## 2.1 Geological background of the study area

Based on regional structural analysis, the Huainan coalfield is located at the southern margin of North China Plate. In the west-east direction, the coal field boundary lies between the Kouziji-Nanzhaoji faults and the Xinchengkou-Changfeng faults. From north to south, the coalfield boundary lies between the Shangtangming-Longshan faults and Yingshang-Dingyuan faults (Fig. 1) 25–26. The coalfield is a near east-west hedge tectonic basin with imbricate fan composed of nappe structures on both sides of the basin and simple synclinic structure in the interior (Fig. 1).

The coal-bearing strata are Taiyuan Formation of upper Carboniferous series, Shanxi Formation of lower Permian series, lower Shihezi Formation, and upper Permian Shangshihezi Formation, with a total thickness of about 900 m and about 40 layers of coal seams 27–28. The coal seams with single-layer thickness greater than 0.7 m on average are 9 ~ 18 layers, the maximum thickness is 12 m, and the total thickness is 23 ~ 36 m, distributed in Shanxi Formation, lower Shihezi Formation and upper Shihezi Formation. The macro-composition of coal is mainly bright coal and semi-bright coal, and the maceral component of vitrinite accounts for about 75%. The reflectance of vitrinite is mostly between 0.75% and 0.85% 16, 28. In this study, the CO2 emission sources were 10 coal-fired power plants in the coalfield with numbered D1-D10, respectively. Deep unworkable seams are CO2 storage sinks, which are bounded by faults and numbered B1-B15, respectively (Fig. 1).

## 2.2 Evaluation method of CO2 geological storage potential

In deep unworkable seam, CO2 geological storage is mainly in adsorbed, dissolved and free states 29. Among them, adsorption storage is the main storage form of coal seam, which is different from other geological bodies 30. Considering the storage differences of different phase CO2, the following potential assessment model of CO2 storage can be adopted 16, 31:

$${M_{CO2}}=0.001{\rho _{CO2}}{M_{Coal}}({m_{ab}}+{m_d}+{m_f})$$

1

Where, *M**CO2* is CO2 storage capacity, t; *ρ**CO2* is the CO2 density, kg/m3; *M**coal* is deep proved coal reserves, t; *m**ab*, *m**d* and *m**f* are the stored quantity of CO2 adsorbed, dissolved and free states in coal per unit mass, m3/t.

In the unit mass coal, the storage potential of CO2 adsorbed state in deep unworkable seam can be characterized by the following formula 16, 31:

$${m_{ab}}={m_{ex}}/(1 - p{T_c}/8Z{p_c}T)$$

2

Where, *P* is the reservoir pressure, which is also CO2 adsorption pressure, MPa; *T**c* is CO2 critical temperature, K; *Z* is the CO2 compression coefficient; *p**c* is CO2 critical pressure, MPa; *T* is the reservoir temperature, and also CO2 adsorption temperature, K; and *m**ex* is the CO2 excess adsorption amount per unit mass of coal, m3/t, which can be calculated using the following D-R adsorption model 16, 31:

$${m_{ex}}={m_0}(1 - {\rho _f}/{\rho _a}){e^{ - D\mathop {\left[ {\ln ({\rho _a}/{\rho _f})} \right]}\nolimits^{{^{2}}} }}+k{\rho _f}$$

3

Where, *m**0* is the maximum CO2 adsorption capacity of coal per unit mass tested by adsorption experiment, m3/t; *ρ**f* and *ρ**a* are the densities of free and adsorbed CO2 under the real temperature and pressure conditions, kg/m3; and *D* is the adsorption constant and *k* is the constant associated with Henry's Law.

In coal reservoir, CO2 density is a function of pressure and temperature, which can be expressed as *ρ**f* = *f(p, T)*, and can be further characterized as follows 16, 31–32

$${\rho _g}=p/((1+\delta \phi _{\delta }^{\tau })\cdot RT)$$

4

Where, *δ* = *ρ**c**/ρ**f* is the CO2reduced density; *ρ**c* is the CO2 critical density, kg/m3; *τ* = *T**c**/T* is the reduced temperature; and *ϕ(δ,τ)* is the Helmholtz free energy, which can be controlled by temperature and density 16, 31–32:

$$\phi (\delta ,\tau )={\phi ^0}(\delta ,\tau )+{\phi ^r}(\delta ,\tau )$$

5

Where, *ϕ**o**(δ, τ)* is the Helmholtz free energy of ideal fluid, and *ϕ**r**(δ, τ)* is the Helmholtz free energy of the residual fluid.

In deep unworkable seam, the storage potential of dissolved CO2 per unit mass of coal is a function of coal porosity, water saturation, coal density and CO2 solubility, which can be characterized as follows 16, 31:

$${m_d}=1000\cdot \varphi {S_w}{S_{CO2}}/{\rho _{Coal}}$$

6

Where, *φ* is the coal porosity, %; *S**w* is the water saturation, %; *S**CO2* is the CO2 solubility, and *ρ**coal* is the coal density, kg/m3.

According to Boyle-Mariotte law, the free CO2 storage potential per unit mass of coal in deep unworkable seam can be characterized as follows 16, 31:

$${m_f}=1000\cdot \varphi {S_g}p{T_0}/({\rho _{visual}}Z{p_0}T)$$

7

Where, *S**g* is the gas saturation, %; *P**0* is the standard atmospheric pressure, MPa; *T**0* is the temperature under the standard condition, K; and *ρ**visual* is the coal apparent density, kg/m3.

## 2.3 Construction of matching model of CCUS source-sink

## 2.3.1 CCUS source and sink matching

CCUS source-sink matching is the basis of CCUS cluster deployment and its pipe network design and construction, with the goal of minimizing CO2 transportation cost and maximizing carbon removal. Its essence is the optimization planning of CCUS cluster system 35–36. Based on CO2 emission source, storage sink, storage geological process, transport network connecting source and sink and corresponding parameter data, the dynamic optimal matching between CO2 source and sink can be achieved in terms of target quantity, continuity and economic efficiency (Fig. 2).

The matching of CCUS source and sink is mainly based on the characteristics of large number, different types and scattered locations of CO2 emission sources (i.e., thermal power, steel, cement, chemical industry, etc.) and storage sinks (i.e., saltwater layer, CO2-ECBM, CO2-EOR, MCO2-ILU, CO2-SDR, etc.). Based on the discussion of constraint conditions and determination of objective function, the influence of regional geographical conditions, traffic, population density, transportation cost and transportation mode on CO2 transport between emission sources and storage sinks is fully considered in the CCUS system. The optimal matching of CO2 emission sources, storage sinks and transportation parameters was realized, so as to determine scientific and reasonable CCUS source and sink matching schemes (Fig. 2).

## 2.3.2 Objective functions

Based on the theory of network analysis in operations research, theoretical models of CO2 source-sink matching within CCUS technology can be constructed in Huainan coalfield by using the minimum support tree method. Among them, the construction of theoretical models should meet the following basic assumptions: (1) Source and sink with the lowest cost should be firstly matched; (2) Allow the matching of one source with multi sinks or one sink with multi sources; (3) Sequestration sink must meet the requirement of CCUS planning period.

In this study, the lowest total cost of matching of CO2 source-sink in CCUS technology is taken as the objective function, namely:

$$COS{T_{\hbox{min} }}=\sum\limits_{{i=1}}^{m} {\sum\limits_{{j=1}}^{n} {({C_C}+C{}_{T}+{C_S}} } )$$

8

Where, *i* refers to the *i**th* CO2 source; *j* means the *j**th* CO2 sink; *m* indicates the number of CO2 sources and the value is 10, and *n* indicates the number of CO2 sinks with the value of 15.

(1) CO2 capture cost (i.e., *C**C*)

Based on the analysis of the industrial sources report published by the National Energy Technology Laboratory of the United States, the average capture cost of CO2 source in coal-fired power plants is 64.35 $/t 30, 37. Therefore, the capture cost of CO2 source in Huainan coalfield can be characterized as follows:

$${C_C}=\sum\limits_{{i=1}}^{m} {\sum\limits_{{j=1}}^{n} {{\omega _{ij}}{X_{ij}}} }$$

9

Where, \({\omega _{ij}}\) represents the CO2 capture cost in the *i* coal-fired power plant, $/t; and *X* *ij* represents CO2 transport amount from the *i* coal-fired power plant to the *j* sequestration sink, t.

(2) CO2 transportation cost (i.e., *C**T*)

CO2 transport is most common by pipeline, ship and tanker. Among them, pipeline transportation is suitable for directional transportation with large capacity, long distance and stable load, which mainly includes construction cost and operation and maintenance cost, and can be characterized as follows:

$${C_T}=(1+0.015N) \times 9970 \times \sum\limits_{{i=1}}^{m} {\sum\limits_{{j=1}}^{n} {{L^{1.13}}X_{{ij}}^{{0.35}}} }$$

10

Where, *N* represents the transportation cycle of the pipeline, year; and *L* is the distance of pipeline transportation, km.

(3) CO2 sequestration cost (i.e., *C**S*)

The cost of CO2 geological storage is closely related to the amount of CO2 storage and the type of storage site, and the average storage cost coefficient in reservoir is 5.59 $/t 30, 37. Therefore, the cost of CO2 geological storage in coal reservoir can be characterized as follows:

$${C_S}=\sum\limits_{{i=1}}^{m} {\sum\limits_{{j=1}}^{n} {{\varepsilon _{ij}}{X_{ij}}} }$$

11

Where, \({\varepsilon _{ij}}\) is the sequestration cost factor of transporting CO2 from coal-fired power plant *i* to sequestration sink *j*, $/t.

In summary, by substituting formulas (9), (10) and (11) into formula (8), the minimum objective function of the total cost of CO2 source-sink matching in CCUS technology can be obtained:

$$MinZ=\sum\limits_{{i=1}}^{m} {\sum\limits_{{j=1}}^{n} {({\omega _{ij}}{X_{ij}}+(1+0.015N) \times 9970 \times {L^{1.13}}X_{{ij}}^{{0.35}}+{\varepsilon _{ij}}{X_{ij}}} } )$$

12

## 2.3.3 Constraint conditions

Based on the basic assumptions of theoretical model, in the planning process of matching pipe network of CO2 source-sink with CCUS technology, the constraint conditions of the lowest total cost objective function are as follows:

(1) The total amount of CO2 captured by all CO2 emission sources is equal to the total amount of pipeline transport, that is:

$${a_i}=\sum\limits_{{j=1}}^{n} {{X_{ij}}}$$

13

Where, *a**i* is the CO2 capture amount of the *i* coal-fired power plant.

(2) The CO2 content transported by the pipeline to the storage site shall not exceed the storage capacity of the storage sink, that is:

$${b_j} \geqslant \sum\limits_{{i=1}}^{m} {{X_{ij}}}$$

14

Where *b**j* is the storage capacity of the *j**th* storage sink.

(3) The amount of CO2 captured in all coal-fired power plants must not exceed the total capacity of all potential sequestration sinks, that is:

$$\sum\limits_{{i=1}}^{m} {{a_i}} \leqslant \sum\limits_{{j=1}}^{n} {{b_j}}$$

15

(4) Non-negative constraint: the pipeline of CO2 transport content is non-negative, that is:

## 2.4 Optimization of matching pipe network of CCUS source-sink

In this study, based on the mileage saving method, the optimization of matching network of CCUS source-sink can be carried out. The core idea of the mileage saving method is to combine the two round-trip routes into a closed loop during the transportation process, and the distance reduction is the largest in the merging process (Fig. 3). Based on the traditional mileage saving method, when the goods are transported from point A to two points B and C, the mileage saved is LAB+LAC-LBC, that is, the difference between 2*(LAB+LAC) and (LAB+LAC+LBC) (Fig. 3-a).

The design and optimization of CCUS source-sink matching pipe network has its own particularities: (1) CO2 transport has a unique direction and there is no loop; (2) A small increase in CO2 transport may change pipeline design and increase transport costs. In this study, the traditional mileage saving algorithm can be improved, while considering the cost reduction caused by distance saving and the cost increase caused by the increase in traffic volume, and automatically determine whether and how to merge the pipelines in the scheme, and finally get the optimization scheme (Fig. 3-b). In the process of transporting CO2 source X to CO2 storage sinks Y and Z, based on the improved mileage saving method, the pipeline mileage saving can be LXY-LZY or LXZ-LYZ, from which the optimal pipeline transportation optimization scheme can be selected (Fig. 3-b).