An Energy Efficiency Root Cause Analysis and Optimization Method for the Thermal System of an Iron and Steel Plant

Abstract

A method for root cause analysis and optimization of the energy efficiency of the thermal system in a steel plant, which solves the problem that the existing thermal systems in the steel industry lack a model that can truly implement energy efficiency analysis, and it is not clear how to apply the existing process mathematical models, resulting in an unsatisfactory actual implementation effect of the energy efficiency analysis work in steel enterprises. This method for root cause analysis and optimization of the energy efficiency of the thermal system in a steel plant hierarchically classifies the energy efficiency problems of the thermal system in the steel plant, establishes energy efficiency models for each level of the thermal system in the steel plant, and establishes an evaluation system; and establishes root cause analysis models for the energy efficiency of each level of the thermal system, provides optimization strategies, and then conducts standardized management of the energy efficiency indicators of the steel plant, conducts root cause analysis on the situations of high energy consumption and low energy efficiency, diagnoses the influencing factors of energy consumption, and gives improvement suggestions to guide the enterprise to use energy efficiently and operate optimally.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial artificial intelligence for metallurgical automation, and specifically relates to a method for optimizing the energy efficiency root cause analysis of a thermal system in a steel plant, which can standardize the management of energy efficiency indicators in a steel plant, perform root cause analysis on high energy consumption and low energy efficiency, diagnose factors affecting energy consumption, and provide improvement suggestions, thereby guiding enterprises to efficiently utilize energy and optimize operation. Background Art

[0002] The production process of steel enterprises requires a large amount of energy, and there are many types of energy; the main energy sources are coal, fuel oil, light diesel, natural gas, liquefied petroleum gas, coal gas, steam, electricity, compressed air, etc. In addition, each type of energy can be further divided according to its quality. For example, coal includes bituminous coal, anthracite, coking coal, etc.; coal gas can be divided into blast furnace gas, coke oven gas, and converter gas; steam is divided into high-pressure steam, medium-pressure steam, and low-pressure steam. At the same time, various types of energy are used in the entire process of steel enterprises, involving branches including coking plants, sintering plants, pellet plants, ironmaking plants, steelmaking plants, rolling mills, power plants, power plants, lime plants, etc., and the main energy-consuming equipment of each branch plant includes coke ovens, sintering ignition furnaces, blast furnaces, hot blast furnaces, converters, heating furnaces, boilers, lime kilns, etc.

[0003] At present, in order to make better use of the above energy in the whole process, steel mills have built various management systems, such as resource planning system (ERP), energy management system (EMS), manufacturing execution system (MES), etc. However, due to the lack of effective energy efficiency management methods, energy consumption has always been a difficult problem for steel companies; how to quickly find high energy consumption points, analyze the factors affecting energy consumption, and give optimization suggestions have become the focus of long-term attention of steel mills. There are many existing thermal system process mathematical models in the steel industry, but the models that can truly implement energy efficiency analysis and how to apply them are not clear, which leads to the urgent need for energy efficiency analysis in steel companies, but the actual implementation effect of various companies is not ideal. Therefore, it is necessary to improve the existing thermal system energy efficiency root cause analysis and optimization methods that guide the energy utilization and optimized operation of enterprises. Summary of the invention

[0004] The present invention aims to solve the above-mentioned problems and provides a method for optimizing the energy efficiency root cause analysis of the thermal system of a steel plant, which can standardize the management of the energy efficiency indicators of the steel plant, perform root cause analysis on high energy consumption and low energy efficiency, diagnose factors affecting energy consumption, and give improvement suggestions, so as to guide the enterprise to efficiently utilize energy and optimize operation.

[0005] The technical solution adopted by the present invention is: the root cause analysis and optimization method of the energy efficiency of the thermal system of the steel plant comprises the following steps:

[0006] Step 1: Classify the energy efficiency problems of the thermal system in the steel plant into the following categories in sequence: process energy efficiency problems, equipment energy efficiency problems, and energy data problems;

[0007] Step 2: Establish a process energy efficiency model with the standard coal consumption per ton of product as the target;

[0008] Step 3: Establish an equipment energy efficiency model with the thermal efficiency as the target;

[0009] Step 4: Establish an energy data model with the energy value or index as the target;

[0010] Step 5: Conduct a root cause analysis of the energy efficiency of the thermal system at each level, including: root cause analysis of energy efficiency at the process level, root cause analysis of energy efficiency at the equipment level, and root cause analysis of energy efficiency at the energy data level;

[0011] Step 6: Establish a root cause analysis model for energy efficiency at the process level, find the areas or equipment causing excessive energy consumption, and determine the energy categories of the excess;

[0012] Step 7: Establish a root cause analysis model for energy efficiency at the equipment level, determine the items with high heat, and further determine the corresponding production operations;

[0013] Step 8: Establish a root cause analysis model for energy efficiency at the energy data level, find the corresponding factors and associated factors causing excessive energy consumption, and feedback and implement improvement suggestions.

[0014] In Step 2, the established process energy efficiency model of the thermal system is shown in Equation (1). Modeling is implemented with the standard coal consumption per unit product at the process level as the target. This model covers various types of energy and energy qualities used in the process, and comprehensively analyzes the energy consumption of various types of energy for producing a unit product in the process;

[0015]

[0016] In the formula: e represents the energy consumption per ton of product of this thermal system;

[0017] Q 能源 represents the total standard coal consumption of the consumed energy;

[0018] P 产品 represents the product output of this process;

[0019] V i 、M j respectively represent the gas energy and solid energy quantities;

[0020] H i 、D j respectively represent the lower calorific values of gas energy and solid energy.

[0021] In Step 3, the energy efficiency model of the thermal engineering system equipment is shown in Equation (2), which combines the effective energy of the furnace, the effective energy of the heat exchange system, and the effective energy of the energy conversion system to ensure comprehensive consideration of the effective utilization of energy.

[0022]

[0023] In the formula: η represents the efficiency of the thermal engineering system of the equipment, which is expressed as the proportion of effective heat here;

[0024] Q 有效 and Q 输入 represent the effective energy entering the thermal engineering system and the total energy input into the system;

[0025] Q 炉 and Q 换热 and Q 转换 represent the effective energy of the furnace equipment, the heat exchange system, and the energy conversion system respectively.

[0026] In Step 4, in the energy efficiency problem of the thermal engineering system, the energy data problem can be divided into two categories. One is the problem of the quantity of energy itself, and the other is the accounting index corresponding to energy production and consumption. Taking the specific energy consumption of energy as an example, the specific energy consumption represents the amount of a certain type of energy consumed per unit product in the process. The established energy data calculation model is shown in Equation (3):

[0027]

[0028] In the formula: g i represents the specific energy consumption corresponding to the consumption of the i-th type of fuel.

[0029] In Step 6, through energy efficiency analysis, diagnose that the process energy consumption exceeds the standard, deduce the area of excessive energy consumption and the corresponding equipment, and output the analysis and diagnosis results and maintenance suggestions; based on the enterprise's production historical data, obtain the benchmark values of the gas energy and solid energy consumption in the energy consumption area as the standard for evaluating energy consumption; and design the energy efficiency root cause analysis rules at the process level:

[0030] ① Design the benchmark energy consumption, including the process benchmark energy consumption E 0 , the benchmark values of various energy consumptions the benchmark value of the total energy consumption in area k the benchmark values of gas and solid energy consumption in each area

[0031] ② Calculate the actual values of the energy consumption of the process, area, and various types of energy;

[0032] ③ Compare the actual energy consumption of the process, the energy consumption of various types of energy, and the energy consumption of the area with the corresponding benchmark values in turn to analyze and diagnose the energy efficiency level of each layer.

[0033] In Step 6, the total model of energy efficiency root cause analysis at the process level:

[0034]

[0035] Energy consumption model of the energy-consuming area k:

[0036]

[0037] The model of the consumption of the i-th type of gas energy in this process:

[0038]

[0039] The model of the consumption of the j-th type of solid energy in this process:

[0040]

[0041] In the formula: E represents the total energy consumption of this process;

[0042] E i and E j represent the consumption of the i-th gas fuel and the j-th solid fuel in this process;

[0043] e k represents the total energy consumption of area k in this process;

[0044] e ki and e kj represent the consumption of gas and solid energy in the energy-consuming area k.

[0045] In the seventh step, the energy consumption situation of energy-consuming equipment is analyzed by calculating models, setting up an energy consumption index system and energy efficiency analysis rules to deduce the excessive energy items, and thus output optimization suggestions for the energy and operation of energy-consuming equipment; the root cause analysis of energy efficiency at the equipment layer is different from that at the process layer. For specific equipment, a thermal engineering model of energy utilization and an energy balance model can be established to guide the root cause analysis process, reduce equipment energy waste, and improve energy utilization efficiency. The specific rules for implementing the root cause analysis of energy efficiency at the equipment layer are as follows:

[0046] ① According to the heat balance principle of the energy-consuming equipment and its system, establish the thermal efficiency model of the equipment;

[0047] ② Establish an evaluation index for the energy consumption level of this equipment, that is, the proportion of each heat quantity in the heat balance model;

[0048] ③ Establish an evaluation rule for whether the heat loss of each item in the equipment model exceeds the standard, and implement the excessive standard judgment;

[0049] ④ Establish a relationship model between each heat quantity in the equipment model and the control operation;

[0050] ⑤ Output the root cause analysis results, and give optimization suggestions for the equipment system in combination with the above relationship model.

[0051] Step 7, the thermal efficiency model of the equipment:

[0052] η sb = 100 - q2 - q3 - q4 - q5 - q6 (8)

[0053]

[0054] In the formula: η sb represents the thermal efficiency of the thermal equipment;

[0055] q2, q3, q4, q5, and q6 respectively represent the proportions of heat loss due to flue gas, incomplete combustion heat loss due to chemistry, incomplete combustion heat loss due to machinery, heat dissipation loss, and other losses relative to the total heat input to the thermal equipment;

[0056] Q f , Q2, Q3, Q4, Q5, and Q6 respectively represent the heat input to the thermal equipment by the fuel, heat loss due to flue gas, incomplete combustion heat loss due to chemistry, incomplete combustion heat loss due to machinery, heat dissipation loss, and other losses;

[0057] The correlation model between the heat losses of the equipment and the control operations:

[0058] Establish a fitting model according to the equipment operation rules to solve the relationship between the heat quantities of the thermal equipment and the equipment type T type and the corresponding operation O operation as shown in formulas (9) and (10):

[0059] V i = f[T type , O operation (9)

[0060] Q i = V i × H i (10).

[0061] Step 8, the energy efficiency root cause analysis model of the energy data layer, as shown in formula (11):

[0062] V j = f[equipment parameters, material parameters, product parameters, energy attribute parameters] (11)

[0063] It can be seen from formula (11) that the parameters affecting the j-th type of energy quantity include: equipment model, size parameters, input quantities of various materials, ratios, qualities, the influence of the characteristics of various products produced, and the parameter attributes of auxiliary energy.

[0064] In Step 8, a mathematical model of the relationship between the blast furnace gas production and influencing factors is established through metallurgical physical chemistry methods to conduct root cause analysis on the high or low blast furnace gas production:

[0065] V g = V CO2 + V CO + V H2 + V CH4 + V N2 (12)

[0066] The blast furnace top gas composition includes CO2, CO, N2, H2, CH4, and the accounting models for each component are as follows:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] In the formula: represents the volume of CH4 per ton of iron in the top gas;

[0077] K represents the coke ratio in blast furnace smelting;

[0078] represents the methane content in coke;

[0079] represents the amount of hydrogen per ton of iron in the top gas;

[0080] represents the total amount of hydrogen entering the blast furnace;

[0081] represents the hydrogen content in coke;

[0082] M represents the pulverized coal injection amount per ton of pig iron smelted;

[0083] represents the hydrogen content in the pulverized coal injection;

[0084] Indicates the moisture content in the pulverized coal injected

[0085] V b Indicates the blast volume per ton of pig iron smelted

[0086] W represents 1 m 3 The amount of oxygen-enriched gas added to the blast

[0087] Indicates the blast humidity

[0088] Indicates the amount of reducing hydrogen, referring to the hydrogen in the H2O generated by reduction

[0089] Indicates the volume of CO2 per ton of pig iron in the top gas

[0090] Indicates the amount of CO2 generated by reduction

[0091] Indicates the amount of CO2 brought in by the burden

[0092] Indicates the content of Fe2O3 in the iron ore

[0093] Indicates the content of MnO2 in the iron ore

[0094] m r,Fe Indicates the amount of reduced iron per ton of pig iron

[0095] r d Indicates the direct reduction degree of iron

[0096] Indicates the reduction degree of hydrogen

[0097] A represents the amount of ore per ton of pig iron

[0098] Indicates the content of CO2 in the iron ore

[0099] Indicates the content of CO2 in the coke

[0100] Φ represents the amount of flux per ton of pig iron

[0101] Indicates the mass fraction of CO2 in the flux (limestone)

[0102] α represents the decomposition rate of limestone in the high-temperature zone

[0103] V CO Indicates the volume of CO per ton of pig iron in the top gas

[0104] m b,C represents the amount of carbon burned in front of the tuyere for smelting 1 ton of pig iron;

[0105] m r,Fe represents the amount of reduced iron per ton of pig iron;

[0106] m da,C represents the amount of CO generated by the reduction of alloying elements such as Si, Mn, and P in pig iron;

[0107] ω K,CO represents the content of CO in the volatile matter of coke;

[0108] represents the volume of N2 in the top gas;

[0109] represents the nitrogen content in the blast;

[0110] represents the nitrogen content in coke;

[0111] represents the nitrogen content in pulverized coal;

[0112] a represents the purity of the oxygen-enriched gas.

[0113] Advantages of the present invention: The method for analyzing and optimizing the energy efficiency root causes of the thermal system in a steel plant classifies the energy efficiency problems of the thermal system in the steel plant hierarchically, establishes energy efficiency models for each hierarchical thermal system in the steel plant, establishes an evaluation system, and establishes an energy efficiency root cause analysis model for each hierarchical thermal system, and provides optimization strategies. This method can standardize the management of energy efficiency indicators in the steel plant, conduct root cause analysis on the situation of high energy consumption and low energy efficiency, diagnose the influencing factors of energy consumption, and give improvement suggestions to guide the enterprise to utilize energy efficiently and optimize operation. Description of the Drawings

[0114] Figure 1 is the logic diagram of the energy efficiency and root cause analysis of the thermal system in the steel plant of the present invention.

[0115] Figure 2 is the logic diagram of the equipment and energy composition of the thermal system in the steel plant of the present invention.

[0116] Figure 3 is the three-level energy consumption hierarchy diagram of the steel plant of the present invention.

[0117] Figure 4 is the schematic diagram of the root cause analysis of the energy efficiency of the process of the present invention.

[0118] Figure 5 is the schematic diagram of the root cause analysis of the energy consumption of the thermal equipment of the present invention.

[0119] Figure 6 is the root cause analysis process of the equipment energy consumption of the present invention.

[0120] Figure 7 is the root cause analysis relationship diagram of the BFG generation amount in the blast furnace smelting process in the embodiment of the present invention. Specific embodiments

[0121] The root cause analysis method for the energy efficiency of the thermal system of the present invention is completed in two stages. As Figure 1 shown, in the first stage, the energy efficiency problems of the thermal system of the steel plant are classified, which are process energy efficiency, equipment energy efficiency, and energy data problems in sequence; in the second stage, root cause analysis is carried out, which is process layer root cause analysis, equipment layer root cause analysis, and energy data layer root cause analysis in sequence.

[0122] According to the above classification, energy efficiency models at all levels of the steel plant are established, and a root cause analysis and diagnosis model is designed to guide the energy efficiency optimization work of the processes and equipment systems in the steel plant. The method of the present invention designs specific energy efficiency analysis and root cause analysis implementation processes. The energy efficiency analysis and modeling of the thermal system are divided into three categories: ① Process energy efficiency model, which is modeled with the standard coal consumption per ton of product as the target; ② Equipment energy efficiency model, which is modeled with the thermal efficiency as the target; ③ Important energy data model, which is modeled with energy values or indicators as the target.

[0123] Root cause analysis: Three levels of root cause analysis for the implementation of the thermal system are designed, and each level of root cause analysis has clear boundaries and the effects to be achieved: ① Root cause analysis at the process level, the purpose is to find the areas or equipment that cause excessive energy consumption and determine the types of energy that exceed the standard; ② Root cause analysis at the equipment level, the purpose is to determine the items with high heat and further determine the corresponding production operations; ③ Root cause analysis of energy data, the purpose is to find the corresponding factors and associated factors that cause excessive energy consumption and feedback improvement suggestions for implementation.

[0124] The specific steps of the present invention are described in detail. The root cause analysis and optimization method for the energy efficiency of the thermal system of the steel plant includes:

[0125] Step 1: Classify the energy efficiency problems of the thermal system of the steel plant, which are classified into: process energy efficiency problems, equipment energy efficiency problems, and energy data problems in sequence.

[0126] The energy and equipment composition logic of the thermal system of the steel plant is as Figure 2 shown. The plant-level system includes various energy-consuming equipment, and each type of equipment consumes multiple types of energy, including Energy 1, Energy 2,..., Energy N; the specific types of energy and equipment can refer to the relevant content in the foregoing background technology.

[0127] Step 2: Establish a process energy efficiency model with the standard coal consumption per ton of product as the target. The established process energy efficiency model of the thermal system is shown in Equation (1). Modeling is carried out with the standard coal consumption per unit product, which is the most representative at the process level, as the target. This model covers various types of energy and energy quality used in the process, and comprehensively analyzes the consumption of various types of energy for producing a unit product in the process.

[0128]

[0129] In the formula: e represents the energy consumption per ton of product of this thermal system;

[0130] Q 能源 represents the total standard coal quantity of the consumed energy;

[0131] P 产品 represents the product output of this process;

[0132] V i 、M j respectively represent the gaseous energy and solid energy quantities;

[0133] H i 、D j respectively represent the lower calorific values of the gaseous energy and solid energy.

[0134] Step 3: Establish an equipment energy efficiency model with the thermal efficiency as the target. The established equipment energy efficiency model of the thermal system is shown in Equation (2). By integrating the effective energy of the furnace, the effective energy of the heat exchange system, and the effective energy of the energy conversion system, it ensures comprehensive consideration of the effective utilization of energy;

[0135]

[0136] In the formula: η represents the efficiency of the thermal system of the equipment, which is expressed as the proportion of the effective heat here;

[0137] Q 有效 、Q 输入 represent the effective energy entering the thermal system and the total energy input into this system;

[0138] Q 炉 、Q 换热 、Q 转换 respectively represent the effective energies of the furnace equipment, the heat exchange system, and the energy conversion system.

[0139] Step 4: Establish an energy data model with the energy value or index as the target. In the energy efficiency problem of the thermal system, the energy data problem can be divided into two categories. One is the problem of the quantity of energy itself, and the other is the accounting index corresponding to energy production and consumption. Taking the specific energy consumption as an example, the specific energy consumption represents the quantity of a certain type of energy consumed by the process for producing a unit product. The established energy data calculation model is shown in Equation (3):

[0140]

[0141] where: g i represents the unit consumption corresponding to the consumption of type-i fuel.

[0142] Through the above model, the energy efficiency of the thermal system is calculated, and the influencing factors of high or low energy consumption are diagnosed. Next, an energy efficiency root cause analysis model is established to analyze the influencing factors of excessive energy consumption to guide the optimized operation of the system.

[0143] Step Five: In order to effectively carry out the energy efficiency root cause analysis work, the energy efficiency root cause analysis process of the steel plant is divided into three levels according to the functional level, namely the process level, the area-equipment level, and the energy data level, as Figure 3 shown. Carry out the energy efficiency root cause analysis of the thermal system at each level, including: the energy efficiency root cause analysis at the process level, the energy efficiency root cause analysis at the equipment level, and the energy efficiency root cause analysis at the energy data level.

[0144] First of all, it is necessary to define the energy efficiency root cause analysis of these three levels of process, area-equipment, and key energy data, clarify the scope of problems solved by each and their mutual relationship, so as to ensure the effectiveness of this work. The scope definition of the three levels of root cause analysis is as follows:

[0145] The first level: Process root cause analysis, find the areas and equipment with excessive energy consumption within the process.

[0146] The second level: Root cause analysis of equipment, using the heat balance principle of thermal equipment, determine the energy input and output items of the equipment, and find the high heat items and corresponding operations.

[0147] The third level: Root cause analysis of energy data, through the analysis of the established energy data index corresponding model, find the corresponding influencing factors that cause the energy data to deviate seriously from the reasonable value.

[0148] Process energy data has its own attributes. The level of energy data reflects the complex production status. Conducting root cause analysis on the situation of excessive deviation of energy data can find the direct influencing factors of high energy consumption, which is conducive to the enterprise to implement operation improvement.

[0149] Step Six: Establish an energy efficiency root cause analysis model at the process level, find the areas or equipment that cause excessive energy consumption, and determine the energy categories of the excess.

[0150] The process energy efficiency root cause analysis process is as Figure 4As shown. Through energy efficiency analysis, diagnose the excessive energy consumption of the process, calculate the excessive energy consumption areas and corresponding equipment, and output the analysis and diagnosis results and maintenance suggestions; based on the enterprise's production historical data, obtain the benchmark values of the gas energy and solid energy consumption of the energy consumption areas (or equipment) as the standards for evaluating energy consumption; and design the energy efficiency root cause analysis rules at the process level:

[0151] ① Design the benchmark energy consumption, including the process benchmark energy consumption E 0 , the benchmark values of various energy consumptions The total benchmark value of energy consumption in area k The benchmark values of gas and solid energy consumption in each area (or equipment)

[0152] ② Calculate the actual values of the energy consumption of the process, area (or equipment), and various energy types;

[0153] ③ Compare the actual energy consumption of the process, various energy consumptions, and area (or equipment) energy consumption with the corresponding benchmark values in turn, analyze and diagnose the energy efficiency levels of each layer; output the high energy consumption areas and prompt for maintenance, output the low energy consumption areas as the standards for excellent operations, and archive them for reference in subsequent operations.

[0154] The total model of energy efficiency root cause analysis at the process level:

[0155]

[0156] The energy consumption model of energy consumption area k:

[0157]

[0158] The model of the consumption of the i-th type of gas energy in this process:

[0159]

[0160] The model of the consumption of the j-th type of solid energy in this process:

[0161]

[0162] In the formula: E represents the total energy consumption of this process;

[0163] E i 、E j represent the consumptions of the gas fuel i and solid fuel j in this process;

[0164] e k represents the total energy consumption of area (or equipment) k in this process;

[0165] e ki 、e kj represent the gas and solid energy consumptions of energy consumption area (or equipment) k.

[0166] Step 7: Establish an energy efficiency root cause analysis model at the equipment layer, determine the items with excessive heat, and further determine the corresponding production operations.

[0167] The principle of root cause analysis of equipment energy consumption is as Figure 5 shown. The energy consumption situation of energy-consuming equipment is analyzed by calculating models, setting up an energy consumption index system and energy efficiency analysis rules, and the exceeded energy items are deduced, so as to output optimization suggestions for the energy and operation of energy-consuming equipment.

[0168] The energy efficiency root cause analysis at the equipment layer is different from that at the process layer. For specific equipment, a thermal engineering model of energy utilization and an energy balance model can be established to guide the root cause analysis process, reduce equipment energy waste, and improve energy utilization efficiency, as Figure 6 shown. The specific rules for implementing energy efficiency root cause analysis at the equipment layer are as follows:

[0169] ① According to the heat balance principle of energy-consuming equipment and its system, establish a thermal engineering efficiency model of the equipment;

[0170] ② Establish an evaluation index for the energy consumption level of the equipment, that is, the proportion of each heat quantity in the heat balance model;

[0171] ③ Establish an evaluation rule for whether the heat loss of each item in the equipment model exceeds the standard, and implement the judgment of exceeding the standard;

[0172] ④ Establish a relationship model between each heat quantity in the equipment model and the control operation;

[0173] ⑤ Output the root cause analysis results, and give optimization suggestions for the equipment system in combination with the above relationship model.

[0174] 1. Equipment thermal efficiency model:

[0175] η sb = 100 - q2 - q3 - q4 - q5 - q6 (8)

[0176]

[0177] In the formula: η sb represents the thermal engineering efficiency of the thermal engineering equipment;

[0178] q2, q3, q4, q5, and q6 respectively represent the proportions of heat loss due to flue gas, incomplete combustion heat loss due to chemistry, incomplete combustion heat loss due to machinery, heat dissipation loss, and other losses relative to the total heat input to the thermal engineering equipment;

[0179] Q f , Q2, Q3, Q4, Q5, and Q6 respectively represent the heat input to the thermal engineering equipment by fuel, heat loss due to flue gas, incomplete combustion heat loss due to chemistry, incomplete combustion heat loss due to machinery, heat dissipation loss, and other losses;

[0180] 2. Association model between various heat losses of the equipment and control operations:

[0181] According to the operation rules of the equipment, a fitting model is established to solve the relationship between the heat quantities of various thermal equipment and the equipment type T type and the corresponding operation O operation as shown in formulas (9) and (10). Since there are many types of energy-consuming equipment in the steel plant, the mathematical model of formula (9) needs to be specifically applied according to the specific equipment to ensure the establishment of a reasonable association model between the various heat losses of the equipment and the operation, and to support the diagnostic work of optimizing the operation of the equipment.

[0182] V i = f[T type , O operation (9)

[0183] Q i = V i ×H i (10)

[0184] Guide the equipment operation according to the above rules to ensure reasonable improvement.

[0185] Step 8. Establish an energy efficiency root cause analysis model for the energy data layer, find the corresponding factors and associated factors that cause excessive energy consumption, and feedback and implement improvement suggestions.

[0186] The deviation of important energy data, whether it is too high or too low, misleads the enterprise's judgment on production operations and equipment energy efficiency. If the cause of the high or low energy data cannot be found, it will lead to no effective basis for production judgment and cause energy waste.

[0187] There are many types of energy-consuming equipment in the steel plant, and their principles are different. Therefore, the specific forms of the accounting process models corresponding to the energy data will also be different. For the convenience of expression, a common analysis is implemented, and the energy efficiency root cause analysis model for the energy data layer is as shown in formula (11):

[0188] V j = f[equipment parameters, material parameters, product parameters, energy attribute parameters] (11)

[0189] It can be seen from formula (11) that the parameters affecting the jth type of energy quantity include: equipment model, size parameters, input quantities of various materials, ratios, qualities, the influence of the characteristics of various products produced, and the parameter attributes of auxiliary energy, etc.

[0190] Through the above three-level energy efficiency high energy consumption root cause analysis, problems such as model confusion, parameter confusion, and application method confusion existing in the industry are avoided. It can help enterprises quickly and accurately find the reasons for the high energy consumption of the thermal system, and guide enterprises to improve operations and optimize the high-level operation of the thermal system.

[0191] Thermal furnace application examples

[0192] During the production process of the blast furnace system in an enterprise's ironmaking plant, in order to increase the recovery of secondary energy, many adjustments have been implemented, including various input material adjustments, pulverized coal injection adjustments, and blast volume adjustments (raising or lowering); the initial purpose was to better achieve index improvement through these adjustments. However, since there was no interrelated model to guide the various adjustment processes, the rationality of the adjusted results could not be judged; as a result, although the measured value of the blast furnace gas (BFG) production increased, it was about 25% higher than the original statistical value. The site seriously suspected this production process, but could not specifically identify the reason for such a serious deviation in the blast furnace gas production, nor did it know how to adjust to restore the blast furnace production to the original reasonable range.

[0193] To solve the problem that such energy data seriously deviates from the reasonable range, the energy efficiency root cause analysis and optimization method of the present invention can be used to quickly find the influencing factors and reasons, provide corresponding operation improvement suggestions, and ensure that the furnace equipment resumes normal production and safe operation in a short time.

[0194] Establish a mathematical model for the correlation between the production of blast furnace gas and influencing factors through metallurgical physical chemistry methods, and conduct root cause analysis on the high or low production of blast furnace gas:

[0195]

[0196] The blast furnace top gas composition includes CO2, CO, N2, H2, CH4, and the accounting models for each component are as follows:

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206] In the formula: represents the volume of CH4 per ton of iron in the top gas of the blast furnace;

[0207] K represents the coke ratio in blast furnace smelting;

[0208] represents the methane content in coke;

[0209] represents the amount of hydrogen per ton of hot metal in top gas;

[0210] represents the total amount of hydrogen entering the blast furnace;

[0211] represents the hydrogen content in coke;

[0212] M represents the amount of pulverized coal injected per ton of pig iron smelting;

[0213] represents the hydrogen content in the injected pulverized coal;

[0214] represents the moisture content in the injected pulverized coal;

[0215] V b represents the amount of blast per ton of pig iron smelting;

[0216] W represents the amount of oxygen - enriched gas added to 1m 3 blast;

[0217] represents the blast humidity;

[0218] represents the amount of reducing hydrogen, referring to the hydrogen in H2O generated by reduction;

[0219] represents the volume of CO2 per ton of hot metal in top gas;

[0220] represents the amount of CO2 generated by reduction;

[0221] represents the amount of CO2 brought in by burden;

[0222] represents the Fe2O3 content in iron ore;

[0223] represents the MnO2 content in iron ore;

[0224] m r,Fe represents the amount of reduced iron per ton of pig iron;

[0225] r d represents the direct reduction degree of iron;

[0226] Represents the reduction degree of hydrogen;

[0227] A represents the amount of ore per ton of pig iron;

[0228] Represents the CO2 content in iron ore;

[0229] Represents the CO2 content in coke;

[0230] Φ represents the amount of flux used per ton of pig iron;

[0231] Represents the mass fraction of CO2 in the flux (limestone);

[0232] α represents the decomposition rate of limestone in the high-temperature zone;

[0233] V CO Represents the volume of CO per ton of iron in the top gas;

[0234] m b,C Represents the amount of carbon burned in front of the tuyere for smelting 1 ton of pig iron;

[0235] m r,Fe Represents the amount of reduced iron per ton of pig iron;

[0236] m da,C Represents the amount of CO generated by the reduction of alloying elements such as Si, Mn, and P in pig iron;

[0237] ω K,CO Represents the CO content in the volatile matter of coke;

[0238] Represents the volume of N2 in the top gas;

[0239] Represents the nitrogen content in the blast;

[0240] Represents the nitrogen content in coke;

[0241] Represents the nitrogen content in pulverized coal;

[0242] a represents the purity of the oxygen-enriched gas.

[0243] Root cause analysis process, establish a root cause analysis correlation model for the generation amount of blast furnace gas as Figure 7As shown in the figure, the BFG (blast furnace gas) generation process is analyzed. The blast furnace production material parameters affecting the gas are: coke volatile content, CH4 content in the volatile matter, blast furnace coke ratio, coal ratio, hydrogen and fixed carbon content in the coke, carbon content in the pulverized coal, blast furnace air volume and air humidity, oxygen-enriched gas volume, content of various elements in the ore, furnace dust and carbon content in the furnace dust, solvent consumption, CO2 mass fraction in the ore and coke, and N2 content in the coke, pulverized coal, and blast air.

[0244] Based on the established BFG production model and root cause analysis model above, and from the relevant enterprise research data, the chemical analysis components of the blast furnace ore are relatively stable, and the gas components are stable. Although the coke ratio and coal ratio change slightly, they will not have a huge impact on the BFG production. The diagnostic reasons are as follows:

[0245] 1. Main reason: The large blast furnace air volume leads to a significant increase in gas recovery.

[0246] An increase in the blast furnace air volume will cause a series of operational changes, such as changes in the blast furnace coal ratio, coke ratio, furnace dust volume, etc. Conversely, these indicators will also affect the BFG gas volume.

[0247] 2. The carbon content in the hot metal has decreased compared to the previous statistical values (about 0.2 percentage points), which will also cause changes in the BFG production.

[0248] Comprehensive analysis: Based on the blast furnace raw materials and BFG components provided by the enterprise, the BFG output per ton of iron should be between 1800 and 1850 Nm 3 which is reasonable. However, due to the relatively large air volume used by the enterprise (2100 Nm of air volume per ton of iron 3 ), although the BFG recovery is high (after calculation, it is between 2000 and 2400 m 3 / t of hot metal), the calorific value quality of the BFG will be severely affected, resulting in an increase in the furnace kiln energy consumption in the subsequent steelmaking process and a decline in the quality of products such as steel billets.

[0249] The correlation between increasing the air volume in the blast furnace system and the BFG recovery is as follows:

[0250] ① When the air volume < reasonable range, increasing the air volume can increase the BFG recovery and ensure stable calorific value;

[0251] ② When the air volume > reasonable range, increasing the air volume can increase the BFG recovery, but the gas calorific value is severely lost;

[0252] ③ When the air volume is within the reasonable range, increasing the air volume can increase the BFG recovery, and the gas calorific value fluctuates little.

[0253] At present, the enterprise is in the second situation. Therefore, the recovery of blast furnace gas will be severely affected, and the quality of products such as billets in the gas-consuming furnace kiln equipment in the downstream process of the production process will also be severely affected.

[0254] 3. Analyze the reasonable range of enterprise BFG output

[0255] There are 2 blast furnaces that the enterprise needs to analyze, and the results are as follows:

[0256] ① A# blast furnace

[0257] The optimal range of BFG production is 1780 - 1850 Nm 3 / t of iron;

[0258] When the BFG recovery amount is 1830 Nm 3 / t of iron, the gas calorific value is stable at 3331 kJ / Nm 3 .

[0259] ② B# blast furnace

[0260] The optimal range of BFG production is 1720 - 1840 Nm 3 / t of iron;

[0261] When the BFG recovery amount is 1815 Nm 3 / t of iron, the gas calorific value is stable at 3386 kJ / Nm 3 .

[0262] With the further improvement of the coverage and effectiveness of the enterprise's metering system, the technical level supporting the root cause analysis of thermal energy efficiency in this patent will be better, and it will be more conducive to effectively analyzing the root cause influencing factors of high and low energy consumption and generating improvement suggestions to guide the enterprise's energy conservation and consumption reduction work.

Claims

1. An energy efficiency root cause analysis and optimization method for the thermal system of an iron and steel plant, characterized in that It includes the following steps: Step 1: Classify the energy efficiency problems of the thermal system in the steel plant, which are successively divided into: process energy efficiency problems, equipment energy efficiency problems, and energy data problems; Step 2: Establish a process energy efficiency model with the standard coal consumption per ton of product as the target; Step 3: Establish an equipment energy efficiency model with the thermal efficiency as the target; Step 4: Establish an energy data model with energy values or indicators as the target; Step 5: Conduct a root cause analysis of the energy efficiency of the thermal system at each level, including: root cause analysis of energy efficiency at the process level, root cause analysis of energy efficiency at the equipment level, and root cause analysis of energy efficiency at the energy data level; Step 6: Establish a root cause analysis model for energy efficiency at the process level, find the areas or equipment causing excessive energy consumption, and determine the types of energy exceeding the standard; Step 7: Establish a root cause analysis model for energy efficiency at the equipment level, determine the items with high heat, and further determine the corresponding production operations; Step 8: Establish a root cause analysis model for energy efficiency at the energy data level, find the corresponding factors and associated factors causing excessive energy consumption, and feedback and implement improvement suggestions; In Step 2, the established process energy efficiency model of the thermal system is shown in Equation (1). Taking the standard coal consumption per unit product, which is the most representative at the process level, as the target, modeling is implemented; this model covers various types of energy and energy qualities used in the process, and comprehensively analyzes the energy consumption of various types of energy for producing a unit product in the process; In the formula: e represents the energy consumption per ton of product of this thermal system; Q 能源 represents the total standard coal consumption of energy consumed; P 产品 represents the product output of this process; V i and M j respectively represent the gas energy source and the solid energy source quantity; H i 、D j respectively represent the lower calorific values of gaseous energy and solid energy; In Step 3, the established equipment energy efficiency model of the thermal system is shown in Equation (2), which comprehensively considers the effective energy of the furnace, the effective energy of the heat exchange system, and the effective energy of the energy conversion system to ensure comprehensive consideration of the effective utilization of energy; In the formula: η represents the efficiency of the thermal system of the equipment, which is expressed as the proportion of effective heat here; Q 有效 and Q 输入 represent the effective energy entering the thermal system and the total energy input to the system; Q 炉 , Q 换热 , Q 转换 respectively represent the effective energy of the furnace equipment, the heat exchange system, and the energy conversion system; In Step 4, the specific energy consumption represents the amount of a certain type of energy consumed for producing a unit product in the process. The established energy data model is shown in Equation (3): where: g i represents the specific consumption corresponding to the consumption of fuel of type i; In Step 6, the root cause analysis model for energy efficiency at the process level: The energy consumption model of the energy-consuming area k: The consumption model of the i-th type of gaseous energy in this process: The consumption model of the j-th type of solid energy in this process: In the formula: E represents the total energy consumption of this process; E i and E j represent the consumption of gaseous fuel i and solid fuel j in this process; e k Represents the total energy consumption of region k for this process; e ki 、e kj represent the gas and solid energy consumption of the energy-consuming area k; In Step 7, the specific rules for implementing the root cause analysis of energy efficiency at the equipment level are as follows: ① According to the heat balance principle of the energy-consuming equipment and its system, establish a thermal efficiency model of the equipment; ② Establish an evaluation index for the energy consumption level of this equipment, that is, the proportion of each heat quantity in the heat balance model; ③ Establish an evaluation rule for whether each heat loss in the equipment model exceeds the standard, and implement the judgment of exceeding the standard; ④ Establish a relationship model between each heat quantity in the equipment model and the control operation; ⑤ Output the root cause analysis result, and give optimization suggestions for the equipment system in combination with the above relationship model; In Step 7, the thermal efficiency model of the equipment: η sb = 100 - q2 - q3 - q4 - q5 - q6 (8) Where: η sb represents the thermal efficiency of the device; q2, q3, q4, q5, and q6 respectively represent the proportions of heat loss due to exhaust gas, incomplete combustion heat loss due to chemistry, incomplete combustion heat loss due to machinery, heat dissipation loss, and other losses relative to the total heat input to the thermal equipment; Q f , Q2, Q3, Q4, Q5, and Q6 respectively represent the heat brought into the thermal equipment by fuel, heat loss due to flue gas, heat loss due to incomplete chemical combustion, heat loss due to incomplete mechanical combustion, heat dissipation loss, and other losses; The relationship model between each heat quantity in the equipment model and the control operation: Establish a fitting model according to the operation law of the equipment to solve the relationship between the heat quantities of various thermal equipment and the equipment type T type and the corresponding operation O operation as shown in equations (9) and (10): V i = f[T type , O operation (9) Q i = V i × H i (10) In Step 8, the root cause analysis model for energy efficiency at the energy data level is shown in Equation (11): V j = f[equipment parameters, material parameters, product parameters, energy attribute parameters] (11) As can be seen from Equation (11), the parameters affecting the energy quantity of the j-th type of energy include: equipment model, size parameters, input quantities of various materials, ratios, qualities, the influence of the characteristics of various products produced, and the parameter attributes of auxiliary energy.

2. The energy efficiency root cause analysis and optimization method for the thermal system of an iron and steel plant according to claim 1, characterized in that: In Step 6, the energy consumption of the process is diagnosed as exceeding the standard through energy efficiency analysis, the energy-consuming areas and corresponding equipment are deduced, and the analysis and diagnosis results and maintenance suggestions are output; based on the enterprise's production historical data, the benchmark values of the gas energy and solid energy consumption in the energy-consuming areas are obtained as the criteria for evaluating energy consumption; and the energy efficiency root cause analysis rules at the process layer are designed: ①Design reference energy consumption, including process reference energy consumption E 0 , reference values of various energy consumptions Reference value of total energy consumption in region k Reference values of gas and solid energy consumptions in each region ② Calculate the actual values of the energy consumption of the process, area, and various types of energy. ③ Compare the actual energy consumption of the process, the energy consumption of various types of energy, and the energy consumption of the area with the corresponding benchmark values in turn, and analyze and diagnose the energy efficiency levels of each layer.

3. The energy efficiency root cause analysis and optimization method for the thermal system of an iron and steel plant according to claim 1, characterized in that: Establish a correlation mathematical model between the blast furnace gas production and the influencing factors through metallurgical physical chemistry methods, and conduct root cause analysis on the high or low blast furnace gas production. The blast furnace top gas composition includes CO2, CO, N2, H2, CH4, and the accounting models for each component are as follows: In the formula: represents the volume of CH4 in the top gas per ton of hot metal; K represents the coke ratio in blast furnace smelting; Indicates the methane content in coke; Indicates the amount of hydrogen per ton of molten iron in the top gas of the blast furnace; Indicates the total amount of hydrogen entering the blast furnace; Indicates the hydrogen content in coke; M represents the pulverized coal injection amount per ton of hot metal smelted; Indicates the hydrogen content in the pulverized coal injected Indicates the moisture content in the pulverized coal injected V b Indicates the blast volume for smelting one ton of pig iron; W represents 1 m 3 the amount of oxygen-enriched gas injected into the blast; Indicates the blast humidity; Indicates the amount of reduced hydrogen, referring to the hydrogen in the H2O generated by reduction; Indicates the volume of CO2 per ton of molten iron in the top gas; Indicates the amount of CO2 produced by reduction; Indicates the amount of CO2 brought in by the burden; Indicates the Fe2O3 content in iron ore; Indicates the content of MnO2 in iron ore; m r,Fe represents the amount of reduced iron per ton of pig iron; r d represents the direct reduction degree of iron; Indicates the reduction degree of hydrogen; A represents the amount of ore per ton of hot metal; Indicates the CO2 content in iron ore; Indicates the CO2 content in coke; Φ represents the flux consumption per ton of hot metal; represents the mass fraction of CO2 in the flux; α represents the decomposition rate of limestone in the high-temperature zone; V CO Indicates the volume of CO per ton of molten iron in the top gas; m b,C It represents the amount of carbon burned in front of the tuyere for smelting 1 ton of pig iron; m r,Fe represents the amount of reduced iron per ton of pig iron; m da,C represents the amount of CO generated by the reduction of alloying elements Si, Mn, and P in pig iron; ω K,CO represents the content of CO in the volatile matter of coke; Indicates the volume of N2 in the top gas; Indicates the nitrogen content in the blast air; represents the nitrogen content in coke; represents the nitrogen content in pulverized coal; a represents the purity of the oxygen-enriched gas.

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