تعداد نشریات | 43 |

تعداد شمارهها | 1,411 |

تعداد مقالات | 11,628 |

تعداد مشاهده مقاله | 19,533,875 |

تعداد دریافت فایل اصل مقاله | 9,234,441 |

## Techno-economic evaluation for development of onshore carbon dioxide pipeline networks | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Gas Processing Journal | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

دوره 10، شماره 1، خرداد 2022، صفحه 45-66 اصل مقاله (2.64 MB) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

نوع مقاله: Research Article | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

شناسه دیجیتال (DOI): 10.22108/gpj.2022.132207.1111 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

نویسندگان | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Ali Mohammad Sakhai^{1}؛ Mohsen Salimi^{2}؛ Majid Amidpour ^{} ^{1}
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{1}Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{2}Renewable Energy Research Department, Niroo Research Institute (NRI), Tehran, | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

چکیده | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

The southern part of Iran has many CO2 emission sources and considerable potential for storage and utilization demand of CO2 such as oil and gas wells. Based on the importance of CO2 pipeline transport in CCS projects, this study was conducted to develop a budget-type techno-economic model for CO2 transmission through pipelines on the southern coasts of Iran. Although the design of a pipeline project with detailed economic investigations has a lower error, it needs spending a lot of time and cost. Therefore, it is necessary to create a budget-type techno-economic model that includes key technical and economic specifications of CO2 transmission pipelines for Iran like similar studies for other countries. In the present study, first, the requirements and the process of construction of a pipeline were described. Then, different economic budget-type models were developed based on the results of different technical models and the investment costs of the pipelines, booster stations, etc. It is worth mentioning that the developed budget-type techno-economic models have uncertainties due to various technical and economic parameters involved in the modeling. Therefore, a stochastic analysis was performed based on the input parameters of the model. For the case study, the pipeline diameter, the investment cost for the 110-km pipeline, and the levelized cost were calculated to be 0.273 m, 18.37 million €, and 1.55 €/ton, respectively which can be for the basic design of CO2 pipelines. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

کلیدواژهها | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

CO2 transmission pipeline؛ carbon dioxide capture؛ and storage؛ techno-economic models؛ levelized cost؛ stochastic analysis | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

اصل مقاله | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

**Introduction**
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 The use of CCS technology is a promising way to reduce the amount of CO 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
It is noteworthy that Iran is one of the most important CO Due to the proximity of the southern part of Iran to CO
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
**Linear modeling**
Van den Broek et al. conducted a study entitled about storing CO
**Modeling based on pipeline weight**
Gao et al. examined all scenarios of CO
**Modeling based on quadratic equations**
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
**CMU model**
One of the most significant techno-economic models is found in the research by (McCoy & Rubin, 2008). The main purpose of this study was to estimate the cost of CO
**Flow modeling based on flow rate**
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 Due to the importance of CO
**Budget-type techno-economic model of CO**_{2}pipelines
According to theoretical principles, CO - 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.
(Veritas, 2010)
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
In the southern part of Iran, especially the southern coasts, there are many sources of CO
According to calculations, the total CO - Abadan, Bandar Abbas, Lavan, Emam Khomeini, Shazand, Kermanshah, and Shiraz Oil Refineries
• Fajr-e-Jam, Parsian, Ilam, Bid Boland, Assaluyeh (different phases), Sarkhon, and Qeshm Natural Gas Refineries
In addition, the petrochemicals in the south of the country, due to pure CO
**Budget-type techno-economic model of CO**_{2}pipeline in the southern costs of Iran
According to the previous techno-economic models, the costs of CO 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 techno-economic parameters of modeling are given in Table 5. For ease of understanding, the modeling method is described in detail below.
To obtain the pipeline diameter, Equations (1) to (3) are solved simultaneously according to the iterative algorithm shown in Figure 2 (McCoy & Rubin, 2008):
Where; D= diameter of the pipeline (m), = average condensability of the fluid, R=
The thickness of the pipeline was also calculated from Equation 5. According to the above-mentioned iterative algorithm by creating a set of data based on the API 5L standard, the diameter, and nominal thickness were calculated (McCoy & Rubin, 2008).
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.
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
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).
**Cost of raw materials and equipment**
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 Raw material cost (pipe):
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:
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:
- Construction and manpower costs:
This section includes the two main sub-sections of project construction costs and labor costs. The reason for this division is the effectiveness of construction costs so in some models, this sub-section 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).
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.
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).
Finally, the investment cost of the pipeline is defined as follows:
Long CO
Where; is the investment cost for the booster station ( ) and W is the capacity of the booster station (MW
**Operating cost of the pressure boosting station:**
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).
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.
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).
After calculating the costs of each section, the levelized cost per ton of CO
In the above equations, is the capital recovery factor, is capacity factor,
In this section, according to the results of economic modeling using the least-squares 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):
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:
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:
The main goal is to minimize the residual error in estimating the model parameters, i.e.:
In the matrix representation, the above expression was rewritten and a partial derivative with respect to b was taken from it:
According to this algorithm, the economic function was obtained as follows:
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. - Comparing the developed model with other available models for different lengths
This section presents and compares the levelized-cost 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.
Reasons for the wide range of the levelized cost per ton of CO - Topographic conditions:
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 off-shore types all, are among the modeling assumptions affecting the levelized cost per ton of CO - Project location:
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. - Different assumptions in cases such as project life, interest rate, and final capacity of the pipeline
- Type of steel, coating, and insulation used in the pipeline
- Details of the parameters involved in the economic modeling of CO
_{2}pipelines
The budget-type techno-economic model has uncertainties due to various technical and economic parameters involved in the modeling. The specifications of the CO 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 technical-economic 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 14-21 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 non-use of other complex distributions is due to their inefficiency in analyzing the statistical data.
As mentioned earlier, the developed model can be generalized to other CO
**Conclusion**
After presenting the modeling results, the main purpose of this section is a comparison and systematic assessment of the techno-economic model in terms of the costs of investment, repair and maintenance, and pressure boosting stations of CO Due to the high levels of CO - Topographic conditions
- Project location
- Different assumptions in cases such as project life span, interest rate, and final capacity of the pipeline
- Type of steel, coating, and insulation used in the pipeline
- detail of the parameters involved in the economic modeling of the CO
_{2}pipeline
First, it should be noted that this model could be used for different lengths and flow rates of a carbon grid. In the developed budget-type model, attempts were made to include the necessary techno-economic details, so the results could be a good criterion for the cost estimate of a CO - Increasing the length of the pipeline in a given flow rate will increase the costs.
- Increasing the discharge over a certain length, while increasing the cost of investment, reduces the levelized cost of CO
_{2}This means that in a given length, although increasing the flow rate increases the diameter and this has technical-economic consequences, due to the reduced levelized cost, in the fixed-length, priority is given to increasing the flow rate. However, it should be noted that with a further increase of the flow rate from a certain range, the decreasing slope of the levelized cost decreases. This means that the increase in flow rate at a certain length should be controlled and evaluated by analyzing the parameters, such as the project location, amount of emissions, optimal flow point, and available CO_{2}exploitation opportunities in a CCS project.
By comparing the developed budget-type techno-economic model with other available models, it was found that the cost of CO In the final part of the research, the cost of transferring 2 million tons of CO The pipeline diameter=0.273 m, the investment cost for the 110-km 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) Developing a techno-economic model for a comprehensive system of CO | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

مراجع | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

An, J., & Peng, S. (2016). Layout optimization of natural gas network planning: Synchronizing minimum risk loss with total cost. API. (2016). API 5L X70 Seamless Line Pipeline | X70 Grade Steel Pipeline | API5L X70 Pipeline | AesteironSteel. (2018). Retrieved May 20, 2018, from http://aesteironsteelpipes.com/5lxpipex42x46x52x56x60x65x70-pipestube/seamlesspipe-type-api-5l-x70-psl1-carbonsteel-pipe-164.html C.E. Smith. (2009). Special report: Pipeline proﬁts, capacity expansion plans grow despite increased costs. C.E. Smith. (2010). Natural gas pipelines continue growth despite lower earnings, oil profits grow. Calculation of thermodynamic state variables of carbon dioxide. (2020). Retrieved February 20, 2020, from http://www.peacesoftware.de/einigewerte/co2_e.html Chandel, M. K., Pratson, L. F., & Williams, E. (2010). Potential economies of scale in CO2 transport through use of a trunk pipeline. Climatic and historical data of Iran. (2020). Retrieved May 20, 2020, from https://irimo.ir/far/wd/2703 Copiello, S. (2018). Expansion of the Italian natural gas network to the Sardinia Island: Economic assessment. CPI and Inflation. (2019). Retrieved January 25, 2019, from https://www.cbi.ir/Inflation/Inflation_en.aspx Dahowski, R., Dooley, J., Davidson, C., Bachu, S., & Gupta, N. (2004). Firozeh Amini, Mohammadali Shafizadeh, Lida Saber Fattahi, Mostafa Tavanpour, Pantea Soleimanpour, Majid Farmad, Nasrin Golghahremani, M. K. (2015). Foreign Exchange Rates. (2019). Retrieved January 25, 2019, from https://www.cbi.ir/ExRates/rates_en.aspx. Gao, L., Fang, M., Li, H., & Hetland, J. (2011). Cost analysis of CO2 transportation: Case study in China. IEA. (2010a). IEA. (2010b). IEA. (2015). IEA. (2016). IEA. (2020). World Energy Prices. Retrieved May 20, 2020, from https://www.iea.org/data-and-statistics?country=WORLD&fuel=Energy supply&indicator=TPESbySource Jafari, M., Ghasemzadeh, K., Amiri, T. Y., & Basile, A. (2019). Comparative Study of Membrane and Absorption Processes Performance and their Economic Evaluation for CO2 Capturing from Flue Gas. Knoope, M. M. J., Ramírez, A., & Faaij, A. P. C. (2013). A state-of-the-art review of techno-economic models predicting the costs of CO2 pipeline transport. Li, Y., Chiu, Y. H., Cen, H., Wang, Y., & Lin, T. Y. (2020). Residential gas supply, gas losses and CO2 emissions in China. Lu, H., Ma, X., Huang, K., Fu, L., & Azimi, M. (2020). Carbon dioxide transport via pipelines: A systematic review. Malek Shariati Niassar. (2018). Development and Optimization of an Integrated Process Conﬁguration for IGCC Power Generation Technology with a Fischer-Tropsch Fuels from Coal and Biomass. Management, I. planning. (2018). McAllister, E. W. (2009). McCollum, D. L., & Ogden, J. M. (2006). McCoy, S. T., & Rubin, E. S. (2008). An engineering-economic model of pipeline transport of CO2 with application to carbon capture and storage. Nikoomaram, H., Amidpour, M., Panahi, M., & Sotoodeh, A. F. (2021). Thermodynamic evaluation of a novel solar-aided biogas polygeneration system integrated with steam network. Parker, N. C. (2015). Using Natural Gas Transmission Pipeline Costs to Estimate Hydrogen Pipeline Costs Institute of Transportation Studies UC Davis Title : Using Natural Gas Transmission Pipeline Costs to Estimate Hydrogen Pipeline Costs Author : Parker , Nathan , ITS-Davis P, (January 2003). Rahimi, M. J., Hamedi, M. H., & Amidpour, M. (2021). Thermodynamic and economic evaluation of a novel configuration for sustainable production of power and freshwater based on biomass gasification. Rahimi, M.J., Hamedi, M. H., & M. Amidpour. (2019). Thermodynamic, Economic and Case Study of Synthesis Gas Using the Biomass Gasification Reactor in Distributed Generation Systems. Rahimi, Mohammad Javad, & Alibabaee, H. (2021). Energy and Environmental Comparison of Different Scenarios for Cogeneration of Power and Desalinated Water in Iran. Research and Technology of the Ministry of Oil of Iran. (2018a). Specific price list of oil and gas facilities: intercity oil and gas transmission pipelines. Research and Technology of the Ministry of Oil of Iran. (2018b). Salehi, G., Jalali, S. S., Noroozieh, M. S., & Araghi, M. I. (2020). Multi-Criteria Analysis for the Use of Carbon Dioxide Generated In the Gas Plant. Shariati, M., & Amidpour, M. (2016). Development of an Integrated Structure of CHP and Heavy Hydrocarbons Liquids Using Fischer-Tropsch Synthesis. Smith, C. E. (2006). US gas carriers’ 2005 net incomes climb; construction costs plummet. Soltanieh, M., Zohrabian, A., Gholipour, M. J., & Kalnay, E. (2016). A review of global gas flaring and venting and impact on the environment: Case study of Iran. Tavanir. (2018). True, W. R. (1995). U.S. interstate pipelines ran more efficiently in 1994. Türkşen, I. B. (2008). Fuzzy functions with LSE. van den Broek, M., Ramírez, A., Groenenberg, H., Neele, F., Viebahn, P., Turkenburg, W., & Faaij, A. (2010). Feasibility of storing CO2 in the Utsira formation as part of a long term Dutch CCS strategy. An evaluation based on a GIS/MARKAL toolbox. Vasconcelos, C. D., Lourenço, S. R., Gracias, A. C., & Cassiano, D. A. (2013). Network flows modeling applied to the natural gas pipeline in Brazil. Veritas, D. N. (2010). Wang, D., Zhang, Y. D., Adu, E., Yang, J. P., Shen, Q. W., Tian, L., & Wu, L. (2016). Influence of dense phase CO2 pipeline transportation parameters. Wei, N., Li, X., Wang, Q., & Gao, S. (2016). Budget-type techno-economic model for onshore CO2 pipeline transportation in China. Yuan, M., Zhang, H., Long, Y., Shen, R., Wang, B., & Liang, Y. (2019). Economic, energy-saving and carbon-abatement potential forecast of multiproduct pipelines: A case study in China. Zhang, Z. X., Wang, G. X., Massarotto, P., & Rudolph, V. (2006). Optimization of pipeline transport for CO2 sequestration. Zhou, X., Zhang, H., Xin, S., Yan, Y., Long, Y., Yuan, M., & Liang, Y. (2020). Future scenario of China’s downstream oil supply chain: Low carbon-oriented optimization for the design of planned multi-product pipelines. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

آمار تعداد مشاهده مقاله: 52 تعداد دریافت فایل اصل مقاله: 24 |