % + DEA 15 wt % (3) MDEA first + DEA second Sulfur Recovery (C) (1) Claus straight

(2) Claus split

Dehydration (D) NGL Separation (E) Fractionation (F) (1) conventional (direct) (2) nonconventional (indirect)

operating conditions

total capital cost (MM $)

annuity of capital, ACCijm (MM $/y)

annual operating cost, AOCijm (MM $/y)

12.4 bar, 151 °C 13.8 bar, 157 °C 15.2 bar, 163 °C

12.2 11.4 11.9

1.48 1.39 1.45

2810 5.32 5.44

55.2 bar, 35 °C 55.2 bar, 35 °C 55.2 bar, 21.1 °C

34.2 34.5 80.4

4.17 4.20 9.78

45.5 380 1380

5 bar, 200 °C 5 bar, 300 °C 5 bar, 400 °C 5 bar, 200 °C 5 bar, 300 °C 5 bar, 400 °C 50 bar, 21.1 °C 14 bar, −62.2 °C

3.06 3.07 3.29 3.27 3.29 3.46 27.6 16.8

0.372 0.373 0.401 0.306 0.307 0.421 3.36 2.04

0.959 0.959 0.960 0.959 0.959 0.960 12.1 2480

20 bar, 143 °C 20 bar, −9.4 °C

8.05 12.8

0.979 1.55

1770 2.46 × 105

Figure 3. Natural gas processing pathways with selected design alternatives for case study 2: (---) dashed lines represent possible technologies/ configurations; () solid lines represent the optimally selected technologies/configurations.

Tables S1−S6 of operating conditions are for streams exiting each processing unit, respectively. However, those in Table 5 are operating conditions for the main operation unit in the process (i.e., the absorber in Unit B and so on). Based on the different yields obtained from ASPEN Plus V7.3 for different products and various capital and operating costs for each option, the MILP model was applied on the processing network. The base MILP model has 66 variables (14 binary variables) and 89 constraints. Although the MILP was

applied on the whole network; it has less variables (compared to case study 1) because just one condensate product was considered in Unit A (i.e., 16 RVP, the optimally selected value from case study 1). The MILP model was solved in LINGO 14.031 via the branch-and-bound method. Figure 3 illustrates all possible design alternatives for this problem where the alternatives with solid line indicate the optimal selection according to the MILP solution. J


Article


tion models. A reformulation of the base model is presented using linearization of bilinear terms based on input streams leading to compact model statistics. Two case studies are presented to demonstrate the key features and applicability of the proposed approach. These case studies illustrate that (i) the proposed modeling approach is computationally feasible, (ii) considering the technology options and their operating modes for each process unit is important for profitability (see the high variance in the costs of different operating modes of the same technology configurations in Table 5), (iii) the integration of simulation/optimization framework proved to be beneficial for designing the network optimally. For example, in the first case study, 56 simulation runs were performed to examine the effects of manipulating pressure of the stabilization column to produce three condensate qualities. The results also provide important insights about the optimal design of the natural gas processing network. For example, our analysis illustrates the importance of incorporating the quality and environmental constraints into the analysis, e.g., in Unit B, using a mixed amine solution of MDEA and DEA is preferred over a more economic option (MDEA 35 wt %) in order to satisfy the quality constraints on CO2 and H2S concentrations. This analysis does not consider the variance in natural gas composition, products prices, demand, and supply which can be addressed by applying stochastic modeling techniques in future studies. We also left expanding the model by considering downstream/production network (e.g., methane utilization through LNG, GTL, gas-to-solid (hydrate), and gas-tochemical, gas-to-petrochemical conversions) for future studies.

The optimal annual profit was found to be MM$3207, and the optimal flow rate values are 1 550 000, 68 815.35, 22 281.26, 1 122 050.702, 61 945.583, 72 682.809, and 15 092.626 kg/h for natural gas feedstock, field condensate, sulfur, methane, ethane, LPG, and plant condensate products, respectively. Furthermore, MILP results showed that the optimal operating mode for the stabilization unit, Unit A, is 13.8 bar and 157 °C for producing 16 RVP condensate. Also, operating acid gas removal unit with (MDEA 30 wt % + DEA15 wt %) at 55 bar and 35 °C, operating the sulfur recovery unit at 5 bar and 200 °C with Claus straight configuration, operating the dehydration unit at 50 bar and 21.1 °C with TEG, operating the NGL recovery unit at 14 bar and −62.2 °C, and operating the fractionation unit at 20 bar and 143 °C with direct sequence configuration were the optimal configurations for Units B−F according to the MILP model solution. Results Using the Reformulated Model. Both the case studies are also solved using the reformulated model presented in section 5, and the results are summarized in Table 6. The optimal solution and the objective values remain same as earlier for both the case studies, but the reformualted model is more compact. Table 6. Comparison of Base and Reformulated Models case study 1 base model binary variables continuous variables constraints objective (MM $/y)

18 60 80 785.27

reformualed model 18 19 39 785.27

case study 2 base model

reformualed model

14 52

14 17

89 3207.19

■

ASSOCIATED CONTENT

S Supporting Information *

38 3207.19

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.7b02624. Details of ASPEN simulation flowsheets for all units are given here (PDF)

■

7. CONCLUSION Natural gas is a nonrenewable energy resource, so this mandates its optimal use. Like for other fossil fuels, the profit margin for natural gas has globally reduced in recent years due to increased supply as a result of recent advances in the natural gas production technologies. Therefore, designing an efficient and economic natural gas production network is crucial for profitability. The two common set of controls for improving efficiency in natural gas production network are the decision choices related to selection of process technologies and their operating modes. Most of the existing studies in superstructure optimization for other fuels or products consider only a single operating mode for each technology option in each processing unit, possibly for simplicity/scalability, and derive the associated product yields from the literature data. We provide a more accurate modeling framework by incorporating these two sets of decisions into a simulation-integrated optimization approach. We propose a comprehensive mixed-integer linear programming (MILP) model for the optimal design and operational management of a generic natural gas upstream processing network with six major processing units. For each unit, various available technologies, and operating modes are successfully considered by estimating the product yield for a given technology and operating mode configuration, based on sequentially solving the Aspen-based simulation and optimiza-

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Munawar A. Shaik: 0000-0002-4364-483X Ali Elkamel: 0000-0002-6220-6288 Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors would like to acknowledge the financial support from NSERC and from Qatar University to conduct this research. M.A.S. and A.E. would also like to acknowledge the Gas Research Center (GRC) at the Petroleum Institute during the later stages of this research. Many thanks to Professor Mahmoud El-Halwagi from Texas A&M University, Chemical Engineering Department, for the valuable inputs and comments about the model formulation and manuscript.

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NOMENCLATURE

Sets

i ∈ {I: A, ..., F} = processing units K


Article

Industrial & Engineering Chemistry Research j ∈ ji = technologies/configurations suitable for processing unit i m ∈ Mi = operational modes of processing unit i p ∈ {P: field C5+, S, C1, C2, LPG, plant C5+} = products k ∈ {K: H2S, CO2} = components s,s′ ∈ S = streams Sini = input streams entering unit i i ∈ Iins = unit consuming stream s Sout = output streams leaving unit i i i ∈ Iout s = units producing stream s Sint = connecting/intermediate streams between units

ytailgas Cj,m = yield value for tailgas stream yC1 Ej,m = yield value for mrthane stream yNGL Ej,m = yield value for NGL stream yC2 Fj,m = yield value for ethane stream yLPG Fj,m = yield value for LPG stream yplantC5+ = yield value for plant C5+ stream Fj,m yis,j,m = yield of outlet stream s ∈ Sout i in unit i for technoglogy j in mode m Superscripts

LB = lower bound of natural gas quantity available UB = upper bound of natural gas quantity available U = a large positive number

Binary Variables

Xijm = binary variable for selection of technology j in unit i in operational mode m

Acronyms

Non-negative Continuous Variables

FNatgas = mass flow rate of natural gas feedstock, kg/h Fresgas = mass flow rate of residual gas stream from Unit A, kg/h FfieldC5+ = mass flow rate of field condensate stream, kg/h Fsweetgas = mass flow rate of sweet gas stream, kg/h Facidgas = mass flow rate of acid gas stream, kg/h Ftailgas = mass flow rate of tailgas stream, kg/h Fsulfur = mass flow rate of sulfur stream, kg/h Fdehgas = mass flow rate of dehydrated gas stream, kg/h FwaterD = mass flow rate of water stream from Unit D, kg/h FNGL = mass flow rate of NGL stream, kg/h FC2 = mass flow rate of ethane stream, kg/h FLPG = mass flow rate of LPG stream, kg/h FplantC5+ = mass flow rate of plant condensate stream, kg/h FC1 = mass flow rate of methane stream, kg/h FLPG = mass flow rate of LPG stream, kg/h FH2S acidgas = mass flow rate of H2S in acidgas stream, kg/h FCO2 acidgas = mass flow rate of CO2 acidgas stream, kg/h FH2S sweetgas = mass flow rate of H2S in sweetgas stream, kg/h FCO2 sweetgas = mass flow rate of CO2 in sweetgas stream, kg/h Fins,j,m = mass flow rate inlet stream s ∈ Sini of technology j in mode m, kg/h out Fout stream, kg/h s = mass flow rate of outlet s ∈ Si

■

C1 = methane C2 = ethane C3 = propane C4 = butane C5 = pentane C5+ = pentane and heavier C6 = hexane C6+ = hexane and heavier CO2 = carbon dioxide DEA = diethanolamine GTL = gas to liquids H2S = hydrogen sulfide LNG = liquefied natural gas MDEA = methyldiethanolamine MILP = mixed integer linear programming MINLP = mixed integer nonlinear programming NGL = natural gas liquids RVP = Reid vapor pressure S = sulfur TEG = triethylene glycol

REFERENCES

(1) Kallrath, J. Mixed Integer Optimization in the Chemical Process Industry. Chem. Eng. Res. Des. 2000, 78 (6), 809−822. (2) Al-Sobhi, S. A.; Elkamel, A. Simulation and Optimization of Natural Gas Processing and Production Network Consisting of LNG, GTL, and Methanol Facilities. J. Nat. Gas Sci. Eng. 2015, 23, 500−508. (3) Liu, P.; Pistikopoulos, E. N.; Li, Z. A Mixed-Integer Optimization Approach for Polygeneration Energy Systems Design. Comput. Chem. Eng. 2009, 33 (3), 759−768. (4) Schulz, E. P.; Diaz, M. S.; Bandoni, J. A. Supply Chain Optimization of Large-Scale Continuous Processes. Comput. Chem. Eng. 2005, 29 (6), 1305−1316. (5) Quaglia, A.; Sarup, B.; Sin, G.; Gani, R. Integrated Business and Engineering Framework for Synthesis and Design of Enterprise-Wide Processing Networks. Comput. Chem. Eng. 2012, 38, 213−223. (6) Floudas, C. A.; Elia, J. A.; Baliban, R. C. Hybrid and Single Feedstock Energy Processes for Liquid Transportation Fuels: A Critical Review. Comput. Chem. Eng. 2012, 41, 24−51. (7) Wood, D. A.; Nwaoha, C.; Towler, B. F. Gas-to-liquids (GTL): A Review of an Industry Offering Several Routes for Monetizing Natural Gas. J. Nat. Gas Sci. Eng. 2012, 9, 196−208. (8) Baliban, R. C.; Elia, J. A.; Floudas, C. A. Toward Novel Hybrid Biomass, Coal, and Natural Gas Processes for Satisfying Current Transportation Fuel Demands, 1: Process Alternatives, Gasification Modeling, Process Simulation, and Economic Analysis. Ind. Eng. Chem. Res. 2010, 49, 7343−7370. (9) Baliban, R. C.; Elia, J. A.; Floudas, C. A. Novel Natural Gas to Liquids Processes: Process Synthesis and Global Optimization Strategies. AIChE J. 2013, 59 (2), 505−531.

Parameters

ACCijm = amoritzed capital cost of unit i for teachnology j in operating mode m AOCijm = annual operating cost of unit i for teachnology j in operating mode m Dp = market demand for product p np = natural gas price qH2S sweetgas = acceptable concentration of H2S in sweet gas qCO2 sweetgas = acceptable concentration of CO2 in sweet gas qksweetgas = acceptable mole fraction of component k in sweet gas MFk,sweetgas = mole fraction of component k in sweet gas from B,j,m unit B for technology j in mode m Qij = upper limit capacity of processing unit i for technology j spp = selling price of product p yresgas Aj,m = yield value for residue gas stream yfieldC5+ = yield value for field C5+ stream Aj,m = yield value for sweetgas stream ysweetgas Bj,m yacidgas = yield value for acidgas stream Bj,m yH2Ssweetgas = yield value for H2S in sweet gas stream Bj,m yCO2sweetgas = yield value for CO2 in sweet gas stream Bj,m yH2Sacidgas = yield value for H2S in acid gas stream Bj,m yC2Sacidgas = yield value for CO2 in acid gas stream Bj,m ysulfur Cj,m = yield value for sulfur stream L


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