Overall energy-saving method and system for coke oven, electronic equipment and storage medium

Abstract

The invention provides an overall energy-saving method and system for a coke oven, electronic equipment and a storage medium. The method comprises the following steps: generating a plurality of candidate operation parameter combinations of the coke oven based on a production target, environmental parameters and equipment parameters of the coke oven; wherein each candidate operation parameter combination comprises values of various types of operation parameters; for each candidate operation parameter combination, calculating a raw material loss index of the candidate operation parameter combination based on a raw material loss model, and calculating an energy loss index of the candidate operation parameter combination based on an energy loss model; and based on the raw material loss index and the energy loss index, selecting a target operation parameter combination from each candidate operation parameter combination, and controlling the coke oven based on the target operation parameter combination. According to the invention, the production loss of raw materials can be reduced, and the energy loss in the production process can be reduced, so that the comprehensive effect of overall energy conservation is realized.

Description

Technical Field

[0001] The present invention relates to the field of energy-saving control, and particularly to an overall energy-saving method and system for coke ovens, an electronic device, and a storage medium. Background Art

[0002] In the iron and steel metallurgy industry, as one of the core devices, the coke oven is crucial for the production process. The main steps in the coking operation process of the coke oven mainly include coal charging, heating (combustion), coking, coke quenching, and coke discharging. The operation of the coke oven not only involves complex chemical reactions but also a large amount of energy conversion and transfer. As the core device in the coking industry, the energy consumption of the coke oven accounts for 70%-80% of the total energy consumption of the coking process, which is a key area for carbon emission reduction.

[0003] However, the traditional energy-saving control of coke ovens is mostly based on the static setting of fixed parameters (such as standard flue temperature, excess air coefficient), focusing on a single goal (such as reducing the heat consumption for coking or reducing the coke cracking rate). Often, after local optimization, the overall energy efficiency improvement is limited, and the full-process energy flow optimization is not achieved. Summary of the Invention

[0004] Embodiments of the present invention provide an overall energy-saving method and system for coke ovens, an electronic device, and a storage medium to solve the problem of overall energy-saving control of coke ovens.

[0005] In a first aspect, embodiments of the present invention provide an overall energy-saving method for coke ovens, including: Generating multiple candidate operation parameter combinations for the coke oven based on production goals, environmental parameters, and equipment parameters of the coke oven; wherein each candidate operation parameter combination includes values of various types of operation parameters; For each candidate operation parameter combination, calculating the raw material loss index of the candidate operation parameter combination based on the raw material loss model, and calculating the energy loss index of the candidate operation parameter combination based on the energy loss model; Selecting a target operation parameter combination from each candidate operation parameter combination based on the raw material loss index and the energy loss index to control the coke oven based on the target operation parameter combination.

[0006] In a possible implementation manner, generating multiple candidate operation parameter combinations for the coke oven based on production goals, environmental parameters, and equipment parameters of the coke oven includes: Determining a first candidate operation parameter range based on the production goal; Determining a second candidate operation parameter range based on the environmental parameters; Determining a third candidate operation parameter range based on the equipment parameters of the coke oven; In the overlapping interval of the first candidate operating parameter range, the second candidate operating parameter range, and the third candidate operating parameter range, select the values of each type of operating parameter and combine them to obtain multiple candidate operating parameter combinations for the coke oven.

[0007] In a possible implementation manner, before calculating the raw material loss index of each candidate operating parameter combination based on the raw material loss model and calculating the energy loss index of each candidate operating parameter combination based on the energy loss model for each candidate operating parameter combination, it further includes: Obtain the raw material loss indexes corresponding to multiple historical operating parameter combinations; Based on the raw material loss indexes corresponding to each historical operating parameter combination, perform a correlation analysis on the operating parameter category and the raw material loss index to obtain the influencing factors of the raw material loss index; Use the influencing factors of the raw material loss index as input variables and the raw material loss index as an output variable for fitting to obtain a raw material loss model.

[0008] In a possible implementation manner, obtaining the raw material loss indexes corresponding to multiple historical operating parameter combinations includes: Obtain the raw material parameters and product parameters corresponding to multiple historical operating parameter combinations; among them, the raw material parameters include the raw material quality and the raw material composition, and the product parameters include the product quality and the product composition; based on the raw material parameters and product parameters corresponding to each historical operating parameter combination, Calculate the difference between the raw material quality and the product quality corresponding to the first historical operating parameter combination as the quality loss corresponding to the first historical operating parameter combination; where the first historical operating parameter combination is any historical operating parameter combination; Calculate the difference between the proportion of the first element in the raw material composition and the proportion of the first element in the product composition corresponding to the first historical operating parameter combination as the composition loss of the first element corresponding to the first historical operating parameter combination; where the first element is any element; Based on a preset weight, perform a weighted sum on the quality loss and the composition losses of each element corresponding to the first historical operating parameter combination to obtain the raw material loss index corresponding to the first historical operating parameter combination.

[0009] In a possible implementation manner, before calculating the raw material loss index of each candidate operating parameter combination based on the raw material loss model and calculating the energy loss index of each candidate operating parameter combination based on the energy loss model for each candidate operating parameter combination, it further includes: Obtain the energy loss indexes corresponding to multiple historical operating parameter combinations; Based on the energy loss indicators corresponding to each historical operation parameter combination, a correlation analysis is performed on the operation parameter categories and the energy loss indicators to obtain the influencing factors of the energy loss indicators; Using the influencing factors of the energy loss indicators as input variables and the energy loss indicators as output variables for fitting, an energy loss model is obtained.

[0010] In one possible implementation, obtaining the energy loss indicators corresponding to multiple historical operation parameter combinations includes: Obtaining the water consumption, power consumption, total heat production, waste heat recovery amount, total gas production, and gas utilization amount corresponding to multiple historical operation parameter combinations; For each historical operation parameter combination, calculate the difference between the total heat production and the waste heat recovery amount corresponding to this historical operation parameter combination to obtain the heat loss, calculate the difference between the total gas production and the gas utilization amount to obtain the gas loss, and calculate the comprehensive energy consumption based on the water consumption, power consumption, heat loss, and gas loss as the energy loss indicator corresponding to this historical operation parameter combination.

[0011] In one possible implementation, based on the raw material loss indicator and the energy loss indicator, selecting the target operation parameter combination from each candidate operation parameter combination includes: Calculate the ratio of the average raw material loss indicator to the raw material loss indicator of the first candidate operation parameter combination as the first energy-saving score of the first candidate operation parameter combination; wherein, the first candidate operation parameter combination is any candidate operation parameter combination; Calculate the ratio of the average energy loss indicator to the energy loss indicator of the first candidate operation parameter combination as the second energy-saving score of the first candidate operation parameter combination; Calculate the average value of the first energy-saving score and the second energy-saving score of the first candidate operation parameter combination to obtain the comprehensive energy-saving score of the first candidate operation parameter combination; Take the candidate operation parameter combination with the highest comprehensive energy-saving score among each candidate operation parameter combination as the target operation parameter combination.

[0012] In a second aspect, an embodiment of the present invention provides a coke oven overall energy-saving system, including: A combination module for generating multiple candidate operation parameter combinations of the coke oven based on the production target, environmental parameters, and equipment parameters of the coke oven; wherein each candidate operation parameter combination includes values of multiple types of operation parameters; A calculation module for, for each candidate operation parameter combination, calculating the raw material loss indicator of this candidate operation parameter combination based on the raw material loss model and calculating the energy loss indicator of this candidate operation parameter combination based on the energy loss model; A selection module is configured to select a target operating parameter combination from each candidate operating parameter combination based on a raw material loss index and an energy loss index, so as to control the coke oven based on the target operating parameter combination.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method according to the first aspect or any possible implementation manner of the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method according to the first aspect or any possible implementation manner of the first aspect are implemented.

[0015] An embodiment of the present invention provides a coke oven overall energy-saving method, system, electronic device, and storage medium. According to production targets, environmental parameters, and equipment parameters of the coke oven, a plurality of possible operating parameter combinations are generated, and a raw material loss model and an energy loss model are used to perform simulation calculations on each candidate operating parameter combination to obtain the loss conditions of raw materials and energy under specific operating parameters. Based on the two indicators, the target operating parameter combination is selected, which can not only reduce the production loss of raw materials but also reduce the energy loss during the production process, thereby achieving the comprehensive effect of overall energy saving. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 is a flowchart of the implementation of a coke oven overall energy-saving method provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a coke oven overall energy-saving system provided by an embodiment of the present invention; Figure 3 is a schematic diagram of the electronic device provided by an embodiment of the present invention. Detailed Embodiments

[0018] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0020] See Figure 1 , which shows the implementation flowchart of an overall energy-saving method for a coke oven provided by an embodiment of the present invention, and is described in detail as follows: Step 101: Based on the production target, environmental parameters, and equipment parameters of the coke oven, generate multiple candidate operation parameter combinations for the coke oven; where each candidate operation parameter combination includes values of various types of operation parameters.

[0021] In this embodiment, the production target refers to the main results or indicators that the coke oven hopes to achieve within a specific production cycle, such as production volume, product quality (such as coke strength), cost control, etc. Environmental parameters refer to external conditions that affect the operation efficiency and safety of the coke oven. These conditions are usually not directly controlled by the factory but have an important impact on operations, such as environmental temperature, humidity, grid electricity price fluctuations, weather changes, etc. Equipment parameters refer to the physical and technical characteristics related to the coke oven and its auxiliary equipment, such as the maximum heating power of the coke oven, the health status of the burner, the type and conditions of the waste heat recovery system, etc.

[0022] The production target clarifies the production direction. Changes in environmental parameters require the coke oven to dynamically adjust operation parameters to adapt to real-time conditions. The capabilities and states of the equipment directly determine the feasible range of certain operation parameters. Therefore, by using the production target, environmental parameters, and equipment parameters of the coke oven, candidate operation parameter combinations that can be adopted are determined, and then the optimal one is selected from them to achieve overall energy-saving control of the coke oven.

[0023] This method can comprehensively consider various factors in the operation of the coke oven, generate diverse parameter combinations, provide a scientific basis for subsequent optimization and decision-making, and thus achieve the production goals of high efficiency, energy saving, and stability.

[0024] Step 102: For each candidate operation parameter combination, calculate the raw material loss index of the candidate operation parameter combination based on the raw material loss model, and calculate the energy loss index of the candidate operation parameter combination based on the energy loss model.

[0025] In this embodiment, calculating the raw material loss index and energy loss index of candidate operation parameter combinations is a key step in optimizing the coke oven operation process. The purpose of this step is to quantify the specific effects of different parameter combinations on production efficiency, cost control, and environmental impact, thereby helping to select the optimal operation parameter combination.

[0026] The raw material loss model is used to predict the amount of raw materials consumed per unit of product output under specific operating conditions. It can help identify the factors that cause high raw material losses and guide how to reduce these losses by adjusting operation parameters. The specific form can be to establish a linear relationship between raw material loss and main operation parameters based on historical data. Or considering that there may be complex non-linear relationships in some process, advanced machine learning methods such as neural networks or support vector machines can be used for modeling. It can also be based on the basic principles in the material conversion process (such as thermodynamics, kinetic equations) to build a model to more accurately describe the process of raw materials being converted into products.

[0027] The energy loss model is used to estimate the total energy consumption in the entire production process under specific operating conditions. This includes direct energy input (such as heat released by fuel combustion) and indirect energy losses (such as equipment heat dissipation, incomplete combustion, etc.). Through this model, energy-saving potential can be discovered and improvement measures can be proposed. The specific forms can include: Heat balance model: By analyzing all energy flows within the system, ensure that the principle of energy conservation is adhered to.

[0028] Energy efficiency ratio model: Calculate the energy consumed per unit of product output, which is an important indicator to measure energy efficiency. Comprehensive energy consumption model: Combine various energy forms (such as electricity, steam, fuel, etc.) and their conversion efficiencies to provide a comprehensive energy consumption assessment framework.

[0029] Through the pre-set models, the raw material loss index and energy loss index of each candidate operation parameter combination can be calculated, so as to select the target operation parameter combination that best meets the overall energy-saving requirements.

[0030] Step 103, based on the raw material loss index and energy loss index, select the target operation parameter combination from each candidate operation parameter combination to control the coke oven based on the target operation parameter combination.

[0031] In this embodiment, the raw material loss index and energy loss index respectively represent two key aspects in the production process: the effective utilization of resources and the effective use of energy. In actual operation, there are often certain conflicts in reducing raw material losses and energy losses. For example, to reduce raw material losses, it may be necessary to add some processing steps, but this may lead to additional energy consumption.

[0032] In this embodiment, by quantitatively analyzing the raw material loss and energy loss, a solid scientific basis can be provided for decision-making, avoiding making decisions solely based on experience or intuition, finding the balance relationship between reducing raw material loss and energy loss, and achieving the overall energy saving of the coke oven.

[0033] In the embodiment of the present invention, according to the production target, environmental parameters, and equipment parameters of the coke oven, multiple possible combinations of operating parameters are generated, and using the raw material loss model and energy loss model, each candidate combination of operating parameters is simulated and calculated to obtain the loss situation of raw materials and energy under specific operating parameters. Based on the two indicators, the target combination of operating parameters is selected, which can not only reduce the production loss of raw materials but also reduce the energy loss in the production process, thereby achieving the comprehensive effect of overall energy saving.

[0034] In a possible implementation manner, based on the production target, environmental parameters, and equipment parameters of the coke oven, multiple candidate combinations of operating parameters of the coke oven are generated, including: Determining the first candidate operating parameter range based on the production target; Determining the second candidate operating parameter range based on the environmental parameters; Determining the third candidate operating parameter range based on the equipment parameters of the coke oven; In the overlapping interval of the first candidate operating parameter range, the second candidate operating parameter range, and the third candidate operating parameter range, the values of each type of operating parameter are selected and combined to obtain multiple candidate combinations of operating parameters of the coke oven.

[0035] In this embodiment, the operating parameters of the coke oven include multiple variables, and each variable has a certain value range. This characteristic of the multi-dimensional parameter space makes the candidate combinations of operating parameters have great flexibility.

[0036] Specifically, the production target may include indicators such as output, quality, cold strength, and hot strength, the environmental parameters may include humidity, temperature, and wind speed, etc., the equipment parameters may include furnace chamber size, heating parameters, furnace body structure, etc., and the operating parameters may include coal material processing parameters, coal charging parameters, carbonization parameters, coke quenching parameters, cooling parameters, and furnace body operating parameters, etc. According to the specific production target, environmental parameters, and equipment parameters of the coke oven, the upper and lower limits of some related categories of operating parameters can be determined respectively, and the specific relevant relationships are as follows: 1. Coal material processing parameters Coal material type: determined according to the production target (such as coke quality standard) and raw material availability.

[0037] Coal material particle size: The optimal particle size range is determined through experiments to meet the requirements of carbonization efficiency and coke quality.

[0038] Moisture content of coal charge: Set the moisture control range according to the raw material characteristics (natural moisture) and production objectives (such as carbonization efficiency).

[0039] Bulk density of coal charge: Determine according to the silo design and requirements for preventing spontaneous combustion of coal charge.

[0040] 2. Coal charging parameters Coal charging method: Determined by the equipment type and production process.

[0041] Coal charging speed: Determine according to the production efficiency and the designed capacity of the carbonization chamber.

[0042] Coal charging density: Determine through experiments and simulations to optimize the carbonization efficiency and coke quality.

[0043] 3. Carbonization parameters Heating time: Determine according to the coal type, coke quality and production efficiency requirements.

[0044] Heating temperature: Set according to the pyrolysis characteristics of the coal type, coke quality standards and environmental protection requirements.

[0045] Heating rate: Determine through experiments to ensure coke quality and production safety.

[0046] Carbonization chamber pressure: Set according to the carbonization process and equipment capacity.

[0047] 4. Coke quenching parameters Coke quenching method: Select according to environmental protection requirements and coke quality standards.

[0048] Coke quenching time: Determine through experiments to ensure that the coke is cooled to a safe temperature.

[0049] 5. Cooling parameters Cooling method: Select according to production efficiency and environmental protection requirements.

[0050] Cooling time: Determine according to the cooling rate of the coke and safety requirements.

[0051] 6. Furnace body operation parameters Furnace body temperature distribution: Determine through heat balance calculations and equipment capacity.

[0052] Furnace body pressure distribution: Determine according to the carbonization process and equipment design.

[0053] Furnace body heating control: Set according to the heating curve and gas calorific value.

[0054] For each operating parameter, three available setting ranges of the operating parameter are determined respectively according to its limitations by production targets, environmental parameters, and equipment parameters. If the value of a certain operating parameter is not affected by the production target, all available values of this operating parameter are taken as the first operating parameter range determined based on the production target, and the same applies to environmental parameters or equipment parameters.

[0055] Find out the overlapping part of the above three ranges. This overlapping interval represents the operating parameter space that simultaneously meets the production target, environmental conditions, and equipment limitations. Then, within this overlapping interval, all feasible values are selected for each type of operating parameter, and these values are combined to form multiple candidate operating parameter combinations that comprehensively consider various factors in the operation of the coke oven.

[0056] For example, the production target of a certain coking plant is high strength (M40 Shatter Strength ≥ 85%) and low ash content (≤ 10%). The environmental parameter is winter with a relatively low environmental temperature (-5°C), and attention needs to be paid to the heat preservation measures during coal charging. The equipment parameters are that the maximum heating power of the coke oven is 10 MW, the temperature resistance limit of the furnace body is 1100°C, and the burner efficiency is 90%.

[0057] The characteristics of coal types available in the market are as follows: Coal type A: Volatile matter = 25%, Ash content = 8%, Sulfur content = 0.6%, Caking index G = 75.

[0058] Coal type B: Volatile matter = 28%, Ash content = 10%, Sulfur content = 0.8%, Caking index G = 85.

[0059] Coal type C: Volatile matter = 22%, Ash content = 6%, Sulfur content = 0.5%, Caking index G = 90.

[0060] For the production target, high-strength coke usually requires a relatively high caking index (G value). General experience shows that coal types with G ≥ 80 are more likely to meet the requirements. The ash content directly affects the quality and strength of coke, so coal types with ash content ≤ 10% need to be selected. It is thus determined that coal type B and coal type C meet the requirements of the production target, and the first candidate operating parameter range of coal types is B and C.

[0061] For the environmental parameter, the low temperature in winter may lead to an increase in heat loss during coal charging. Therefore, coal types with moderate volatile matter (20% - 30%) need to be selected to ensure combustion stability and thermal efficiency. It is thus determined that coal types A, B, and C all meet the requirements of the environmental parameter, and the second candidate operating parameter range of coal types is A, B, and C.

[0062] For equipment parameters, too high volatile matter content (>30%) may lead to unstable combustion and exceed the upper limit of the equipment heating power (10 MW). Therefore, it is necessary to ensure that the volatile matter content of the coal type is within a reasonable range. The ash fusion temperature of the coal type should be higher than the furnace body temperature resistance limit (1100 °C) to avoid furnace body damage. The burner efficiency is 90%, and it is necessary to select coal types with a relatively low sulfur content (<1%) to reduce emissions and improve combustion efficiency. Thus, it is determined that coal type B and coal type C meet the requirements of the equipment parameters, and the third candidate operating parameter range of the coal type is B and C.

[0063] Finally, values are taken in their overlapping intervals, and coal type B and coal type C are respectively combined with the values of other operating parameters to obtain multiple candidate operating parameter combinations for the coke oven.

[0064] In a possible implementation manner, before calculating the raw material loss index of each candidate operating parameter combination based on the raw material loss model and calculating the energy loss index of each candidate operating parameter combination based on the energy loss model for each candidate operating parameter combination, it further includes: Obtaining the raw material loss indexes corresponding to multiple historical operating parameter combinations; Based on the raw material loss indexes corresponding to each historical operating parameter combination, performing a correlation analysis on the operating parameter categories and the raw material loss indexes to obtain the influencing factors of the raw material loss indexes; Using the influencing factors of the raw material loss indexes as input variables and the raw material loss indexes as output variables for fitting to obtain a raw material loss model.

[0065] In this embodiment, the historical operating parameter combination refers to the set of specific values of various operating parameters used in the coke oven production process. For example, different value combinations of parameters such as the air / fuel ratio and the heating rate can form a historical operating parameter combination. By statistically analyzing historical data, identifying which operating parameters have a significant correlation with raw material loss can simplify the calculation steps and improve the accuracy when calculating the raw material loss index in the future.

[0066] Specifically, the correlation coefficient (such as the Pearson correlation coefficient) between each operating parameter and the raw material loss index can be calculated to determine the strength of their correlation. Then, factors with significant influence are selected according to the correlation coefficient (for example, setting a threshold to only retain factors with an absolute value of the correlation coefficient greater than 0.5). Finally, using the selected influencing factors as input variables and the raw material loss index as the output variable, an appropriate mathematical method (such as linear regression, neural network, etc.) is used to construct a model that can predict the raw material loss index.

[0067] After the model is built, only the values of the influencing factors belonging to the raw material loss index in the candidate operation parameter combinations are input into the model, and the raw material loss index of the candidate operation parameter combinations can be obtained.

[0068] In a possible implementation, obtaining the raw material loss indexes corresponding to multiple historical operation parameter combinations includes: Obtaining the raw material parameters and product parameters corresponding to multiple historical operation parameter combinations; among them, the raw material parameters include raw material quality and raw material composition, and the product parameters include product quality and product composition; based on the raw material parameters and product parameters corresponding to each historical operation parameter combination, Calculating the difference between the raw material quality and the product quality corresponding to the first historical operation parameter combination as the quality loss corresponding to the first historical operation parameter combination; where the first historical operation parameter combination is any historical operation parameter combination; Calculating the difference between the proportion of the first element in the raw material composition and the proportion of the first element in the product composition corresponding to the first historical operation parameter combination as the composition loss of the first element corresponding to the first historical operation parameter combination; where the first element is any element; Based on the preset weights, performing weighted summation on the quality loss and the composition losses of each element corresponding to the first historical operation parameter combination to obtain the raw material loss index corresponding to the first historical operation parameter combination.

[0069] In this embodiment, through precise measurement and recording of the quality of the input and output materials for mass balance analysis, recording the total amount of coal material entering the coke oven for each batch, and comparing it with the quality of the by-products and waste (such as ash slag) such as coke, coke oven gas, tar, and ammonia water produced, the theoretical raw material loss rate can be calculated.

[0070] By analyzing the chemical compositions of the raw coal and its products, such as the proportion changes of elements such as carbon, hydrogen, oxygen, nitrogen, and sulfur, the efficiency of the conversion of useful components in the raw materials into the final products is evaluated.

[0071] Finally, by combining the weights of the quality loss and the composition loss of each element to comprehensively quantify various losses, the raw material utilization efficiency under different operation parameter combinations can be evaluated. The weights of the quality loss and the composition loss of each element can be determined by the analytic hierarchy process.

[0072] In a possible implementation, before calculating the raw material loss index of each candidate operation parameter combination based on the raw material loss model and calculating the energy loss index of each candidate operation parameter combination based on the energy loss model for each candidate operation parameter combination, it further includes: Obtaining the energy loss indexes corresponding to multiple historical operation parameter combinations; Based on the energy loss indicators corresponding to each historical operation parameter combination, perform a correlation analysis on the operation parameter categories and the energy loss indicators to obtain the influencing factors of the energy loss indicators; Take the influencing factors of the energy loss indicators as input variables and the energy loss indicators as output variables for fitting to obtain an energy loss model.

[0073] In this embodiment, similar to the construction process of the raw material loss model, statistical analysis is performed on historical data, and the correlation coefficient (such as the Pearson correlation coefficient) between each operation parameter and the energy loss indicator is calculated to determine the strength of their correlation. Then, factors with significant influence are selected according to the correlation coefficient (for example, set a threshold to only retain factors with an absolute value of the correlation coefficient greater than 0.5). Finally, use the selected influencing factors as input variables and the energy loss indicator as the output variable, and adopt an appropriate mathematical method (such as linear regression, neural network, etc.) according to the specific change relationship to construct a model that can predict the energy loss indicator.

[0074] After the model is constructed, only input the values of the influencing factors of the energy loss indicator in the candidate operation parameter combination into the model to obtain the energy loss indicator of the candidate operation parameter combination.

[0075] In a possible implementation, obtaining the energy loss indicators corresponding to multiple historical operation parameter combinations includes: Obtain the water consumption, electricity consumption, total heat production, waste heat recovery amount, total gas production, and gas utilization amount corresponding to multiple historical operation parameter combinations; For each historical operation parameter combination, calculate the heat loss by subtracting the waste heat recovery amount from the total heat production corresponding to this historical operation parameter combination, calculate the gas loss by subtracting the gas utilization amount from the total gas production, and calculate the comprehensive energy consumption based on the water consumption, electricity consumption, heat loss, and gas loss as the energy loss indicator corresponding to this historical operation parameter combination.

[0076] In this embodiment, the water consumption is the total amount of water consumed during the production process under a specific combination of operating parameters, including cooling water, coke quenching water, etc. The electricity consumption is the total amount of electricity consumed during the production process under a specific combination of operating parameters, usually used for driving equipment, lighting, etc. The total heat production is the total amount of heat generated during the production process under a specific combination of operating parameters, mainly from the energy released by fuel combustion. The waste heat recovery amount is the heat recovered and reused from waste gas or other waste heat sources through the waste heat recovery system under a specific combination of operating parameters. The total gas production is the total amount of gas generated during the production process under a specific combination of operating parameters, usually a by-product. The gas utilization amount is the amount of gas actually effectively utilized under a specific combination of operating parameters, and the unutilized part is regarded as waste or emission. The heat loss is the difference between the total heat production and the waste heat recovery amount, reflecting the heat loss that cannot be effectively utilized during the production process. The gas loss is the difference between the total gas production and the gas utilization amount, reflecting the gas loss that cannot be effectively utilized during the production process.

[0077] Calculating the comprehensive energy consumption can be based on the water consumption, electricity consumption, heat loss, and gas loss. Appropriate conversion factors are used to unify each energy consumption item to the same energy unit (such as tons of standard coal or megawatt-hours), and then weighted summation is performed to obtain a comprehensive energy consumption index to evaluate the energy consumption level during the production process.

[0078] In a possible implementation manner, based on the raw material loss index and the energy loss index, a target operating parameter combination is selected from each candidate operating parameter combination, including: Calculate the ratio of the average raw material loss index to the raw material loss index of the first candidate operating parameter combination as the first energy-saving score of the first candidate operating parameter combination; wherein, the first candidate operating parameter combination is any candidate operating parameter combination; Calculate the ratio of the average energy loss index to the energy loss index of the first candidate operating parameter combination as the second energy-saving score of the first candidate operating parameter combination; Calculate the average value of the first energy-saving score and the second energy-saving score of the first candidate operating parameter combination to obtain the comprehensive energy-saving score of the first candidate operating parameter combination; Select the candidate operating parameter combination with the highest comprehensive energy-saving score among each candidate operating parameter combination as the target operating parameter combination.

[0079] In this embodiment, the average raw material loss index is the arithmetic mean of the raw material loss indexes corresponding to all historical operating parameter combinations, and the average energy loss index is the arithmetic mean of the energy loss indexes corresponding to all historical operating parameter combinations, serving as the benchmark reference.

[0080] The first - stage energy - saving score is used to evaluate the score of a certain candidate operation parameter combination in terms of raw material saving. By comparing the raw material loss index of a certain candidate operation parameter combination with the average raw material loss index, the relative performance of this combination in raw material saving can be evaluated. Directly sorting using the loss index can only reflect the absolute value level, but cannot intuitively show the improvement degree of a certain candidate combination compared to the reference value (such as the historical average level). By calculating the energy - saving score through a ratio, the energy - saving effect can be intuitively reflected, the sensitivity to small improvements can be increased, subtle but important optimization points can be identified, and a reference benchmark can be provided to enhance the comparability between candidate combinations. The larger the ratio, the higher the raw material utilization rate of this combination.

[0081] The second - stage energy - saving score is used to evaluate the score of a certain candidate operation parameter combination in terms of energy saving. By comparing the energy loss index of a certain candidate operation parameter combination with the average energy loss index, the relative performance of this combination in energy saving can be evaluated. The larger the ratio, the higher the energy utilization efficiency of this combination.

[0082] Taking the average of the first - stage energy - saving score and the second - stage energy - saving score can obtain a quantitative score of the overall energy - saving performance of this candidate operation parameter combination, eliminating the influence of dimensions and numerical ranges, more scientifically and reasonably evaluating the comprehensive energy - saving effect of the candidate operation parameter combination, and ultimately helping to select the optimal solution.

[0083] It should be understood that the magnitudes of the sequence numbers of the steps in the above - mentioned embodiments do not mean 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 to the implementation process of the embodiments of the present invention.

[0084] The following is an embodiment of the system of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiments above.

[0085] Figure 2 The structural schematic diagram of an overall coke oven energy - saving system provided by an embodiment of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows: As Figure 2 shown, an overall coke oven energy - saving system 2 includes: A combination module 21, configured to generate multiple candidate operation parameter combinations of the coke oven based on production targets, environmental parameters, and equipment parameters of the coke oven; wherein, each candidate operation parameter combination includes values of various types of operation parameters; A calculation module 22, configured to, for each candidate operation parameter combination, calculate the raw material loss index of this candidate operation parameter combination based on the raw material loss model, and calculate the energy loss index of this candidate operation parameter combination based on the energy loss model; The selection module 23 is used to select a target operating parameter combination from each candidate operating parameter combination based on the raw material loss index and the energy loss index, so as to control the coke oven based on the target operating parameter combination.

[0086] In a possible implementation, the combination module 21 is specifically used for: Determine the first candidate operating parameter range based on the production target; Determine the second candidate operating parameter range based on the environmental parameters; Determine the third candidate operating parameter range based on the equipment parameters of the coke oven; In the overlapping interval of the first candidate operating parameter range, the second candidate operating parameter range, and the third candidate operating parameter range, select the values of each type of operating parameter and combine them to obtain multiple candidate operating parameter combinations of the coke oven.

[0087] In a possible implementation, the calculation module 22 is further used for: Before calculating the raw material loss index of each candidate operating parameter combination based on the raw material loss model and calculating the energy loss index of each candidate operating parameter combination based on the energy loss model, obtain the raw material loss indexes corresponding to multiple historical operating parameter combinations; Based on the raw material loss indexes corresponding to each historical operating parameter combination, perform a correlation analysis on the operating parameter category and the raw material loss index to obtain the influencing factors of the raw material loss index; Use the influencing factors of the raw material loss index as input variables and the raw material loss index as the output variable for fitting to obtain the raw material loss model.

[0088] In a possible implementation, the calculation module 22 is specifically used for: Obtain the raw material parameters and product parameters corresponding to multiple historical operating parameter combinations; where the raw material parameters include the raw material quality and the raw material composition, and the product parameters include the product quality and the product composition; based on the raw material parameters and product parameters corresponding to each historical operating parameter combination, Calculate the difference between the raw material quality and the product quality corresponding to the first historical operating parameter combination as the quality loss corresponding to the first historical operating parameter combination; where the first historical operating parameter combination is any historical operating parameter combination; Calculate the difference between the proportion of the first element in the raw material composition and the proportion of the first element in the product composition corresponding to the first historical operating parameter combination as the component loss of the first element corresponding to the first historical operating parameter combination; where the first element is any element; Based on the preset weights, perform a weighted sum of the quality loss corresponding to the first historical operation parameter combination and the component losses of each element to obtain the raw material loss index corresponding to the first historical operation parameter combination.

[0089] In a possible implementation, the calculation module 22 is further configured to: Before calculating the raw material loss index of each candidate operation parameter combination based on the raw material loss model and calculating the energy loss index of each candidate operation parameter combination based on the energy loss model, obtain the energy loss indexes corresponding to multiple historical operation parameter combinations; Based on the energy loss index corresponding to each historical operation parameter combination, perform a correlation analysis on the operation parameter category and the energy loss index to obtain the influencing factors of the energy loss index; Use the influencing factors of the energy loss index as input variables and the energy loss index as an output variable for fitting to obtain an energy loss model.

[0090] In a possible implementation, the calculation module 22 is specifically configured to: Obtain the water consumption, power consumption, total heat production, waste heat recovery amount, total gas production, and gas utilization amount corresponding to multiple historical operation parameter combinations; For each historical operation parameter combination, calculate the difference between the total heat production and the waste heat recovery amount corresponding to the historical operation parameter combination to obtain the heat loss, calculate the difference between the total gas production and the gas utilization amount to obtain the gas loss, and calculate the comprehensive energy consumption based on the water consumption, power consumption, heat loss, and gas loss as the energy loss index corresponding to the historical operation parameter combination.

[0091] In a possible implementation, the selection module 23 is specifically configured to: Calculate the ratio of the average raw material loss index to the raw material loss index of the first candidate operation parameter combination as the first energy-saving score of the first candidate operation parameter combination; wherein, the first candidate operation parameter combination is any candidate operation parameter combination; Calculate the ratio of the average energy loss index to the energy loss index of the first candidate operation parameter combination as the second energy-saving score of the first candidate operation parameter combination; Calculate the average value of the first energy-saving score and the second energy-saving score of the first candidate operation parameter combination to obtain the comprehensive energy-saving score of the first candidate operation parameter combination; Take the candidate operation parameter combination with the highest comprehensive energy-saving score among all candidate operation parameter combinations as the target operation parameter combination.

[0092] In an embodiment of the present invention, multiple possible combinations of operating parameters are generated based on production targets, environmental parameters, and equipment parameters of a coke oven. By using a raw material loss model and an energy loss model, simulation calculations are performed on each candidate combination of operating parameters to obtain the loss situations of raw materials and energy under specific operating parameters. The selection of the target combination of operating parameters is based on two indicators, which can not only reduce the production loss of raw materials but also reduce the energy loss during the production process, thereby achieving the comprehensive effect of overall energy conservation.

[0093] Figure 3 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, the electronic device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in each of the above embodiments of a method for overall energy conservation of a coke oven are implemented, such as Figure 1 the steps 101 to 103 shown. Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in each of the above system embodiments are implemented, such as Figure 2 the functions of the modules / units 21 to 23 shown.

[0094] Exemplarily, the computer program 32 can be divided into one or more modules / units. One or more modules / units are stored in the memory 31 and executed by the processor 30 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 can be divided into Figure 2 the modules / units 21 to 23 shown.

[0095] The electronic device 3 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electronic device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art can understand that Figure 3 this is only an example of the electronic device 3 and does not constitute a limitation on the electronic device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, a bus, etc.

[0096] The so-called processor 30 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0097] The memory 31 may be an internal storage unit of the electronic device 3, such as the hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk equipped on the electronic device 3, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 31 may also include both the internal storage unit and the external storage device of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0099] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0100] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0101] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0104] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described embodiments of a coke oven overall energy-saving method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0105] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for overall energy saving of coke oven, characterized in that, Including: Generating a plurality of candidate operating parameter combinations for the coke oven based on production targets, environmental parameters, and equipment parameters of the coke oven; wherein each candidate operating parameter combination includes values of various types of operating parameters; For each candidate operating parameter combination, calculating a raw material loss index for the candidate operating parameter combination based on a raw material loss model, and calculating an energy loss index for the candidate operating parameter combination based on an energy loss model; Selecting a target operating parameter combination from each candidate operating parameter combination based on the raw material loss index and the energy loss index, so as to control the coke oven based on the target operating parameter combination.

2. The overall energy-saving method for a coke oven according to claim 1, characterized in that The generating a plurality of candidate operating parameter combinations for the coke oven based on production targets, environmental parameters, and equipment parameters of the coke oven includes: Determining a first candidate operating parameter range based on the production target; Determining a second candidate operating parameter range based on the environmental parameters; Determining a third candidate operating parameter range based on the equipment parameters of the coke oven; Selecting values of each type of operating parameter and combining them in the overlapping interval of the first candidate operating parameter range, the second candidate operating parameter range, and the third candidate operating parameter range to obtain a plurality of candidate operating parameter combinations for the coke oven.

3. The overall energy-saving method for a coke oven according to claim 1, characterized in that, Before calculating the raw material loss index for each candidate operating parameter combination based on the raw material loss model and calculating the energy loss index for the candidate operating parameter combination based on the energy loss model, it further includes: Obtaining raw material loss indexes corresponding to a plurality of historical operating parameter combinations; Performing a correlation analysis on the operating parameter categories and the raw material loss indexes based on the raw material loss indexes corresponding to each historical operating parameter combination to obtain influencing factors of the raw material loss index; Performing fitting with the influencing factors of the raw material loss index as input variables and the raw material loss index as an output variable to obtain the raw material loss model.

4. The overall energy-saving method for coke ovens according to claim 3, characterized in that, The obtaining raw material loss indexes corresponding to a plurality of historical operating parameter combinations includes: Obtaining raw material parameters and product parameters corresponding to a plurality of historical operating parameter combinations; wherein the raw material parameters include raw material quality and raw material composition, and the product parameters include product quality and product composition; calculating the difference between the raw material quality and the product quality corresponding to the first historical operating parameter combination as the quality loss corresponding to the first historical operating parameter combination based on the raw material parameters and the product parameters corresponding to each historical operating parameter combination; wherein the first historical operating parameter combination is any one of the historical operating parameter combinations; Calculating the difference between the proportion of a first element in the raw material composition corresponding to the first historical operating parameter combination and the proportion of the first element in the product composition as the component loss of the first element corresponding to the first historical operating parameter combination; wherein the first element is any element; Performing a weighted sum of the quality loss corresponding to the first historical operating parameter combination and the component losses of each element based on a preset weight to obtain the raw material loss index corresponding to the first historical operating parameter combination.

5. A method for overall energy conservation of a coke oven according to claim 1, characterized in that, Before calculating the raw material loss index of each candidate operating parameter combination based on the raw material loss model and the energy loss index of each candidate operating parameter combination based on the energy loss model, it further includes: Obtaining the energy loss indexes corresponding to a plurality of historical operating parameter combinations; Based on the energy loss indexes corresponding to each historical operating parameter combination, performing a correlation analysis on the operating parameter categories and the energy loss indexes to obtain the influencing factors of the energy loss indexes; Using the influencing factors of the energy loss indexes as input variables and the energy loss indexes as output variables for fitting to obtain the energy loss model.

6. The overall energy-saving method for coke ovens according to claim 5, characterized in that, The obtaining the energy loss indexes corresponding to a plurality of historical operating parameter combinations includes: Obtaining the water consumption, power consumption, total heat production, waste heat recovery amount, total gas production, and gas utilization amount corresponding to a plurality of historical operating parameter combinations; For each historical operating parameter combination, calculating the difference between the total heat production and the waste heat recovery amount corresponding to the historical operating parameter combination to obtain the heat loss, calculating the difference between the total gas production and the gas utilization amount to obtain the gas loss, and calculating the comprehensive energy consumption based on the water consumption, power consumption, heat loss, and gas loss as the energy loss index corresponding to the historical operating parameter combination.

7. A method for overall energy conservation of a coke oven according to claim 1, characterized in that The selecting the target operating parameter combination from each candidate operating parameter combination based on the raw material loss index and the energy loss index includes: Calculating the ratio of the average raw material loss index to the raw material loss index of the first candidate operating parameter combination as the first energy-saving score of the first candidate operating parameter combination; wherein, the first candidate operating parameter combination is any candidate operating parameter combination; Calculating the ratio of the average energy loss index to the energy loss index of the first candidate operating parameter combination as the second energy-saving score of the first candidate operating parameter combination; Calculating the average value of the first energy-saving score and the second energy-saving score of the first candidate operating parameter combination to obtain the comprehensive energy-saving score of the first candidate operating parameter combination; Taking the candidate operating parameter combination with the highest comprehensive energy-saving score among each candidate operating parameter combination as the target operating parameter combination.

8. An overall energy-saving system for coke ovens, characterized in that, It includes: A combination module for generating a plurality of candidate operating parameter combinations of the coke oven based on the production target, environmental parameters, and equipment parameters of the coke oven; wherein, each candidate operating parameter combination includes values of various types of operating parameters; A calculation module for calculating the raw material loss index of each candidate operating parameter combination based on the raw material loss model and the energy loss index of each candidate operating parameter combination based on the energy loss model; A selection module for selecting the target operating parameter combination from each candidate operating parameter combination based on the raw material loss index and the energy loss index to control the coke oven based on the target operating parameter combination.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7 above.

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

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