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GB/T 41571-2022 English PDF (GBT41571-2022)
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GB/T 41571-2022: The diagnosis method of energy efficiency in industry automation
GB/T 41571-2022
NATIONAL STANDARD OF THE
PEOPLE’S REPUBLIC OF CHINA
ICS 25.040
CCS N 10
The diagnosis method of energy efficiency in industry
automation
ISSUED ON: JULY 11, 2022
IMPLEMENTED ON: FEBRUARY 01, 2023
Issued by: State Administration for Market Regulation;
Standardization Administration of the People's Republic of China.
Table of Contents
Foreword ... 3
1 Scope ... 4
2 Normative references ... 4
3 Terms and definitions ... 4
4 Abbreviations ... 5
5 Overview of energy efficiency diagnosis ... 5
6 Energy efficiency diagnosis process ... 8
7 Assessment of energy efficiency improvement potential ... 10
8 Determination of energy efficiency improvement target ... 13
9 Energy efficiency improvement plan ... 14
Bibliography ... 16
The diagnosis method of energy efficiency in industry
automation
1 Scope
This document specifies the general method for energy efficiency diagnosis for industry
automation.
This document is applicable to energy efficiency analysis and energy efficiency
diagnosis of industry automation.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
3.1 energy
Electricity, fuel, steam, heat, compressed air and other similar media.
NOTE: Energy includes various forms including renewable energy. It can be purchased, stored,
disposed of, used in equipment or processes and recycled.
[Source: GB/T 23331-2020, 3.5.1]
3.2 energy consumption
The amount of energy used.
[Source: GB/T 23331-2020, 3.5.2, modified]
3.3 energy efficiency
The ratio or other quantitative relationship of output performance, service, product,
commodity or energy to input energy.
Example 1: Conversion efficiency: Energy demand/energy actual use.
Example 2: Output/input: Energy amount of theoretical operation/energy amount of
actual operation.
[Source: GB/T 23331-2020, 3.5.3, modified]
3.4 energy efficiency benchmark line
Provide quantitative reference basis for energy efficiency comparison.
3.5 efficiency indicator
Indicative value of energy efficiency.
3.6 energy management
A coordinated activity that directs and controls the energy use of an entity.
3.7 specific energy consumption
Energy consumption per physical unit output.
4 Abbreviations
The following abbreviations apply to this document.
DCS: Distributed Control System
MES: Manufacturing Execution System
PLC: Programmer Logic Controller
SCADA: Supervisory Control And Data Acquisition
5 Overview of energy efficiency diagnosis
5.1 Energy efficiency diagnosis model
Energy efficiency diagnosis is a key link in implementing energy efficiency
improvement. For manufacturing enterprises to carry out energy efficiency diagnosis,
it is necessary to consider the production organization status, status quo of enterprise
and industry status quo, and to have sufficient energy efficiency data as the support to
complete the energy efficiency diagnosis. The production organization status includes
production process, production execution, personnel management, material
management. The status quo of the enterprise refers to the enterprise's operation,
manufacturing level, and funds that can be invested in energy efficiency improvement.
The industry status quo refers to the manufacturing level and energy efficiency level of
the industry divided by product or process. The production status quo can be formed by
the aspects of production status, management status, and informatization status.
First, review the production status, mainly including production process flow,
production takt and production efficiency of each process/equipment, material flow and
energy flow around the production process, production equipment around the process
flow, energy production or supply equipment. Second, review the management status,
mainly including total energy consumption and expenses, energy consumption of
various media, production energy consumption, production auxiliary energy
consumption, personnel usage in production and production auxiliary links, effective
use of materials, equipment maintenance. Last, review the informatization status,
mainly including enterprise-level management, workshop-level operation management,
energy management, on-site monitoring and other information system deployment,
equipment digitization capabilities, on-site data collection, integration between
information systems, integration between information system and equipment.
In the process of reviewing the status quo of the enterprise according to the above
content, quantitative data or indicators shall be obtained as the goal. If part of the
content of the status quo of the enterprise may not be available due to the insufficient
degree of refinement of enterprise management and incomplete relevant data,
communicate with enterprise management, workshop management, production
operators. Through the comparison and analysis of the status quo of related industries,
qualitatively describe and judge some of the contents of the status quo of the enterprise.
5.2.2 Energy efficiency data collection
Energy efficiency data is the basis and premise of developing enterprise energy
efficiency diagnosis. The requirement for energy efficiency data is to cover all aspects
related to energy efficiency, not only energy consumption data, but also equipment,
production process and other data related to energy efficiency. In addition to the internal
data of the factory, energy efficiency data shall also include related data of similar
industries and enterprises. These data can help enterprises determine energy efficiency
benchmark lines and position their energy efficiency in the industry. From the
perspective of timeliness, energy efficiency data shall include historical data and real-
time field data. It is required to ensure data accuracy and meet certain clock
synchronization requirements, so as to establish time correlation between data. Energy
efficiency data shall be processed, analyzed and managed uniformly. An energy
efficiency data model shall be established, so as to facilitate the management and
integration of energy efficiency data.
There are two main ways to collect energy efficiency data. One is to collect through the
existing system. For example, obtain energy efficiency-related equipment, units,
production lines and other data through equipment communication interfaces, PLC,
DCS, SCADA. Obtain relevant data of production operation management through MES
system. The other is that, for data that cannot be obtained, data collection needs to be
realized by modifying the equipment or deploying new devices or equipment. For
example, deploy energy measurement devices on the device. Deploy flow measuring
instruments, pressure measuring instruments on pipelines. For the inability to deploy
an installation, equipment or system due to reasons such as reduced economic input or
physical environmental conditions which may cause multiple devices or units to share
energy efficiency data, it may conduct statistical analysis of the data. Combined with
the working conditions of equipment and units, carry out the quantitative analysis of
different equipment or units.
The types of energy efficiency data collected shall include but are not limited to: At the
equipment and manufacturing unit level: various energy consumption, various material
consumption, average o...
Get QUOTATION in 1-minute: Click GB/T 41571-2022
Historical versions: GB/T 41571-2022
Preview True-PDF (Reload/Scroll if blank)
GB/T 41571-2022: The diagnosis method of energy efficiency in industry automation
GB/T 41571-2022
NATIONAL STANDARD OF THE
PEOPLE’S REPUBLIC OF CHINA
ICS 25.040
CCS N 10
The diagnosis method of energy efficiency in industry
automation
ISSUED ON: JULY 11, 2022
IMPLEMENTED ON: FEBRUARY 01, 2023
Issued by: State Administration for Market Regulation;
Standardization Administration of the People's Republic of China.
Table of Contents
Foreword ... 3
1 Scope ... 4
2 Normative references ... 4
3 Terms and definitions ... 4
4 Abbreviations ... 5
5 Overview of energy efficiency diagnosis ... 5
6 Energy efficiency diagnosis process ... 8
7 Assessment of energy efficiency improvement potential ... 10
8 Determination of energy efficiency improvement target ... 13
9 Energy efficiency improvement plan ... 14
Bibliography ... 16
The diagnosis method of energy efficiency in industry
automation
1 Scope
This document specifies the general method for energy efficiency diagnosis for industry
automation.
This document is applicable to energy efficiency analysis and energy efficiency
diagnosis of industry automation.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
3.1 energy
Electricity, fuel, steam, heat, compressed air and other similar media.
NOTE: Energy includes various forms including renewable energy. It can be purchased, stored,
disposed of, used in equipment or processes and recycled.
[Source: GB/T 23331-2020, 3.5.1]
3.2 energy consumption
The amount of energy used.
[Source: GB/T 23331-2020, 3.5.2, modified]
3.3 energy efficiency
The ratio or other quantitative relationship of output performance, service, product,
commodity or energy to input energy.
Example 1: Conversion efficiency: Energy demand/energy actual use.
Example 2: Output/input: Energy amount of theoretical operation/energy amount of
actual operation.
[Source: GB/T 23331-2020, 3.5.3, modified]
3.4 energy efficiency benchmark line
Provide quantitative reference basis for energy efficiency comparison.
3.5 efficiency indicator
Indicative value of energy efficiency.
3.6 energy management
A coordinated activity that directs and controls the energy use of an entity.
3.7 specific energy consumption
Energy consumption per physical unit output.
4 Abbreviations
The following abbreviations apply to this document.
DCS: Distributed Control System
MES: Manufacturing Execution System
PLC: Programmer Logic Controller
SCADA: Supervisory Control And Data Acquisition
5 Overview of energy efficiency diagnosis
5.1 Energy efficiency diagnosis model
Energy efficiency diagnosis is a key link in implementing energy efficiency
improvement. For manufacturing enterprises to carry out energy efficiency diagnosis,
it is necessary to consider the production organization status, status quo of enterprise
and industry status quo, and to have sufficient energy efficiency data as the support to
complete the energy efficiency diagnosis. The production organization status includes
production process, production execution, personnel management, material
management. The status quo of the enterprise refers to the enterprise's operation,
manufacturing level, and funds that can be invested in energy efficiency improvement.
The industry status quo refers to the manufacturing level and energy efficiency level of
the industry divided by product or process. The production status quo can be formed by
the aspects of production status, management status, and informatization status.
First, review the production status, mainly including production process flow,
production takt and production efficiency of each process/equipment, material flow and
energy flow around the production process, production equipment around the process
flow, energy production or supply equipment. Second, review the management status,
mainly including total energy consumption and expenses, energy consumption of
various media, production energy consumption, production auxiliary energy
consumption, personnel usage in production and production auxiliary links, effective
use of materials, equipment maintenance. Last, review the informatization status,
mainly including enterprise-level management, workshop-level operation management,
energy management, on-site monitoring and other information system deployment,
equipment digitization capabilities, on-site data collection, integration between
information systems, integration between information system and equipment.
In the process of reviewing the status quo of the enterprise according to the above
content, quantitative data or indicators shall be obtained as the goal. If part of the
content of the status quo of the enterprise may not be available due to the insufficient
degree of refinement of enterprise management and incomplete relevant data,
communicate with enterprise management, workshop management, production
operators. Through the comparison and analysis of the status quo of related industries,
qualitatively describe and judge some of the contents of the status quo of the enterprise.
5.2.2 Energy efficiency data collection
Energy efficiency data is the basis and premise of developing enterprise energy
efficiency diagnosis. The requirement for energy efficiency data is to cover all aspects
related to energy efficiency, not only energy consumption data, but also equipment,
production process and other data related to energy efficiency. In addition to the internal
data of the factory, energy efficiency data shall also include related data of similar
industries and enterprises. These data can help enterprises determine energy efficiency
benchmark lines and position their energy efficiency in the industry. From the
perspective of timeliness, energy efficiency data shall include historical data and real-
time field data. It is required to ensure data accuracy and meet certain clock
synchronization requirements, so as to establish time correlation between data. Energy
efficiency data shall be processed, analyzed and managed uniformly. An energy
efficiency data model shall be established, so as to facilitate the management and
integration of energy efficiency data.
There are two main ways to collect energy efficiency data. One is to collect through the
existing system. For example, obtain energy efficiency-related equipment, units,
production lines and other data through equipment communication interfaces, PLC,
DCS, SCADA. Obtain relevant data of production operation management through MES
system. The other is that, for data that cannot be obtained, data collection needs to be
realized by modifying the equipment or deploying new devices or equipment. For
example, deploy energy measurement devices on the device. Deploy flow measuring
instruments, pressure measuring instruments on pipelines. For the inability to deploy
an installation, equipment or system due to reasons such as reduced economic input or
physical environmental conditions which may cause multiple devices or units to share
energy efficiency data, it may conduct statistical analysis of the data. Combined with
the working conditions of equipment and units, carry out the quantitative analysis of
different equipment or units.
The types of energy efficiency data collected shall include but are not limited to: At the
equipment and manufacturing unit level: various energy consumption, various material
consumption, average o...
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