2024年2月18日发(作者：)

ARTICLE

J. Cent. South Univ. (2019) 26: 2149−2159

DOI: /10.1007/s11771-019-4162-z

Online monitoring and assessment of energy efficiency

for copper smelting process

CHEN Zhuo(陈卓), ZHU Zhen-yu(祝振宇), WANG Xiao-na(王晓娜), SONG Yan-po(宋彦坡)

School of Energy Science and Engineering, Central South University, Changsha 410083, China

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Abstract: The copper flash smelting process is characterized by its involvement of wide energy sources and high

energy consumption, so the energy conservation is usually a highly concerned topic for the flash smelting enterprises.

However, due to the complexity of the system, it is quite difficult to perform a timely comprehensive analysis of the

energy consumption of the whole production system. Aiming to realize an online assessment of the energy consumption

of the system, great effort was first made in Jinguan Copper, Tongling Nonferrous Metals Group Co. Ltd. Methods were

proposed to solve technical difficulties such as the acquisition and processing of data with different sampling

frequencies, the online evaluation of the electricity consumption, and timely evaluation of product output in the periodic

process. As a result, a software system was developed to make the online analysis of the energy consumption and

efficiency from the three levels ranging from the system to the equipment. The analytical results at the system level was

introduce. It’s found that electricity is the most consumed energy in the system, accounting for 77.3% of the total energy

consumption. The smelting unit has the highest energy consumption, accounting for 52.8% of the total energy

consumed in the whole enterprise.

Key words: energy consumption; energy efficiency; online assessment; copper flash smelting

Cite this article as: CHEN Zhuo, ZHU Zhen-yu, WANG Xiao-na, SONG Yan-po. Online monitoring and assessment

of energy efficiency for copper smelting process [J]. Journal of Central South University, 2019, 26(8): 2149−2159. DOI:

/10.1007/s11771-019-4162-z.

1 Introduction

As the energy shortage and the environmental

protection are better acknowledged by the society,

the energy conservation has become an important

theme nowadays in the industrial enterprises, and to

some extent becomes a key issue to their

development. To achieve the energy-saving targets,

the energy-saving transformation has been one of

the most widely used measures, including

eliminating the high energy-consuming equipments,

improving the energy efficiency through system

modification, and adopting waste heat recovery

technologies [1, 2]. Meanwhile, optimization of the

energy system is also an important method for the

energy-saving purpose. It is not only to expand the

energy sources from heavy oil and natural gas to

renewable fuel such as biofuel [3] and solar energy

[4], but also to give guidance for process operations

based on the analysis of equipment/system energy

efficiency.

The evaluation of energy efficiency has been

widely used in studies of buildings and indoor

facilities, for example, analyzing the energy

consumption in offices [5, 6], air-conditioning [7, 8],

ventilation systems [9] and elevator controllers [10].

Models have also been developed for energy

Foundation item: Project(1301021018) supported by Science and Technology Research Project of Anhui Province, China

Received date: 2019-06-19; Accepted date: 2019-07-22

Corresponding author: SONG Yan-po, PhD, Associate Professor; Tel: +86-; E-mail: songyanpo@; ORCID:

0000-0003-0246-8566

2150efficiency analysis of industrial equipments, such as

electric and induction motors [11, 12], turbine

generator [13], condensing boiler [14] and

MEE-MSF (multi effect evaporation and hybrid

multi effect evaporation multi-stage flash) systems

[15]. Applications were then quickly spread to

energy studies of the wastewater treatment plants

[16], manufacturing systems [17, 18] and waste

heat recovery systems [19, 20].

Heat balance is the basis of most models for

the energy efficiency analysis. The result of the heat

balance calculation generally tells how the energy

quantities change in the system, and it cannot

reflect the difference in the quality of energy

flowing through the equipment/system. Therefore,

the exergetic analysis was adopted, and models

introduced by ARAUJO [21], YARI [22], KELLY et

al [23] and SINGH et al [24] have been developed

in analysis of the distillation processes, geothermal

power plants, solar thermal power system and so on.

The techno-economic assessment models were also

developed and applied in the study of the renewable

energy system [25, 26].

The energy efficiency evaluation and analysis

have also been carried out in the high-energy

consumption enterprises such as the steel,

non-ferrous metallurgical and petrochemical

industries. JIANG et al [27] and GE [28] calculated

the income and expense distribution of energy and

exergy in the energy analysis of the blast furnace

and alumina production, respectively. ANDERSEN

[29] carried out analysis of the material flow and

energy flow in a steel industry with the end-use

model and the process-step model respectively, and

made a detailed comparison of the differences

between the two models. NAJDENOV et al [30]

and HE [31] provided suggestions to improve the

energy efficiency after making an analytical study

of the energy consumption in the copper smelting

process. SIITONEN et al [32] calculated the

specific energy consumption with different

boundaries at the process level and the mill level,

and developed an index to analyze the energy

efficiency. LIU [33] summarized the energy flow at

the process level and constructed a mathematic

model to calculate the energy flow in the whole

steelworks according to the input and output

balance. LIU et al [34] developed a hierarchical

SDA model to analyze the key factors resulting in

the energy consumption changes in a steel plant.

J. Cent. South Univ. (2019) 26: 2149−2159

GAO et al [35] built the energy index system of

different layers and developed a multi-stage energy

efficiency evaluation method to make a

comprehensive evaluation for a petrochemical

enterprise.

Though the analysis of energy consumption

and efficiency has been carried out widely for the

production process, it is still limited to offline

running and the equipment or process level. The

difficulties encountered in the online energy

analysis of the whole process as resulted from three

aspects, i.e., the synchronous data acquisition, the

unified measurements of different energy media,

and the timely measurement of the production

output. This is particularly difficult for the

non-ferrous metallurgical enterprises, because they

are usually characterized by their complex

production processes, involvement of a good

variety of energy media, and the lack of modern

testing measures. Therefore, seeking solutions to

these problems is the premise for carrying out an

online analysis of the energy utilization and

efficiency in a non-ferrous metallurgical enterprise.

Sponsored by the Science and Technology

Research Project of Anhui Province, China, efforts

were first made in the Jinguan Copper, Tongling

Non-ferrous Metal Group Co. Ltd., to realize an

online monitoring and analysis of energy

consumption and efficiency at three levels, i.e. the

equipment level, the process level and the system

level. The energy analysis at the system level

includes two parts. One is to calculate the amount

of different energy media (electricity, natural gas,

diesel and water) consumed in the production

system, and the other is to show how the energy

media flow and distribute in the system. For the

energy analysis at the process level, the whole

production system is divided into six units

according to the functional partitioning, that is, the

mineral processing unit, the smelting unit, the

electrolysis unit, the sulfuric acid unit, the public

utility unit and the office unit. Except that the

public utility unit and the office unit are consisted

of processes for the same service function, other

units at the process level are defined based on the

production process, and characterized by the

purpose of producing a certain product, from the

beginning of the main raw material entering the

process to the completion of the intermediate

product exiting the process. The energy analysis at

J. Cent. South Univ. (2019) 26: 2149−2159

2151

the process level then includes the calculation of the

energy consumption, the energy efficiency and the

unit product energy consumption in the process of

each unit. At the equipment level, the analysis is

only for the key energy-consuming equipment in a

process, and it is to calculate the values of the

energy consumption, the energy efficiency, and the

energy consumption per unit of product of these key

equipments. Solutions to difficulties such as the

acquisition and processing of data with different

sampling frequencies, the online evaluation of the

unit electricity consumption, and the timely

evaluation of the product output for the periodic

process are to be introduced in the paper, and

examples of the online analysis of the energy flow

in the system are illustrated as well.

2 Energy consumption in Jinguan

Copper

The Jinguan Copper company adopts the

double flash technology (i.e. the flash smelting and

the flash converting technology) to produce the

cathode copper, the sulfuric acid and other

by-products. A good variety of energy media

including electricity, diesel, natural gas and water

are used in the production process [36]. The main

material and energy flows in the production system

are shown in Figure 1.

On the whole, the production and the energy

consumption system in the enterprise is a typical

hierarchical structure. The production system is on

the top of the structure. The six process units can be

regarded as the primary subsystems at the second

level. A number of processes in a particular unit are

the secondary subsystems at the third level. And the

single equipment in a specific process is the fourth

level at the bottom of the structure. Hence, a

multi-level structure for the online assessment of

the system consumption and efficiency is

established.

3 Solutions to technical difficulties in

online energy analysis

The energy consumption system of Jinguan

Figure 1 Material and energy flows of whole plant

2152

Copper is complicated. The complexity lies in two

aspects. One is that the system consists of both

pyro- and hydro- metallurgical processes and many

types of energy media, and the other is that the

statistical time scale of the product and energy data

is inconsistent between processes of the system. For

example, the acquisition frequency of the

production data is 1 s for the flash smelting furnace.

However, the statistical cycle of the product output

for the anode furnace is a shift (8 h). The large

difference between the data sampling time of the

processes thus makes it difficult to perform the data

acquisition, statistical analysis, and the real-time

assessment of the system energy consumption.

Therefore, efforts are made to solve the problems of

energy data acquisition and timely measurement of

product output.

3.1 Structure of online analysis system

The original data needed for the energy

analysis in Jinguan Copper includes three types.

The first is the data acquired directly from the DCS

system in real time, such as the concentrate feed

rate and the air flow rate. The second is the manual

input data, such as XRF results of the concentrate

and the product output of the cathode copper. The

third is some constant data that usually does not

need to be modified, such as the material density

and the enthalpy value required for the energy

efficiency calculation.

The sampling frequencies for different types of

data are also different. For instance, the sampling

period for the instantaneous data collected from the

DCS system is 1 min, and the frequency for the

J. Cent. South Univ. (2019) 26: 2149−2159

cumulative data collected from the DCS system is

once an hour, while for the manual input data and

constant data, no sampling and modification with

be made until it is required.

In order to analyze the data with different

sampling frequency, the data must be first collected

and stored, until it is needed to be processed to

obtain kinds of energy efficiency indicators. Hence,

the software system for the online energy analysis

consists of three functional modules to achieve the

desired functions, that is, the database module, the

energy efficiency calculation module and the results

display module (Figure 2). In the data acquisition

module, the real-time data are copied from the PI

system to the Historian database, and synchronized

together with the manual input data as well as the

data from the distribution room to the MongoDB

where all the data are stored. Afterwards, the energy

efficiency calculation module is activated, in which

the multi-thread high-speed calculation method is

adopted in order to accelerate the calculation speed.

The results are then authorized to different users

and finally displayed based on the Process book.

3.2 Online data acquisition and correction

The flash copper smelting enterprise has a high

degree of automation. Though lots of important

energy consumption data can be measured

online from the distributed control system, some

important data cannot be obtained due to the lack of

necessary meters or even proper measurement

methods.

The data acquisition refers to the direct

collection of raw data required for the calculation of

Figure 2 Structure of online energy analysis software system

J. Cent. South Univ. (2019) 26: 2149−2159

the

energy consumption and efficiency. The

difficulty results from that the data acquisition

frequencies are different for different processes and

different data types. For example, for most

continuous processes, the data is usually sampled

at an interval of 1 s. However, for a period process,

the data may not be updated for every 8 h. Besides,

the instantaneous data is copied from the DCS

system per min, but the accumulated data is

collected from the DCS system once an hour, such

as the electricity from the distribution room. For the

manual input data and some constant data, it will

not be revised until needed.

As the instantaneous data is read directly from

the online measurement, its accuracy is easily

influenced by either the internal fault of spot meters

or the external random interference. When the

detection error becomes large, reliability of the data

must be judged and correction must be made in

order to guarantee the quality of the data.

The way to eliminate the abnormal data is to

save at least six consecutive values of the same

variable, i. e. the current value I′(k) and the previous

five values I(k−i), i=1, 2, 3, 4, 5. If the current value

I(k) is beyond the set variation range of the

variable, it will then be replaced by the average of

the previous five consecutive values, so that the

unexpected fluctuation in the data metering can be

avoided. The formula for the data correction is

shown in Eq. (1).

Ik155Iki (1)

i1

3.3 Online estimation of electricity consumption

The power supply in Jinguan Copper includes

two stages. The 110 kV central step-down

substation supplies power to the 10 kV high-voltage

power distribution room through the main

transformer. The high-voltage power distribution

room then supplies power to the low-voltage power

distribution rooms and some high-power electrical

apparatuses. As the “nearby” principle is adhered in

the power supply to the production processes, the

supply relationship of the power distribution rooms

is complicated. On the one hand, a low-voltage

power distribution room may supply power to

different processes at the same time, and on the

other hand, a production process receives power

supply from several distribution rooms. Specifically,

in the six process units, there are only two units of

2153

which the processes receive the electricity supply

respectively from the unique corresponding

low-voltage power distribution room. For these

processes, the electricity data required for the

energy efficiency assessment at the process level

can be obtained directly by reading the inlet cabinet

display of the low-voltage distribution room.

However, the situation is much more

complicated in the units such as the smelting unit,

the sulfuric acid unit and the public utility unit. In

these units, one low-voltage power distribution

room supplies power to several processes, and the

power supplied to most equipments is even not

metered. So, the electricity supply is hard to be

calculated accurately.

In order to solve the technical problem for the

online analysis of the energy consumption, a

calculation method was proposed to make a timely

evaluation of the electricity utilization in different

processes.

Supposing the electricity supplied to the

process Pi comes from several high-voltage power

distribution rooms and several low-voltage power

distribution rooms, its power consumption is

calculated by:

ABQPiQHTa_PiQLTb_Pi (2)

a1b1In which, A is the number of the high-voltage

power distribution rooms that directly supply power

to the process Pi, and

QHTof electricity power supplied

a_Pi denotes the amount

to the process by the

high-voltage power distribution room; B is the

number of the low-voltage power distribution

rooms that directly supply power to the process Pi,

and

QLTsupplied

b_Pi denotes the amount of electricity

power to the process Pi from the

low-voltage power distribution room.

In the high-voltage power distribution room

HTa, since the facilities running for the process Pi

have their own meters to record the electricity

consumption,

QHTa_Pi can be calculated by Eq. (3).

KQHTa_PiQHTa_Pi_Ek (3)

k1where

QHTa_Pi_Ek is the total electricity

consumption of power-consuming facility Ek; K is

the number of facilities in the process Pi receiving

their electricity supply from the high-voltage power

distribution room.

2154 In the low-voltage power distribution room

LTb, although some of the high-power facilities in

the process Pi have their corresponding meters,

most low-power facilities are not metered, and one

power distribution room generally provides power

supplies to several processes. In the case where one

low-voltage distribution room LTb supplies power

to m processes (respectively referred to as P1,

P2, …, Pm,

Pi{P1,P2, ,Pm}), the procedure to

calculate the electricity utilization in the process Pi

provided by the low-voltage distribution room LTb

is as follows.

1) Adding up all the metered electricity of

high-power electrical facilities belonging to the

process Pi in the low-voltage power distribution

room LTb.

2) Subtracting the electricity consumption of

all the electrical facilities that have meters in the

low-voltage power distribution room LTb from the

total electricity consumption in the same room, to

obtain the electricity of all processes that are not

metered in the room LTb.

3) Multiplying the electricity consumption that

is not metered by the distribution coefficient of the

process Pi, and get the amount of the electricity

which is consumed but not metered by the process

Pi.

Therefore, the

QLTEq. (4).

b_Pi can be calculated by

KmQLTb_PiQLTbcK_PiQLT

k1i_Eki1bQLTk1b_Ek

(4)

where

QLTb_Pi_Ek is the electricity consumption of

Pi which is metered in the low-voltage power

distribution room LTb; K is the number of the

power-consuming facilities;

QLTin the low-voltage

b is the total

power consumption power

distribution room LTb;

QLTb_Econsumed by all the electrical

k is the electricity

facilities which

receive electricity supply from the low-voltage

power distribution room LTb ; ci is the distribution

coefficient describing the ratio of the un-metered

electricity in the power distribution room supplied

to the process Pi.

In particular, the distribution coefficient of

each power distribution room needs a manual input.

It can be estimated according to the power and

operating rate of the equipment. For example,

supposing a power distribution room suppling

J. Cent. South Univ. (2019) 26: 2149−2159

power to m processes, the distribution coefficient

can be calculated by Eq. (5).

KPi_kwi_kcik1mK (5)

Pi_kwi_k

i1k1where

Pi_k and

wi_k are the power and operating

rate of the k-th facility in the process Pi

respectively.

3.4 Online estimation of product output of

periodic process

The electrolytic refinery process is a typical

periodic process in the double flash production

system. One of the technical difficulties

encountered in the online energy analysis of the

process is that the estimation of its product output is

too much lagged in time, which is generally made

every electrolytic period (210−240 h). So a timely

evaluation and correction method is proposed for

this periodic process in order to meet the data

requirement of the online energy analysis.

Known by the mechanism of the electrolysis

process, the generation rate of the cathode copper

can be calculated by Eq. (6).

kIN (6)

where α is the generation rate of the cathode copper,

t/h; η is the current efficiency of the electrolytic cell;

k is the copper electrochemical equivalent,

1.185×10−6 t/(A∙h); I is the current intensity, A; And

N is the number of the electrolytic cells.

Among the four parameters in Eq. (6), k and N

are constants, and I can be measured in real time. In

normal situations, η ranges in a small range, which

can be set according to historical data or with

experience. Once obtaining the generation rate of

the cathode copper by Eq. (6), the product output of

the cathode copper can be calculated for different

statistical periods. However, the calculated value is

suggested to be compared with the actual

production data, and the current efficiency then

needs to be revised in order to improve the

evaluation accuracy.

4 Results and discussion

The online energy analysis of the double flash

system is performed from three levels. As the

J. Cent. South Univ. (2019) 26: 2149−2159

energy analysis has been reported a lot at the

equipment and process level, here it gives only

examples to demonstrate the online analysis of

energy consumption of the whole production

system.

4.1 Energy consumption of production system

There are a good variety of energy media

involved in the copper production of Jinguan

Copper. Some of the media like the electricity,

natural gas, diesel and water, are the energy sources

that need to pay. Others such as the steam, oxygen,

nitrogen, and compressed air are self-produced

sources and are generally not included in the cost of

production of the enterprise. So in calculation of the

energy consumption of the system, only outsourced

energy media are included, and the consumption is

converted into the standard coal to calculate the

proportion of each medium. As illustrated in

Figure 3, the electricity is the most consumed

energy source in the system, which accounts for

77.3%. It is then followed by the natural gas and the

diesel, accounting for 12.2% and 10.2%

respectively in the total energy consumption of the

system.

Figure 3 Consumption of different energy media

consumed by the production system

For the energy analysis of each process unit,

since the steam, oxygen, nitrogen and compressed

air are no longer the self-produced sources in the

unit, it must be included in the energy consumption

by converting all the consumed energy media into

the equivalent standard coal. As found in Figure 4,

the highest energy consumption takes place in the

smelting process unit, accounting for 52.8% of the

total energy consumption of the system. This is

because the smelting unit has the main processes

such as the flash smelting (FS), the flash converting

2155

(FC), and the anode refining. All these pyro-

metallurgical processes consume a large amount of

electricity, natural gas, oxygen and steam. This is

followed by the public utility unit and the

electrolysis unit. The public utility unit is

responsible for suppling the self-produced energy to

other units, and it consumes nearly a quarter of the

total energy in the system. In addition, the sulfuric

acid unit and the mineral processing unit consume

relatively less energy, accounting for only 8.4% and

4.3% in the total amount of energy consumed in the

system.

Figure 4 Energy consumption in different process units

4.2 Flow of steam

The steam is a self-produced energy source,

however, the steam used in the production system is

at three different pressure levels: the low-pressure

(0−0.6 MPa), the medium-pressure (0.6−2.5 MPa)

and the high-pressure (2.5−6 MPa). Aiming to make

a more efficient utilization of the stream at different

pressure levels, a steam flow chart through the

system was included in the online energy analysis,

as shown in Figure 5.

From the perspective of the whole production

system, the steam mainly comes from the waste

heat boilers, and is consumed by the drying process,

the power generation and the flash smelting and

converting processes.

Specifically, the high-pressure steam of

5.5 MPa is produced by the waste heat boilers of

the flash smelting furnace (FSF) and the flash

converting furnace (FCF). It is then used to

generate electricity, discharging the steam of 2.2

MPa which enters the medium-pressure steam pipe

network. In parallel, the medium-pressure steam of

2 MPa produced by the waste heat boiler in the

sulfuric acid unit enters the medium-pressure pipe

network as well. These medium-pressure steam

2156

J. Cent. South Univ. (2019) 26: 2149−2159

Figure 5 Flow of steam in the production system

processing unit. An example of the daily energy

consumption of the three units is listed in Table 1.

The smelting unit has the highest energy

consumption in the enterprise. The energy media

involved in this unit are electricity, natural gas and

oxygen. This is because the smelting unit has the

main processes of the copper pyro-extraction, it

requires a large amount of oxygen. In addition, the

excess oxygen over supplied to the processes is

usually directly discharged. This causes a kind of

waste of the oxygen and to some extent results in a

high value of the energy consumption of these

processes. In contrast, the sulfuric acid system and

the mineral processing system are auxiliary

processes, so their energy consumption is relatively

low.

streams are then mainly consumed in processes,

such as the steam drying, the flash smelting, the

flash converting, the circulating water system and

the oxygen production. The low-pressure steam of

0.6 MPa is produced by the waste heat boiler in the

sulfuric acid unit. It enters the low-pressure pipe

network, and is mainly used in the electrolysis, the

deaeration water supply and the waste heat power

generation. The auxiliary boiler is responsible for

supplying insufficient steam to the smelting process

or others.

4.3 Energy consumption of three main process

units

The three major process units are the smelting

unit, the sulfuric acid unit and the mineral

J. Cent. South Univ. (2019) 26: 2149−2159

2157

Table 1 Example of daily energy consumption of three major units

Type

Electricity

Natural gas

Low-pressure steam

Medium-pressure steam

Compressed air

Oxygen

Processed air

Water

Diesel

Total

1 kgce=29306 kJ.

Smelting unit

kgce*

79702

62201

224

1932

3590

141264

2480

383

1479

293255

%

27.2

21.2

0.1

0.7

1.2

48.2

0.8

0.1

0.5

100

Sulfuric acid unit

kgce

22415

—

1

—

1252

—

—

529

72855

97052

%

23.1

—

0.0

—

1.3

—

—

0.5

75.1

100

Mineral processing unit

kgce

9322

—

—

—

208

—

—

1

—

9531

%

97.8

—

—

—

2.2

—

—

0.0

—

100

5 Conclusions

Due to the complexity of the system and the

diversity of energy media involved in the system, it

is difficult to perform an online analysis of the

energy consumption and efficiency for large

enterprise, especially for the non-ferrous enterprise.

Work has been carried out first in Jinguan Copper,

to perform an online analysis of the energy

consumption and efficiency from the system,

process and equipment levels respectively. The

main work and conclusions are summarized as

follows.

1) Based on the characteristics of the

production processes, the whole double flash

production system is divided into six process units

according to the functional partitioning. A

multi-level energy analysis method was proposed to

make a comprehensive analysis of the energy

consumption and efficiency from the system level,

the process level and the equipment level

respectively.

2) In order to tackle the technical difficulties

for the online energy analysis of the double flash

production system, methods were also proposed for

problems such as the online acquisition and

processing of data with different sampling

frequencies, the online evaluation of the unit

electricity consumption, and the timely evaluation

of the product output for the periodic process. The

solutions to these problems are of great help for the

implementation of the online energy analysis.

3) A software system was developed and put

into practice for the multi-level assessment of the

energy consumption and efficiency in the copper

double flash enterprise. As a demonstration, the

flow of the steam and the energy consumption at

the system level were analyzed. The results show

that electricity is the most consumed energy in the

system, accounting for 77.3% of the total energy

consumption. And the smelting unit has the highest

energy consumption, accounting for 52.8% of the

total energy consumed in the whole enterprise.

Acknowledgements

The authors are sincerely grateful to all the

technical support from the Jinguan Copper

Company, Tongling Non-ferrous Metal Group Co.

Ltd.

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中文导读

闪速炼铜工艺的能效在线监测与分析

摘要：铜闪速熔冶炼工艺的特点是能源介质种类多、系统能耗高。因此，节能是闪速炼铜企业高度关注的主题之一。然而，由于系统的复杂性，要实现对生产系统的能耗进行综合分析具有一定难度。为此，关于“闪速炼铜企业系统能耗在线监测与分析”的研究工作率先在铜陵有色金属集团有限公司金冠铜业展开。解决了能耗在线分析工作中存在的诸如不同采样频率数据采集和处理问题、过程用电量在线测算、周期性过程产品产量估测等技术难题，并在此基础上完成了系统软件开发，实现了从系统到设备的三个级别分别对全厂能源消耗与用能效率的在线监测与分析。系统投入实际运行后的结果表明：电力是双闪企业生产系统中消耗最多的能源介质，其用量占总能源消耗量的77.3%；生产系统中，冶炼单元能耗最高，占整个企业总能耗的52.8%。

关键词：能耗；能效；在线分析；闪速炼铜