Energy efficiency improving method, device and system for iron and steel industry

Abstract

The invention provides an energy efficiency improving method, device and system for the iron and steel industry, and relates to the technical field of iron and steel resources. According to the method, comprehensive analysis is carried out on data of each dimension of the iron and steel enterprise, and a plurality of energy efficiency evaluation indexes of each dimension are calculated by adopting an energy efficiency evaluation architecture constructed by a target layer, a factor layer and a base layer. And analyzing the energy efficiency evaluation index by adopting a D-S evidence theory to obtain an energy efficiency evaluation result of the iron and steel enterprise, and formulating an energy efficiency improvement scheme of the iron and steel enterprise based on the energy efficiency evaluation result. According to the method, the data of each dimension of the iron and steel enterprise is comprehensively analyzed, so that the energy consumption of the iron and steel enterprise is comprehensively evaluated, the energy efficiency evaluation of the iron and steel enterprise is improved, and the accuracy of a scheme is improved.

Description

Technical Field

[0001] This invention relates to the field of steel resource technology, and in particular to a method, apparatus and system for improving energy efficiency in the steel industry. Background Technology

[0002] As a key energy-intensive industry, the steel sector is a fundamental raw material industry within the industrial system, holding a pivotal position. Steel enterprises should actively respond to energy conservation and emission reduction policies, starting from their own needs for cost reduction and efficiency improvement, and actively promote green transformation. In the current global context of energy conservation and carbon reduction, the analysis of energy efficiency improvement potential plays a crucial role.

[0003] In-depth data analysis and evaluation of energy usage and equipment management can help key high-energy-consuming enterprises fully understand their potential for energy efficiency improvement and identify areas for improvement. As a typical high-energy-consuming industry, the assessment of the steel industry's potential for energy efficiency improvement is particularly important.

[0004] However, current domestic methods for improving energy efficiency potential often focus on energy consumption studies within specific energy-consuming sectors, resulting in somewhat one-sided energy consumption assessments. Implementing energy efficiency improvements based on these incomplete analyses may actually increase energy consumption for steel companies. For example, energy consumption studies focusing on process flows may overlook the impact of economic performance, general equipment, and energy management on energy consumption, leading to inaccurate analysis results and hindering the implementation of energy-saving strategies. Summary of the Invention

[0005] This invention provides a method, device, and system for improving energy efficiency in the steel industry, which can comprehensively assess the energy consumption of steel enterprises and improve the accuracy of energy efficiency assessment and improvement plans for steel enterprises.

[0006] In a first aspect, the present invention provides an energy efficiency improvement method for the steel industry. The method includes: acquiring data from various dimensions of the steel enterprise to be optimized; calculating multiple energy efficiency evaluation indicators based on the data from various dimensions and a pre-constructed energy efficiency evaluation architecture; the energy efficiency evaluation architecture includes a target layer, a factor layer, and a foundation layer; the target layer includes multiple factors, and each factor in the factor layer includes multiple energy efficiency evaluation indicators from the foundation layer; calculating the energy efficiency assessment results of the steel enterprise to be optimized using the DS evidence theory based on the multiple energy efficiency evaluation indicators; and determining the energy efficiency improvement scheme for the steel enterprise to be optimized based on the energy efficiency assessment results and the multiple energy efficiency evaluation indicators.

[0007] In one possible implementation, based on multiple energy efficiency evaluation indicators, the DS evidence theory is used to calculate the energy efficiency assessment results of the steel enterprise to be optimized. This includes: constructing a basic trust allocation function using multiple energy efficiency evaluation indicators as evidence bodies for the DS evidence theory; using each energy efficiency level in the target layer as the identification framework for the basic trust function; calculating the basic trust function value corresponding to each evidence body; determining the weighted credibility of each energy efficiency level in the identification framework under each evidence body based on the basic trust function value corresponding to each evidence body and the preset weight values ​​of each evidence body; calculating a conflict factor based on the energy efficiency assessment framework and each evidence body, where the conflict factor represents the degree of conflict between different evidence bodies; obtaining the predicted probability of each energy efficiency level in the identification framework based on the weighted credibility and the conflict factor; and determining the energy efficiency assessment results of the steel enterprise to be optimized based on the predicted probabilities of each energy efficiency level in the identification framework.

[0008] In one possible implementation, before calculating the energy efficiency assessment results of the steel enterprise to be optimized based on multiple energy efficiency evaluation indicators and using the DS evidence theory, the following steps are included: obtaining the pairwise evaluation values ​​of each expert for any two evidence bodies, and the priority of each expert; the pairwise evaluation value represents the comparative evaluation result of an expert on one evidence body and the other evidence body; determining the weight coefficient of each expert based on the priority of each expert; optimizing the weight coefficient of each expert based on the Jaynes maximum entropy principle to obtain the optimized weight coefficient of each expert; determining the initial weight coefficient of each evidence body based on the pairwise evaluation values ​​of each expert for each evidence body and the optimized weight coefficient of each expert; calculating the sum of the initial weight coefficients of each evidence body; and determining the weight coefficient of each evidence body based on the initial weight coefficients of each evidence body and the sum of the initial weight coefficients of each evidence body.

[0009] In one possible implementation, the energy efficiency levels in the target layer include high potential, medium potential, and low potential; the factor layer includes: economic performance energy efficiency indicators, communication energy system energy efficiency indicators, process energy efficiency indicators, and energy management energy efficiency indicators; the basic layer of economic performance energy efficiency indicators includes energy consumption per unit output value, energy consumption per unit output, and a comparison of energy consumption per unit output; the basic layer of communication energy system energy efficiency indicators includes the energy efficiency index of the power distribution system, the energy efficiency index of the pump system, the energy efficiency index of the fan system, and the energy efficiency index of the air compressor system; the basic layer of process energy efficiency indicators includes coke... The energy efficiency index includes the following: energy efficiency index for chemical process, energy consumption per unit of product in coking process, energy efficiency index for sintering process, energy consumption per unit of product in sintering process, energy efficiency index for pelletizing process, energy consumption per unit of product in pelletizing process, energy efficiency index for blast furnace ironmaking process, energy efficiency index for blast furnace ironmaking process, energy efficiency index for converter process, energy consumption per unit of product in converter process, energy efficiency index for rolling mill process, and energy consumption per unit of product in rolling mill process. The basic layer of energy management energy efficiency indicators includes the equipment rate of energy metering instruments, the degree of energy organization construction, the degree of document management, the degree of information technology construction, and the degree of publicity and training.

[0010] In one possible implementation, the data for each dimension includes basic user information, energy management information, general energy system information, production process system information, and expert evaluation data. The expert evaluation data includes the pairwise evaluation values ​​of experts for each energy efficiency evaluation indicator and the priority of each expert.

[0011] In one possible implementation, based on the energy efficiency assessment results and multiple energy efficiency assessment indicators, an energy efficiency improvement plan for the steel enterprise to be optimized is determined, including: if the energy efficiency assessment result is at a high potential level, then the multiple energy efficiency assessment indicators are ranked to obtain a ranked energy efficiency assessment sequence; based on the ranked energy efficiency assessment sequence, high potential indicators are determined; based on the high potential indicators, factors to be optimized are determined; based on the factors to be optimized and the high potential indicators, optimization measures corresponding to each high potential indicator are determined; based on the optimization measures corresponding to each high potential indicator, an energy efficiency improvement plan for the steel enterprise to be optimized is determined.

[0012] In one possible implementation, based on the factors to be optimized and high-potential indicators, optimization measures corresponding to each high-potential indicator are determined, including: if the factors to be optimized are process energy efficiency indicators, then high-potential processes are identified based on the high-potential indicators; operating data of high-potential processes are extracted; and based on the operating data of high-potential processes, the actual energy consumption data of high-potential processes is determined; the actual energy consumption data includes unit product energy consumption, fuel consumption, and electricity consumption; based on the actual energy consumption data of high-potential processes, the reasons for high energy consumption in high-potential processes are determined; the reasons for high energy consumption include low equipment efficiency, unreasonable process flow, or energy waste; based on the reasons for high energy consumption in high-potential processes, optimization measures are determined, including optimizing the process flow, replacing energy-saving equipment, or automating precise control.

[0013] Secondly, embodiments of the present invention provide an energy efficiency improvement device for the steel industry. The device includes: a data collection module, an indicator calculation module, and a scheme formulation module. The data collection module is used to acquire data from various dimensions of the steel enterprise to be optimized. The indicator calculation module is used to calculate multiple energy efficiency evaluation indicators based on the data from each dimension and a pre-constructed energy efficiency evaluation architecture. The energy efficiency evaluation architecture includes a target layer, a factor layer, and a foundation layer. The target layer includes multiple factors, and each factor in the factor layer includes multiple energy efficiency evaluation indicators from the foundation layer. Based on the multiple energy efficiency evaluation indicators, the DS evidence theory is used to calculate the energy efficiency assessment result of the steel enterprise to be optimized. The scheme formulation module is used to determine the energy efficiency improvement scheme for the steel enterprise to be optimized based on the energy efficiency assessment result and the multiple energy efficiency evaluation indicators.

[0014] Thirdly, embodiments of the present invention provide an energy efficiency improvement system for the steel industry. The energy efficiency improvement system includes an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to perform the steps of the method as described in the first aspect and any possible implementation thereof.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the method described in the first aspect and any possible implementation thereof.

[0016] This invention provides a method, device, and system for improving energy efficiency in the steel industry. It comprehensively analyzes data from various dimensions of steel enterprises, employing an energy efficiency evaluation framework constructed with target, factor, and foundation layers to calculate multiple energy efficiency evaluation indicators for each dimension. Then, using the DS evidence theory, it analyzes the energy efficiency evaluation indicators to obtain the energy efficiency assessment results for the steel enterprises. Based on these results, it formulates energy efficiency improvement plans for the steel enterprises. This invention achieves a comprehensive assessment of the energy consumption of steel enterprises through comprehensive analysis of data from various dimensions, improving the accuracy of energy efficiency assessments and improvement plans. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an energy efficiency improvement method for the steel industry provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of an energy efficiency improvement device for the steel industry provided in an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0022] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0023] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0024] To make the objectives, technical solutions, and advantages of the present invention clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0025] like Figure 1 As shown, this embodiment of the invention provides a method for improving energy efficiency in the steel industry. The method includes steps S101-S104.

[0026] S101. Obtain data from various dimensions of the steel enterprise to be optimized.

[0027] In some embodiments, the data for each dimension includes basic user information, energy management information, general energy system information, production process system information, and expert evaluation data. The expert evaluation data includes the pairwise evaluation values ​​of experts for each energy efficiency evaluation indicator and the priority of each expert.

[0028] For example, embodiments of the present invention can be obtained through online digital system platforms such as the State Grid Corporation of China (SGCC) system, the State Grid Corporation of China (SGCC) website, and the State Grid Corporation of China (SGCC) website, or offline (front-end staff) collection, including basic user information, energy management information, general energy system information, and production process system information.

[0029] In some embodiments, the data in each dimension also includes enterprise organizational structure information, product capacity information, energy management status, production process equipment and raw materials, general equipment and systems, energy statistics and measurement, energy consumption and energy balance, major energy consumption indicators, and the application and effects of energy-saving technologies.

[0030] S102. Based on data from various dimensions and a pre-built energy efficiency evaluation framework, calculate multiple energy efficiency evaluation indicators.

[0031] In this embodiment of the application, the energy efficiency evaluation architecture includes a target layer, a factor layer, and a basic layer; the target layer includes multiple factors, and each factor in the factor layer includes multiple energy efficiency evaluation indicators in the basic layer.

[0032] In some embodiments, the energy efficiency levels in the target tier include high potential, medium potential, and low potential.

[0033] In some embodiments, the factor layer includes: economic performance energy efficiency index, communication energy system energy efficiency index, process energy efficiency index and energy management energy efficiency index;

[0034] For example, the basic layer of economic performance energy efficiency indicators includes energy consumption per unit of output value, energy consumption per unit of output, and a comparison of energy consumption per unit of output.

[0035] Wherein, energy consumption per unit of output is the ratio of total energy consumption to total output, representing the amount of energy consumed per unit of output, denoted as R. 1,1 .

[0036] Energy consumption per unit of output is the ratio of total energy consumption to total output, representing the amount of energy consumed per unit of product output, denoted as R. 1,2 .

[0037] Energy consumption per unit of output is the ratio of a company's energy consumption per unit of output to the industry average. It compares a company's energy consumption per unit of output with the industry average to assess energy efficiency, and is denoted as R. 1,3 .

[0038] For example, the basic layer of energy efficiency indicators for communication energy systems includes the energy efficiency index of power distribution systems, the energy efficiency index of pump systems, the energy efficiency index of fan systems, and the energy efficiency index of air compressor systems.

[0039] For example, the energy efficiency index of a general energy system is expressed by the following formula:

[0040]

[0041] Among them, R 2,i Let T be the energy efficiency index of the i-th general energy system; i = {1, 2, 3, 4} represent the power distribution system, pump system, fan system, and air compressor system, respectively; iS represents the total score of the energy efficiency diagnostic standard for the i-th general-purpose energy system; i Let be the actual total score for the energy efficiency diagnosis of the i-th general energy system.

[0042] It should be noted that the total score for the General Energy System Energy Efficiency Diagnostic Standard is 100 points. The actual total score for the General Energy System Energy Efficiency Diagnostic Standard is obtained based on the "General Energy System Assessment Table." The specific content of the "General Energy System Assessment Table" is shown below.

[0043] The scoring sources for the power distribution system are: whether the operation plan is adjusted according to the load / production plan; whether the transformer is regularly maintained; whether the transformer operating load rate (=the company's monthly electricity consumption / average monthly operating hours / total transformer capacity) is between 60% and 80%; whether an intelligent control system is configured; and whether the operating voltage is reasonable.

[0044] The scoring sources for the pump system are: whether the pump system has undergone energy efficiency assessment within the past five years; whether the pump system is regularly maintained; the proportion of pumps with variable frequency control (= number of variable frequency units / total number of units); the proportion of pump motors with first-level energy efficiency (or equivalent energy efficiency level) (first-level energy efficiency (or equivalent energy efficiency level) motors / total number of units); and whether the non-supply water flow branches in the pump system are shut off in a timely manner.

[0045] The scoring criteria for the wind turbine system are: whether the wind turbine system has undergone energy efficiency assessment within the past five years; whether the wind turbine system is regularly maintained; the proportion of wind turbines with variable frequency control (= number of variable frequency units / total number of units); whether the medium transported by the wind turbine system is clean; and the proportion of wind turbines equipped with intelligent control systems (= number of units equipped with intelligent control systems / total number of units).

[0046] The scoring criteria for air compressor systems include: whether the compressor operates at a high load rate (70%-80%); whether the air compressor system is equipped with an intelligent group control system and achieves coordinated control of multiple units; whether the air compressor system is equipped with heat recovery; the proportion of compressors using frequency conversion control (= number of frequency conversion units / total number of units); the proportion of compressors using Level 1 energy efficiency (or equivalent) equipment (= Level 1 energy efficiency (or equivalent) motors / total number of units); whether the air compressor system's supply pressure is 10% higher than the demand pressure; and whether the air compressor system implements leakage management.

[0047] In this embodiment of the invention, the actual total score of each general energy system can be determined based on the scoring source of each general energy system, namely the "General Energy System Evaluation Table".

[0048] For example, the basic layer of process energy efficiency indicators includes the coking process energy efficiency index, the unit energy consumption of coking process products, the sintering process energy efficiency index, the unit energy consumption of sintering process products, the pelletizing process energy efficiency index, the unit energy consumption of pelletizing process products, the blast furnace ironmaking process energy efficiency index, the unit energy consumption of blast furnace ironmaking process products, the converter process energy efficiency index, the unit energy consumption of converter process products, the steel rolling process energy efficiency index, and the steel rolling process product unit energy consumption.

[0049] For example, the calculation model for the unit energy consumption of each process in the process energy efficiency index is as follows:

[0050]

[0051] Among them, R 3,j Let be the unit energy consumption of the product in the j-th process flow; j = {1, 2, 3, 4, 5, 6} represent the coking process, sintering process, pelletizing process, blast furnace ironmaking process, converter process, and steel rolling process, respectively. and These represent the actual fuel and electricity consumption in the j-th process flow, respectively; PRO j Let be the product output of the j-th process flow.

[0052] For example, the product of the coking process is coke, the product of the sintering process is sintered ore, the product of the pelletizing process is pelletized ore, the product of the blast furnace ironmaking process is steel billet, the product of the converter process is steel billet, and the product of the rolling process is finished steel.

[0053] The calculation model for the energy efficiency index of each process in the process energy efficiency index is as follows:

[0054]

[0055] Among them, R 3,j+6 Let be the energy efficiency index of the j-th process flow; The advanced value of unit product energy consumption for the j-th process flow; This is the unit product energy consumption limit for the j-th process flow.

[0056] The advanced values ​​and limit values ​​of unit product energy consumption for each process flow are the energy efficiency standards for the steel industry, as shown in Table 1. Table 1 is a benchmarking analysis table for the steel industry. By comparing the actual energy consumption indicators of each process flow with the standard indicator values, the key processes of the enterprise to be evaluated can be diagnosed, and it can be determined whether there is potential for improvement in a certain process of the enterprise.

[0057] Table 1

[0058]

[0059] For example, the basic layer of energy management efficiency indicators includes the energy metering equipment coverage rate, the degree of energy organization construction, the degree of document management, the degree of information technology construction, and the degree of publicity and training.

[0060] For example, the calculation model for energy management level related indicators is expressed by the following formula:

[0061]

[0062] Among them, R 4,k Let Q be the value of the k-th energy management level indicator; k = {1, 2, 3, 4} represents the degree of energy organization construction, document management, information technology construction, and publicity and training, respectively; k The score for the kth energy management level indicator; This represents the standard score for the k-th energy management level indicator.

[0063] The calculation model for the energy metering instrument coverage rate is expressed by the following formula:

[0064]

[0065] Among them, R 4,5 Energy metering equipment coverage rate; N act N represents the actual number of energy metering devices installed. the This represents the theoretical requirement for energy metering instruments.

[0066] For example, energy metering instruments include scales, electricity meters, oil flow meters, gas flow meters, water flow meters, thermometers, pressure gauges, etc.

[0067] Among them, the following instruments are used: Weighing instruments: for static and dynamic measurement of fuel entering and leaving energy-consuming units. Electricity meters: for AC power measurement of Class IV users and DC power measurement of energy-consuming units. Oil flow meters: for oil entering and leaving energy-consuming units (gasoline, diesel, heavy oil). Gas flow meters: for gas entering and leaving energy-consuming units (coal gas, natural gas, steam). Water flow meters: for water entering and leaving energy-consuming units (pipe diameter <= 250mm, pipe diameter > 250mm). Thermometers: for temperature measurement of liquid and gaseous energy; temperature measurement related to gas and steam mass calculation. Pressure gauges: for pressure measurement of gaseous and liquid energy; pressure measurement related to gas and steam mass calculation.

[0068] The standard scores for energy management level indicators can be set at 21%, 21%, 48%, and 10% respectively. For example, assuming the total score for energy management level is 100 points, the standard scores for each energy management level indicator would be 21 points, 21 points, 48 ​​points, and 10 points respectively.

[0069] S103. Based on multiple energy efficiency evaluation indicators, the DS evidence theory is used to calculate the energy efficiency assessment results of the steel enterprise to be optimized.

[0070] As one possible implementation, step S103 can be specifically implemented as steps S1031-S1036.

[0071] S1031. Using multiple energy efficiency evaluation indicators as evidence for the DS evidence theory, a basic trust allocation function is constructed.

[0072] In some embodiments, the basic trust function uses the energy efficiency levels in the target layer as the identification framework.

[0073] In some embodiments, the basic trust function can be expressed as the following formula.

[0074] A standardized framework for identifying assessment targets: Θ = {high potential, medium potential, low potential} = {A1, A2, A3};

[0075] R1~R4, R 1,1 ~R 1,3 R 2,1 ~R 2,4 R 3，1 ~R 3,12 R 4,1 ~R 4,5 All evaluation indicators are used as evidence to represent uncertainty.

[0076] Basic Trust Assignment Function (BPA) mass:2 Θ →[0,1], satisfying:

[0077]

[0078] Where, m j (A i () is the j-th piece of evidence on Θ, under A i The mass function represents the focus of each piece of evidence on element A. i The degree of trust; φ represents the empty set.

[0079] S1032. Calculate the basic trust function value corresponding to each piece of evidence.

[0080] For example, in embodiments of the present invention, the basic trust function value corresponding to each piece of evidence can be calculated based on the calculation formula of each energy efficiency evaluation index.

[0081] S1033. Based on the basic trust function value corresponding to each piece of evidence and the preset weight value of each piece of evidence, determine the weighted credibility of each energy efficiency level in the identification framework under each piece of evidence.

[0082] For example, embodiments of the present invention can determine the weighted credibility based on the following formula: The weights of each piece of evidence are calculated using the Moody's chart method, which optimizes weights based on the Jaynes maximum entropy principle, and the BPA of each piece of evidence is adjusted accordingly.

[0083]

[0084] in, Indicates that under the weighted and adjusted j-th piece of evidence, A i The credibility of m is the weighted credibility. j (A i For identifying the j-th piece of evidence A on the framework Θ i The mass function, also known as the basic trust function value; ω j Let be the weight value of the j-th piece of evidence.

[0085] S1034. Based on the energy efficiency assessment framework and each piece of evidence, calculate the conflict factor.

[0086] In some embodiments, the conflict factor represents the degree of conflict between different pieces of evidence.

[0087] For example, embodiments of the present invention can calculate the conflict factor based on the following formula. The conflict factor reflects the degree of conflict between different pieces of evidence, and the specific calculation method is as follows:

[0088]

[0089] Where K is the conflict factor. For the j-th piece of evidence, A i Weighted credibility Indicates taking A i The intersection of sets that are not empty is included in the calculation, such as when j=1, A i Let A1 be an empty set. The intersection of A1 and A1 is equal to the intersection of A1 and A1 being not an empty set. Then, the intersections of A2 and A3 with A1 are both empty sets. ∩A i =φ means taking the AND with A i The opposite of the above examples is when the intersection is an empty set.

[0090] S1035. Based on weighted confidence and conflict factor, the predicted probability of each energy efficiency level in the identification framework is obtained.

[0091] For example, embodiments of the present invention can utilize DS synthesis rules for step-by-step fusion.

[0092]

[0093] Where, m ω (A i) represents the confidence level when a certain level indicator is fused to the previous level indicator. Based on the above synthesis rules, the fusion proceeds step by step from the base layer to the factor layer and then to the target layer. Let the final fusion result obtained from the factor layer be [m]. ω (A i )], i=1,2,3, which is the probability of each energy efficiency level supported by the evidence.

[0094] For example, m ω (A1) represents the probability of having a high potential energy efficiency rating, m ω (A2) represents the probability that the energy efficiency level is of medium potential, m ω (A3) indicates the probability that the energy efficiency level is low potential.

[0095] For example, in this embodiment of the invention, the energy efficiency improvement potential is determined based on the principle of maximizing probability, that is, the final energy efficiency improvement potential P is P = argmax{m ω (A i ), i = 1, 2, 3}.

[0096] Where, argmax{m ω (A i )} represents the level A corresponding to the probability reaching its maximum value. i .

[0097] S1036. Based on the predicted probabilities of each energy efficiency level in the identification framework, determine the energy efficiency assessment results of the steel enterprise to be optimized.

[0098] S104. Based on the energy efficiency assessment results and multiple energy efficiency assessment indicators, determine the energy efficiency improvement plan for steel enterprises to be optimized.

[0099] As one possible implementation, step S104 can be specifically implemented as steps S1041-S1045.

[0100] S1041. If the energy efficiency assessment result is at the high potential level, then the multiple energy efficiency assessment indicators are sorted to obtain the sorted energy efficiency assessment sequence.

[0101] It should be noted that a high potential level indicates that steel companies have high energy consumption and significant potential for energy efficiency improvement. Energy consumption optimization and energy-saving measures, i.e., energy efficiency improvement plans, can be developed for steel companies with high potential levels.

[0102] For example, all energy efficiency assessment indicators (such as energy consumption per unit of output, power distribution system energy efficiency index, etc.) are quantitatively scored based on their gap with industry standards or best practices. Using a reasonable ranking algorithm (such as a weighted total score method based on weights), these indicators are arranged from highest to lowest score, forming an energy efficiency assessment sequence. A higher score indicates greater potential for energy efficiency improvement under that indicator.

[0103] S1042. Based on the sorted energy efficiency assessment sequence, identify high-potential indicators.

[0104] For example, embodiments of the present invention may select the top few highest-scoring indicators as "high-potential indicators." These indicators represent opportunities to achieve maximum energy savings under current business conditions through improvement. A threshold may be set (e.g., a score higher than a certain value), and only indicators exceeding this threshold are considered high-potential indicators.

[0105] S1043. Based on high-potential indicators, identify the factors to be optimized.

[0106] For example, embodiments of the present invention can analyze the factor layer (such as economic performance, general equipment, process flow, energy management) to which each high-potential indicator belongs, thereby determining which aspects require key attention and optimization. For instance, if the high-potential indicators are concentrated in "process energy efficiency indicators," it indicates that there is significant energy-saving potential in certain processes during steel production, requiring further analysis of specific process steps.

[0107] S1044. Based on the factors to be optimized and the high-potential indicators, determine the optimization measures corresponding to each high-potential indicator.

[0108] For example, step S1044 can be specifically implemented as steps A1-A6.

[0109] A1. If the factor to be optimized is the process energy efficiency index, then the high-potential process is determined based on the high-potential index.

[0110] For example, embodiments of the present invention can identify specific high-energy-consuming processes (such as coking processes, sintering processes, etc.) based on high-potential indicators and mark them for subsequent in-depth analysis.

[0111] A2. Extract operational data from high-potential processes.

[0112] For example, the present invention can collect historical operating data of selected processes, including but not limited to information such as production volume, raw material usage, and equipment operating status.

[0113] A3. Based on the operational data of high-potential processes, determine the actual energy consumption data of high-potential processes.

[0114] In some embodiments, actual energy consumption data include energy consumption per unit product, fuel consumption, and electricity consumption.

[0115] For example, the present invention can calculate actual energy consumption data such as unit product energy consumption, fuel consumption, and electricity consumption for selected processes in order to accurately understand the current energy consumption level.

[0116] A4. Based on the actual energy consumption data of high-potential processes, determine the reasons for the high energy consumption of high-potential processes.

[0117] In some embodiments, high energy consumption may be caused by low equipment efficiency, unreasonable process flow, or energy waste.

[0118] For example, the present invention can analyze energy consumption data to identify the main causes of high energy consumption, such as low equipment efficiency, unreasonable process design, or obvious energy waste.

[0119] A5. Based on the reasons for the high energy consumption of high-potential processes, determine optimization measures.

[0120] In some embodiments, optimization measures include optimizing the process flow, replacing energy-efficient equipment, or automating precise control.

[0121] For example, the present invention can formulate specific optimization measures for the problems found. For instance, if the problem is equipment efficiency, consider replacing it with high-efficiency and energy-saving equipment; if the process flow is unreasonable, redesign or adjust the existing process; and for cases of energy waste, introduce an automated control system to achieve precise control and reduce unnecessary energy consumption.

[0122] S1045. Based on the optimization measures corresponding to each high-potential indicator, determine the energy efficiency improvement plan for the steel enterprise to be optimized.

[0123] For example, this invention can integrate all optimization measures developed for different high-potential indicators to form a comprehensive and systematic energy efficiency improvement plan. The plan should include a detailed implementation plan, expected goals, cost-benefit analysis, and a tracking and monitoring mechanism to ensure that all measures are successfully implemented and achieve the expected energy-saving effects. The plan should be reviewed and updated regularly, and strategies adjusted according to actual conditions to continuously improve the company's energy efficiency management level.

[0124] This invention provides an energy efficiency improvement method for the steel industry. It comprehensively analyzes data from various dimensions of steel enterprises, employing an energy efficiency evaluation framework constructed with target, factor, and foundation layers to calculate multiple energy efficiency evaluation indicators for each dimension. Then, using DS evidence theory, it analyzes the energy efficiency evaluation indicators to obtain the energy efficiency assessment results for steel enterprises. Based on these results, it formulates energy efficiency improvement plans for steel enterprises. This invention achieves a comprehensive assessment of energy consumption in steel enterprises through comprehensive analysis of data from various dimensions, improving the accuracy of energy efficiency assessments and improvement plans.

[0125] Optionally, the energy efficiency improvement method for the steel industry provided in this embodiment of the invention further includes steps S201-S206 before step S103.

[0126] S201. Obtain the pairwise evaluation values ​​of each expert for any two pieces of evidence in each body of evidence, as well as the priority of each expert.

[0127] In some embodiments, pairwise evaluation values ​​represent an expert's comparative evaluation of one of any two pieces of evidence against the other.

[0128] S202. Determine the weight coefficient of each expert based on their priority.

[0129] S203. Based on Jaynes' maximum entropy principle, the weight coefficients of each expert are optimized to obtain the optimized weight coefficients of each expert.

[0130] S204. Based on the pairwise evaluation values ​​of each expert for each piece of evidence, and the optimized weight coefficients of each expert, determine the initial weight coefficients for each piece of evidence.

[0131] In some embodiments, the present invention can determine the number of indicators, denoted as Q, according to the specific calculation requirements of the indicator weights at each level during the stepwise fusion process from the basic layer to the factor layer and then to the target layer in the improved DS evidence theory; and calculate the expert's weight coefficient for different evaluation indicators, denoted as n, where the number of experts participating in the decision-making is n.

[0132] The power coefficient is determined based on Jaynes' maximum entropy principle: Assume n experts have a priority sequence: z1>z2>…>z n Expert Z j The weighting coefficient is q j That is, q1≥q2≥…≥q n This is valid, and to avoid extreme values ​​in the expert scoring process of the Moody's chart method, we consider using a non-dictatorial weighting condition to improve the scientific nature of the weighting, namely: the preceding q j It cannot be greater than the q that follows. j The sum. Optimizing the weights using Jaynes' maximum entropy principle, we have:

[0133]

[0134] This allows us to obtain the values ​​of n power coefficients.

[0135] S205. Calculate the sum of the initial weight coefficients for each piece of evidence.

[0136] S206. Based on the initial weight coefficients of each piece of evidence and the sum of the initial weight coefficients of each piece of evidence, determine the weight coefficients of each piece of evidence.

[0137] For example, the Moody's chart method is used to obtain the indicator weights: all indicators are compared pairwise in terms of importance, with those considered more important being assigned 1, and others 0. The sum of the weight coefficients for each indicator is obtained by combining the evaluation results of each expert.

[0138]

[0139] Among them, W i H represents the sum of the weight coefficients of the i-th indicator; (i,k),j This represents the evaluation result of the j-th expert on the comparison between the i-th and k-th indicators, i.e., the pairwise evaluation value, which takes the value of 1 or 0.

[0140] The sum of the weight coefficients for all indicators is:

[0141] Therefore, the weight of each indicator is: ω i =W i / W total That is, the weighting coefficient of each piece of evidence.

[0142] This invention analyzes energy usage in steel enterprises from four dimensions: economic performance, general equipment, process flow, and energy management. It aims to comprehensively understand the energy usage of steel enterprises, identify potential energy-saving opportunities, and formulate comprehensive energy-saving strategies that take into account both comprehensiveness and holistic approach.

[0143] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0144] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0145] Figure 2 This diagram illustrates the structure of an energy efficiency improvement device for the steel industry, as provided in an embodiment of the present invention. The energy efficiency improvement device 300 includes a data collection module 301, an index calculation module 302, and a scheme formulation module 303.

[0146] The data collection module 301 is used to acquire data from various dimensions of the steel enterprise to be optimized.

[0147] The indicator calculation module 302 is used to calculate multiple energy efficiency evaluation indicators based on data from various dimensions and a pre-built energy efficiency evaluation architecture. The energy efficiency evaluation architecture includes a target layer, a factor layer, and a basic layer. The target layer includes multiple factors, and each factor in the factor layer includes multiple energy efficiency evaluation indicators from the basic layer. Based on multiple energy efficiency evaluation indicators, the DS evidence theory is used to calculate the energy efficiency assessment results of the steel enterprise to be optimized.

[0148] The solution development module 303 is used to determine the energy efficiency improvement plan for steel enterprises to be optimized based on the energy efficiency assessment results and multiple energy efficiency assessment indicators.

[0149] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 400 includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, it implements the steps in the above-described method embodiments, for example... Figure 1 The steps S101-S104 are shown. Alternatively, when the processor 401 executes the computer program 403, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of the data collection module 301, the indicator calculation module 302, and the scheme formulation module 303 are shown.

[0150] For example, the computer program 403 can be divided into one or more modules / units, which are stored in the memory 402 and executed by the processor 401 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 403 in the electronic device 400. For example, the computer program 403 can be divided into... Figure 2 The data collection module 301, the indicator calculation module 302, and the scheme formulation module 303 are shown.

[0151] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for improving energy efficiency in the steel industry, characterized in that, include: Obtain data from various dimensions of steel enterprises that need optimization; Based on data from various dimensions and a pre-built energy efficiency evaluation framework, multiple energy efficiency evaluation indicators are calculated. The energy efficiency evaluation framework includes a target layer, a factor layer, and a basic layer. The target layer includes multiple factors, and each factor in the factor layer includes multiple energy efficiency evaluation indicators from the basic layer. Based on the aforementioned multiple energy efficiency evaluation indicators, the DS evidence theory is used to calculate the energy efficiency assessment results of the steel enterprise to be optimized. Based on the energy efficiency assessment results and the multiple energy efficiency assessment indicators, an energy efficiency improvement plan for the steel enterprises to be optimized is determined.

2. The energy efficiency improvement method for the steel industry according to claim 1, characterized in that, Based on the aforementioned multiple energy efficiency evaluation indicators, and using the DS evidence theory, the energy efficiency assessment results for the steel enterprise to be optimized are calculated, including: A basic trust assignment function is constructed using multiple energy efficiency evaluation indicators as evidence bodies for the DS evidence theory; the basic trust function uses each energy efficiency level in the target layer as the identification framework. Calculate the basic trust function value corresponding to each piece of evidence; Based on the basic trust function value corresponding to each piece of evidence and the preset weight value of each piece of evidence, the weighted credibility of each energy efficiency level in the identification framework under each piece of evidence is determined. Based on the energy efficiency assessment framework and each piece of evidence, a conflict factor is calculated, which represents the degree of conflict between different pieces of evidence. Based on the weighted confidence level and the conflict factor, the predicted probability of each energy efficiency level in the identification framework is obtained. Based on the predicted probabilities of each energy efficiency level in the identification framework, the energy efficiency assessment results of the steel enterprises to be optimized are determined.

3. The energy efficiency improvement method for the steel industry according to claim 1, characterized in that, Before calculating the energy efficiency assessment results of the steel enterprise to be optimized based on the multiple energy efficiency evaluation indicators and using the DS evidence theory, the following steps are also included: Obtain the pairwise evaluation values ​​of each expert for any two pieces of evidence in each body of evidence, as well as the priority of each expert; the pairwise evaluation values ​​represent the comparative evaluation results of an expert on one piece of evidence and the other piece of evidence in any two bodies of evidence. Based on the priority of each expert, the weight coefficient of each expert is determined; Based on Jaynes' maximum entropy principle, the weight coefficients of each expert are optimized to obtain the optimized weight coefficients of each expert. Based on the pairwise evaluation values ​​of each expert for each piece of evidence, and the optimized weighting coefficients of each expert, the initial weighting coefficients of each piece of evidence are determined. Calculate the sum of the initial weight coefficients for each piece of evidence; The weight coefficient of each piece of evidence is determined based on the initial weight coefficient of each piece of evidence and the sum of the initial weight coefficients of each piece of evidence.

4. The energy efficiency improvement method for the steel industry according to claim 1, characterized in that, The target layer includes energy efficiency levels of high potential, medium potential, and low potential. The factor layer includes: economic performance energy efficiency indicators, communication energy system energy efficiency indicators, process energy efficiency indicators, and energy management energy efficiency indicators. The foundation layer of the economic performance energy efficiency indicators includes energy consumption per unit output value, energy consumption per unit output, and a comparison of energy consumption per unit output. The foundation layer of the communication energy system energy efficiency indicators includes the power distribution system energy efficiency index, pump system energy efficiency index, fan system energy efficiency index, and air compressor system energy efficiency index. The foundation layer of the process energy efficiency indicators includes coking chemical industry... The energy efficiency index includes the following: energy efficiency index, unit energy consumption of coking process products, energy efficiency index of sintering process products, energy efficiency index of pelletizing process products, energy efficiency index of blast furnace ironmaking process products, energy efficiency index of converter process products, energy efficiency index of rolling mill process products, and energy consumption of rolling mill process products. The basic layer of the energy management energy efficiency index includes the energy metering instrument equipment rate, the degree of energy organization construction, the degree of document management, the degree of information technology construction, and the degree of publicity and training.

5. The energy efficiency improvement method for the steel industry according to claim 1, characterized in that, The data in each dimension includes basic user information, energy management information, general energy system information, production process system information, and expert evaluation data. The expert evaluation data includes the pairwise evaluation values ​​of experts for each energy efficiency evaluation indicator and the priority of each expert.

6. The energy efficiency improvement method for the steel industry according to claim 1, characterized in that, The process of determining energy efficiency improvement plans for steel enterprises to be optimized based on the energy efficiency assessment results and the multiple energy efficiency assessment indicators includes: If the energy efficiency assessment result is at the high potential level, then the multiple energy efficiency assessment indicators are sorted to obtain the sorted energy efficiency assessment sequence. Based on the sorted energy efficiency assessment sequence, high-potential indicators are identified. Based on high-potential indicators, factors to be optimized were identified; Based on the factors to be optimized and the high-potential indicators, the optimization measures corresponding to each high-potential indicator are determined. Based on the optimization measures corresponding to each high-potential indicator, energy efficiency improvement plans for steel enterprises to be optimized are determined.

7. The energy efficiency improvement method for the steel industry according to claim 6, characterized in that, The step of determining the optimization measures corresponding to each high-potential indicator based on the factors to be optimized and the high-potential indicators includes: If the factor to be optimized is the process energy efficiency index, then high-potential processes are identified based on high-potential indicators. Extract operational data from high-potential processes; Based on the operational data of high-potential processes, the actual energy consumption data of high-potential processes are determined; the actual energy consumption data includes energy consumption per unit product, fuel consumption, and electricity consumption. Based on the actual energy consumption data of high-potential processes, the reasons for high energy consumption in high-potential processes are determined; the reasons for high energy consumption include low equipment efficiency, unreasonable process flow, or energy waste. Based on the reasons for the high energy consumption of high-potential processes, optimization measures are determined, including optimizing the process flow, replacing energy-saving equipment, or automating precise control.

8. An energy efficiency improvement device for the steel industry, characterized in that, include: The data collection module is used to acquire data from various dimensions of the steel enterprise to be optimized; The indicator calculation module is used to calculate multiple energy efficiency evaluation indicators based on data from various dimensions and a pre-built energy efficiency evaluation architecture. The energy efficiency evaluation architecture includes a target layer, a factor layer, and a basic layer. The target layer includes multiple factors, and each factor in the factor layer includes multiple energy efficiency evaluation indicators from the basic layer. Based on the multiple energy efficiency evaluation indicators, the DS evidence theory is used to calculate the energy efficiency assessment results of the steel enterprise to be optimized. The solution formulation module is used to determine the energy efficiency improvement plan for the steel enterprise to be optimized based on the energy efficiency assessment results and the multiple energy efficiency assessment indicators.

9. An energy efficiency improvement system for the steel industry, characterized in that, The energy efficiency improvement system includes an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor is used to execute the steps of the method as described in any one of claims 1 to 7 when it calls and runs the computer program stored in the memory.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7 above.

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