Document Type : Research Article
Authors
^{1} Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran
^{2} Renewable Energy Research Department, Niroo Research Institute (NRI), Tehran,
Abstract
Keywords
Nomenclature
API 
American Petroleum Institute 


Annual operating cost (energy consumption cost) 

Annual repair and maintenance costs 


Investment costs 
CCS 
Carbon Capture and Storage 

CRF 
Capital recovery factor 
CI 
Cost index 


Cost of raw materials 
CMU 
Carnegie Mellon University 

CPI 
Consumer Price Index 

Ancillary costs 


Drilling costs 

Cost of hydrostatic testing 


Construction and labor costs 

Stirring costs 


Transportation costs 

Costs of valves 


Cost for the booster station 
D 
Diameter of the pipeline (m) 


Height difference (m) 
EOR 
Enhanced oil recovery 

E 
Seam factor 
F 
Design factor 


Coefficient of friction 

Correction coefficients for construction operations 


Correction coefficients for additional costs for manpower 
H 
Drilling depth 

L 
Pipeline length (m) 
LC 
Levelized costs per ton of CO2 transport 

P_{1} and P_{2} 
Inflow and outflow pressures of the pipeline (Pa) 

Project duration (months) 


Price of electricity 
P_{ave} 
Average flow pressure 


Maximum pressure e in the pipeline (Pa) 
µ 
Dynamic viscosity 


The density of steel used in the pipeline ( ) 
M 
Mass flow rate passing through the pipeline (kg/s) 


Fluid molecular mass ( ) 

Equipment transportation costs 


Project duration (months) 
R 
Gas constant 

Re 
Reynolds number 
S 
Minimum yield strength 


Average fluid temperature (K) 
W 
Capacity of the pressure boosting station (kW) 


Number of manpower 
ɛ 
Pipeline roughness 

t 
Pipeline thickness (m) 
Climate change in recent years has impacted all areas of the world. Among the main mitigation of climate change methods, the use of carbon capture and storage (CCS) technology is one of the promising technologies. This process is a robust way to reduce the emission of CO_{2} into the atmosphere. To reduce CO_{2} emissions in each sector of the economy, the International Energy Agency has proposed the utilization share of each technology in the world by 2050. According to this study (IEA, 2015), 5 effective solutions have been identified to reduce CO_{2} emissions in 5 different sectors of the economy; power plants, heavy industry (such as steel and cement industry), transportation, construction, and refining and petrochemical industries, which are: utilization of renewable energy (Rahimi et al., 2021) (Nikoomaram et al., 2021), changing the fuel consumed in power plants (Rahimi et al., 2019) (Shariati, 2018), industries, etc., utilization of nuclear power plants, increasing energy efficiency (Rahimi and Alibabaee, 2021) (Shariati and Amidpour, 2016), and CO_{2} capture and storage. According to the conditions of each country, all methods of mitigating CO_{2} emissions have their limitations in the implementation phase. It should be noted that for Iran due to its huge oil and gas resources and a high share of power generation from fossil fuels, the importance of developing CCS technology would be even greater (IEA, 2015).
The use of CCS technology is a promising way to reduce the amount of CO_{2} in the atmosphere and mitigate the effects of climate change. CCS technology includes three main phases, which are: Capture of CO_{2} from existing emission sources, Transmission of CO_{2}, and Storage and operation of CO_{2}. In CO_{2 }transport, there are different methods such as transport through pipeline and ship, which vary according to the volume transferred and the distance from the source of emissions to the place of exploitation of CO_{2}. However, according to existing experiences, a pipeline is used for large volumes. To achieve these goals, preliminary estimates indicate that between 20,000 and 50,000 km of pipelines are needed in 2050 globally to transport CO_{2} (IEA, 2010a) (IEA, 2010b).
Historically, the United States, Canada, and Norway have been leaders in CCS technology. One of the most internationally important projects is that done in the Permian Basin (USA) for EOR by CO_{2} injection. Another important project is the Weyburn in Canada, which has also been done with the aim of EOR. Table 1 demonstrates some of the most important CO_{2} pipeline projects and the purpose of their implementation (IEA, 2016).
Table 1. Some of the most important CCS projects in the world (IEA, 2016)
Goal of project 
Annual captured carbon (million tons) 
Operational beginning year 
The Project Name 
EOR 
1.3 
1972 
Natural Gas Plants  Val Verde (USA) 
EOR 
7 
1986 
Gas Processing Facility  Shute Creek (USA) 
deep saline reservoir 
0.9 
1996 
CO_{2} Storage Project  Sleipner (Norway) 
EOR 
3 
2000 
WeyburnMidale Project and Great Plains Synfuel Plant(Canada) 
Depleted Gas Reservoir 
1 
2004 
CO_{2} Storage  In Salah (Algeria) 
EOR 
0.7 
2013 
CCS Project  Petrobras Lula Oil Field (Brazil) 
saline aquifer and possible EOR 
1 
2015 
Quest (Canada) 
EOR 
0.8 
2015 
CO_{2} EOR Demonstration Project  Uthmaniyah (Saudi Arabia) 
EOR 
0.8 
2016 
CCS Project  Abu Dhabi (UAE) 
It is noteworthy that Iran is one of the most important CO_{2} emitters among the countries of the world. According to the data, industry, refineries, and power plants accounted for 48.5% of the total emissions. As the prominent role of CCS in the power plant and industry sector was emphasized above, there is an opportunity for Iran to use this technology and enjoy its benefits in these sectors (Firozeh Amini et al. 2015). Jafari et al. studied the performance of different CO_{2} capture processes from the flue gas and evaluated their economic aspects (Jafari et al., 2019). The capital cost of the membrane unit of flow gas carbon capture is much higher than the capital cost of carbon capture through the absorption process for Iran (2.3 times higher). In another study, Salehi et al. used multicriteria analysis to prioritize different applications for CO_{2} utilization based on technical, economic, and environmental criteria (Salehi et al., 2020). It was found that methanol production is the best option.
Due to the proximity of the southern part of Iran to CO_{2} emission sources and suitable storage and operation areas such as oil and gas wells for Enhanced Oil Recovery (EOR), high CO_{2 }emissions by power plants and industries, and the country's needs (Table 2) to control and reduce CO_{2 }emissions, this research was conducted to develop a budgettype technoeconomic model for CO_{2} transmission through pipelines in the southern coasts of Iran.
Table 2. CO2 emissions and share of different emission sources in Iran (2015) (Firozeh Amini et al., 2015)
CO2 emission sources 
Amount of CO2 emission (Megatons) 
Percentage of CO2 emission sources 
Industries, refineries, and power plants 
283 
48.5% 
Total amount of CO2 emission 
584 
100% 
Different studies in the literature were performed to assess the economic aspects of natural gas pipeline networks for China (An and Peng, 2016) (Li et al., 2020), Italy (Copiello, 2018), and Brazil (Vasconcelos et al., 2013). According to the literature, the cost of CO_{2} pipeline projects is generally divided into three main sections, the cost of pipeline investment, the cost of maintenance, and the cost of pressure booster stations. It should be noted that the investment sector accounts for a significant share of costs, so in most existing economic models, the cost of the other two sectors (especially maintenance costs) is applied as a percentage of the investment costs. It should be noted that the modeling studies do not lead to the exact cost of the CO_{2} pipeline, and the modeling results of a CCS system and CO_{2} pipeline have a range of variations for investment and operation costs. Here are mentioned five types of technoeconomic modeling that include all existing models:
Van den Broek et al. conducted a study entitled about storing CO_{2} feasibility in the Utsira formation (van den Broek et al., 2010). One of the most significant studies of linear modeling is element energy research. This modeling has been done to optimize the transfer of CO_{2 }from emission sources to injection and storage sites worldwide by 2030 and 2050.
Gao et al. examined all scenarios of CO_{2} emissions, such as maritime, rail, and pipeline transportation in China (Gao et al., 2011). The project site was located in China without considering the correction factors. As mentioned, in this study, the feasibility of different transfer methods such as ship transport, rail transport, and pipelines were studied. It is worth mentioning that in the CO_{2} transfer method by ship and train, respectively, at the end of the route, CO_{2} is transferred to the desired oil fields with a pipeline of 25 and 20 km in length.
Parker conducted research on the transport of natural gas, oil, and petroleum products, specifically in the field of hydrogen transport (Parker, 2015). Like most of the models, the investment costs of pipeline construction in this study were divided into four main sections, the costs of raw materials and equipment, workforce, land ownership, and ancillary costs. Due to the existing similarities, the results of this study can also be used for CO_{2} transmission. There were found significant changes in the distribution of all costs except for labor cost, which was mainly between 40 to 50% depending on changes in the pipeline diameter.
One of the most significant technoeconomic models is found in the research by (McCoy & Rubin, 2008). The main purpose of this study was to estimate the cost of CO_{2 }pipeline for different regions of the United States without considering the correction coefficients.
Dohowski et al. studied a CCS system in a study (Dahowski et al. 2004). The main purpose of this study was to analyze the modeling results by presenting different curves of the estimated CO_{2} transfer and storage cost according to different parameters. In this modeling, the distance between the emission source and the injection or storage site was considered a straight line. Since this would not be possible in the real condition for a variety of reasons, a correction factor was applied in the modeling to correct it. In addition, to correct the length of the path to the suitable storage location, a distance of 16 km (10 miles) was added to the pipeline length when estimating the cost of constructing the pipeline. In another study, carbon dioxide transport via pipelines was reviewed with similar modeling. Pipeline design, risk, safety, process, standard, and specification of CO_{2} pipelines were studied (Lu et al., 2020). Yuan et al. and Zhou et al. worked on the future of multiproduct pipelines in China for CO_{2} abatement (Yuan et al., 2019) and (Zhou et al., 2020).
Due to the importance of CO_{2} pipeline transmission in CCS projects, technical and economic analysis of transmission pipelines is inevitable. Although the project design and detailed economic investigation has a lower error rate but requires spending a lot of time and cost. Therefore, it is necessary to develop a budgettype technoeconomic model that includes key technical and economic features of the CO_{2} transmission pipeline. The advantage of technoeconomic models is the estimation of important parameters in CCS projects without the need to consider technical and economic details and project design, which saves time and money. Similar studies were performed for many countries as it was mentioned above. But there are no studies about Iran as one of the GHG emitters. Unlike most models, standardization and nominal diameters of the pipelines were considered in the study which results in the diagrams having several jump points. Also, stochastic analysis in this paper is another novelty the paper considering uncertainties of the technoeconomic models. Therefore, this study was conducted to estimate the cost of CO_{2} transfer based on technical and economic characteristics and parameters.
2.1. Conditions and properties of CO_{2 }for pipeline transmission
According to theoretical principles, CO_{2 }can be transported through pipelines in liquid, gaseous, supercritical, and biphasic (liquidgas) states, although there are limitations and recommendations in this regard. Note that for pipeline transmission, it is very important to study the CO_{2} phase curve, so the following is a detailed analysis of the limitations and operational recommendations in this field. The critical point of CO_{2} is at a temperature of 31.1 °C and a pressure of 7.3 MPa, above which it transforms into the supercritical state. If the pressure remains above 7.3 MPa and the temperature drops below 31.1 °C using coolers, the liquid phase is formed, which is called dense liquid. Operational experiences in the field of pipeline transmission show that CO_{2},_{ }due to its high density and low viscosity, is transported in pipes mainly in two states supercritical and dense liquid (Zhang et al., 2006). As a result, large amounts of CO_{2 }can be transported with low losses. For more insight, the properties of CO_{2 }in different states with respect to operating temperature and pressure are given in Table 3 below (Wang et al., 2016).
 Supercritical fluid: pressure and temperature more than 7.3 MPa and 31.1°C, respectively
 Dense liquid: pressure more than 7.3 MPa and temperature less than 31.1 °C and more than 56 °C
 Liquid phase: pressure less than 7.3 MPa and more than 0.52 MPa, temperature less than 31.1 °C and more than 56 °C.
Table 3. Thermodynamic Properties of CO_{2} (Veritas, 2010)
CO_{2} thermodynamic properties 
Amount 
Molar mass 
44.01 g. 
Critical pressure 
73.8 bar 
Critical temperature 
31.1 °C 
Critical density 
467 kg. 
Triple point pressure 
5.18 bar 
Triple point temperature 
56.6 °C 
Density in the gas state (at 0 °C and 1 bar) 
1.976 kg. 
Density in the liquid state (at 0 °C and 70 bar) 
995 kg. 
2.2. CO2 emission sources in the southern coasts of Iran
As described in the previous sections, in this study, the southern coasts of Iran were selected due to the existence of refinery and power plants with a large volume of CO_{2 }emissions, as well as the capacity of the southern part of the country to use CO_{2 }in different ways. This section will first describe the sources of CO_{2} emissions along with the number of emissions.
2.2.1. CO_{2} emission sources
In the southern part of Iran, especially the southern coasts, there are many sources of CO_{2} emissions. Among these, industries and power plants have a significant share (48.5% of total emissions) and the southern parts account for a larger part due to the centralization of industries and power plants. Considering the criteria of "distance to suitable operation and storage sites" as well as "emission volume per year", the possible emission resources were analyzed to select suitable ones. Table 4 shows the selected power plant emission sources along with the emission rates calculated through CO_{2} emission intensity (Firozeh Amini et al., 2015) (Tavanir, 2018).
Table 4. Selected onshore power plants in southern Iran (Tavanir, 2018)

Power Plant Name 
CO_{2} emissions (tons per year) 
Actual average power output (MW) 
1 
Bandar Abbas power plant 
4718008 
1280 
2 
Bistoon power plant 
2833974 
640 
3 
Ramin power plant 
6817528 
1823 
4 
Khalije Fars gas power plant 
2712879 
871 
5 
Abadan Combined cycle power plant 
1987592 
674 
6 
Khorramshahr power plant 
2661148 
818 
7 
Asalooyeh gas power plant 
3156784 
826 
8 
Eslamabad gharb (Shian) power plant 
209465 
82 
9 
Isin power plant 
2012932 
550 
10 
Sanandaj Combined cycle power plant 
2076368 
769 
11 
Bushehr gas power plant 
31028.5 
36 
12 
Kerman Combined cycle power plant 
5227317 
1451 
13 
Bandar Abbas gas power plant 
73454.7 
33 
14 
Fars Combined cycle power plant 
2518854 
794 
15 
Hafez power plant 
2798810 
716 
16 
Chabahar gas power plant 
833515 
338 
17 
Behbahan power plant 
208422 
269 
18 
Zargan power plant 
182434.5 
82 
19 
Zagros power plant 
1415881 
521 
20 
Genaveh power plant 
1932907 
415 
21 
Kazeroon Combined cycle power plant 
3479681 
1111 
22 
Jahrom Combined cycle power plant 
1979329 
720 
According to calculations, the total CO_{2} emission from the selected power plants in the southern part of the country is about 48 million tons per year. This amount of emissions is very significant and creates many opportunities for the exploitation of CO_{2} as well as environmental measures to reduce emissions. However, there are other important sources of emissions on the southern coast of the country, such as refineries. According to available sources, the amount of CO_{2} emissions from the country's refineries in 2015 was about 15 million tons, which considering the concentration of a large number of refineries in the south, it can be said that refineries are important sources of emissions on the southern coasts. These refineries are (Firozeh Amini et al., 2015):
In addition, the petrochemicals in the south of the country, due to pure CO_{2}, are considered attractive sources of emissions for the exploitation of CO_{2}. The global gas flaring emissions from the petroleum refineries, natural gas processing plants, and petrochemical plants on the southern coasts of Iran were reviewed previously (Soltanieh et al., 2016).
According to the previous technoeconomic models, the costs of CO_{2 }pipelines in this project were divided into three main categories of pipeline investment cost, maintenance cost, and cost of booster stations. Since investment cost accounts for a significant share of costs, in some economic models, the cost of the other two sectors (especially maintenance costs) is applied as a percentage of investment costs. The advantage of technoeconomic modeling is the estimation of important parameters in CCS projects without the need for technical and economic details and project design, which saves time and money. It should be noted that economic modeling does not give us the exact cost of the CO_{2 }pipeline, and the modeling results have a range of variations for investment and operation costs (Figure 1) (Knoope et al., 2013).
In the modeling, the pipeline diameter was first obtained through an iterative algorithm. Then, based on the relevant standard, the calculated diameter was modified according to the nominal diameter of the pipe, and then the modeling is continued according to the obtained values, and next, the modeling was continued based on the obtained values. The technoeconomic parameters of modeling are given in Table 5. For ease of understanding, the modeling method is described in detail below.

Figure 1. Configuration of the economic model of pipeline construction (Knoope et al., 2013) 
3.1. Calculation of the pipeline diameter
To obtain the pipeline diameter, Equations (1) to (3) are solved simultaneously according to the iterative algorithm shown in Figure 2 (McCoy & Rubin, 2008):
(1) 

(2) 

(3) 

Where; D= diameter of the pipeline (m), = average condensability of the fluid, R= gas constant that is equal to 8.31 , = average fluid temperature (K), M=mass flow rate passing through the pipeline (kg/s), = coefficient of friction, L= pipeline length (m), = fluid molecular mass ( ), =height difference (m), P_{1} and P_{2}= inflow and outflow pressures of the pipeline (Pa), ɛ= Pipeline roughness assumed to be 0.0457 mm for this pipeline, Re= Reynolds number, µ= dynamic viscosity, and P_{ave}= average flow pressure in the pipeline calculated from Equation (4) (McCoy & Rubin, 2008).
(4) 

The thickness of the pipeline was also calculated from Equation 5. According to the abovementioned iterative algorithm by creating a set of data based on the API 5L standard, the diameter, and nominal thickness were calculated (McCoy & Rubin, 2008).
(5) 

In which, t is the pipeline thickness (m), S is the minimum yield strength, is the maximum pressure in the pipeline (Pa), D is the pipeline diameter (m), and E and F are seam and design factors, which were assumed to be 1 and 0.72, respectively (McCoy & Rubin, 2008). The results of the calculations are shown in Figure 3. The resulting diagram shows the nominal diameters at different lengths and flow rates of the pipeline. As observed, unlike most models, the diagrams have different jump points due to considering standardization and nominal diameters of the pipes. Using the results from calculating the diameter, economic modeling and budget estimation are discussed below.

Figure 2. Algorithm for calculating pipeline diameter 
Table 5. Modeling parameters (Climatic and historical data of Iran, 2020) (Calculation of thermodynamic state variables of carbon dioxide, 2020) (API, 2016) (IEA, 2020) (API 5L X70 Seamless Line Pipeline  X70 Grade Steel Pipeline  API5L X70 Pipeline  AesteironSteel, 2018) (Foreign Exchange Rates, 2019) (CPI and Inflation, 2019) (Management, 2018) (Research and Technology of the Ministry of Oil of Iran, 2018b) (Research and Technology of the Ministry of Oil of Iran, 2018a)
Parameter 
Amount 
Unit 
Mass flow 
135 
Mt.a^{1} 
Pipeline length 
502000 
Km 
Pipeline diameter 
Calculation from the results of technical modeling 
m 
Input Pressure 
14 
MPa 
Output pressure 
10 
MPa 
Mean Temperature 
22 
°C 
Average distance between pressure boosting stations 
200 
km 
Carbon dioxide compressibility coefficient 
0.26 
 
Density of carbon dioxide 
845 
kg.m^{3} 
Type of pipeline used based on API 5L Standard 
X70 
 
Steel yield stress 
483 
MPa 
Density of steel 
7.9 
g.cm^{3} 
X70 Steel pipeline price 
1.75 
/€kg 
Cost of transportation of materials and equipment (pipes) 
2 
/€ton 
Correction factor of civil operations 
1.05 
 
Manpower correction factor 
1.3 
 
Number of working days per month 
22 
day 
Monthly salary of manpower 
Depending on the type of civil works and technical specifications of the pipeline 
€ 
Cost of civil works 
Depending on the type of civil works and technical specifications of the pipeline 
€ 
Drilling depth 
2 
m 
Pipeline lifetime 
25 
year 
Inflation rate 
9.6 
% 
Electricity price 
0.02 
€/kW.h 
Capacity factor 
0.8 
 
Figure 3. Results of technical modeling (calculation of nominal diameter in different flow rates and pipeline lengths)
3.2. Economic modeling
3.2.1. Pipeline investment costs
This section is generally divided into four main subsections, including the cost of raw materials and equipment, construction and manpower costs, access road costs (right of easement), and miscellaneous and ancillary costs, which are defined and detailed below. There is limited information available worldwide regarding CO_{2} pipelines and the costs of their various sections, which may be due to the low experience and proximity of the project implementation process to natural gas pipeline projects (Knoope et al., 2013). Generally, considering a series of correction factors, theoretical models, and segmentation, the investment costs of natural gas pipelines are also used for the CO_{2} pipelines. However, enough attention should be paid to the operational differences between the two projects. Among the major differences between natural gas and CO_{2} pipelines are type of steel, type of pipes, coating and insulation of the pipes, operating pressure, temperature, and pipeline buffer area. For example, the operating pressure of a CO_{2} pipeline is higher than that of a natural gas pipeline, which leads to a greater thickness of the CO_{2} pipeline (Knoope et al., 2013). In addition, crack propagation in the CO_{2} pipeline is a serious problem that could be caused by corrosion or an earthquake. To prevent this, the thickness of the pipeline is increased, or crack arresters are used (to reduce the length of the cracks). Another difference is the legal buffer area of the pipeline to the residential units as the environmental and safety regulations impose more restrictions on the CO_{2} pipelines. These differences make the cost of a CO_{2} pipeline higher than a natural gas pipeline if the pipeline diameter is the same. In the present model, due to the lack of sufficient information, the available data of oil and gas pipelines were used for budget type calculation of the pipeline (Veritas, 2010) (Smith, 2006) (C.E. Smith, 2010) (True, 1995). It should be noted that in the modeling and economic analysis ahead, whenever the reference equations were used (in the comparison of different modeling and cost estimation of pressure boosting station), cost estimation was done according to the year of publication of the equation using Equation 6 and the price change index provided by Marshall and Swift.
(6) 

where the CI value represents the ratio of the price coefficient announced by authorities such as Marshall Swift. The CI value can be calculated using Equation (7).
(7) 
CI= Cost index for the required year (Cost index for reference year) 
This section includes the costs of pipes, coating, cathodic protection, and insulation of pipes. Other costs include blocking valves, crack arrestors, and other miscellaneous equipment.
For example, if the diameter of the pipeline increases, distribution costs in different parts of the CO_{2} pipeline investment models will change so that the cost of raw materials and equipment will increase and the share of manpower costs in the distribution costs will decrease, as well. These changes can be seen in the results of this modeling. The cost estimate of raw materials and equipment was done using the following equations et al., 2016).
Raw material cost (pipe):
(8) 

Where, is the cost of raw materials (€), is the density of steel used in the pipeline ( ), the price of X70 steel pipe, and the inner diameter (m), which is calculated according to the standardized diameter and thickness in the technical modeling stage.
Ancillary costs (insulation, cathodic protection, and miscellaneous costs) and transportation of pipes and equipment (assumed for a distance of 30 km) and valves:
(9) 

(10) 

(11) 

In which, , , and are respectively transportation costs, ancillary costs, and the costs of valves (€) and is the unit of equipment transportation costs.
Finally, the cost of raw materials and equipment was defined as follows:
(12) 

This section includes the two main subsections of project construction costs and labor costs. The reason for this division is the effectiveness of construction costs so in some models, this subsection is considered as a separate section. Construction and labor costs include annual labor salaries and costs related to necessary infrastructure, pipeline installation, pipeline welding, and ancillary construction works.
Most pipelines are installed underground to limit the impact on their surroundings, so in this type of project, excavation, burial, and clearing steps are performed to install the pipes. It is worth mentioning that the technical and economic principles governing the construction of the carbon dioxide pipeline generally follow the principles of the oil and gas pipelines. The costs considered in this section include the costs of groups of welders, workshop supervision, pipeline bending, drivers of heavy machinery and transportation, stringing of pipes, hydrostatic testing, construction of infrastructure for pipeline construction, canal drilling, etc. The duration of the project was calculated according to Equation 13 (McAllister, 2009).
(13) 

Where PD is the duration of the project (months), L is the length of the pipeline (km), S is the daily progress of the construction of the pipeline (0.6 km), and D is the number of working days per month. Due to the inseparability of construction and labor costs, in this model, the cost of these two parts was estimated together under one group, as this assumption is common in these models. To estimate the construction and labor costs, based on available sources and a function of pipeline specifications such as pipeline diameter, pipeline length, etc., a data set was formed including project duration, number of labor required for each section, monthly salary, and construction operations required for pipeline construction. Using the dataset and the results of technical modeling, the cost of this section was calculated.
(14) 


(15) 


(16) 


(17) 


In which, , , , and, are respectively construction and labor costs, drilling costs, stirring costs, and the cost of hydrostatic testing (€), is the number of manpower in each section, is the monthly salary of manpower in each section (€), L is the length of the pipeline (m), H is drilling depth, is project duration (months), and are respectively correction coefficients for construction operations and additional costs for the manpower (hardship pay for the harsh environmental conditions in the southern regions of Iran and accommodation), and , , and are respectively the costs of drilling, stirring the pipelines, and hydrostatic testing, which is a function of pipeline diameter (m/€). The cost of land ownership and miscellaneous and ancillary costs are expressed as a percentage of investment costs (C.E. Smith, 2009).
(18) 

(19) 

(20) 
+ 
Finally, the investment cost of the pipeline is defined as follows:
3.2.2. Investment cost of pressure boosting station
Long CO_{2} pipelines need pressureboosting stations to compensate for the pressure drop along the route. Therefore, onshore pipelines have pressureboosting stations due to pressure drops. However, for pipelines that are installed and operated at sea, due to technical complexities and reduced investment costs, it is recommended to compensate for the pressure drop by increasing the inlet pressure and pipeline diameter and preventing or limiting the installation of booster stations (Knoope et al., 2013). There is a relationship between the number of pressure boosting stations based on the overall pressure drop and the characteristics of the booster station. Given that no specific route was determined in the present study, the distance between each pressure boosting station was assumed to be 200 km, which seems reasonable given the previous studies. To obtain the investment cost, booster stations were modeled at different assumed flow rates in HYSYS software. Using the capacity of the modeled pressure boosting stations, the investment cost of the pipeline was calculated from the following equation and the price change index provided by Marshall and Swift (Chandel, Pratson, & Williams, 2010).
(21) 

Where; is the investment cost for the booster station ( ) and W is the capacity of the booster station (MW_{e}).
The operating cost and energy consumed by the pressure boosting station depend on the price of electricity and the capacity of the station, which is calculated by the following equation (Knoope et al., 2013).
(22) 

In which; is the annual operating cost (energy consumption cost), W capacity of the pressure boosting station (kW), and and were assumed to be the operating hours (7500 hours per year) and the price of electricity (0.02 €/kW.h), respectively.
3.2.3. Repair and maintenance cost of pipeline and pressure boosting station
Repair and maintenance costs include the cost of repairing valves, pipes, possible leaks, etc., which are part of the operational costs of the pipeline project. These types of costs, due to the probability of various failures and repairs in pipeline construction projects, are expressed in most models annually and as a percentage of pipeline investment costs or a fixed cost per unit length of the pipeline. For the pressure boosting station, the repair and maintenance cost is generally expressed as a percentage of pipeline investment costs. According to the literature, the maintenance cost was assumed to be 2.5% of the pipeline investment cost and 4% of the pressure boosting station investment cost, respectively (McCollum & Ogden, 2006).
3.2.4. Levelized cost
After calculating the costs of each section, the levelized cost per ton of CO_{2} transport was calculated according to the following procedure.
(23) 

(24) 

(25) 

(26) 

(27) 

In the above equations, is the capital recovery factor, is capacity factor, M is the flow rate in terms of millions of tons per year and, , , and are respectively the annual repair and maintenance costs, investment costs, and levelized costs per ton of CO_{2} transport (€). The diagrams in Figures 4 and 5 show the levelized costs modeled for different lengths and different flow rates. As expected in the same flow rate, the cost of the project increases with increasing the pipeline length. In addition, at a given length, with increasing flow rate to a certain level, the levelized cost decreases and after which point the cost will remain almost constant.

Figure 4. levelized cost per ton of CO_{2} transport (at different pipeline lengths and flow rates) 

Figure 5. levelized cost per ton of CO2 transport (at different pipeline lengths and flow rates)

3.2.5. Obtaining the economic function
In this section, according to the results of economic modeling using the leastsquares estimation method, the economic function was obtained. In this method, the dependent variable (y) is considered as a linear function of the independent input variables and ϵ error (Türkşen, 2008):
(28) 

Where; j = 1,…, nv are indices of input variables, nv is the number of inputs, and ϵ is an independent error term that was assumed to be normally distributed. The purpose of this method is to estimate anonymous parameters. βj shows the effect of changing the independent variable on the dependent variable. In the matrix display, the general linear model would b as follows:
(29) 

Where Y is a vector [nd, 1] of the response values, X is a matrix [nd, nv + 1] of inputs with fixed constants, nd is the number of input and output vectors in the training data category, nv is the number of selected inputs, b is a vector [nv + 1, 1] of the parameters, and ϵ is a vector [nd, 1] of errors, for example:
(30) 

The main goal is to minimize the residual error in estimating the model parameters, i.e.:
(31) 

In the matrix representation, the above expression was rewritten and a partial derivative with respect to b was taken from it:
(32) 

According to this algorithm, the economic function was obtained as follows:
(33) 


(34) 


L and M in these equations are the length and flow, respectively, in meters and million tons per year. The coefficients of the above equations are given in Table 6.
This section presents and compares the levelizedcost diagrams of this model with those of other available models (Figures 6 to 13). As observed, this model is less expensive than the other models. The reason for this difference is the area assumed for the project and other related details such as topographic conditions, the level of details included in the model, lower manpower costs in Iran than in other countries, etc.
Table 6. Coefficients of economic equations
Amount 
Coefficients 
Amount 
Coefficients 
Amount 
Coefficients 
Amount 
Coefficients 
0.00011894 

0.0064 

0.0846 

0.3331 

0.0015 

0.1001 

2.2561 

26.0502 

0.00083993 

0.0533 

1.0548 

10.0359 


Figure 6. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 50 km) 

Figure 7. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 150 km) 

Figure 8. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 250 km) 

Figure 9. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 450 km) 

Figure 10. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 700 km) 

Figure 11. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 1000 km) 

Figure 12. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 1500 km) 

Figure 13. Comparing the levelized costs of different technoeconomic models (for a pipeline length of 2000 km)

Reasons for the wide range of the levelized cost per ton of CO_{2} transport in the studied models are:
One reason could be different topographic and geographical conditions and terrain smoothness and unevenness as model assumptions. For example, locating in flat surfaces, forestlands, or desert lands, as well as onshore or offshore types all, are among the modeling assumptions affecting the levelized cost per ton of CO_{2} transport in different projects.
The location of projects is one of the important assumptions that play a key role in modeling parameters such as labor costs and right of way and consequently, in the cost of investment and operation. For example, labor costs vary significantly between China and the United States.
3.2.6. Stochastic analysis
The budgettype technoeconomic model has uncertainties due to various technical and economic parameters involved in the modeling. The specifications of the CO_{2} pipeline projects such as the project life span, interest rates, raw materials and equipment, land ownership, labor, pipeline construction operations, parameters related to investment costs, tax policies, etc. all affect the final cost of the pipeline. For this purpose, a stochastic analysis was performed for the economic model.
Table 7 lists the stochastic parameters that the analysis was done based on their changes. Stochastic analysis on different parameters requires determining the variable costs of technicaleconomic parameters. To generate indeterminate numbers, the normal and uniform distribution of key parameters was used and the test was performed 1000 times. Finally, with the results of normal distribution of data, a cumulative probability function was created for the levelized cost. Figures 1421 give a probability of 90 and 10% for the levelized cost per different lengths and flow rates. The analysis results can be seen in the following diagrams. The nonuse of other complex distributions is due to their inefficiency in analyzing the statistical data.
Table 7. Effective parameters in the stochastic analysis
Parameter 
stochastic parameters variation range 
Unit 
Mass flow 
135 
Mt.a^{1} 
Distance 
502000 
km 
Input Pressure 
Normal distribution (1315) 
MPa 
Output pressure 
Normal distribution (911) 
MPa 
Mean Temperature 
Normal distribution (2026) 
°C 
Average distance between pressure boosting stations 
Normal distribution (150250) 
km 
Type of pipeline used based on API 5L Standard 
X70 
 
Steel yield stress 
483 
MPa 
Density of steel 
7.9 
g.cm^{3} 
Correction factor of civil operations 
Uniform distribution (11.05) 
 
Correction factor of manpower 
Uniform distribution (1.21.35) 
 
Correction factor of equipment transportation 
Uniform distribution (15) 
 
Pipeline lifetime 
Uniform distribution (2030) 
Year 
Inflation rate 
Normal distribution (0.050.15) 
% 
Electricity price 
Uniform distribution (0.020.04) 
€ 
Capacity factor 
Normal distribution (0.70.9) 
 

Figure 14. Levelized cost range at different flow rates for a 50km pipeline (at probabilities of 10% and 90%) 

Figure 15. Levelized cost range at different flow rates for a 150km pipeline (at probabilities of 10% and 90%) 

Figure 16. Levelized cost range at different flow rates for a 250km pipeline (at probabilities of 10% and 90%) 

Figure 17. Levelized cost range at different flow rates for a 400km pipeline (at probabilities of 10% and 90%) 
Figure 18. Levelized cost range at different flow rates for a 700km pipeline (at probabilities of 10% and 90%) 

Figure 19. Levelized cost range at different flow rates for a 1000km pipeline (at probabilities of 10% and 90%) 
Figure 20. Levelized cost range at different flow rates for a 1500km pipeline (at probabilities of 10% and 90%) 

Figure 21. Levelized cost range at different flow rates for a 2000km pipeline (at probabilities of 10% and 90%) 
3.2.7. Case study
As mentioned earlier, the developed model can be generalized to other CO_{2} pipeline transport with different flow rates, pipeline diameter, inlet, and outlet pressure, ambient temperature, and other influential parameters, as well as from any emission sources to the place of CO_{2} operation sites. However, in this section, due to the significant amount of CO_{2} emissions from Ramin Power Plant (about 7 million tons per year), the economic analysis was done for the CO_{2} transmission pipeline from Ramin Power Plant to Ramshir Oil Field. Ramin Power Plant is one of the largest power plants in Iran, which has been built to supply electricity to Khuzestan Province and the national grid. The power plant, with a production capacity of 1850 MW, is one of the major emission sources in the southern part of the country. Ramshir Oil Field is located in Ramshir City, 15 km southwest of Omidieh City in Khuzestan Province. The oil field is adjacent to Shadegan, Ragesefid, and Aghajari Fields. The geographical location of the Ramin Power Plant and Ramshir Oil Field is shown in Figure 22. The key modeling parameters and modeling results are given in Table 8. According to the capacity of the emission source, the stochastic analysis of the levelized cost up to the capacity of 4 million tons per year is shown in Figure 23. It is worth mentioning that the assumptions for the economic analysis of the CO_{2} pipeline from Ramin Power Plant to Ramshir Oil Field are the same as the basic model, which is not listed in Table 8 to avoid duplication.

Figure 22. Schematic of CO2 transmission pipeline from the emission source (Ramin Power Plant) to the place of CO2 operation (Ramshir Oil Field) 
Table 8. Key Parameters and results of economic modeling of Ramin Power PlantRamshir Oil Field CO2 transmission pipeline
Parameter 
Amount 
Unit 
Pipeline length 
110 
Km 
Mass flow 
2 
Mt.a^{1} 
Pipeline lifetime 
25 
Year 
Inflation rate 
9.6 
% 
Capacity factor 
0.8 
 
Input pressure 
14 
MPa 
Minimum outlet pressure 
10 
MPa 
Calculated diameter of pipe 
0.273 
m 
Material and equipment cost 
8.36 
M € 
Manpower cost 
5.05 
M € 
Capital cost of pipeline 
18.37 
€M 
Levelized cost 
1.55 
€/ton 
Levelized cost (Cumulative probability of 10%) 
1.37 
€/ton 
Levelized cost (Cumulative probability of 90%) 
2.07 
€/ton 

Figure 23. levelized cost range of Ramin Power PlantRamshir Oil Field CO2 transmission pipeline 
After presenting the modeling results, the main purpose of this section is a comparison and systematic assessment of the technoeconomic model in terms of the costs of investment, repair and maintenance, and pressure boosting stations of CO_{2} pipelines. According to the model, it is concluded that:
Due to the high levels of CO_{2} emissions in the southern part of the country and the shortcomings in the increase of oil extraction, which has led to the waste of oil in recent years, increasing oil extraction by injecting CO_{2} is a very good option to solve this problem. It is worth mentioning that Iran has a capacity of 10 Gt CO_{2} storage. This potential for the exploitation and storage of CO_{2} highlights the need to address this issue. Comparing the developed model with other models revealed that the range of levelized cost in different flow rates is wider, which may be due to the following reasons:
First, it should be noted that this model could be used for different lengths and flow rates of a carbon grid. In the developed budgettype model, attempts were made to include the necessary technoeconomic details, so the results could be a good criterion for the cost estimate of a CO_{2} pipeline. The main findings of this study are:
By comparing the developed budgettype technoeconomic model with other available models, it was found that the cost of CO_{2 }pipeline in Iran is lower than that in other countries, which may be to the particular conditions of Iran, such as lower labor costs. This can increase the attractiveness of CCS projects for Iran. Considering the uncertainties in CO_{2} pipeline projects resulting from changes in technicaleconomic parameters in different conditions, the statistical analysis of the budgettype model is very important. According to the results of this analysis, from an economic point of view, a better understanding may be obtained of the CCS chain, especially the CO_{2} pipeline, which acts as a connector in this chain.
In the final part of the research, the cost of transferring 2 million tons of CO_{2} from Ramin Power Plant to Ramshir Oil Field was calculated according to the basic model. The model results are as follows:
The pipeline diameter=0.273 m, the investment cost for the 110km pipeline= 18.37 million €, the levelized cost = 1.55 €/ton, the levelized cost with a cumulative probability of 10% = 1.37 €/ton, and the levelized cost with a cumulative probability of 90% = 2.07 €/ton.
Based on the results of the previous section, two suggestions are presented to continue the present study:
1) Investigating the CO_{2} exploitation potentials in Iran and using decisionmaking algorithms to rank emission priorities for CO_{2} capture and operation sites
2) Developing a technoeconomic model for a comprehensive system of CO_{2} capture, transmission, and storage.