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GB/T 41571-2022 English PDF (GBT41571-2022)

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

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.
GB/T 41571-2022
NATIONAL STANDARD OF THE
PEOPLE 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 operating time, auxiliary processing time, number of unplanned equipment downtime, equipment working status, rework rate, defective product rate. At the auxiliary unit level: various energy supply, energy conversion efficiency, lighting energy consumption, air conditioning energy consumption. At the industry level: advanced value of energy efficiency of similar processes, advanced value of energy consumption per unit product, and per capita labor productivity of enterprises. 6 Energy efficiency diagnosis process
The general process of industrial automation energy efficiency diagnosis is shown in Figure 2.
The general process of energy efficiency diagnosis is mainly divided into 4 stages: preparation for energy efficiency diagnosis, assessment of energy efficiency improvement potential, determination of energy efficiency improvement target, energy efficiency improvement plan.
The preparation for energy efficiency diagnosis is the basic work that needs to be completed before carrying out energy efficiency diagnosis. First, complete energy efficiency data collection. Energy efficiency data types include energy consumption data of various manufacturing equipment, auxiliary equipment in the production process, as well as data in the production process related to energy efficiency. The main ways to obtain energy efficiency data are: through equipment communication interface, adding sensors and instrumentation, workshop manufacturing operation management system or similar information system. Secondly, it is necessary to analyze the influencing factors of energy efficiency. It is divided into 3 levels to sort out the influencing factors of energy efficiency. At the equipment level, analyze the impact on energy efficiency from factors such as equipment working mode, equipment utilization, and key process parameters. At the production process level, analyze the impact on energy efficiency from factors such as technological process and workshop production operation management. At the energy supply level, analyze the impact on energy efficiency from factors such as energy consumption, energy supply and demand balance. The assessment of energy efficiency improvement potential is to calculate and evaluate the energy efficiency improvement potential as a whole. It is the basis for subsequent determination of energy efficiency improvement goals and energy efficiency single product structure, the ideal unit energy consumption or reference unit energy consumption recognized by the industry can be used as the benchmark line value. For enterprises with multiple product varieties but stable output results but no obvious difference in the production energy efficiency of different products, the ideal unit energy consumption or reference unit energy consumption of all products recognized by the industry can be used as the benchmark line value. For enterprises with multiple product varieties, but the output is unstable or different product varieties have a greater impact on energy efficiency, product variety and product quantity can be used as influencing factors. The energy efficiency reference value is obtained by data fitting. The total energy consumption benchmark line of the enterprise can also be calculated based on the number of enterprise products. Example 1: Iron and steel production enterprises usually use standard coal per ton of steel as an indicator to compare the overall energy efficiency level. Auto parts processing enterprises usually use the energy consumption per unit number as the benchmark indicator of energy efficiency.
b) Process-level energy efficiency benchmark line
A process can consist of a single production process or multiple production processes. The following methods can be used to determine the energy efficiency benchmark line at the process level: reference value method, mechanism modeling method, data fitting method. The reference value method can refer to the reference energy consumption per unit output of the production process provided by the relevant industry associations and organizations, or the energy consumption per unit output of the theoretical design or take energy efficiency optimization as the energy efficiency benchmark line. The mechanism modeling method is based on the production process principle of the process and considers the constraints such as materials and energy to establish a process energy efficiency model. Calculate the reference value of process energy efficiency based on the process energy efficiency model. The data fitting method is based on the relevant status data and product data during the operation of the production process. Determine the influencing factors that affect the production process. The relationship between process energy efficiency and influencing factors is obtained by mathematical regression method. Based on the relationship, the reference value of process energy efficiency can be obtained. Determination of process-level energy efficiency benchmark line needs to consider their applicable operating conditions. Example 2: For atmospheric and vacuum processes in oil refining production, the "heat balance method" based on process mechanism is used to calculate the benchmark energy efficiency. For the hot rolling process in metallurgical production, considering the thickness of incoming materials, the thickness of the rolled piece exit and other factors, the polynomial regression method is used to calculate the benchmark energy efficiency.
c) Equipment-level energy efficiency benchmark line
The following methods can be used to determine the equipment-level energy efficiency benchmark line: reference value method, mechanism modeling method, data fitting method. The reference value method is based on parameters such as the rated power of the equipment provided by the equipment manufacturer and the energy consumption data of typical operating conditions. Through the verification of relevant parameters and data in the actual working environment, it is used as the energy efficiency benchmark line of the equipment. The mechanism modeling method is to establish the energy efficiency model of the equipment based on the working principle of the equipment and the physical or chemical change
mechanism during the operation process. Based on the energy efficiency model, the energy efficiency of the equipment under ideal conditions can be calculated and used as the energy efficiency benchmark line of the equipment. The data fitting method is based on the state data during the operation of the equipment and the analysis of the factors affecting the energy efficiency of the equipment. The relationship between influencing factors and energy efficiency is obtained by mathematical regression method. Based on the established mathematical
relationship, the reference value of equipment energy efficiency under different working conditions can be obtained as the energy efficiency benchmark line of the equipment.
Example 3: CNC machine tools, based on the working principle, are divided into load-independent energy consumption subsystems (including lubrication and cooling systems, auxiliary systems, peripheral systems, hydraulic systems) and load-related energy consumption subsystems (including main drive system, feed system). Establish a CNC machine tool energy consumption model including the above components. For ball mill, establish a regression model including grinding particle size, mill parameters, mill energy consumption and other data. The relationship between the key influencing factors and the energy consumption of the mill is formed.
7.2 Assessment of energy efficiency improvement potential
Evaluate the energy efficiency improvement potential of energy efficiency diagnostic objects such as equipment, processes, and enterprises. It needs to determine the indicators that describe the energy efficiency improvement potential first. Evaluate the energy efficiency improvement potential through indicator calculation. Formula (1) is the calculation formula of the energy efficiency improvement potential indicator. Where,
EER - The indicator for energy efficiency improvement potential;
Es - The actual energy consumption of the manufacturing enterprise, or process, or energy efficiency are determined. Finally, the overall energy efficiency improvement potential assessment, the positioning of key processes and main influencing factors in the production process, and the positioning of key equipment and key influencing factors at the equipment level are formed.
8.3 Determination of energy efficiency improvement goals
Based on the accurate positioning of the energy efficiency problems of manufacturing enterprises, through effective communication with the management of the enterprise, considering the current economic status quo, operation status quo, technology status quo, and personnel status quo of the enterprise, develop a corporate energy efficiency improvement plan. The enterprise energy efficiency improvement plan shall include but not limited to technical transformation for equipment, process and energy supply system, energy management or energy efficiency management information system construction or upgrade, management optimization for energy efficiency, qualitative and quantitative evaluation of the effect after the implementation of energy efficiency improvement. The completion period of the enterprise energy efficiency improvement plan can be set at 3 to 5 years. Energy efficiency improvement goals can be divided into long-term goals and periodic goals. A long-term goal can help enterprises better achieve energy-saving goals, avoid short-term behavior, and be sustainable. Periodic goals are usually achieved annually and shall be coordinated with long-term goals. Energy efficiency improvement goals shall be qualitatively and quantitatively measured. It may conduct quantitative calculation of technical transformation of equipment, process and energy supply system. Carry out qualitative analysis and effect estimation of energy management or information system construction or upgrade of energy efficiency management. This is used as the basis for formulating energy efficiency improvement goals. In addition, it is necessary to calculate the economic input and evaluate the effect of input and output on the energy efficiency improvement plan and energy efficiency improvement goal. If the input and output expectations of the enterprise are not met, the energy efficiency improvement plan and energy efficiency improvement goals can be adjusted.
9 Energy efficiency improvement plan
Based on the energy efficiency improvement plan and energy efficiency improvement goals of the enterprise, the energy efficiency improvement plan is formulated around the technical transformation of equipment/process/energy supply system, the construction or upgrade of energy management or energy efficiency management information system, and the management optimization for energy efficiency. For the technical transformation of equipment, processes and energy supply systems, improvement plans shall be formulated from the following aspects: improvement of digital capabilities, provision of data collection and communication interfaces for equipment, processes, and energy supply systems; replacement or energy-saving transformation of high-energy-consuming equipment, such as the use of energy-saving

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