Energy efficiency evaluation of a natural gas pipeline based on an analytic hierarchy process

Abstract

A long-distance natural gas pipeline system consists of considerable equipment and many pipe segments, but the conventional energy efficiency index of a natural gas pipeline is considered as a whole. Because the specific energy consumption of each unit cannot be determined, the index system is not perfect, and evaluating the energy efficiency of a natural gas pipeline system is difficult. The energy efficiency evaluation model for a natural gas pipeline was established using the analytic hierarchy process. A judgment matrix was constructed based on the energy efficiency index system of a long-distance natural gas pipeline, and the weight coefficient was calculated using the characteristic root method. Then, the consistency of the established judgment matrix was verified. The energy efficiency evaluation model successfully quantified the qualitative factors that affect natural gas pipelines. The comprehensive energy efficiency coefficient G of the natural gas pipeline was obtained from the operational data of the natural gas pipeline; then, the equipment or pipe segments with high energy consumption can be identified. The energy efficiency evaluation program of the natural gas pipeline was developed using Visual Basic for Applications, which significantly reduced the evaluation workload. The natural gas pipeline energy efficiency evaluation model is used to evaluate the energy efficiency of a natural gas pipeline, to identify the high energy consumption equipment or pipe segments, and to propose measures to improve the energy efficiency. The results show that the gas pipeline energy efficiency evaluation model and evaluation procedures can identify high energy consumption equipment or pipe sections in complex natural gas pipelines.

Keywords

Analytic hierarchy process natural gas pipeline energy efficiency coefficient index weight energy efficiency evaluation visual basic for applications

Introduction

Recently, energy has become the key to national development, and the most effective way to ease energy shortages is to save energy rather than to develop new energy. Energy saving in this sense refers to decreasing energy consumption, and the most important aspect of this is reducing the energy consumption of a unit product, that is, to improve the energy efficiency. The main transportation mode for natural gas is through pipelines. However, considerable energy is consumed and lost in a long-distance natural gas pipeline system.¹ The development of energy management technology for natural gas pipelines is of great significance for the development of China’s natural gas industry. In China, domestic energy management technology and methods for natural gas pipelines are still in their infancy, and it is difficult to identify the energy consumption and energy savings potential of each constituent unit or device in the pipeline system.² The energy management tasks of natural gas pipelines fall into two categories: guiding the operation optimization and promoting the optimization of planning and design. The aim of modern energy management is to combine pipeline automation technology, simulation technology, and information technology with optimized energy-saving operational technology and management technology and to use them to improve the energy efficiency of natural gas pipeline systems.³

After many years of development, various methods have been derived from the analytic hierarchy process (AHP), such as improved analytic hierarchy process (IAHP), fuzzy analytic hierarchy process (FAHP), extended fuzzy analytic hierarchy process (EFAHP), and gray analytic hierarchy process (GAHP). These methods each have their own scope according to the actual situation. AHP is mainly used in the field of safety science and environmental science, especially in oil and natural gas engineering.^4,5 Currently, AHP is widely used. Y Han et al.⁶ presented a performance analysis and optimal temperature selection method for ethylene cracking furnaces using the data envelopment analysis cross-model (DEACM) integrated AHP. FAHP has been used to accurately evaluate the energy consumption of petrochemical plants based on production data.⁷ A novel energy evaluation framework of petrochemical industrial processes based on a data envelopment analysis (DEA)-integrated AHP model has been proposed. This approach was first applied to the energy efficiency analysis of the ethylene production process in petrochemistry to improve the energy efficiency.⁸ In this article, the AHP is used to establish a natural gas energy efficiency evaluation model.

In this article, based on the analysis and statistics of a large amount of energy consumption data, an energy efficiency evaluation model of a natural gas pipeline is established. The energy efficiency evaluation index is divided into a measurable parameter index and a comprehensive evaluation index. The measurable parameter index was directly obtained from station equipment, and the comprehensive evaluation index was calculated from the measurable parameter index to comprehensively evaluate each piece of equipment and pipe section. The comprehensive energy efficiency coefficient G of the natural gas pipeline integrates the impact of various energy consumption factors and can identify the pipe or station that the operation and management department need to retrofit first to improve the investment efficiency.⁹ Through real-time collection and monitoring of the energy consumption data, the energy efficiency evaluation model can determine the energy consumption of each piece of equipment and pipe section in the natural gas pipeline system and identify any high energy consumption equipment or pipe sections for technological improvements. Considering the energy consumption and pipeline loss, establishing the natural gas pipeline energy efficiency evaluation model is very important for the comprehensive evaluation of each piece of equipment and pipe section.

Energy efficiency evaluation model of a natural gas pipeline based on an AHP

An AHP is a multi-criteria analysis and decision-making method that combines qualitative analysis with quantitative analysis; this method was proposed by T.L. Saaty, an American operational research scientist.^10,11 On the basis of expert knowledge and experience, the subjective elements can be eliminated using mathematical methods with strict logic. The experts should be authoritative persons in a particular field, and the required number of experts depends on the circumstances. These experts clarify the ranking of factors that are sensitive to the weights using sensitivity analysis. Based on the relationships among the internal factors of the natural gas pipeline system, the complex problem is decomposed into several hierarchies and several factors. Based on the elements of the upper hierarchy, the importance of each pair of elements in the same hierarchy is compared; then, the judgment matrix is established. Next, the maximum eigenvalues of the judgment matrix and the corresponding eigenvectors are calculated. The reasonableness of the weight values can be verified based on whether the judgment matrix has satisfactory consistency. Thus, the reliability, accuracy, and objective fairness of the evaluation results can be improved.¹²

The determination of the energy efficiency index weight of a natural gas pipeline

By ranking the relative importance of the indexes at the same hierarchy level, the degree (numerical importance) of the indexes at each hierarchy, that is, the index weight, is determined. The energy efficiency index weight in a long-distance natural gas pipeline can be determined using the following method.

Establish the hierarchy model

First, the energy efficiency index system of the natural gas pipeline is divided into several hierarchies. Then, the hierarchical structure is established from the top to the bottom in a “dominant” and “being dominated” relationship to calculate each index weight and evaluate the system. The energy efficiency index system of a natural gas pipeline consists of actual object-level indexes (referred to as O-level indexes), intensity-level indexes (referred to as I-level indexes), efficiency-level indexes (referred to as E-level indexes), and index-level indexes (referred to as T-level indexes). O-level indexes include the turnover, volume, energy consumption, power consumption, pressure ratio, electricity consumption, air consumption, loss of throttle, pressure, and power consumption,¹³ where the energy consumption includes the production energy consumption and auxiliary energy consumption. The energy efficiency index system of a natural gas pipeline is shown in Figure 1.

Figure 1.

The energy efficiency index system of a natural gas pipeline.

The O-level index is the basic data related to the energy consumption index of a natural gas pipeline and provides the basis for calculating the I-level index, the E-level index, and the T-level index. The I-level index is the intensity-level index, which is calculated from the O-level index and the actual operational data of a natural gas pipeline; this index can reflect different aspects of the energy consumption level of the pipeline. The E-level index is the efficiency-level index, which is calculated from the O-level index and the actual operational data of a natural gas pipeline; this index can reflect different aspects of the efficiency, utilization, and loss of the pipeline. The T-level index is a comprehensive index-level index that considers all relevant factors and forms a certain algorithm. The T-level index is calculated from the O-level index, I-level index, and E-level index. The T-level index reflects the level of pipeline energy consumption and can be directly used to evaluate the pipeline energy consumption. The influences of the T-level indexes, E-level indexes, or O-level indexes in a hierarchy on their upper hierarchy target should be compared and analyzed.

Construct the judgment matrix

A pairwise comparison is used to analyze each hierarchy in the energy efficiency index system of a natural gas pipeline. By introducing a relative scale called the proportional scale, the influence of the factors in a hierarchy on their upper hierarchy target is compared and analyzed. Then, the two-by-two judgment matrixes between different hierarchies are constructed. The judgment matrix is given as $A_{ij} = (a_{ij})_{m \times n}$ , $a_{ij} = f (x_{i}, x_{j})$ , $a_{ij} > 0, a_{ij} = 1 / a_{ji}$ , where the a_ij scale is a “1–9 scale method” and a_ij indicates the importance ratios of $x_{i}$ and $x_{j}$ to the target. This solves the problem of the quantitative representation of uncertainties in the single-criterion ranking.¹⁴

Calculate the index weights

Reducing the subjectivity of the weight determination in the energy efficiency evaluation model of a natural gas pipeline has very important significance,¹⁵ especially for improving the accuracy and sensitivity of the energy efficiency evaluation and for improving the quality and credibility of the evaluation results. Based on the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ , the maximum eigenvalues λ_max of the judgment matrix are calculated. Then, the eigenvectors $ξ = (x_{1}, x_{2}, x_{3}, \dots, x_{n})$ corresponding to the maximum eigenvalues λ_max of the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ are calculated. The values of $x_{i}$ after the normalization treatment give the weight of each factor, and the summation of these weights for each criteria or sub-criteria must be equal to one. The qualitative factors affecting the energy efficiency of a long-distance natural gas pipeline thus achieve a more quantitative nature. The priority of the factors in each hierarchy in the energy efficiency index system of a natural gas pipeline can also be sorted based on their weight values.

Check the consistency

To determine the reasonableness of the energy efficiency index weights for natural gas pipeline and to ensure consistency in the decision-makers’ thinking, it is necessary to test the consistency of the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ . When the judgment matrix consistency ratio CR = CI/RI < 0.1, the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ is applicable.¹⁶CI and RI are defined as $CI = (λ_{max} - N) / (N - 1)$ and $CR = CI / RI$ , where CI is the consistency index value of the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ , λ_max is the maximum eigenvalue of the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ , N is the dimension of the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ , CR is the consistency ratio of the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ , and RI is the stochastic consistency index. The RI values with the judgment matrix dimension of N are shown in Table 1.

Table 1.

The stochastic consistency index.

N	2	3	4	5	6	7	8
RI	0	0.58	0.9	1.12	1.24	1.32	1.41

If it does not satisfy the consistency criterion, the judgment matrix $A_{ij} = (a_{ij})_{m \times n}$ needs to be adjusted until it does.

The calculation of the energy efficiency index of a natural gas pipeline

Based on the established energy efficiency index and production data, the energy efficiency of a long-distance natural gas pipeline can be achieved. To calculate the comprehensive energy efficiency index, dimensionless processing is adopted to calculate the membership degree.¹⁷ As an example to illustrate the method, N factors in a hierarchy are used to calculate the upper hierarchy target in the energy efficiency index system of a long-distance natural gas pipeline. Each factor is calculated based on m basis values. The values of the n factors are C₁, C₂, C₃,…, C_n, and the membership degree is as follows

a_{11, j} = 1 - \frac{C_{1, max} - C_{1, j}}{C_{1, max} - C_{1, min}}, j = 1, 2, \dots, m

(1)

a_{12, j} = 1 - \frac{C_{2, max} - C_{2, j}}{C_{2, max} - C_{2, min}}, j = 1, 2, \dots, m

(2)

a_{1 n, j} = \frac{C_{n, j} - C_{n, 0}}{C_{n, max} - C_{n, min}}, j = 1, 2, \dots, m

(3)

The energy efficiency coefficient of the upper hierarchy target is as follows

M_{1} = \bar{a_{11}} w_{11} + \bar{a_{12}} w_{12} + \dots + \bar{a_{1 n}} w_{1 n}

(4)

where $a_{11, j}$ is the membership degree of the first factor, $w_{11}$ is the weight value of the first factor, $a_{12, j}$ is the membership degree of the second factor, $w_{12}$ is the weight value of the second factor, $a_{1 n, j}$ is the membership degree of the nth factor, $w_{1 n}$ is the weight value of the nth factor, $C_{1, max}$ is the maximum value of the first factor, $C_{1, min}$ is the minimum value of the first factor, $C_{1, j}$ is the value of the first factor, $C_{2, max}$ is the maximum value of the second factor, $C_{2, min}$ is the minimum value of the second factor, $C_{2, j}$ is the value of the second factor, $C_{n, max}$ is the maximum value of the nth factor, $C_{n, min}$ is the minimum value of the nth factor, $C_{n, j}$ is the value of the nth factor, $C_{n, 0}$ is the average value of the nth factor, $M_{1}$ is the energy efficiency coefficient of the upper hierarchy target, $\bar{a_{11}}$ is the average value of the membership degree of the first factor, $\bar{a_{12}}$ is the average value of the membership degree of the second factor, and $\bar{a_{1 n}}$ is the average value of the membership degree of the nth factor.

We calculate the energy efficiency coefficient of each hierarchy of a natural gas pipeline using the same method and then obtain the comprehensive energy efficiency coefficient G of the natural gas pipeline.

Energy efficiency evaluation program of a natural gas pipeline

Energy efficiency evaluation algorithm process for a natural gas pipeline

Visual Basic for Applications (VBA), as a simple derivative of Visual Basic (VB) programming, is easy to learn, convenient to use, and can quickly execute Office functions. VBA is compatible with Word, Excel, Access, SQL, VFP, and many other data text procedures.¹⁸ VBA code is simple to write and can be generated by recording a macro.¹⁹ VBA has been widely used in the field of oil and gas. Based on the operational basic data of natural gas pipelines and the relevant principles of the AHP, Excel VBA can achieve any of the energy efficiency evaluations of a natural gas pipeline. The energy efficiency evaluation algorithm process for natural gas pipelines is shown in Figure 2.

Figure 2.

Energy efficiency evaluation algorithm process for natural gas pipeline.

To improve the readability of the data and to make it convenient for later analysis and processing, the basic data for a natural gas pipeline are input into Excel based on the designed data format. Then, we determine the index weight and check the consistency using the AHP method. Next, we rank the priority of each factor in the energy efficiency index system of the natural gas pipeline according to the weight. Then, the energy efficiency coefficient G of the natural gas pipeline is calculated using the calculation method. The energy consumption of various equipment and pipe sections of the pipeline system could be shown at any time by collecting and monitoring the energy efficiency data of the evaluation index system. High energy consumption equipment or pipe sections can be identified, which is advantageous for adjusting or transforming technology in a timely manner and for saving energy costs.²⁰

Energy efficiency evaluation VBA operational interfaces of a natural gas pipeline

The natural gas pipeline energy efficiency evaluation program using Excel VBA consists of six interfaces: the basic data, weight calculation, index calculation, energy efficiency evaluation, results output, and operational help. The program is saved as a Microsoft Excel macro-enabled worksheet. The purpose of the energy efficiency evaluation of a natural gas pipeline is to find pipe sections and months with low energy efficiency through analysis and comparison. The pipeline is divided into sections according to certain rules. The measurable parameters of compressor stations, pipelines, and other energy efficiency indexes are recorded from January to December. The basic data input interface is simple and flexible; any data input for a long-distance natural gas pipeline can be used. Figure 3 shows the weight calculation interface of the energy efficiency evaluation in VBA, and Figure 4 shows the index calculation interface of the energy efficiency evaluation in VBA.

Figure 3.

The weight calculation interface of the energy efficiency evaluation in VBA.

Figure 4.

The index calculation interface of the energy efficiency evaluation in VBA.

The order of the judgment matrix is selected by rotating the button in the VBA weight calculation interface. The “output” button is then clicked to calculate the matrix judgment matrix consistency ratio CR, the maximum eigenvalue, and its eigenvector. The “2” button at the bottom right is selected based on the value of CR. When CR < 0.1, clicking “write” will automatically write to the Excel file. When CR > 0.1, clicking on “adjust” will adjust the matrix and recalculate. When calculating the weight of other indicators, clicking on “clear” will initialize all the values.

In the VBA weight calculation interface, clicking on “output 1” will calculate the corresponding membership based on the basic data and the order of the judgment matrix, and clicking on “output 2” will calculate the energy efficiency coefficient of the upper index. Clicking “write” will automatically write to the Excel file, and clicking “clear” will initialize all the values.

The main difficulties of VBA development are understanding and applying the corresponding object model.²¹ The VBA development methods for solving a problem are very flexible. Through VBA programming, the energy efficiency evaluation of a long-distance natural gas pipeline can be successfully achieved, which can free oil and gas industry personnel from a large number of tedious operations. Users need to input only a few values to make modifications and adjustments based on their different needs. Then, the high energy consumption equipment or pipe sections of the natural gas pipeline can be quickly identified.

Application example

Establish a hierarchical model

A long-distance natural gas pipeline is taken as an example for analysis. There is only one compressor station along the pipeline, and the natural gas pipeline uses unheated transport. The total energy supplied by the pipeline system is partly used to compensate for the energy consumption of the compressor station and the heat loss of the air cooler, and the rest of the energy is supplied to overcome the loss from frictional drag along the path, conveying both pressure loss and heat transfer loss. The final residual energy is accompanied by the gas entering the end of the pipe. The energy consumption structure of the natural gas pipeline is shown in Figure 5. Based on the principle of the AHP, to establish the hierarchical structure model, the energy efficiency index system of the long-distance natural gas pipeline is established, as shown in Figure 6.

Figure 5.

The energy consumption structure of the natural gas pipeline.

Figure 6.

The energy efficiency index system of the natural gas pipeline.

G, M, and C represent the indexes. The index system is divided into three hierarchies. The first hierarchy is the comprehensive influence of all the sub-hierarchies. The three indexes in the second hierarchy are the three main aspects of the pipeline’s energy consumption. The energy efficiency indexes of the compressor station reflect the main energy consuming equipment in the compressor station and reflect the energy efficiency of the compressor station and the entire pipeline from a macro point of view. The comprehensive energy efficiency index of the natural gas pipeline reflects the overall energy consumption of the pipeline. The pipeline loss ratio reflects the overall loss of the natural gas pipeline. During the operation of a natural gas pipeline, factors that affect the energy consumption include the pipe diameter, pipe length, pipe material, pipe insulation, physical properties of natural gas, volume of natural gas, surrounding climate, and heat transfer coefficient of the soil.²² In addition to the energy consumed during the operation of a natural gas pipeline, losses are caused by pipeline venting and measurement errors. The total loss determines the operation and management of the pipeline.

Determine the index weights

Construct a judgment matrix based on the comprehensive energy efficiency index G of the natural gas pipeline, the energy efficiency index of the compressor station and the loss ratio and energy efficiency index of the pipeline. Another judgment matrix is established based on the energy efficiency coefficient M₁ of the compressor station, the polytropic efficiency of the compressor, the air cooler energy efficiency index, and the air cooler pressure drop per volume. A third judgment matrix is established based on the energy efficiency M₃ of the pipeline, the pressure drop per length of pipe, and the temperature drop per length of pipe. The largest eigenvalue and the eigenvector of the judgment matrix are calculated, and then the relative weight is determined. After the normalization treatment, the summation of weights for each criteria or sub-criteria will be equal to one. The judgment matrixes and weight values of the three groups are shown in Tables 2 –4.

Table 2.

The judgment matrix G–M and weight values.

G	M ₁	M ₂	M ₃	$ξ$	w
M ₁	1	9	9/5	0.8701	0.6000
M ₂	1/9	1	1/5	0.0967	0.0667
M ₃	5/9	5	1	0.4834	0.3333

Table 3.

The judgment matrix M₁–C and weight values.

M ₁	C ₁	C ₂	C ₃	$ξ$	w
C ₁	1	3/2	9	0.8285	0.5625
C ₂	2/3	1	6	0.5523	0.3750
C ₃	1/9	1/6	1	0.0921	0.0625

Table 4.

The judgment matrix M₃–C and weight values.

M ₃	C ₁	C ₂	$ξ$	w
C ₁	1	1/5	0.1961	0.1667
C ₂	5	1	0.9806	0.8333

The calculations show the following:

The G–M judgment matrix: λ_max = 3, w = (0.6000, 0.0667, 0.3333), CR = 0 < 0.1;

The M₁–C judgment matrix: λ_max = 3, w = (0.5625, 0.3750, 0.0625), CR = 0 < 0.1;

The M₃–C judgment matrix: λ_max = 2, w = (0.1667, 0.8333), CR = 0 < 0.1.

The degree of inconsistency for the matrixes G, M₁, and M₃ is within the allowable range, so they can be used. Through the calculation and analysis of Tables 2 –4, Table 5 shows the overall ranking of the C hierarchy indexes to the G hierarchy.

Table 5.

The overall ranking of the C hierarchy indexes to the G hierarchy.

The energy efficiency indexes	The weight values			The weight values of the overall ranking
	w ₁	w ₂	w ₃
	0.6000	0.0667	0.3333
C ₁	0.5625	–	–	w ₁₁	0.3375
C ₂	0.3750	–	–	w ₁₂	0.2250
C ₃	0.0625	–	–	w ₁₃	0.0375
C ₄	–	–	0.1667	w ₃₁	0.0556
C ₅	–	–	0.8333	w ₃₁	0.2777

Calculate the energy efficiency index

Based on the operational data of the natural gas pipeline, the pipe is divided into 10 sections that are numbered from 1 to 10. The energy efficiency index of the compressor station, the energy efficiency coefficient of the pipe section, and the comprehensive energy efficiency coefficient G of the pipeline are calculated based on the operational base data of the compressor station, air cooler, and pipeline from April to October. The energy efficiency evaluation indexes and calculation methods are shown in Table 6, the basic data of each pipe section of the main line are shown in Table 7, and the basic data of the compressor station and loss ratio are shown in Table 8.

Table 6.

Energy efficiency evaluation indexes and calculation methods.

	Energy efficiency evaluation indexes	Calculation methods
Natural gas pipeline	Energy consumption per turnover (MJ/10⁴ m³ km)	The ratio of electricity consumption to turnover
Natural gas pipeline	Loss ratio (%)	The ratio of loss to volume
Pipe section	Pressure drop per length of pipe (Pa/m)	The ratio of pressure drop to pipe length
Pipe section	Temperature drop per length of pipe (°C/km)	The ratio of temperature drop to pipe length
Compressor station	Overall operation efficiency of compressor station (%)	The ratio of the pressure energy obtained from natural gas to the total electricity consumption of the compressor station
Compressor	Polytropic efficiency (%)	The ratio of the polytropic compression work to the compressor energy consumption
Air cooler	Energy efficiency coefficient	The ratio of heat removal from natural gas to its energy consumption
Air cooler	Pressure drop per volume (Pa/m³)	The pressure difference between the import and export per volume

Table 7.

The basic data of each pipe section of the main line.

The pipe section number	Pipe length (km)	Pressure drop per length of pipe (Pa/m)	Temperature drop per length of pipe (°C/km)
1	93.471	3.1	0.35
2	105.384	7.21	0.01
3	104.597	3.15	0.03
4	35.145	5.98	−0.1
5	83.841	6.92	0
6	72.596	4.96	0.04
7	111.327	1.44	−0.07
8	87.077	3.79	0.05
9	80.854	6.56	0.05
10	115.638	6.31	−0.05

Table 8.

The basic data of the compressor station and loss ratio.

Month	The energy efficiency coefficient of the air cooler	Pressure drop per volume of air cooler (Pa/N m³)	Polytropic efficiency (%)	Loss ratio (%)
April	83.040	0.306	72.733	0.015
May	84.352	0.305	74.1	0.016
June	95.880	0.288	72.667	0.016
July	90.653	0.303	72.167	0.017
August	70.340	0.307	71.9	0.016
September	91.975	0.289	69.8	0.016
October	93.349	0.215	70.567	0.016

Based on the above evaluation analysis method, the comprehensive energy efficiency coefficient of pipe sections No. 1 to No. 10 is calculated, and the comprehensive energy efficiency coefficient of the compressor station and the pipeline is calculated for April to October. The following is a brief introduction to the calculation method of the energy efficiency coefficient M₁ of the compressor station. The energy efficiency coefficient M₃ of the pipeline and the comprehensive energy efficiency coefficient G of the natural gas pipeline are calculated using the same method.

The calculation method of the energy efficiency coefficient M₁ of the compressor station is as follows. The polytropic efficiency C₁ of the compressor, the energy efficiency coefficient C₂ of the air cooler, the pressure drop C₃ per volume of the air cooler, and their membership degrees are as follows

a_{11, j} = 1 - \frac{C_{1, max} - C_{1, j}}{C_{1, max} - C_{1, min}}, j = 1, 2, \dots, m

(5)

a_{12, j} = 1 - \frac{C_{2, max} - C_{2, j}}{C_{2, max} - C_{2, min}}, j = 1, 2, \dots, m

(6)

a_{13, j} = \frac{C_{3, j} - C_{3, 0}}{C_{3, max} - C_{3, min}}, j = 1, 2, \dots, m

(7)

The energy efficiency coefficient M₁ of the compressor station is as follows

M_{1} = \bar{a_{11}} w_{11} + \bar{a_{12}} w_{12} + \bar{a_{13}} w_{13}

(8)

where $a_{11, j}$ is the membership degree of the polytropic efficiency of the compressor, $w_{11}$ is the weight value of the polytropic efficiency of the compressor, $a_{12, j}$ is the membership degree of the energy efficiency coefficient of the air cooler, $w_{12}$ is the weight value of the energy efficiency coefficient of the air cooler, $a_{13, j}$ is the membership degree of the pressure drop per volume of the air cooler, $w_{13}$ is the weight value of the pressure drop per volume of the air cooler, $C_{1, max}$ is the maximum value of the polytropic efficiency of the compressor, $C_{1, min}$ is the minimum value of the polytropic efficiency of the compressor, $C_{1, j}$ is the value of the polytropic efficiency of the compressor, $C_{2, max}$ is the maximum value of the energy efficiency coefficient of the air cooler, $C_{2, min}$ is the minimum value of the energy efficiency coefficient of the air cooler, $C_{2, j}$ is the value of the energy efficiency coefficient of the air cooler, $C_{3, max}$ is the maximum value of the pressure drop per volume of the air cooler, $C_{3, min}$ is the minimum value of the pressure drop per volume of the air cooler, $C_{3, j}$ is the value of the pressure drop per volume of the air cooler, $C_{3, 0}$ is the average value of the pressure drop per volume of the air cooler, $M_{1}$ is the energy efficiency coefficient of the compressor station, $\bar{a_{11}}$ is the average value of the membership degree of the polytropic efficiency of the compressor, $\bar{a_{12}}$ is the average value of the membership degree of the energy efficiency coefficient of the air cooler, and $\bar{a_{13}}$ is the average value of the membership degree of the pressure drop per volume of the air cooler.

Energy efficiency evaluation and results analysis

The comprehensive energy efficiency coefficients of pipe sections No. 1 to No. 10 are calculated as follows: 0.65, 0.40, 0.73, 0.48, 0.43, 0.58, 0.86, 0.67, 0.44, and 0.46, respectively. The results of the comprehensive energy efficiency evaluation of the natural gas pipeline from April to October are mapped to the analysis, as shown in Figure 7.

Figure 7.

The comprehensive energy efficiency evaluation of the natural gas pipeline from April to October.

The energy consumption per turnover of the natural gas pipeline from April to October is shown in Table 9.

Table 9.

Energy consumption per turnover of the natural gas pipeline.

Month	Electricity consumption (10⁴ kW h)	Turnover (10⁷ m³ km)	Energy obtained per mass of natural gas (kJ/kg)	Energy consumption per turnover (MJ/10⁴ m³ km)
April	861.07	34,362.32	104.86	3.01
May	808.01	35,395.11	105.28	2.74
June	865.58	33,396.09	109	3.11
July	893.43	34,173.13	109.38	3.14
August	900.79	33,550.58	111.48	3.22
September	891.51	32,291.31	111.17	3.31
October	854.2	34,625.85	102.25	2.96

The analysis of the energy efficiency of the long-distance natural gas pipeline, combining Tables 7 –9 and Figure 7, is shown in the following:

For the long-distance pipeline, the energy efficiency coefficients of pipe sections No. 2 (0.40), No. 4 (0.48), No. 5 (0.43), No. 9 (0.44), and No. 10 (0.46) are lower than the average value. It would be helpful to strengthen the regulation of these segments during their daily operations, especially in section No. 2, under the current energy management status. From the comprehensive energy efficiency evaluation results, the pressure drop per length for section No. 2 is much higher than that of the other pipe section, which may be caused by ice blocking the inside of the pipe or by sewage accumulation.²³ A regular pipeline cleaning process would reduce the pipeline internal hydrate and impurity deposition and reduce the friction loss along the pipeline. Reducing the friction loss along the pipeline will reduce the pressure drop per pipe length.

For the compressor station, the comprehensive energy efficiency coefficient for June was significantly higher than those for the other months, and the energy efficiency coefficients for August and September were significantly smaller than the average. The reason for a low energy efficiency coefficient is a low polytropic efficiency of the compressor; therefore, the opening number of air coolers and the operating time of the air coolers increase. For the entire pipeline, the comprehensive energy efficiency coefficient of the pipeline in June is the highest, providing more energy savings than other months. The comprehensive energy efficiency coefficient in August is much lower than that in the other months; the reason for this is the lower polytropic efficiency of the compressor and the low energy efficiency coefficient of the air cooler. Energy consumption per turnover was higher in August and September than in other months, which is consistent with the lower efficiency coefficient of the compressors in August and September. The polytropic efficiency of the compressor can be improved by cleaning the compressor regularly and removing fouling from the impeller. The most effective method for improving the energy efficiency coefficient of the air cooler is to reduce the energy consumption of the air cooler. At the compressor station, the air cooler fan motor is recommended to be replaced with a variable frequency motor.

By analyzing the energy efficiency coefficients and operational basic data of different months in natural gas pipelines, the inefficient months and the reason for the inefficiencies can be identified. An effective supervision and management strategy for inefficient months can be formulated and combined with the actual operation of the natural gas pipeline. Then, measures can be taken to improve the energy efficiency in inefficient months, which will have a positive impact on improving the efficiency in other months.

Conclusion

Based on the calculation principle of the AHP, the hierarchical structure of the influencing factors is established. The judgment matrix is constructed, and then, the weight coefficient is calculated using the characteristic root method. Next, the consistency of the judgment matrix is verified. The major factors that affect a long-distance natural gas pipeline are thus determined. The comprehensive energy efficiency coefficient G of a natural gas pipeline was obtained from the operational data of the pipeline. Then, the high energy consumption equipment or pipe segments can be identified by the lower energy efficiency coefficients.

Using the energy efficiency evaluation model and procedure for natural gas pipelines, the major factors that affect a long-distance natural gas pipeline can be determined, and the high energy consumption equipment or pipe segments can be identified. This is conducive to formulating an effective supervision and management strategy for long-distance pipelines. Then, measures can be taken to improve the energy efficiency of the pipeline, and the energy consumption can be optimized, which will increase energy savings.

Footnotes

Academic Editor: Jun Ren

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Sichuan Provincial Key Discipline Construction Foundation of China (no. SZD0416).

References

Wang

Research on energy saving technology of natural gas booster pipeline. J Nat Gas Technol Econ 2013; 7: 46–47, 79.

Wei

Jianting

Hancheng

. Assessment indexes on operation energy efficiency of natural gas pipeline. J Oil Gas Storage Transp 2010; 29: 313–316.319.

Bing

A study on gas piping energy efficiency index systems. J Sin Glob Energy 2012; 17: 96–102.

Afgan

Carvalho

Pilavachi

. Evaluation of natural gas supply options for Southeast and Central Europe: part 2. Multi-criteria assessment. Energ Convers Manage 2008; 49: 2345–2353.

Fengjuan

Xuefei

. Optimization of long distance natural gas pipeline design proposals based on multi-objective AHM method. In: Proceedings of the 2012 2nd international conference on computer and information application (ICCIA 2012), Taiyuan, China, 8–9 December 2012, vol. 2. IEEE.

Han

Geng

Wang

. Performance analysis and optimal temperature selection of ethylene cracking furnaces: a data envelopment analysis cross-model integrated analytic hierarchy process. J Anal Appl Pyrol 2016; 122: 35–44.

Han

Geng

Zhu

. Energy consumption analysis and evaluation of petrochemical industries using an improved fuzzy analytic hierarchy process approach. J Intell Fuzzy Syst 2016; 37: 2085–2095.

Han

Geng

Energy efficiency hierarchy evaluation based on data envelopment analysis and its application in a petrochemical process. J Chem Eng Technol 2014; 37: 2085–2095.

Wei

Jian

Assessment on operation efficiency of Sichuan-Chongqing looped gas pipeline network. J Oil Gas Storage Transp 2013; 32: 1112–1117.

10.

Xiaodong

Hierarchy gray relevancy analysis technique and its applications in the optimization of the sewage treatment programs. Master’s Thesis, Taiyuan University of Technology, Taiyuan, China, 2006, pp.5–8.

11.

Xiaohuan

Research on the consistency problem of AHP. Master’s Thesis, Guangxi University, Nanning, China, 2006, pp.1–3.

12.

Taha

Rostam

A fuzzy AHP-ANN-based decision support system for machine tool selection in a flexible manufacturing cell. Int J Adv Manuf Tech 2011; 57: 719–733.

13.

Bing

Energy efficiency management of oil and gas pipeline. Beijing, China: Geological Publishing, 2011.

14.

Kaidi

Yanjun

Shaoling

. Path problem in multi-criteria ranking and generalization of AHP. J Syst Eng 2015; 35: 973–983.

15.

Zhonggang

Anlin

Xuefen

. Research on weight assignment method of risk factors in oil and gas pipeline. J Nat Gas Ind 2007; 27: 103–105, 108.

16.

Guangfu

Xuejun

Balbir

. Quantitative system evaluation method for equipment state using fuzzy and analytic hierarchy process. J Syst Eng 2010; 30: 744–749.

17.

Xia

Evaluation and analysis of the comprehensive efficiency in oil field pipe network system. J Chem Eng Equip 2008; 29: 53–60.

18.

Lei

Long

Mingwei

Research and application of VBA programming in optical transmission system of Sichuan-East gas transmission. J Manage Technol SME 2015; 21: 187–188.

19.

Yingcheng

Honghui

Xinguo

Application of excel VBA to logging data normalization. J Chengdu Univ Technol 2009; 36: 216–220.

20.

Ballal

DR.

Energy usage in natural gas pipeline applications. J Eng Gas Turb Power 2012; 134: 1–9.

21.

Meifei

Ning

Jianchuan

. Application of VBA language in digital data management. J Value Eng 2013; 22: 253–254.

22.

Shuman

Analysis and optimization of energy consumption of natural gas pipeline. J Bus 2015; 2015: 289, 293.

23.

Zhifang

Libo

WY.

The causes and measures of ice blockage in natural gas pipeline. J Oil Gas Storage Transp 2014; 32: 43–46.