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Optimal energy consumption analysis of natural gas pipeline.

1. Introduction

Gas pipelines are the bond that connects gas production and consumption; therefore, their operation must be safe, smooth, and effective. In 1961, a US gas pipeline company collaborated with IBM to simulate and optimize the operation of gas pipelines [1]. This represented the prelude to additional optimal operation research on gas transmission pipelines.

In 1983, Goldberg introduced a genetic algorithm, which was one of the most popular optimization algorithms of the time, to optimize the operation of a natural gas pipeline [2]. The optimal solution of this optimization model considered the minimum energy consumption to be the objective function and promoted research on long-distance pipeline operation optimization using intelligent optimization algorithms. Between 1984 and 1997, many scholars, such as Mantri, Renji, Bhaduri, Anglard, Wilson, Ryan, and Berry et al., continued to improve the operation optimization model of gas transmission pipelines, as well as the methods for obtaining solutions [3-10]. In 1998, Carter took advantage of the dynamic programming algorithm for constructing a steady-state operation optimization model of a gas transmission pipeline [11]. Based on his calculations, he concluded that the dynamic programming algorithm converged faster than the annealing and genetic algorithms. By the end of the 20th century, network simulation models and the optimization of the operation technology for natural gas transmission pipelines had reached maturity. The nonlinear operation optimization model for long-distance gas transmission pipelines (including a discrete variable and objective function for minimum energy consumption) had also been recognized. Since then, researchers have made a sustained effort, taking into consideration the various aspects of the optimization algorithm, to solve the network operation optimization model for a gas transmission pipeline more quickly and effectively. For example, in 2000, Sun and others established a comprehensive pipeline operation optimization expert system [12]. This expert system was capable of detecting the pipeline filling state such that the system could decide the control requirements. It was also able to work out the demand of the corresponding energy consumption. Based on these two steps, a fuzzy model can be used to determine the exact extent to which the compressor should be open. In 2002, Cobos-Zaleta and Rios-Mercado used the equation relaxation and expansion valve method to solve the operation optimization model for a gas pipeline [13]. In 2004, Rusnak et al. used the steady optimization simulator for dynamic optimization analysis of long-distance pipelines, with the goal of simulating the minimal energy consumption [14, 15]. After 2008, Yi et al. studied the problem of steady-state optimization operation of a main gas transmission pipeline network under a determined throughput. In these studies, the optimal rule was adopted based on the minimum energy consumption cost [16-19].

In this paper, we aim to characterize long-distance natural gas pipeline operation management. For a given throughput, with the minimum pipeline operation energy consumption as the goal, the gas pipeline optimal operation model can be established. This model is solved using a dynamic programming method to obtain the best operation scheme and the minimum energy consumption for the natural gas pipeline.

2. Minimum Energy Consumption Prediction Model of a Natural Gas Pipeline

Natural gas pipeline systems are complicated. They are composed of pipelines, stations, compressors, fluids, external environmental factors, and other components. Based on the Chinese policy for energy savings and emission reduction and the premise of the transportation quantity plan (intake quantity or delivery quantity), the pipeline operation department must configure each station's compressors and determine the operating parameters for each station to reach the lowest energy consumption for the pipeline system.

To study the minimum energy consumption of a natural gas pipeline system, we need to establish a corresponding mathematical model. A reasonable and accurate mathematical model is the key to obtaining the best results.

2.1. The Objective Function. During operation, the pipeline's main energy consumption is from the compressor's drive. Therefore, we established an objective function as the goal for minimum production unit consumption, which is expressed as

min F = ([S.sub.p], [[omega].sub.1] + [S.sub.g][[omega].sub.2])/[T.sub.ur], (1)

where F is the production unit consumption of the pipeline in kgce/([10.sup.7]N[m.sup.3] x km), [S.sub.p] is the power consumption in kW x h, [S.sub.g] is the gas consumption in [m.sup.3], [[omega].sub.1] is the electric coal conversion coefficient based on the Chinese National Standard GB2589-81 of 0.1229 kgce/(kW-h), [[omega].sub.1] is the gas coal conversion coefficient based on the Chinese National Standard GB2589-81 of 1.33 kgce/[m.sup.3], and [T.sub.ur] is the turnover in [10.sup.7] N[m.sup.3]-km.

The power consumption [S.sub.p] can be expressed as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (2)

The gas consumption [S.sub.g] can be expressed as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (3)

where n is the number of compressors, [N.sub.i] is the shaft power of the ith compressor in kW, [t.sub.i] is the running time of the ith compressor in h, [[eta].sub.ei] is the drive motor efficiency of the ith compressor, [[eta].sub.gi]i is the turbine efficiency of the ith compressor, and ge is the gas loss rate of the gas turbine in N[m.sup.3]//(kW x h).

The turnover [T.sub.ur] can be expressed as

[T.sub.ur] = [10.sup.-4] [n.summation over (i=1)] [Q.sub.i][L.sub.i]t, (4)

where [Q.sub.i] is the volume flow of the ith section of the pipeline in N[m.sup.3]/d, [L.sub.i] is the length of the ith section of the pipeline in km, and t is the delivery time in d.

2.2. Optimization Variables. The power of the compressor depends on the compression ratio, flow rate, and temperature. Because the inbound traffic of the compressor station is known, the power of the compressor can be simplified into a function of the pressure ratio and temperature. The compressor inlet and outlet temperatures depend on the compression ratio; therefore, the optimization variables can be converted into the compression ratio and thus can be converted into the outbound pressure. The optimization variables of the optimization model, that is, the outbound pressures and the boot number, can be expressed as

[X.sub.k] = ([P.sub.dk], [O.sub.i]), (5)

where [P.sub.dk] is the outbound pressure of the kth compressor station and [O.sub.i] is the boot number of the ith compressor station.

2.3. Constraint Condition. To guarantee the safe operation of the pipeline and the devices, both the operation parameters of the pipelines and the operation parameters of the devices must be within the permitted range. Namely, the parameters must be satisfied with a series of constraint conditions.

(1) Inlet and Outlet Pressure Constraint. According to the user's need, there are some requirements for the pressures of the subair node. These are expressed as

[P.sub.i min] [less than or equal to] [P.sub.i] [less than or equal to] [P.sub.i max] (i = 1, 2, ..., [n.sub.s]), (6)

where [P.sub.i] is the pressure of the ith node in Pa, [P.sub.i min] is the minimum permissible pressure of the ith node in Pa, and [P.sub.i max] is the maximum allowable pressure of the ith node in Pa.

(2) Pipeline Strength Constraints. To ensure the safe operation of the pipelines, the gas pressure must be less than the maximum allowable operating pressure such that

[P.sub.k] [less than or equal to] [P.sub.k max] (k = 1, 2, ..., [n.sub.p]), (7)

where [P.sub.k] is the pressure of the kth pipe in Pa and [P.sub.k max] is the maximum allowable pressure of the kth pipe in Pa.

(3) Compressor Performance Constraints. The compressor power equation is

N = MH/[eta], (8)

where M is the overflow rate of the compressor in kg/s, H is the polytropic head of the compressor, and [eta] is the efficiency of the compressor.

The head curve is calculated according to

-H = [h.sub.1][S.sup.2] + [h.sub.2]SQ + [h.sub.3][Q.sup.2], (9)

where [h.sub.1], [h.sub.2], and [h.sub.3] are the fitting coefficients of the head curve, S is the speed of the compressor, and Q is the actual overflow rate of the compressor in [m.sup.3]/d.

The efficiency curve is calculated according to

-H/[eta] = [e.sub.1][S.sup.2] + [e.sub.2]SQ, (10)

where [e.sub.1] and [e.sub.2] are the fitting coefficients of the power curve. The buzz curve is calculated according to

[Q.sub.surge] = [s.sub.1] + [s.sub.2]H, (11)

where [Q.sub.surge] is the surging flow in [m.sup.3]/d and [s.sub.1] and [s.sub.2] are the fitting coefficients of the buzz curve.

The stagnation curve is calculated according to

[Q.sub.stone] = [s.sub.3] + [s.sub.4]H, (12)

where [Q.sub.stone] is the stagnation flow in [m.sup.3]/d and [s.sub.3] and [s.sub.4] are the fitting coefficients of the stagnation curve.

From (9) to (12) are plotted in the figure, forming a closed area. This area is the operating area of the compressor.

(4) Compressor Power Constraints. The power constraints are represented by

[N.sub.min] < N < [N.sub.max], (13)

where [N.sub.min] is the minimum allowable power of the compressor in MW and [N.sub.max] is the maximum allowable power of the compressor in MW.

(5) Compressor Speed Constraints. The speed constraints are represented by

[S.sub.min] < S < [S.sub.max], (14)

where [S.sub.min] is the minimum speed of the compressor in rpm/min and [S.sub.max] is the maximum speed of the compressor in rpm/min.

(6) Compressor Outlet Temperature Constraints. The temperature constraints are represented by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (15)

where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the maximum outlet temperature of the compressor in K.

(7) Pipeline Pressure Drop Equation. The pressure of the pipeline is determined by two factors: the value of the frictional pressure drop and the pressure change due to the elevation change. The calculation of the pressure drop is based on the continuity and momentum equations. Introducing the mass flow rate of the gas [20], we obtain

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (16)

where M is the flow of the gas through the pipes in kg/s, [P.sub.Q] is the starting pressure of the pipeline in Pa ([P.sub.Q] = [P.sub.d]), [P.sub.Z] is the end pressure of the pipeline in Pa ([P.sub.Z] = [P.sub.s]), T is the average of the gas flow temperature in K, L is the length of the pipeline in m, D is the diameter in m, [DELTA]h is the elevation difference between the start and end of the pipeline in m, Z is the gas compressibility (i.e., the pressure computation of the BWRS state equation), and [lambda] is the friction factor.

(8) Pipe Temperature Drop Formula. The pipe temperature drop is calculated according to

T = [T.sub.0] + ([T.sub.Q] - [T.sub.0])[e.sup.-ax], (17)

where T is the temperature of length x of the pipeline in K, [T.sub.0] is the temperature of the pipeline where it is deeply buried in K, and [T.sub.Q] is the temperature at the start of the pipeline in K.

(9) Pipe Network Node Flow Balance Constraints. For a natural gas pipeline, in any node, according to the law of conservation of mass, the inflow and outflow of the gas should be 0. In general, for a natural gas pipeline network system with Nn node, the gas flow equilibrium equations of the node can be written as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (18)

where [C.sub.i] is the set connected to the ith node element, [M.sub.ik] is the absolute value of element k into/out of the node flow connected to the ith node, [Q.sub.i] is the flow in the node exchange with the outside world (flow into the positive, flow out of the negative), and [a.sub.ik] is the coefficient (when traffic flows in, the k node components are +1 and when traffic flows out, the k node components are -1).

The mathematical model can be written in the standard form for optimization models as

min f(x)

s.t: [g.sub.i](x) [less than or equal to] 0 (i = 1, 2, ..., m), (19)

where x represents the optimization variables and m is the number of constraints.

3. Method for Modeling Based on Dynamic Programming

The gas pipeline branch is simplified to a point. The operation process of the pipeline can be regarded as a multistage process. Thus, we can use a dynamic programming algorithm to distribute the optimal ratio of the compressor stations (i.e., the optimal discharge pressure).

Suppose the number of compressor stations is n when establishing the dynamic programming model. Treat the gas transmission process from the compressor station of the (k - 1)th to the kth as the kth phase of the correspondence problem. The kth stage of the state variables [X.sub.k] (corresponding to the starting point of the state) is the discharge pressure [P.sub.d,k-1] of the kth station. The phase effect for the kth station energy consumption (i.e., the power, as shown in formula (1)), with respect to the pipeline total energy consumption of the optimization goal, can build the optimized dynamic programming model of the pipeline's compressor station pressure ratio.

The algorithm for solving the model is composed of the following components: "determine the state space," "recursive between stations" "recursive within the station" and "backtracking algorithm"

3.1. Determine the State Space. In the dynamic programming algorithm, a certain compressor station out of all of the feasible discharge pressures is the state space. The upper boundary of the state space can give the design pressure of the pipeline. The lower boundary, also called the lowest discharge pressure, is difficult to determine. If it is too large, it will increase the unnecessary computation; however, if it is too small, it may miss the optimal solution. We calculated the lowest discharge pressure for the previous compressor station with the limitations of the lowest discharge pressure of this compressor station.

The compressor with the gas turbine or motor drive performs stepless speed regulation, so that the discharge pressure of the compressor station can be within the scope of feasible continuous change. Thus, we must process the state space to obtain the finite state point. In this paper, the outlet pressure range of each compressor station is divided into 300 points to determine the compression ratio of the space.

When the pipeline is running with low throughput, the station operation plan is always run more economically than with a low compression ratio. This must be taken into consideration for circumstances where the pressure is above the permitted level for one of the compressor stations. By setting each station's entrance pressure as part of the state space, the state transition will not leak.

3.2. Recursive between Stations. Recursion between stations is a calculation through which the entrance condition of the next compressor station is determined by the outlet condition of the current compressor station, which mainly involves hydraulic and thermodynamic calculation between stations. On the basis of a certain outlet pressure of the compressor station, (16) and 17) can be used to calculate the pressure and temperature at the ends of the pipeline. This provides the inlet pressure and the temperature of the next station.

Taking the recursive between stations shown in Figure 1 as an example, use number (i - 1) station's operation condition corresponding to output pressure [X.sup.1.sub.i] to recursive between stations to obtain the ith station outlet condition corresponding to the inlet pressure [P.sup.1.sub.s,i]. The main steps are as follows. Carry out the pipeline's hydraulic and thermodynamic calculation between the i-1th station and ith station. The starting point's parameters are [Q.sup.1.sub.d,i-1], [P.sup.1.sub.d,i-1], [T.sup.1.sub.d,i-1]. The flow should provide the corresponding changes if there is an injection or disengagement point. The final figures for flow, pressure, and temperature are obtained from the inlet operation. In the end, the optimal index [C.sup.1.sub.d,i-1] corresponding to [X.sup.1.sub.i] should be recorded as the energy consumption of the inlet operation, which reflects the pipeline's energy consumption under the optimal operation scheme from the beginning to the ith station.

3.3. Recursive within the Station. The recursive within the station gives the outlet station's operation based on the compressor station's inlet operation, which is dominated by the state transfer. For the state before the transfer, in addition to determining the state space, the feasible compression ratio range of compression for every inlet condition should also be obtained, based on the constraint conditions of the decision variables.

Taking the recursive within the station shown in Figure 1 as an example, for [X.sup.1.sub.i+1], the method of state transition is as follows. Inspect whether the path from [P.sup.1.sub.s,i] to [X.sup.1.sub.i+1] is feasible, which indicates whether [d.sup.1.sub.1] gained by [X.sup.1.sub.i+1] divides [P.sup.1.sub.s,i] (station pressure ratio, namely, the decision variables) is in line with the pressure ratio range inlet condition [X.sup.1.sub.i+1]. If not, make the energy consumption of the path a maximum value; otherwise, call for station optimization to obtain the compressor station's optimal scheme under the condition of [X.sup.1.sub.i+1] corresponding to the inlet condition and the station pressure ratio of [d.sup.1.sub.1] and obtain the energy consumption of the station at the program (if [d.sup.1.sub.1] is equal to zero, then so is the energy consumption), namely, the stage effect of the stage. The stage effect and [P.sup.1.sub.s,i] of the corresponding inlet condition recorded from the beginning to the energy consumption of this station are added. Then, the total energy consumption from [P.sup.1.sub.s,i] to [X.sup.1.sub.i+1] can be obtained.

Use the same method to calculate the total energy consumption from [P.sup.1.sub.s,i] to [X.sup.1.sub.i+1]. Compared with the former, the smaller one is the [X.sup.1.sub.i+1] state transfer result.

3.4. Backtracking Algorithm. After the completion of the recursive within the station, we will obtain all of the total energy costs corresponding to several inlet conditions in the terminal station. To obtain the operation program within the minimum energy consumption limit to meet the terminal station's pressure, backtracking of the whole scheme is required.

Backtracking is performed according to the compression station's inlet and outlet operations recorded in the optimal program to determine the optimal operation scheme of the pipeline. Backtracking starts from the gate station's optimal inlet condition, according to every state transfer's recorded results, to find out every compressor station's outlet condition corresponding to the last station's outlet condition.

4. Operation Optimization of the XQ Gas Pipeline

4.1. Basic Parameters of the XQ Gas Pipeline

4.1.1. Pipe Parameters. The length of the pipeline is 3840 km, the design capacity is 170 x [10.sup.8] N[m.sup.3]/year, the design pressure is 10 MPa, and the diameter is [PHI]1016 x 17.5 mm. The elevation and mileage of the XQ gas pipeline are shown in Figure 2. We can see that the elevation change is large, with the highest point at 1900 m and the lowest point at 1 m.

There are 40 stations in the XQ gas pipeline, including 22 compressor stations and 18 distribution stations, as listed in Table 1.

4.1.2. The Compressor Performance Curve. There are two manufacturers for the compressors used in the XQ gas pipeline (GE and RR). Part of the compressor's coefficients for (9)-(12) is shown in Table 2.

4.1.3. Constraint Conditions. The maximum outbound pressure is 9.8 MPa, while minimum pitted pressure is 5 MPa. The minimum pitted temperature is 15[degrees]C, while the maximum outbound temperature is 65[degrees]C.

4.2. Optimization Research and Analysis. Take the parameters in May 2012 as an example for the optimization calculation. The pitted pressure of the first station is 6.5 MPa and the temperature is 15[degrees]C. Each station's gas transmission capacity is shown in Table 3. There are 5 points for admission and 37 points distributed along the line. Through 50 iterations, the optimum operation is determined, as shown in Table 4, for 23 running compressors. Compressors are connected in parallel at all stations. By means of the energy consumption amount, the energy consumption of the scheme is shown in Table 5. The unit consumption for production is 138.37 kgce/([10.sup.7] N[m.sup.3] x km), and the actual measurement of energy consumption is lower by -12.70% compared with the same month, indicating that the pipeline has great potential for saving energy.

Using the same method to optimize the operation for 1-7 months in 2012, the energy consumption optimization results can be obtained. As shown in Table 6, 1-3 months is the gas use peak in the winter. The first station's intake is approximately 4800 x [10.sup.4] N[m.sup.3] per day at full load. The period from 4 to 7 months without heating gas is the low point. The first station's intake is approximately 3500 x [10.sup.4] N[m.sup.3] per day, according to the optimal operation scheme proposed in this paper.

We can acquire the operating parameters through the SCADA systems of the pipeline, including the gas consumption and electricity consumption. Therefore, we can obtain the actual energy of the pipeline in Table 6.

The data in Table 6 is plotted in Figure 3. Compared with the measured values, the production unit consumption can be reduced by approximately 11%~17%. Therefore, the pipeline has great energy-saving potential.

5. Conclusions

Our conclusions are as follows.

(1) Based on a full understanding of actual demands of a pipeline company, we introduce production consumption indicators to establish an objective function of the minimum energy consumption of the gas pipeline and use dynamic programming to solve the model quickly and efficiently.

(2) When setting the constraints, it is necessary to consider the pipeline, station, power equipment, topography, and climate and to simplify these constraints reasonably such that the mathematical model can accurately describe not only the energy consumption of crude oil pipeline but also the convenient mathematical operations.

(3) According to the dynamic programming method, we compiled the natural gas pipeline running optimization software, which can be used to guide the natural gas pipeline running program analysis and optimize the energy savings. Through the optimization analysis of the XQ nature gas pipeline with the actual working condition, we discovered that the optimal operation scheme can reduce energy consumption by 11%~16%.

http://dx.doi.org/10.1155/2014/506138

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported by the special fund of China's central government for the development of local colleges and universities--the project of National First-Level Discipline in Oil and Gas Engineering, the Scientific Research Cultivate Project of SWPU, the National Natural Science Foundation of China (no. 51174172), and a subproject of the National Science and Technology Major Project of China (no. 2011ZX05054).

References

[1] L.-G. Li and Z.-H. Zhang, "Operation optimization based on improved pattern search algorithm in gas transmission networks," Journal of China University of Petroleum, vol. 36, no. 4, pp. 139-143, 2012.

[2] D. E. Goldberg, Computer-aided gas pipeline operation using genetic algorithms and rule learning [Ph.D. dissertation], University of Michigan, Ann Arbor, Mich, USA, 1983.

[3] V. B. Mantri, L. B. Preston, and C. C. Pringle, "Computer program optimizes nature gas pipeline operation," Pipeline Industry, vol. 6, no. 6, pp. 39-45, 1986.

[4] Z. Renji, S. Selandari, and H. G. Nicolai, "A system for control and optimum operation of a gas transmission network," in Proceedings of the 19th Annual Pipeline Simulation Interest Group Meeting (PSIG '87), Tulsa, Okla, USA, 1987.

[5] M. J. Ryan and P. B. Percell, "Steady state optimization of gas pipeline network operation," in Proceedings of the 19th Annual Pipeline Simulation Interest Group Meeting (PSIG '87), Tulsa, Okla, USA, 1987

[6] T. Williams and G. E. Broadbent, "Optimization in the operation of compressor stations on the moomba to adelaide gas pipeline network," in Proceedings of the 21th Annual Pipeline Simulation Interest Group Meeting (PSIG '89), El Paso, Tex, USA, October 1989.

[7] M. A. R. Berry, "Optimizing compressor operation by effective application of performance models," in Proceedings of the 23th Annual Pipeline Simulation Interest Group Meeting (PSIG '91), Houston, Tex, USA, 1991.

[8] S. Bhaduri and R. K. Talachi, "Optimization of natural gas pipeline design," ASME Petroleun Division, vol. 16, pp. 67-75, 1988.

[9] J. G. W. Wilson, "Optimization of large gas networks," in Proceedings of the European Confercence on Mathematics in Industry, The University of Stranthclyde, Glasgow, UK, August 1988.

[10] P. Anglard and P. David, "Hierarchical steady state optimization of very large gas pipelines," in Proceedings of the 20th Annual Pipeline Simulation Interest Group Meeting (PSIG '88), Toronto, Canada, 1988.

[11] R. Carter, "Pipeline optimization: dynamic programming after 30 years," in Proceedings of the 30th Annual Pipeline Simulation Interest Group Meeting (PSIG '98), Denver, Colo, USA, 1998.

[12] C. K. Sun, V. Uraikul, C. W. Chan, and P. Tontiwachwuthikul, "An integrated expert system/operations research approach for the optimization of natural gas pipeline operations," Engineering Applications of Artificial Intelligence, vol. 13, no. 4, pp. 465-475, 2000.

[13] D. Cobos-Zaleta and R. Z. Rios-Mercado, "A MINLP Model for a Minimizing Fuel Consumption on Natural Gas Pipeline Networks," 2002, http://yalma.fime.uanl.mx/.

[14] D. R. Rusnak and E. P. Ferber, "Automated Pipeline Optimization (APO) for nonimations management & comperssor fuel minimization," in Proceedings of the 36th Annual Pipeline Simulation Interest Group Meeting (PSIG '04), 2004.

[15] J. Stoffregen, K. K. Botros, and D. J. Sennhauser, "Pipeline network optimization application of genetic algorithm methodologies," in Proceedings of the 37th Annual Pipeline Simulation Interest Group Meeting (PSIG '05), San Antonio, Tex, USA, 2005.

[16] Y. Yi and Z. Hongwei, "Research on steady optimization operation of China petroleum main gas transmission pipe network," Shanghai Gas, vol. 2, pp. 10-14, 2008.

[17] C. Borraz-Snchez and R. Z. Ros-Mercado, "Minimizing fuel cost in gas transmission networks by dynamic programming and adaptive discretization," Computers & Industrial Engineering, vol. 1, no. 2, pp. 364-372, 2011.

[18] C. Monteiro, T Santos, L. A. Fernandez-Jimenez, I. J. Ramirez-Rosado, and M. S. Terreros-Olarte, "Short-term power forecasting model for photovoltaic plants based on historical similarity," Energies, vol. 6, no. 5, pp. 2624-2643, 2013.

[19] B. Tomoiaga, M. Chindric, and A. Sumper, "Pareto optimal reconfiguration of power distribution systems using a genetic algorithm based on NSGA-II," Energies, vol. 6, no. 3, pp. 1439-1455, 2013.

[20] C. Li, The Transportation of Natural Gas Pipeline, Oil Industry Press, Beijing, China, 2000.

Enbin Liu, (1,2) Changjun Li, (1,2) and Yi Yang (3)

(1) Southwest Petroleum University, Chengdu 610500, China

(2) CNPC Key Laboratory of Oil & Gas Storage and Transportation, Southwest Petroleum University, Chengdu 610500, China

(3) Beijing Oil and Gas Control Center, Beijing 100191, China

Correspondence should be addressed to Enbin Liu; sunriselebpsb@163.com

Received 20 March 2014; Accepted 29 April 2014; Published 13 May 2014

Academic Editor: Arman Siahvashi

TABLE 1: Equipment at each station.

        Station           Compressor     Drive type

Number       Type       Model   Number

1         Compressor      1       2         Gas
2         Compressor      2       2         Gas
3         Compressor      3       1         Gas
4         Compressor      4       2         Gas
5         Compressor      5       2         Gas
6         Compressor      6       1         Gas
7         Compressor      7       2         Gas
8         Compressor      8       2         Gas
9         Compressor      9       2       Electric
10        Compressor     10       2         Gas
11        Compressor     11       2         Gas
12        Compressor     12       1         Gas
13        Compressor     13       1         Gas
14       Distribution
15        Compressor     14       1         Gas
16        Compressor     15       2         Gas
17        Compressor     16       1         Gas
18       Distribution
19        Compressor     17       1         Gas
20        Compressor     18       2       Electric
21       Distribution
22        Compressor     19       2       Electric
23       Distribution
24        Compressor     20       2       Electric
25       Distribution
26        Compressor     21       2       Electric
27       Distribution
28       Distribution
29        Compressor     22       2         Gas
30       Distribution
31       Distribution
32       Distribution
33       Distribution
34       Distribution
35       Distribution
36       Distribution
37       Distribution
38       Distribution
39       Distribution
40       Distribution

TABLE 2: Coefficients for the equation for the compressor
performance curves.

Model   [H.sub.1]   [H.sub.2]   [H.sub.3]  [e.sub.1]   [e.sub.2]

1       -0.000282   -0.000393   0.000090   -0.001170   0.000144
2       -0.001200   0.000167    0.000045   -0.001470   0.000332
3       -0.000403   -0.000348   0.000064   -0.001440   0.000140
4       -0.001200   0.000167    0.000045   -0.001470   0.000332
5       -0.000390   -0.001090   0.000334   -0.002180   0.000392
6       -0.000640   0.000012    0.000023   -0.000883   0.000141
7       -0.000183   -0.001100   0.000314   -0.001990   0.000362
8       -0.001190   0.000161    0.000042   -0.001450   0.000317
9       -0.000644   -0.000679   0.000252   -0.001790   0.000324
10      -0.001190   0.000161    0.000042   -0.001450   0.000317

Model   [S.sub.1]   [S.sub.2]   [S.sub.3]   [S.sub.4]

1         4620        0.396       8310        1.44
2         3840        0.145       4910        0.533
3         5920        0.412       10700       1.47
4         3840        0.145       4910        0.533
5         3080        0.145       5970        0.585
6         5010        0.342       8520        1.26
7         3260        0.173       6270        0.79
8         3610        0.149       4640        0.554
9         2970        0.165       5340        0.504
10        3610        0.149       4640        0.554

TABLE 3: Transmission capacity, [10.sup.4] [Nm.sup.3]/d.

Station number   Injection   Distribution
                  volume        volume

1                  3552           0
14                   0           291
15                 1277           0
21                   0            35
22                  188           0
23                   0           158
24                   0           379
25                  219          220
26                   0           589
27                   0            55
28                   0            68
29                   0           130
30                   0            39
31                   0           351
32                  817           56
33                   0           522
34                   0            66
35                   0           416
36                   0           149
37                   0           183
38                   0           751
39                   0           508

TABLE 4: Optimal operation scheme.

Station    Pitted     Outbound       Pitted        Outbound
number    pressure,   pressure,   temperature,   temperature,
             MPa         MPa       [degrees]C     [degrees]C

1            6.5        8.43           15           37.28
2           6.41        9.08          6.61          35.87
3           7.78        9.75          8.69          27.56
4            8.5         8.5          6.32           6.32
5           6.62        9.17          5.09          32.36
6           8.17        9.78          8.24          23.14
7           7.96         9.8          6.88          24.09
8           8.73        8.73          6.73           6.73
9           6.98         9.8          5.15          33.56
10          8.54        8.54          6.35           6.35
11          6.85        9.24          5.15          30.07
12          8.43         9.8          9.73          22.27
13          8.81        8.81          7.54           7.54
15          7.84        9.76          5.18          23.15
16          7.03         9.7          6.93          33.98
17          8.05         9.8          11.8          28.32
19          8.05         9.8          9.41           25.8
20          7.95        9.74          9.44          26.35
22          7.68        9.34          8.9           25.18
24           7.5        9.26          8.36          25.87
26          7.24        9.03          7.37          25.72
29          6.29        7.84          5.24          23.39

Station   Compressor
number       boot
           program

1           1 set
2           2 set
3           1 set
4           0 set
5           2 set
6           1 set
7           1 set
8           0 set
9           2 set
10          0 set
11          2 set
12          1 set
13          0 set
15          1 set
16          2 set
17          1 set
19          1 set
20          1 set
22          1 set
24          1 set
26          1 set
29          1 set

TABLE 5: Energy consumption of the optimal operation scheme.

Turnover                  452892.63 x [10.sup.7] [Nm.sup.3] x km
Gas consumption               4154.5 x [10.sup.4] [Nm.sup.3]
Gas unit consumption   91.7 [Nm.sup.3]/([10.sup.7] [Nm.sup.3] x km)
Production unit           135.4 kgce/([10.sup.7] [Nm.sup.3] x km)
  consumption
Power consumption                4195 x [10.sup.4] kW x h
Total energy                           61314.19 tce
  consumption
Power unit               108.9 kW x h/([10.sup.7] [Nm.sup.3] x km)
  consumption

TABLE 6: XQ1 energy consumption.

Month     Production unit consumption, Turnover,
          kgce/([10.sup.7] [Nm.sup.3]  [10.sup.7]
                      x km)            [Nm.sup.3]
                                          x km
        Optimal   Measured   Energy
         value     value     saving
                              rate

1        205.1     241.46    -15.07%     521623
2        210.1     237.6     -11.56%     496704
3        236.0      283      -16.59%   476826.49
4        147.0      174      -15.53%   452892.63
5        135.4     158.5     -14.58%   452892.63
6        148.6     171.2     -13.20%   463643.35
7        157.4     187.3     -15.97%   486394.54

Month           Power consumption,
                [10.sup.4] kW x h

        Optimal   Measured   Deviation
         value     value

1        5446       5302       2.72%
2        4944       5825      -15.12%
3        5267       6996      -24.71%
4       4449.69     5345      -16.75%
5       4930.3      4195      17.53%
6       5024.59     5194      -3.26%
7       5049.2      4612       9.48%

Month           Gas consumption,
             [10.sup.4] [Nm.sup.3]

        Optimal   Measured   Deviation
         value     value

1        7540       8980      -16.04%
2        7391       8335      -11.33%
3        7976       9529      -16.30%
4        4594       4377       4.96%
5       4154.5      5011      -17.09%
6       4716.23     5487      -14.05%
7       5289.32     6424      -17.66%
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Title Annotation:Research Article
Author:Liu, Enbin; Li, Changjun; Yang, Yi
Publication:The Scientific World Journal
Article Type:Report
Date:Jan 1, 2014
Words:5735
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