An energy-saving bottleneck diagnosis method for industrial system applied to circulating cooling water system

Journal of Cleaner Production 232 (2019) 224e234

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

An energy-saving bottleneck diagnosis method for industrial system applied to circulating cooling water system Xiaochen Zhu a, *, Fuli Wang b, a, Dapeng Niu a, Yuming Guo a, Mingxing Jia a a

College of Information Science and Engineering, Northeastern University, Heping District no.3, lane No.11, Northeastern University, Liaoning, China State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), Heping District no.3, lane No.11, Northeastern University, Liaoning, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 December 2018 Received in revised form 9 April 2019 Accepted 27 May 2019 Available online 28 May 2019

Research on energy-saving retrofits of the existing industrial systems can effectively improve the systems’ energy-saving level and increase company revenue. The energy-saving bottleneck diagnosis for systems is the foundation of the energy-saving retrofit design. The diagnosis also aims to yield a more targeted energy-saving retrofit, thus ensuring the energy-saving effect and investment income of the transformation project. Therefore, this paper proposes a new method to diagnose the energy-saving bottleneck of the existing industrial systems. First, the energy-saving bottleneck diagnosis is decomposed into two parts: the diagnosis of energy-saving bottleneck units and the diagnosis of energy-saving bottleneck factors. Then, the energy density decomposition method is improved and a multi-mode energy efficiency density decomposition method is proposed. Next, an energy-saving bottleneck diagnosis method for industrial systems based on multi-mode energy efficiency density decomposition and an orthogonal test method is proposed. The proposed method can analyze the energy-saving potential of existing systems, accurately locate energy-saving bottlenecks, and avoid the dependence on a large amount of expert experience or complete production data in the existing methods. Finally, the proposed method is applied to an actual circulating cooling water system and the system energy-saving bottleneck diagnosis is realized, thus verifying the practicability of the method. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Energy-saving retrofit Industrial system Energy-saving bottleneck diagnosis Energy efficiency density decomposition Orthogonal test Circulating cooling water system

1. Introduction With the increase in global energy prices and the depletion of natural resources, research on energy conservation in industrial systems has received increasing attention; further, energy-saving retrofits are important in this research field. The energy-saving retrofits of the existing industrial systems can reduce energy consumption and resource waste and are effective measures to save operating costs and improve regional economy. Additionally, energy-saving retrofits can maximally use the resources and configurations of the existing system, avoid the construction of new projects, and significantly reduce capital investment and environmental impact (Iskin and Daim, 2016). In industrial systems, certain equipment attributes and system conditions limit the degree of system energy conservation (Akpomiemie and Smith, 2017). These limitations, called system energy-saving bottlenecks, are the core of

* Corresponding author. E-mail address: [email protected] (X. Zhu). https://doi.org/10.1016/j.jclepro.2019.05.322 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

the process of energy-saving retrofits. Energy-saving retrofit research is also called energy efficiency improvement research. Energy efficiency is not a fixed concept; rather, it differs according to application areas (Zhou et al., 2016). Currently, energy efficiency is the most widely used indicator for describing the energy-saving level of industrial systems. This index generally indicates the ratio of the system useful energy to the total energy consumption or the ratio of the system energy consumption to the system effective output. Therefore, the research on energysaving retrofits aims at improving the energy efficiency of industrial systems. Recently, research on energy-saving retrofits of industrial systems has been divided into three categories: macroscopic research on system energy efficiency, application of energy-saving technology, and design of system energy-saving retrofit scheme. Macroscopic research on system energy efficiency generally uses statistical methods to analyze a system, including energy consumption status, energy-saving measures, or application effects of energy-saving technologies (Wang et al., 2017a; Du et al., 2011;
n ~ez et al., 2018). For Hong et al., 2010; Nikolaidis et al., 2009; Ya

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example, Wang et al. (2017a) analyzed the relationship between water consumption, energy consumption, and pollutant emissions in the steel industry; they adopted the quantitative modeling method to analyze the energy-saving status of various processes and the popularity of energy-saving technologies. Similar studies were conducted by Du et al. (2011) and Hong et al. (2010). The former conducted statistics and analysis on energy-saving measures in various links of a steel enterprise, and the latter used simple statistics method to analyze the energy consumption of the Taiwanese textile industry. Nikolaidis et al. (2009) used some postproject evaluation indicators to evaluate various energy-saving measures for building energy conservation, and analyzed the ~ez et al. (2018) designed application effects of these measures. Y
an certain energy-saving evaluation indicators and evaluated the energy use of the petroleum industry with a large amount of production data. This type of research can help clarify the research situation of an application field, but it is difficult to guide a specific application process. The application research of energy-saving technology generally introduces the specific application process of a certain energysaving improvement method and its retrofit effect (Meng et al.,
n-Cruz 2008; Zhao and Tan, 2014; Kuo and Lo, 2013; Maruga et al., 2014; Chen, 2015; Oh et al., 2016). For example, Meng et al. (2008) applied the frequency conversion technology to the pump station of circulating cooling water system (CCWS) and designed the optimal energy-saving control scheme. Zhao et al. (Zhao and Tan, 2014) applied a heat storage device to a thermoelectric system and verified the energy-saving effect from two aspects of cooling: energy efficiency and energy consumption. Kuo et al. (Kuo and Lo, 2013) applied a CCWS to a solar power generation system to
n-Cruz improve its power generation efficiency, whereas Maruga et al. (2014) applied a solar power generation system to a regional cooling system to use excess solar energy for cooling. Chen (2015) performed an energy-saving retrofit using a turbine instead of a cooling tower motor to achieve the full utilization of the pump residual pressure. Oh et al. (2016) improved the cooling efficiency of a cooling system by replacing the coolant to achieve system energy savings. Through such research, the application effect of an energy-saving technology can be clearly realized. However, such research often does not compare or comprehensively apply the same type of energy-saving technologies, and does not conduct a system-wide analysis; therefore, it is difficult to achieve the best energy-saving effect. The design research of the energy-saving retrofit scheme is generally more complete than the previous two types of research. This type of research realizes the design or optimization of the energy-saving retrofit scheme for industrial systems through specific system analysis. Moreover, this research has a high application value and can be directly applied to an actual operation system. Some researchers conducted a comprehensive analysis of the systems’ retrofit measures to propose the best retrofit plan (Piacentino, 2011; Wang et al., 2012, 2017b). For example, Piacentino et al. (Piacentino, 2011) combined pinch analysis, heat transfer drive, and exergy analysis to design a comprehensive energy-saving modification scheme for a heat transfer system to obtain the maximum heat recovery. Wang et al. (2012) comprehensively considered various energy-saving retrofit measures such as topology adjustment, heat transfer area increase, and heat transfer capacity enhancement in the retrofit process of a heat transfer network system. Wang et al. (2017b) studied the energysaving retrofit of a steel system and set the priorities for various energy-saving measures by considering the impact on water saving. In addition, some scholars designed the optimal energy-saving retrofit scheme through the overall analysis or partial analysis of a system (Hu and Wang, 2012; Gang et al., 2015; Foo et al., 2014;

225

Sreepathi and Rangaiah, 2014; Jahromi and Beheshti, 2017). For example, Hu et al. (Hu and Wang, 2012) performed an energysaving retrofit of the cogeneration system, primarily analyzed the impact on the operating efficiency of pumps when the system thermal performance changed, and designed the corresponding retrofit scheme. Gang et al. (2015) analyzed and compared the improved regional cooling system with the traditional cooling system, and also compared the energy consumption status under different loads; eventually, they evaluated the effect of the systemimproved scheme. Contrary to the previous two references, Foo et al. (2014) designed a network retrofit scheme with the highest heat transfer efficiency under the condition of constant thermal performance. Sreepathi et al. (Sreepathi and Rangaiah, 2014) transformed the network retrofit problem into an optimization problem by a mathematical programming method, and performed single-objective optimization and multi-objective optimization with the goals of reducing investment cost and annual operating cost. Jahromi et al. (Jahromi and Beheshti, 2017) proposed a bridge analysis method based on the pinch method, and proposed an energy-saving retrofit scheme through the thermal analysis of the heat transfer network. From the current industrial energy efficiency research, although a certain degree of system energy-saving effect can be achieved, the final design of the energy-saving retrofit scheme often exhibits some problems, because most studies do not analyze the system energy-saving bottleneck. First, owing to the insufficient selective analysis of energy-saving retrofits, priority designs for various retrofit measures do not exist, thus resulting in the high cost and difficult implementation of energy-saving retrofit programs. Furthermore, the energy-saving potential of some measures is insufficient; therefore, it is difficult to achieve the expected energysaving effect, thereby resulting in the low utilization of investment funds. Therefore, the diagnosis of energy-saving bottlenecks of industrial systems is highly required, which can help researchers produce more targeted energy-saving retrofits to achieve the optimal energy-saving effect. Currently, scholars recognize the importance of energy-saving bottleneck research in industrial systems. From the summary of energy-saving bottlenecks researches in recent years, energysaving bottleneck diagnosis methods can be classified into three categories: data statistics method, evaluation weight method, and experiment analysis method. Energy-saving bottleneck diagnosis with data statistics implies using system operation data or industry data to establish the corresponding statistical indicators or statistical models to analyze the systems' energy-saving bottleneck (Zhao and Gao, 2014; Li and Duncan, 2010). For example, in Alkaya et al.‘s (Alkaya and Demirer, 2015) water-saving study of the Turkish soft-drink industry, the evaluation benchmark was determined based on the operation data from the same type of systems, and the energysaving potential of each link of system was analyzed. Brange et al. (2017a) used questionnaires to directly consult enterprises and analyzed the energy-saving application bottlenecks of district heating systems. Di et al. (2011) used the Laspeyres index decomposition method to analyze the energy consumption and energysaving potential of a region and determined the primary influence factors. Hasanbeigi et al. (2012) and Yang et al. (Yang and Liu, 2016) both used energy intensity indicators as an analysis method for subsystem energy-saving bottlenecks. The former primarily analyzed the energy-saving core of the textile industry, and the latter primarily designed the subsystem retrofit plan of the natural gas purification system. The evaluation weight method is used to determine the weight of the index through expert knowledge and other bases for the energy-saving evaluation of industrial systems. Further, this weight

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information can be used as the analysis basis for the system energysaving bottleneck. For example, Iskin et al. (Iskin and Daim, 2016) established a hierarchical decision model to comprehensively evaluate the selection of 13 emerging energy efficiency projects in North America, and expert knowledge was applied to determine the weight of the indexes to analyze the energy-saving bottlenecks of each project. Urbaniec et al. (2000) and Li (2012) also used similar weighting methods to diagnose energy-saving bottlenecks in the sugar industry and energy systems, respectively. The experiment analysis method diagnoses the system energysaving bottleneck by designing experiments and analyzing the system experimental results. For example, Akpomiemie et al. (Akpomiemie and Smith, 2017) and Jiang et al. (2014) used the sensitivity analysis method to diagnose energy-saving bottlenecks in energy-saving retrofit research on heat transfer networks, and designed the retrofit scheme based on the analysis results. Shamseddini et al. (2015) designed simulation experiments to analyze the energy-saving bottlenecks of crude oil refining systems to produce energy-saving retrofits. Brange et al. (2017b) used the simulation experiment method and sensitivity analysis method to analyze the bottleneck retrofit measures of a district heating system to design the optimal retrofit plan. The summary of the relevant research on system energy-saving bottleneck diagnosis in recent years suggests that it has some limitations. First, some methods are vague and could not guide the energy-saving retrofit program of a specific industrial system. Next, the analysis scope of some methods is narrow because only a part of the equipment in the system is diagnosed. However, many types of equipment and complex structures exist in actual industrial systems, and a comprehensive analysis and diagnosis of the whole system are required. Subsequently, some diagnosis methods rely heavily on the subjective judgment of experts and lack the objective analysis of the system itself. Finally, some diagnosis methods rely heavily on the process data, with high requirements of data integrity. However, owing to the imperfect work in monitoring and data collection in the actual production, it is difficult to obtain the perfect process data statistics. In summary, an energy-saving bottleneck diagnosis method for industrial systems based on the system structure and equipment parameters, without relying on subjective evaluation information and complete process data, is urgently required. To solve the aforementioned problems, an energy-saving bottleneck diagnosis method based on the energy efficiency density decomposition method and orthogonal test analysis method is proposed with the combination of a simulation test and the decomposition of energy-saving bottleneck diagnosis problem. Additionally, based on the energy density decomposition, we propose an improved multi-mode energy efficiency density decomposition analysis method to expand the application range of this method. Finally, the proposed diagnosis method is applied to the energy-saving bottleneck diagnosis process of a CCWS in iron and steel enterprises, and the practicability of the proposed method is verified. 2. Analysis and decomposition of energy-saving bottleneck in industrial system The energy-saving bottleneck of industrial systems can be defined as the primary limiting factor of the systems’ energy-saving level. Dividing an industrial system into multiple parts, each part may have different impacts on the energy-saving level of the system. The most influential part or parts are the energy-saving bottleneck of the system, and these bottlenecks are parts of the system with the most significant energy-saving potential and retrofit value. For the energy-saving bottleneck diagnosis of

industrial systems, it is first necessary to describe the energysaving level of industrial systems. Currently, various energy efficiency indicators are generally used to describe the energy-saving level of industrial systems. Among them, the typical and effective indicator is the specific energy consumption (SEC), which can be expressed as (Estrada et al., 2018)

SEC0 ¼

Energy consumption Compliant production

(1)

This indicator can reflect the ratio of the system input to the system effective output, which is the efficiency of the system energy use. This is a minuscule indicator: if the indicator value is small, the energy consumed by the system to output a specific product is less, and the system has a higher level of energy saving. Conversely, if the indicator value is large, more energy is consumed during system operation, and the system has a lower level of energy saving. In addition, the energy efficiency indicator is not constant, and the appropriate adjustments are required in the application of different systems. Most industrial auxiliary systems provide specific functions for the primary process system and do not output the product; therefore, the standard SEC indicator does not apply. Therefore, for such systems, we must improve the SEC indicator. Taking the CCWS as an example, the new indicator can be expressed as:

SEC ¼

Energy consumption Epump þ Etower ¼ Useful work Q

(2)

where, Epump is the total energy consumption of the circulating pumps in the system, Etower is the total energy consumption of the cooling towers in the system, Q is the cooling capacity of the system, which is also the effective output of the system, and it can be expressed as:

Q¼

 X  Nl tlin  tlout

(3)

l

where, l is the number of heat exchanger, Nl is the flow of the hot fluid in the heat exchangerl, tlin ; tlout is the inlet temperature and outlet temperature of the hot fluid in the heat exchanger l. The SEC indicator can describe the energy efficiency of the CCWS and reflects the energy-saving level of such systems. Therefore, in energy-saving bottleneck diagnosis, the influence of the equipment configuration and design conditions on the system energy-saving level (corresponding SEC indicator) can be comprehensively analyzed to determine the system energy-saving bottleneck. In industrial systems, the factors that affect the systems’ energysaving level include many aspects, such as the scheduling and planning of production tasks, equipment configuration and design conditions, and operating status of various devices. The problems in different aspects must be adjusted in different ways. For example, management planning can improve the planning problem of production tasks; the optimization method can design the optimal system operation plan, and the improvement of system attributes and equipment configuration must rely on the system energysaving retrofit. Therefore, as the basis of energy-saving retrofit research, energy-saving bottleneck diagnosis must include the analysis of the system attributes and equipment configuration problems. On the one hand, the structure of most industrial systems is complex and the systems contain a large number of devices; therefore, researchers generally divide the system into several

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operation units according to the operational tasks and system structures in the analysis. In energy-saving bottleneck diagnosis, it is necessary to first diagnose the energy-saving bottleneck unit of the system. This type of diagnosis can determine the horizontal position of the system energy-saving bottleneck through the influence of the energy efficiency of each operation unit on the system energy-saving level. On the other hand, another major task of energy-saving bottleneck diagnosis is to analyze the system attributes and equipment configuration of the existing system, and determine the factors that contribute most negatively to the current system energy saving, i.e., the primary factors that limit the improvement in energy-saving levels. It is also possible to assess the factors that contribute most positively to the system energy saving, which are part of the system with the most significant energy-saving potential. These two ideas are not different in that both can vertically analyze the system energy-saving bottlenecks and determine the primary direction for further improvements. In this study, the energy-saving bottleneck diagnosis is decomposed into two independent analyses: the system operation unit and system equipment attributes, both of which are diagnosed separately. These two analyses do not affect each other, but they can complement each other and accurately locate the system energysaving bottleneck horizontally and vertically. For the former process, we propose a multi-mode energy efficiency density decomposition method by improving the energy density decomposition method. For the latter process, the orthogonal test analysis method combined with simulation experiments is proposed to achieve a complete system energy-saving bottleneck diagnosis. These two methods are described in Section 3 and Section 4, respectively. 3. Method design 3.1. Energy-saving bottleneck unit analysis based on energy efficiency density decomposition method Generally, several types and quantities of operating devices exist in an industrial system. These devices are divided into several operation units to complete the system operation mission; further, the influence degree of each operation unit on the system energysaving level is different. Therefore, it is necessary to analyze the operation units in the horizontal direction to determine the bottleneck unit with large energy-saving potential in energy-saving bottleneck diagnosis for designing the retrofit scheme and ensuring the optimal rate of retrofit return. Therefore, we propose a multimode energy efficiency density decomposition method to diagnose the energy-saving bottleneck of system operation units. The energy efficiency density decomposition method is an improved method proposed herein for the energy density decomposition method. The core idea of the energy density decomposition method is to decompose the changes in system energy consumption into energy use changes and product structure changes; this method is generally used for the macro analysis of energy conservation in an industrial field or region (Hasanbeigi et al., 2012; Yang and Liu, 2016; Ang and Zhang, 2014). Based on this method, Lu et al. (2000) proposed an energy-production analysis method to analyze the energy-saving status of iron and steel enterprises. Several other energy-saving bottleneck analysis methods for steel enterprises have been designed by other researchers based on Lu's research. However, some industrial systems do not have direct product production and output, such as CCWSs, resulting in the inability to decompose product structures. Hence, we improve the energy density decomposition method, transform the decomposition of the SEC index into the decomposition of system useful energy efficiency, and consider the unit contribution

227

degree of different operating modes in the system operation. Finally, a diagnosis method based on multi-mode energy efficiency density decomposition is proposed to realize the energy-saving bottleneck unit diagnosis of this type of industrial system. The basic indicator of energy efficiency density decomposition is the system energy efficiency, which describes the energy consumed by the system output-specific useful work, and can reflect the system energy use level. Using the CCWS as an example, the corresponding energy efficiency index is the energy consumption per specific cooling capacity, expressed as e, and the calculation formula is

e¼

P X E Ei X Qi Ei ¼ ¼  ¼ qi  ei Q Q Q Qi

(4)

where, E is the system energy consumption, Q is the system cooling capacity, Ei is the energy consumption of the operating unit i, and Qi is the cooling capacity of the operating unit i. Subsequently, three system indicators are defined for calculation and analysis. Cooling capacity coefficient:

Qi Q

qi ¼

(5)

It is the ratio of the cooling capacity of each operating unit to the system total cooling capacity. Energy efficiency coefficient:

Ei Qi

ei ¼

(6)

It is the ratio of energy consumption to the cooling capacity of each operating unit, and describes the energy efficiency of the operating unit. Energy consumption weight:

ui ¼

Ei E

(7)

It is the ratio of the energy consumption of each operating unit to the system total energy consumption, and can reflect the importance of the operating unit from the perspective of energy consumption. The analysis of the energy efficiency density decomposition method is similar to that of the energy density decomposition method, and the change in system energy efficiency is decomposed into the change in the cooling coefficient and the change in the energy efficiency coefficient of each unit. In the diagnosis, the contribution of each operating unit to the change in the system overall energy efficiency is determined by comparing the indicators under different operating modes, thereby diagnosing the system energy-saving bottleneck unit. It is noteworthy that the change in the operating mode is caused by changes in the system cooling tasks and equipment operating points, while the system structure and equipment conditions remain unchanged. Next, the specific analysis process of the energy efficiency density decomposition method is introduced. First, any operation mode in the system is set as the benchmark mode, and its system energy efficiency is e0 . The energy efficiency of the operation mode j is ej , and the change in system energy-saving level from the benchmark mode 0 to the operation mode j is defined as

V¼

ej e0

(8)

According to the decomposition process in previous researches (Ang and Zhang, 2014; Lu et al., 2000; Li et al., 2011; Zhen et al.,

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2012), equation (7) can be decomposed into the following by the Laspeyres factor decomposition method and the NewtoneLeibnitz formula:

V ¼ Dc  De

(9)

hX  . i Dc ¼ exp u*i ln qji q0i

(10)

hX  . i De ¼ exp u*i ln eji e0i

(11)

where qji is the cooling capacity coefficient of operating unit i under j operation mode j, and ei is the energy efficiency coefficient of operating unit i under operation mode j. Under different operation modes, the energy consumption weight ui of each operating unit may change; therefore, this indicator must be corrected. The method is



ui ¼

uji  u0i



uj

(12)

ln u0i i

u u*i ¼ P i ui

(13)

where uji is the energy consumption weight of the operating unit i under operation mode j, and u*i is the corrected weight. The change in system energy-saving level is decomposed into two indicators: Dc andDe , where Dc is the change in cooling capacity coefficient and De is the change in energy efficiency coefficient. Dc can describe the impact of the cooling task changes in each unit on the system energy efficiency, which can reflect the level of the system production task design and operation scheduling. However, these factors need not to be analyzed during the energysaving bottleneck diagnosis process. De can reflect the influence of the energy efficiency coefficient of the operation unit on the system energy efficiency and also consider the influence of unit energy consumption weight. Therefore, De is the primary indicator for energy-saving bottleneck unit diagnosis. Through the calculation of the index contribution coefficient, the impact of the two indicators on the overall energy efficiency change can be analyzed. The index contribution is defined. If Z ¼ A  B, the contribution CðA /ZÞ of A to Z can be expressed as

CðA/ZÞ ¼

lnA  100% lnZ

(14)

Subsequently, De can be further decomposed, and the influence of the energy efficiency coefficient changes in each operating unit on the total index is determined, thereby determining the influence of the energy efficiency coefficient of each unit on the system energy-saving level. Each unit indicator meets the following requirement:

De ¼

Y Dei

(15)

Finally, it is necessary to consider the change in the energy efficiency contribution of each operating unit under different operation modes; therefore, the final contribution calculation with the operation mode information is required. The formula of the final calculation is

3 2 1 4X j CðDe /De Þ ¼ C ðDe /De Þ5 j ¼ 1; 2; …; n  1 n1 j

(16)

where n is the number of operation modes for the analysis; CðDei /De Þ is the average contribution of the energy efficiency coefficient changes in the operating unit i under multi-mode conditions. Through the multi-mode energy efficiency density decomposition method, the energy efficiency index of industrial systems can be decomposed, and the contribution of energy consumption weight and energy efficiency change in each operating unit to system energy-saving level is analyzed. According to different system states and operation modes, the system energy efficiency may have two change directions: energy saving and non-energy saving. Further, the energy efficiency coefficient of the operating unit has two states: positive contribution and negative contribution. From the diagnosis results, the contribution of the operating unit can be summarized as two states: limiting system energy saving and promoting system energy saving. In both cases, the unit contribution represents the ability to limit the system energy efficiency or the energy-saving potential of the unit. Therefore, the operating unit with the most significant contribution can be diagnosed as a system energy-saving bottleneck unit. 3.2. Energy-saving bottleneck factor analysis based on orthogonal test analysis method Industrial systems generally contain multiple operating devices, resulting in a variety of factors that affect the system energy-saving level. Therefore, only the bottleneck unit diagnosis cannot accurately locate the energy-saving bottleneck of industrial systems. Further, it is difficult to compare and analyze various factors using single-factor analysis methods such as a simple statistical analysis. Therefore, a multiple factor analysis method must be designed to accurately analyze the energy-saving bottleneck factors of the system. The orthogonal test analysis method is a typical multi-factor and multi-level analysis method, which is a high-efficiency, fast, and economical experimental design method and generally used for fractional factorial design. Because the orthogonal test method can analyze the influence degree of each factor on the test index through a few representative experiments, we apply this method to diagnose the energy-saving bottleneck factor. The determination of the system test evaluation index is required to conduct an orthogonal test for energy-saving bottleneck analysis. Because the test purpose is to analyze the impact on system energy-saving level, the energy efficiency index, i.e., the SEC, is generally selected as the benchmark indicator. Subsequently, it is necessary to determine the test factors and the level of each factor. This step relies on the researcher's understanding of the diagnosed system, and the choice with experience and analytical objectives to ensure that the test is evenly dispersed and comparable. Of course, these factors are not the final bottlenecks factors. They are only a scope of analysis and further analysis is needed. By the way, the proposed method can make the test complexity and engineering knowledge complement each other. If the engineering knowledge is more complete and accurate, the range of selected factors will be smaller, and the analysis experiment will be simpler. Conversely, if the engineering knowledge is poor, the range of factors will be large, and the test complexity will be high. Next, an orthogonal table is designed according to the factors and levels. An orthogonal table is a test design tool proposed by Taguchi (1960) to ensure the orthogonality of the design tests, which can be

X. Zhu et al. / Journal of Cleaner Production 232 (2019) 224e234

expressed as

SST ¼ SSf þ SSe

m

Ln ðr Þ

(17)

where L is the orthogonal table code, n is the number of rows (the number of tests), r is the number of factor levels, and m is the number of columns (the number of factors that can be tested at most). The general form of the orthogonal table is shown in Table 1, and the numbers in the table represent the levels of the respective test factors. It is difficult to conduct physical tests for large-scale industrial systems owing to their complex structures; therefore, simulation experiments are generally conducted. Subsequently, the tests were performed according to the scheme designed as shown in the orthogonal table. The test results were recorded and a statistical analysis was performed. The results analysis of the orthogonal test includes two categories: range analysis and variance analysis (Yuan et al., 2013; Liu et al., 2010). The range analysis assesses the influence degree of different factors by comparing the difference in the test results at different factor levels. This method contains a small calculation complexity and can assess the trend of system energysaving level with various influence factors. The range analysis requires the calculation of three indicators: Kjl : The sum of indicators corresponding to factor j and level i; kjl : The average of Kjl that is calculated as

kjl ¼

229

Kjl ; l ¼ 1; 2; …; s s

(18)

where s is the occurrence number of each level on any column. Rj : The range of factor j; the larger the range, the more significant the influence of this factor on the test index. The calculation formula is

    Rj ¼ max Kj  min Kj

(19)

Variance analysis is based on the principle of the significance test, constructs the statistics obeying the F distribution, and assesses the significance level of each factor's influence on the test results by comparing the F threshold values. Range analysis cannot distinguish between the indicator fluctuations caused by factor changes and the indicator fluctuations caused by errors; however, variance analysis can accommodate for this defect. The specific steps of variance analysis are as follows: (1) Decomposition of sum of square of deviance The total sum of squares of deviations can be decomposed into the sum of squares of deviations of the factors, and the sum of squares of deviations of the errors; the error column is the empty column designed in the orthogonal table. The formulas are as follows (Chen et al., 2017):

Table 1 General form of orthogonal table. Test number

Column number

Test result

1

2

…

m

1 2 … n

1 1 1 r

1 2 3 r

1 2 3 2

1 2 3 1

x1 x2 xn

SST ¼

n X

(20) Pn

x2i 

i¼1

SSf ¼

m X

i¼1 xi

2 (21)

n

SSj

(22)

j¼1 r 1X Kjl2  SSj ¼ t l¼1

Pn

i¼1 xi

2

n

; j ¼ 1; 2; …; m

(23)

where SST is the total sum of squares of deviations, SSf is the total sum of squares of deviations of each factor, SSe is the sum of squares of deviations of the errors, SSj is the sum of squares of deviations of factors j, t is the number of repetitions per level, and t ¼ n=r. (2) Decomposition of degree of freedom The degree of freedom can also be decomposed into the degree of freedom of factors and the degree of freedom of errors. The formulas are

dfT ¼ dff þ dfe

(24)

dfT ¼ n  1

(25)

dfj ¼ r  1

(26)

where dfT is the total degree of freedom; dfj is the degree of freedom of the factor j; dfe is the degree of freedom of the error. (3) Variance calculation The formula for calculating the variance of each factor is

MSj ¼

SSj dfj

(27)

The formula of the variance of error is

MSe ¼

SSe dfe

(28)

where MSj is the variance of factor j; MSe is the error variance. The error test must be performed first before the significance test. If MSj < MSe , it implies that this factor has little effect on the system test index; this factor can be classified into the error column in the calculation process. (4) Construct F statistics The F statistic of each factor is constructed according to the variance, and the formula is

Fj ¼

MSj MSe

(29)

(5) Construct a variance analysis table and perform F test According to the calculated F value, the significance test is performed. If Fj > Fa and Fa is the F test value, it indicates that factor j

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has a significant influence on the test result; if Fj < Fa , factor j has no significant effect on the test result. Orthogonal test analysis can determine the system factors that significantly affect the system energy efficiency level. The system factors are ranked in the descending order according to the significance test value. Next, the threshold is set according to experience. The factor whose significant value exceeds this threshold is called the energy-saving bottleneck factor of the system, and the higher the ranking, the greater is the improvement value. These energy-saving bottleneck factors can significantly affect the energy-saving level of the current system; therefore, energy-saving retrofits for these factors can yield the best energy-saving effect. However, it is difficult to accurately locate the system energysaving bottleneck only by energy-saving bottleneck factor diagnosis. Therefore, it is necessary to combine the energy efficiency density decomposition method with the orthogonal test analysis method to comprehensively analyze and complement the two diagnosis results. The two-step analysis method can be used to first analyze the energy-saving retrofit value of each operating unit, and subsequently analyze the system energy-saving factors to determine the exact location of the system energy-saving bottleneck. The overall quantitative analysis method can also be used to standardize and comprehensively calculate the results of the factor diagnosis and unit diagnosis, and determine a quantitative system energy-saving bottleneck indicator to achieve bottleneck diagnosis. Hence, the system energy-saving bottleneck can be located from two directions through the comprehensive application of the two diagnosis methods, and the accurate diagnosis of the energy-saving bottleneck of industrial systems can be realized. 4. Case study To verify the industrial system energy-saving bottleneck diagnosis method proposed herein, this method was applied to an actual energy-saving bottleneck diagnosis process of a CCWS. CCWS is a typically used industrial auxiliary system, which is widely used in petroleum, chemical, steel, power station, food production, and other industries. This system is a high-energy consumption unit in the production process and has significant energy-saving potential. The working principle of this system is as follows: water is used as a cooling medium and it obtains kinetic energy through pumps, and is subsequently transmitted to the network; the heat contained in the equipment or materials is absorbed by the heat exchanger, and subsequently fully contacted with the air by the cooling device; the heat is released into the atmosphere, the cooling task is completed, and water recycling is achieved. The general structure of this system is shown in Fig. 1. The system diagnosed in this study is part of the production process in a steel plant, and it is a representative and commonly used industrial auxiliary system. In the previous project, we obtained the complete equipment parameters of this system for

further research. In order to facilitate analysis, the entire system is divided into three equipment groups according to the operation task and system structure. The structure of this system is shown in Fig. 2. Wherein, the operating unit 1 is responsible for cooling the heat exchangers E1, E2 and E3, unit 2 is responsible for cooling E4 and E5, and unit 3 is responsible for cooling E6 and E7. The cooled hot fluid in the heat exchanger is responsible for cooling the operating equipment in the steel plant. As an actual industrial system, the CCWS exhibits limitations in terms of scale and cost, so it is difficult to perform physical tests on the system. Therefore, a simulation experiment is adopted in this research combined with self-developed simulation software for the CCWS. This software was developed using MATLAB software as a development platform, and the superstructure modeling method was used as the software core. Through the input of the network structure and the equipment parameters, the software can realize the simulation operation of the circulating cooling water system and obtain the system's operating state. The software is used to generate operation data, and more detailed modeling ideas can be found in the previous research result (ZhuXWangFNiuDet al, 2017). Subsequently, energy-saving bottleneck unit diagnosis is performed based on test data. The network system of this case is first divided into three operation units according to the structure and operation tasks, as shown in Fig. 2. Next, three stable system operating modes are selected, and the operating data is shown in Table 2. These data include three types: energy efficiency, energy consumption and cooling capacity. The energy consumption includes the energy consumption of each three operating units and the overall energy consumption of the system; the cooling capacity includes the cooling capacity of each three operating units and overall cooling capacity of the system. According to formulas (7)e(15), the relevant indicators of the energy efficiency density decomposition diagnosis method are calculated, and the calculation results of each index are shown in Tables 3 and 4. According to formulas (7)e(15), the relevant indicators of energy efficiency density decomposition are calculated, and the calculation results are shown in Tables 3 and 4 To more intuitively observe the decomposition of the system energy-saving state and the decomposition of the energy-saving state of each operating unit, the energy efficiency decomposition and the energy-saving bottleneck unit diagnosis figures are drawn according to the energy efficiency density decomposition data; this is performed to analyze the relationship between the system energy-saving level of each operating unit, as shown in Figs. 3 and 4. By analyzing the results of the energy efficiency density decomposition, we found that operation modes 1 and 2 contain smaller system energy efficiency indicators and higher energysaving levels compared to the benchmark mode. Among them, the contribution of De at operation mode 1 reached 106%, and the contribution of De at operation mode 2 reached 85%. Both

Cooling tower

Circulating cooling water

Waterhead

Water supply pump

Hot fluid

Heat exchanger

Fig. 1. Basic structure of CCWS.

Heat source of process

X. Zhu et al. / Journal of Cleaner Production 232 (2019) 224e234

231

Fig. 2. Application example of CCWS.

Table 2 System operation data. Operation mode

e

ET

E1

E2

E3

QT

Q1

Q2

Q3

0 1 2

6.93 6.22 6.00

328 198 266

224 140 183

76 43 62

28 15 21

47256778 31805686 44334297

29220422 20073189 26142857

11626222 7886416 10343968

6410134 3846080 7847472

Table 3 Overall indicators of energy efficiency density decomposition. Indicator

V

De

De contribution

Dc

Dc contribution

Operation mode 1 Operation mode 2

0.8970 0.8650

0.8909 0.8845

106% 85%

1.0068 0.9773

6% 15%

Table 4 Unit indicators of energy efficiency density decomposition. Indicator Operation mode 1 Operation mode 2 Average contribution

Dei Dei contribution Dei Dei contribution

Operating unit 1

Operating unit 2

Operating unit 3

0.9364 56.9% 0.9396 53.1% 55%

0.9601 35.2% 0.9606 30.5% 32.9%

0.9909 7.9% 0.9800 16.4% 12.2%

contributed positively, indicating that the change in the energy efficiency coefficient of the operating unit promoted the improvement in the system energy-saving level. Through further decomposition, the contribution of each operating unit in this network to the system energy-saving level can be analyzed. The average contribution rate of operating unit 1 is 55%, indicating that this unit has the greatest energy-saving potential, and is the energy-saving bottleneck unit of the current system. The average contribution rate of operating unit 2 is 32.9%, which is a secondary bottleneck unit with a certain degree of energy-saving potential. The energysaving potential of operating unit 3 is small and the retrofit value is low. Next, the energy-saving bottleneck factors of this system must

be determined. According to the system operation mechanism and the accumulated engineering knowledge in the early research, the scope of bottleneck factors can be determined from the main operation links, including the water supply, water transfer, heat exchange, water cooling, and water treatment. One or more factors significantly affect the system energy-saving level in these factors. Therefore, it is necessary to further analyze the factor scope and diagnose the energy-saving bottleneck factors. The diagnosis factors selected herein are shown in Table 5. After determining the analysis factors, the factor levels in the test are set. Because the CCWS has a large number of same-type equipment, it is necessary to set an operating state as a baseline and subsequently scale the settings of each equipment parameter.

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Fig. 3. System energy efficiency decomposition.

Fig. 4. Energy-saving bottleneck unit diagnoses.

The level of each test factor is set as follows: Level 1: The baseline data of each factor data (acquired from a system stable operating state); Level 2: The factor data are reduced by 5%; Level 3: The factor data are increased by 5%. Based on the above settings, the orthogonal table L18 ð37 Þ is constructed, and the simulation tests are performed. The test results are shown in Table 6. Test index e is the system energy consumption for completing a specific cooling capacity. According to the test results, the results of the orthogonal test were analyzed, and the range analysis data are shown in Table 7. According to the R value in the range analysis result, the primary and secondary relationships of energy-saving bottleneck factors can be analyzed. In the current system, the impact of each factor on the system energy-saving level is ranked from large to small:

F1 > F4 > F5 > F6 > F2 > F3 . Further, the tendency chart of the factors and indicators can be drawn based on kjl , as shown in Fig. 5. According to the tendency chart, it is possible to more intuitively observe the trend of system energy-saving level with various factors, and the energy-saving effect of each factor level can be analyzed. As shown from the tendency chart, with the change in the factor level, factors F1 , F4 , and F5 have a greater impact on the system energy efficiency, whereas factors F2 , F3 , and F6 have less influence on the system energy efficiency. The variance analysis results of the energy-saving bottleneck factors diagnosis are shown in Table 8. Through the variance analysis results, a more accurate diagnosis of energy-saving bottleneck factors can be performed. The order of influence of each factor is consistent with the range analysis result for the system studied herein. The results demonstrate that the performance coefficient of circulating pumps and the static head of the network are the primary energy-saving bottleneck factors of this system; their impact on the system energy-saving level is significant and their retrofit value is large. Circulating water density is a secondary energy-saving bottleneck factor with a certain degree of retrofit value. The heat transfer coefficient of the heat exchanger, the resistance coefficient of the pipeline, and the cooling coefficient of the cooling tower have a low degree of influence, and their energy-saving potential are small; therefore, these factors are not recommended to perform the retrofit. In summary, when designing the energy-saving retrofit scheme of this system, it is necessary to prioritize the improvement in the selection and configuration of the circulating pump and the height difference of the equipment, followed by improving the water treatment problem. This targeted energy-saving design can achieve better energysaving effect. Finally, the bottleneck unit diagnosis method based on energy efficiency density decomposition and the bottleneck factor diagnosis method based on orthogonal test analysis are combined to realize a complete and accurate energy-saving bottleneck diagnosis of the CCWS. The overall analysis of this system is performed based on the two-part diagnosis. The results show that the performance coefficient of the pumps and the static head of the network in

Table 5 Energy-saving bottleneck factor setting. Number

Factor indicator

Factor analysis

F1 F2 F3 F4 F5 F6

Performance coefficient of circulating pumps Average heat transfer area of heat exchanger Resistance coefficient of pipeline Static head of network Circulating water density Cooling coefficient of cooling tower

Influence Influence Influence Influence Influence Influence

of of of of of of

the design and matching of circulating pumps the heat transfer capacity of heat exchangers pipe head loss equipment height difference water treatment the design and matching of cooling towers

X. Zhu et al. / Journal of Cleaner Production 232 (2019) 224e234 Table 6 Result statistics of orthogonal test analysis. Test number

Table 8 Variance analysis of energy-saving bottleneck factor.

Factor

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

e

F1

F2

F3

F4

F5

F6

Null

1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 2 3 1 2 3 2 3 1 3 1 2 2 3 1 3 1 2

1 2 3 2 3 1 1 2 3 3 1 2 3 1 2 2 3 1

1 2 3 2 3 1 3 1 2 2 3 1 1 2 3 3 1 2

1 2 3 3 1 2 2 3 1 2 3 1 3 1 2 1 2 3

1 2 3 3 1 2 3 1 2 1 2 3 2 3 1 2 3 1

6.93 8.11 6.62 6.01 5.50 6.15 8.69 8.76 6.78 5.97 7.61 8.06 5.21 5.63 7.06 8.84 7.90 7.85

Table 7 Range analysis of energy-saving bottleneck factor. Indicator

K1 K2 K3 k1 k2 k3 Rj

233

Factor

Sum of square of deviance

Freedom

F test value

F threshold value

F1 F2 F3 F4 F5 F6 Error Total value

14.7892 0.2887 0.1934 6.5641 1.3640 0.4441 0.0951 23.7386

2 2 2 2 2 2 5 17

389.19 7.60 5.09 172.74 35.89 11.69

F0:05 ð2; 5Þ ¼ 5.786 F0:01 ð2; 5Þ ¼ 13.274

good energy-saving effects. Through this case application, the practical application effect of the proposed method is verified. Based on the energy-saving bottleneck diagnosis, the related research can be further performed, such as energy-saving retrofit design. Furthermore, this method can also be applied to other industrial systems, which requires researchers to make minor adjustments to achieve the best application effect.

5. Conclusion

Factor F1

F2

F3

F4

F5

F6

43.30 35.56 48.82 7.22 5.93 8.14 13.26

42.52 43.51 41.65 7.09 7.25 6.94 1.86

42.29 43.42 41.97 7.05 7.24 7.00 1.45

42.86 46.84 37.98 7.14 7.81 6.33 8.86

43.01 40.35 44.32 7.17 6.73 7.39 3.97

41.74 43.88 42.06 7.01 7.31 6.96 2.14

Fig. 5. Tendency charts of energy-saving factors and indicators.

operating unit 1 are the most important energy-saving bottlenecks of the system. Therefore, in the energy-saving retrofit process, the design and selection of pumps and the improvement in equipment height difference in operating unit 1 can be achieved for the best energy-saving effect. In addition, the performance coefficient of the pumps in operating unit 2 and circulating water density are the secondary energy-saving bottlenecks. After prioritizing the improvement in the primary bottleneck, the retrofit of the system water treatment and pump selection in operation unit 2 can yield

In this study, we realized the importance of the current energysaving bottleneck research on industrial systems through an indepth analysis. Further, we summarized the limitations of the existing bottleneck diagnosis investigations. To achieve the research goal, we proposed an energy-saving bottleneck diagnosis method for industrial systems based on the multi-mode energy efficiency density decomposition method and orthogonal test analysis method. The proposed method decomposed the energysaving bottleneck diagnosis problem into energy-saving bottleneck unit diagnosis and energy-saving bottleneck factor diagnosis. We improved the energy density decomposition method and proposed a multi-mode energy efficiency density decomposition method that could be applied to industrial systems that do not directly output products, and performed the analysis for multimode systems. This method could diagnose the energy-saving bottleneck unit in system operating units, thereby determining the energy-saving potential of each operating unit. Furthermore, we used the orthogonal test analysis method to analyze the configuration and attribute factors of the system equipment to diagnose the system energy-saving bottleneck factors and determine the primary direction of the energy-saving retrofit. With the combination of the two diagnosis methods, the energy-saving bottleneck of the existing industrial systems could be accurately diagnosed from two directions. The proposed method can comprehensively analyze the energy-saving state of industrial systems and accurately locate the system energy-saving bottlenecks. In the practical application of the industrial field, the optimal design of the energy-saving retrofit scheme can be realized based on this method, so as to perform targeted energy-saving retrofit. It can help industrial systems effectively improve energy-saving level and increase corporate profits. The proposed energy-saving bottleneck diagnosis method for industrial systems was applied to an energy-saving retrofit process of the CCWS, and diagnosed the energy-saving bottleneck of the existing network system. The diagnosis results showed that the proposed method could accurately locate the energy-saving bottleneck of the applied system and determine the priority of the energy-saving retrofit. Finally, the practicality of the method was verified. In addition, there is still room for improvement in this study. The simulation process of orthogonal test analysis is realized

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