A method and system for overall energy saving of a coke oven, electronic equipment, and storage medium.

Abstract

This invention provides a method and system for overall energy saving in coke ovens, as well as electronic devices and storage media. The method includes: generating multiple 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 ​​for multiple types of operating parameters; for each candidate operating parameter combination, calculating a raw material loss index based on a raw material loss model and an energy loss index based on an energy loss model; and selecting a target operating parameter combination from the candidate operating parameter combinations based on the raw material loss index and the energy loss index, thereby controlling the coke oven based on the target operating parameter combination. This invention can reduce both raw material production losses and energy losses during the production process, thus achieving a comprehensive energy-saving effect.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control, and in particular to a method and system for overall energy saving of a coke oven, electronic equipment, and storage medium. Background Technology

[0002] In the iron and steel metallurgical industry, the coke oven, as one of the core pieces of equipment, is crucial to the production process. The coke oven operation mainly includes several key steps such as coal charging, heating (combustion), coking, quenching, and coke discharge. The operation of the coke oven involves not only complex chemical reactions but also a large amount of energy conversion and transfer. As the core equipment in the coking industry, the coke oven accounts for 70%-80% of the total energy consumption of the coking process, making it a key area for carbon emission reduction.

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

[0004] This invention provides a method and system for overall energy saving of coke ovens, electronic equipment, and 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 a method for overall energy saving of a coke oven, comprising:

[0006] Based on production targets, environmental parameters, and coke oven equipment parameters, multiple candidate combinations of operating parameters for the coke oven are generated; each candidate combination of operating parameters includes values ​​for multiple types of operating parameters.

[0007] For each candidate combination of operating parameters, the raw material loss index of the candidate combination of operating parameters is calculated based on the raw material loss model, and the energy loss index of the candidate combination of operating parameters is calculated based on the energy loss model.

[0008] Based on raw material loss indicators and energy loss indicators, a target operating parameter combination is selected from various candidate operating parameter combinations to control the coke oven.

[0009] In one possible implementation, based on production targets, environmental parameters, and coke oven equipment parameters, multiple candidate combinations of operating parameters for the coke oven are generated, including:

[0010] The range of the first candidate operating parameters is determined based on production targets;

[0011] The range of the second candidate operating parameters is determined based on environmental parameters;

[0012] The range of the third candidate operating parameters is determined based on the equipment parameters of the coke oven.

[0013] In the overlapping range 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 operating parameter combinations for the coke oven.

[0014] In one possible implementation, 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, the method further includes:

[0015] Obtain raw material loss indicators corresponding to multiple combinations of historical operating parameters;

[0016] Based on the raw material loss index corresponding to each historical combination of operating parameters, a correlation analysis was conducted between the operating parameter category and the raw material loss index to obtain the influencing factors of the raw material loss index.

[0017] By fitting the raw material loss index with the influencing factors as input variables and the raw material loss index as output variables, a raw material loss model is obtained.

[0018] In one possible implementation, raw material loss indicators corresponding to multiple combinations of historical operating parameters are obtained, including:

[0019] Obtain raw material parameters and product parameters corresponding to multiple historical operation parameter combinations; whereby raw material parameters include raw material quality and raw material composition, and product parameters include product quality and product composition; based on the raw material parameters and product parameters corresponding to each historical operation parameter combination,

[0020] Calculate the difference between the raw material quality and the product quality corresponding to the first historical operating parameter combination, and use it as the quality loss corresponding to the first historical operating parameter combination; wherein, the first historical operating parameter combination is any historical operating parameter combination.

[0021] 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 is calculated as the component loss of the first element corresponding to the first historical operating parameter combination; where the first element is any element.

[0022] Based on preset weights, the mass loss and component loss of each element corresponding to the first historical operating parameter combination are weighted and summed to obtain the raw material loss index corresponding to the first historical operating parameter combination.

[0023] In one possible implementation, 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, the method further includes:

[0024] Obtain energy loss indicators corresponding to multiple combinations of historical operating parameters;

[0025] Based on the energy loss index corresponding to each historical combination of operating parameters, a correlation analysis is conducted between the operating parameter category and the energy loss index to obtain the influencing factors of the energy loss index.

[0026] By fitting the influencing factors of energy loss index as input variables and the energy loss index as output variable, an energy loss model is obtained.

[0027] In one possible implementation, energy loss metrics corresponding to multiple historical combinations of operating parameters are obtained, including:

[0028] Obtain water consumption, electricity consumption, total heat production, waste heat recovery, total gas production, and gas utilization corresponding to multiple historical operating parameter combinations;

[0029] For each historical operating parameter combination, the heat loss is calculated by the difference between the total heat production and the waste heat recovery corresponding to that historical operating parameter combination. The gas loss is calculated by the difference between the total gas production and the gas utilization. The comprehensive energy consumption is calculated based on water consumption, electricity consumption, heat loss and gas loss, and is used as the energy loss index corresponding to that historical operating parameter combination.

[0030] In one possible implementation, a target operating parameter combination is selected from various candidate operating parameter combinations based on raw material loss indicators and energy loss indicators, including:

[0031] The ratio of the average raw material loss index to the raw material loss index of the first candidate operating parameter combination is calculated and used as the first energy-saving score of the first candidate operating parameter combination; wherein, the first candidate operating parameter combination is any combination of candidate operating parameters.

[0032] The ratio of the average energy loss index to the energy loss index of the first candidate operating parameter combination is calculated and used as the second energy saving score of the first candidate operating parameter combination.

[0033] Calculate the average 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;

[0034] The candidate operating parameter combination with the highest overall energy-saving score among all candidate operating parameter combinations is taken as the target operating parameter combination.

[0035] Secondly, embodiments of the present invention provide an overall energy-saving system for a coke oven, comprising:

[0036] The combination module is used to generate multiple candidate combinations of operating parameters for the coke oven based on production targets, environmental parameters, and equipment parameters of the coke oven; each candidate combination of operating parameters includes values ​​of multiple types of operating parameters.

[0037] The calculation module is used to calculate the raw material loss index of each candidate combination of operating parameters based on the raw material loss model and the energy loss index of each candidate combination of operating parameters based on the energy loss model.

[0038] The selection module is used to select a target operating parameter combination from various candidate operating parameter combinations based on raw material loss indicators and energy loss indicators, so as to control the coke oven based on the target operating parameter combination.

[0039] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0040] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0041] This invention provides a method and system for overall energy saving in coke ovens, electronic equipment, and storage medium. Based on production targets, environmental parameters, and coke oven equipment parameters, multiple possible combinations of operating parameters are generated. Using raw material loss models and energy loss models, simulation calculations are performed on each candidate combination of operating parameters to obtain the loss of raw materials and energy under specific operating parameters. Based on these two indicators, a target combination of operating parameters is selected, which can reduce both raw material production losses and energy losses during the production process, thereby achieving a comprehensive energy-saving effect. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1This is a flowchart illustrating the implementation of an overall energy-saving method for a coke oven provided in an embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of the structure of an overall energy-saving system for a coke oven provided in an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0046] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0047] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0048] See Figure 1 The diagram illustrates a flowchart of an overall energy-saving method for coke ovens provided by an embodiment of the present invention, which is described in detail below:

[0049] Step 101: Based on production targets, environmental parameters, and coke oven equipment parameters, generate multiple candidate operating parameter combinations for the coke oven; wherein each candidate operating parameter combination includes values ​​for multiple types of operating parameters.

[0050] In this embodiment, production targets refer to the main results or indicators that the coke oven hopes to achieve within a specific production cycle, such as output, product quality (e.g., coke strength), and cost control. Environmental parameters refer to external conditions that affect the operating efficiency and safety of the coke oven. These conditions are usually not under the direct control of the plant but have a significant impact on operation, such as ambient temperature, humidity, fluctuations in grid electricity prices, and weather changes. 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 burners, and the type and conditions of the waste heat recovery system.

[0051] Production targets clarify the direction of production. Changes in environmental parameters require coke ovens to dynamically adjust their operating parameters to adapt to real-time conditions. The capacity and status of the equipment directly determine the feasible range of certain operating parameters. Therefore, by considering production targets, environmental parameters, and coke oven equipment parameters, we can determine the possible combinations of candidate operating parameters and then select the best one to achieve overall energy-saving control of the coke oven.

[0052] This method can comprehensively consider various factors in coke oven operation, generate diverse parameter combinations, provide a scientific basis for subsequent optimization and decision-making, and thus achieve the goal of efficient, energy-saving and stable production.

[0053] Step 102: For each candidate combination of operating parameters, calculate the raw material loss index of the candidate combination of operating parameters based on the raw material loss model, and calculate the energy loss index of the candidate combination of operating parameters based on the energy loss model.

[0054] In this embodiment, calculating the raw material loss and energy loss indices for candidate operating parameter combinations is a key step in optimizing coke oven operation. 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 operating parameter combination.

[0055] Raw material loss models are used to predict the amount of raw materials required to produce a unit of product under specific operating conditions. They help identify factors that lead to high raw material losses and guide how to reduce these losses by adjusting operating parameters. Specifically, they can establish a linear relationship between raw material losses and key operating parameters based on historical data. Alternatively, considering the potentially complex nonlinear relationships in certain processes, advanced machine learning methods such as neural networks or support vector machines can be used for modeling. Models can also be built based on fundamental principles of material conversion processes (such as thermodynamics and kinetic equations) to more accurately describe the process of converting raw materials into products.

[0056] Energy loss models are used to estimate the total energy consumption of an entire production process under specific operating conditions. This includes direct energy inputs (such as the heat released from fuel combustion) and indirect energy losses (such as equipment heat dissipation and incomplete combustion). Through this model, energy-saving potential can be identified and improvement measures can be proposed. Specific forms may include:

[0057] Thermal equilibrium model: By analyzing all energy flows within the system, it ensures that the principle of energy conservation is followed.

[0058] Energy efficiency ratio model: Calculates the energy consumed per unit of product output, serving as an important indicator for measuring energy efficiency.

[0059] Integrated Energy Consumption Model: This model combines multiple energy forms (such as electricity, steam, fuel, etc.) and their conversion efficiencies to provide a comprehensive energy consumption assessment framework.

[0060] By using a pre-set model, the raw material loss index and energy loss index of each candidate combination of operating parameters can be calculated, thereby selecting the target combination of operating parameters that best meets the overall energy-saving requirements.

[0061] Step 103: Based on the raw material loss index and energy loss index, select the target operating parameter combination from the various candidate operating parameter combinations, and control the coke oven based on the target operating parameter combination.

[0062] In this embodiment, the raw material loss index and energy loss index represent two key aspects of the production process: the efficient utilization of resources and the efficient use of energy. In practice, reducing raw material loss and energy loss often conflict. For example, reducing raw material loss may require adding certain processing steps, but this could lead to additional energy consumption.

[0063] In this embodiment, by quantitatively analyzing raw material loss and energy loss, a solid scientific basis can be provided for decision-making, avoiding decisions based solely on experience or intuition, finding a balance between reducing raw material loss and energy loss, and achieving overall energy saving of the coke oven.

[0064] According to the embodiments of the present invention, multiple possible combinations of operating parameters are generated based on production targets, environmental parameters, and equipment parameters of the coke oven. The raw material loss model and energy loss model are used to simulate and calculate each candidate combination of operating parameters to obtain the loss of raw materials and energy under specific operating parameters. The target combination of operating parameters is selected based on the two indicators, which can reduce the production loss of raw materials and reduce the energy loss in the production process, thereby achieving a comprehensive energy-saving effect.

[0065] In one possible implementation, based on production targets, environmental parameters, and coke oven equipment parameters, multiple candidate combinations of operating parameters for the coke oven are generated, including:

[0066] The range of the first candidate operating parameters is determined based on production targets;

[0067] The range of the second candidate operating parameters is determined based on environmental parameters;

[0068] The range of the third candidate operating parameters is determined based on the equipment parameters of the coke oven.

[0069] In the overlapping range 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 operating parameter combinations for the coke oven.

[0070] In this embodiment, the operating parameters of the coke oven include multiple variables, each with a certain range of values. This multi-dimensional parameter space allows for great flexibility in the combination of candidate operating parameters.

[0071] Specifically, production targets can include indicators such as output, quality, cold strength, and hot strength; environmental parameters can include humidity, temperature, and wind speed; equipment parameters can include furnace dimensions, heating parameters, and furnace structure; and operating parameters can include coal handling parameters, coal charging parameters, carbonization parameters, quenching parameters, cooling parameters, and furnace operating parameters. Based on the specific production targets, environmental parameters, and coke oven equipment parameters, upper and lower limits for some related categories of operating parameters can be determined. The specific relationships are as follows:

[0072] 1. Coal processing parameters

[0073] Coal type: determined based on production objectives (such as coke quality standards) and raw material availability.

[0074] Coal particle size: The optimal particle size range is determined through experiments to meet the requirements of carbonization efficiency and coke quality.

[0075] Coal moisture content: The moisture control range is set based on the characteristics of the raw materials (natural moisture content) and production targets (such as carbonization efficiency).

[0076] Coal bulk density: determined based on silo design and requirements for preventing spontaneous combustion of coal.

[0077] 2. Coal charging parameters

[0078] Coal loading method: determined by equipment type and production process.

[0079] Coal loading speed: determined based on production efficiency and the design capacity of the carbonization chamber.

[0080] Coal loading density: determined through experiments and simulations to optimize carbonization efficiency and coke quality.

[0081] 3. Carbonization parameters

[0082] Heating time: Determined based on coal type, coke quality, and production efficiency requirements.

[0083] Heating temperature: set according to the pyrolysis characteristics of coal, coke quality standards and environmental protection requirements.

[0084] Heating rate: determined experimentally to ensure coke quality and production safety.

[0085] Carbonization chamber pressure: set according to the carbonization process and equipment capacity.

[0086] 4. Coke quenching parameters

[0087] Quenching method: Selected according to environmental protection requirements and coke quality standards.

[0088] Quenching time: determined through experiments to ensure that the coke cools to a safe temperature.

[0089] 5. Cooling parameters

[0090] Cooling method: Selected based on production efficiency and environmental protection requirements.

[0091] Cooling time: Determined based on the cooling rate of the coke and safety requirements.

[0092] 6. Furnace operating parameters

[0093] Furnace body temperature distribution: determined through heat balance calculations and equipment capacity.

[0094] Furnace pressure distribution: determined based on carbonization process and equipment design.

[0095] Furnace heating control: set according to the heating curve and the calorific value of the gas.

[0096] For each operating parameter, three settable ranges are determined based on the constraints imposed by production targets, environmental parameters, and equipment parameters. If the value of a certain operating parameter is not affected by production targets, then all selectable values ​​of that operating parameter are taken as the first operating parameter range determined based on production targets. The same principle applies to environmental parameters or equipment parameters.

[0097] Identify the overlapping portion of the three ranges mentioned above. This overlapping interval represents the operational parameter space that simultaneously satisfies production targets, environmental conditions, and equipment limitations. Then, within this overlapping interval, select all feasible values ​​for each type of operational parameter and combine these values ​​to form multiple candidate operational parameter combinations that comprehensively consider various factors of coke oven operation.

[0098] For example, a coking plant's production target is high strength (M40 crushing strength ≥85%) and low ash content (≤10%). The environmental parameters are winter, with low ambient temperature (-5℃), requiring attention to insulation measures during coal loading. The equipment parameters are a maximum coke oven heating power of 10MW, a furnace body temperature resistance limit of 1100℃, and a burner efficiency of 90%.

[0099] The characteristics of coal types available on the market are as follows:

[0100] Coal type A: Volatile matter = 25%, Ash content = 8%, Sulfur content = 0.6%, Caking index G = 75.

[0101] Coal type B: Volatile matter = 28%, Ash content = 10%, Sulfur content = 0.8%, Caking index G = 85.

[0102] Coal type C: Volatile matter = 22%, Ash content = 6%, Sulfur content = 0.5%, Caking index G = 90.

[0103] To meet production targets, high-strength coke typically requires a high caking index (G value). General experience suggests that coal types with a G value ≥ 80 are more likely to meet these requirements. Ash content directly affects the quality and strength of coke; therefore, coal types with an ash content ≤ 10% should be selected. Based on this, coal types B and C are determined to meet the production target requirements, and the first candidate operating parameter ranges for these coal types are B and C.

[0104] Regarding environmental parameters, low temperatures in winter may lead to increased heat loss during coal loading. Therefore, it is necessary to select coal with moderate volatile matter (20%-30%) to ensure combustion stability and thermal efficiency. Based on this, coal types A, B, and C all meet the requirements of environmental parameters, and the second candidate operating parameter range for coal types is A, B, and C.

[0105] Regarding equipment parameters, excessively high volatile matter content (>30%) may lead to unstable combustion, exceeding the equipment's heating power limit (10MW). Therefore, it is essential to ensure that the volatile matter content of the coal is within a reasonable range. The ash fusion point of the coal must be higher than the furnace body's temperature resistance limit (1100℃) to avoid furnace damage. With a burner efficiency of 90%, coal with low sulfur content (<1%) should be selected to reduce [burden / damage]. This improves emissions and combustion efficiency. Based on this, coal types B and C are determined to meet the equipment parameter requirements, thus the third candidate operating parameter range for coal types is B and C.

[0106] Finally, values ​​are selected from their overlapping intervals, and the values ​​of coal type B and coal type C are combined with the values ​​of other operating parameters to obtain multiple candidate operating parameter combinations for the coke oven.

[0107] In one possible implementation, 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, the method further includes:

[0108] Obtain raw material loss indicators corresponding to multiple combinations of historical operating parameters;

[0109] Based on the raw material loss index corresponding to each historical combination of operating parameters, a correlation analysis was conducted between the operating parameter category and the raw material loss index to obtain the influencing factors of the raw material loss index.

[0110] By fitting the raw material loss index with the influencing factors as input variables and the raw material loss index as output variables, a raw material loss model is obtained.

[0111] In this embodiment, the historical operating parameter combination refers to the specific set of values ​​for various operating parameters used in the coke oven production process. For example, different combinations of values ​​for parameters such as air / fuel ratio and heating rate can constitute 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 accuracy when calculating raw material loss indicators in the future.

[0112] 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 the correlation. Then, factors with significant influence can be screened based on the correlation coefficient (for example, setting a threshold to retain only factors with an absolute correlation coefficient greater than 0.5). Finally, using the screened influencing factors as input variables and the raw material loss index as output variables, an appropriate mathematical method (such as linear regression, neural networks, etc.) can be used to construct a model that can predict the raw material loss index.

[0113] Once the model is built, simply inputting the values ​​of the influencing factors that belong to the raw material loss index in the candidate operating parameter combination into the model will yield the raw material loss index of the candidate operating parameter combination.

[0114] In one possible implementation, raw material loss indicators corresponding to multiple combinations of historical operating parameters are obtained, including:

[0115] Obtain raw material parameters and product parameters corresponding to multiple historical operation parameter combinations; whereby raw material parameters include raw material quality and raw material composition, and product parameters include product quality and product composition; based on the raw material parameters and product parameters corresponding to each historical operation parameter combination,

[0116] Calculate the difference between the raw material quality and the product quality corresponding to the first historical operating parameter combination, and use it as the quality loss corresponding to the first historical operating parameter combination; wherein, the first historical operating parameter combination is any historical operating parameter combination.

[0117] 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 is calculated as the component loss of the first element corresponding to the first historical operating parameter combination; where the first element is any element.

[0118] Based on preset weights, the mass loss and component loss of each element corresponding to the first historical operating parameter combination are weighted and summed to obtain the raw material loss index corresponding to the first historical operating parameter combination.

[0119] In this embodiment, by accurately measuring and recording the quality of input and output materials to perform quality balance analysis, the total amount of coal entering the coke oven in each batch is recorded and compared with the quality of by-products and waste (such as ash) produced, such as coke, coke oven gas, tar, ammonia water, etc., to calculate the theoretical raw material loss rate.

[0120] By analyzing the chemical composition of raw coal and its products, such as the changes in the proportions of elements like carbon, hydrogen, oxygen, nitrogen, and sulfur, the efficiency of converting useful components in the raw materials into final products can be evaluated.

[0121] Finally, by combining the weights of mass loss and loss of each element, various losses are comprehensively quantified, allowing for the evaluation of raw material utilization efficiency under different combinations of operating parameters. The weights of mass loss and loss of each element can be determined using the analytic hierarchy process (AHP).

[0122] In one possible implementation, 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, the method further includes:

[0123] Obtain energy loss indicators corresponding to multiple combinations of historical operating parameters;

[0124] Based on the energy loss index corresponding to each historical combination of operating parameters, a correlation analysis is conducted between the operating parameter category and the energy loss index to obtain the influencing factors of the energy loss index.

[0125] By fitting the influencing factors of energy loss index as input variables and the energy loss index as output variable, an energy loss model is obtained.

[0126] In this embodiment, similar to the construction process of the raw material loss model, historical data is statistically analyzed to calculate the correlation coefficient (such as the Pearson correlation coefficient) between each operating parameter and the energy loss index, determining the strength of the correlation. Then, factors with significant influence are selected based on the correlation coefficients (for example, a threshold is set to retain only factors with an absolute correlation coefficient greater than 0.5). Finally, using the selected influencing factors as input variables and the energy loss index as the output variable, an appropriate mathematical method (such as linear regression, neural networks, etc.) is employed to construct a model capable of predicting the energy loss index based on the specific changing relationships.

[0127] Once the model is built, simply inputting the values ​​of the factors influencing the energy loss index from the candidate operating parameter combinations into the model will yield the energy loss index for the candidate operating parameter combinations.

[0128] In one possible implementation, energy loss metrics corresponding to multiple historical combinations of operating parameters are obtained, including:

[0129] Obtain water consumption, electricity consumption, total heat production, waste heat recovery, total gas production, and gas utilization corresponding to multiple historical operating parameter combinations;

[0130] For each historical operating parameter combination, the heat loss is calculated by the difference between the total heat production and the waste heat recovery corresponding to that historical operating parameter combination. The gas loss is calculated by the difference between the total gas production and the gas utilization. The comprehensive energy consumption is calculated based on water consumption, electricity consumption, heat loss and gas loss, and is used as the energy loss index corresponding to that historical operating parameter combination.

[0131] In this embodiment, water consumption refers to the total water consumed during production under a specific combination of operating parameters, including cooling water and quenching water. Electricity consumption refers to the total electricity consumed during production under a specific combination of operating parameters, typically used for driving equipment and lighting. Total heat production refers to the total heat generated during production under a specific combination of operating parameters, mainly derived from the energy released by fuel combustion. Waste heat recovery refers to the heat recovered and reused from waste gas or other waste heat sources through a waste heat recovery system under a specific combination of operating parameters. Total gas production refers to the total amount of gas generated during production under a specific combination of operating parameters, typically a byproduct. Gas utilization refers to the amount of gas actually effectively utilized under a specific combination of operating parameters; unutilized portions are considered waste or discharged. Heat loss is the difference between total heat production and waste heat recovery, reflecting the heat lost due to ineffective utilization during production. Gas loss is the difference between total gas production and gas utilization, reflecting the amount of gas lost due to ineffective utilization during production.

[0132] Comprehensive energy consumption can be calculated based on water consumption, electricity consumption, heat loss, and gas loss. By using appropriate conversion factors to unify each energy consumption to the same energy unit (such as ton of standard coal or megawatt-hour), and then weighted summing, a comprehensive energy consumption index can be obtained to assess the energy consumption level in the production process.

[0133] In one possible implementation, a target operating parameter combination is selected from various candidate operating parameter combinations based on raw material loss indicators and energy loss indicators, including:

[0134] The ratio of the average raw material loss index to the raw material loss index of the first candidate operating parameter combination is calculated and used as the first energy-saving score of the first candidate operating parameter combination; wherein, the first candidate operating parameter combination is any combination of candidate operating parameters.

[0135] The ratio of the average energy loss index to the energy loss index of the first candidate operating parameter combination is calculated and used as the second energy saving score of the first candidate operating parameter combination.

[0136] Calculate the average 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;

[0137] The candidate operating parameter combination with the highest overall energy-saving score among all candidate operating parameter combinations is taken as the target operating parameter combination.

[0138] 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 a benchmark reference.

[0139] The first energy-saving score is used to evaluate the raw material saving performance of a candidate combination of operating parameters. By comparing the raw material loss index of a candidate combination with the average raw material loss index, the relative performance of that combination in terms of raw material saving can be assessed. Directly using loss indices for ranking only reflects the absolute value, but cannot intuitively show the degree of improvement of a candidate combination compared to a benchmark (such as the historical average). Calculating the energy-saving score using a ratio provides a more intuitive reflection of energy-saving effects, improves sensitivity to small improvements, identifies subtle but important optimization points, provides a benchmark reference, and enhances the comparability between candidate combinations. The larger the ratio, the higher the raw material utilization rate of the combination.

[0140] The second energy-saving score is used to evaluate the energy-saving performance of a candidate combination of operating parameters. By comparing the energy loss index of a candidate combination of operating parameters with the average energy loss index, the relative energy-saving performance of the combination can be evaluated. The larger the ratio, the higher the energy utilization efficiency of the combination.

[0141] By averaging the first and second energy-saving scores, a quantitative score of the overall energy-saving performance of the candidate operating parameter combination can be obtained. This eliminates the influence of dimensions and numerical range, allowing for a more scientific and reasonable evaluation of the comprehensive energy-saving effect of the candidate operating parameter combination, and ultimately helping to select the optimal solution.

[0142] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0143] The following are system embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0144] Figure 2 A schematic diagram of an overall energy-saving system for a coke oven according to an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0145] like Figure 2 As shown, a coke oven overall energy-saving system 2 includes:

[0146] The combination module 21 is used to generate multiple 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 multiple types of operating parameters;

[0147] Calculation module 22 is used to calculate the raw material loss index of each candidate combination of operating parameters based on the raw material loss model and the energy loss index of each candidate combination of operating parameters based on the energy loss model.

[0148] Module 23 is used to select a target operating parameter combination from various candidate operating parameter combinations based on raw material loss index and energy loss index, so as to control the coke oven based on the target operating parameter combination.

[0149] In one possible implementation, the compositing module 21 is specifically used for:

[0150] The range of the first candidate operating parameters is determined based on production targets;

[0151] The range of the second candidate operating parameters is determined based on environmental parameters;

[0152] The range of the third candidate operating parameters is determined based on the equipment parameters of the coke oven.

[0153] In the overlapping range 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 operating parameter combinations for the coke oven.

[0154] In one possible implementation, the computing module 22 is also used for:

[0155] 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 the candidate operating parameter combination based on the energy loss model, the raw material loss index corresponding to multiple historical operating parameter combinations is obtained.

[0156] Based on the raw material loss index corresponding to each historical combination of operating parameters, a correlation analysis was conducted between the operating parameter category and the raw material loss index to obtain the influencing factors of the raw material loss index.

[0157] By fitting the raw material loss index with the influencing factors as input variables and the raw material loss index as output variables, a raw material loss model is obtained.

[0158] In one possible implementation, the computing module 22 is specifically used for:

[0159] Obtain raw material parameters and product parameters corresponding to multiple historical operation parameter combinations; whereby raw material parameters include raw material quality and raw material composition, and product parameters include product quality and product composition; based on the raw material parameters and product parameters corresponding to each historical operation parameter combination,

[0160] Calculate the difference between the raw material quality and the product quality corresponding to the first historical operating parameter combination, and use it as the quality loss corresponding to the first historical operating parameter combination; wherein, the first historical operating parameter combination is any historical operating parameter combination.

[0161] 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 is calculated as the component loss of the first element corresponding to the first historical operating parameter combination; where the first element is any element.

[0162] Based on preset weights, the mass loss and component loss of each element corresponding to the first historical operating parameter combination are weighted and summed to obtain the raw material loss index corresponding to the first historical operating parameter combination.

[0163] In one possible implementation, the computing module 22 is also used for:

[0164] 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, the energy loss index corresponding to multiple historical operating parameter combinations is obtained.

[0165] Based on the energy loss index corresponding to each historical combination of operating parameters, a correlation analysis is conducted between the operating parameter category and the energy loss index to obtain the influencing factors of the energy loss index.

[0166] By fitting the influencing factors of energy loss index as input variables and the energy loss index as output variable, an energy loss model is obtained.

[0167] In one possible implementation, the computing module 22 is specifically used for:

[0168] Obtain water consumption, electricity consumption, total heat production, waste heat recovery, total gas production, and gas utilization corresponding to multiple historical operating parameter combinations;

[0169] For each historical operating parameter combination, the heat loss is calculated by the difference between the total heat production and the waste heat recovery corresponding to that historical operating parameter combination. The gas loss is calculated by the difference between the total gas production and the gas utilization. The comprehensive energy consumption is calculated based on water consumption, electricity consumption, heat loss and gas loss, and is used as the energy loss index corresponding to that historical operating parameter combination.

[0170] In one possible implementation, module 23 is specifically used for:

[0171] The ratio of the average raw material loss index to the raw material loss index of the first candidate operating parameter combination is calculated and used as the first energy-saving score of the first candidate operating parameter combination; wherein, the first candidate operating parameter combination is any combination of candidate operating parameters.

[0172] The ratio of the average energy loss index to the energy loss index of the first candidate operating parameter combination is calculated and used as the second energy saving score of the first candidate operating parameter combination.

[0173] Calculate the average 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;

[0174] The candidate operating parameter combination with the highest overall energy-saving score among all candidate operating parameter combinations is taken as the target operating parameter combination.

[0175] According to the embodiments of the present invention, multiple possible combinations of operating parameters are generated based on production targets, environmental parameters, and equipment parameters of the coke oven. The raw material loss model and energy loss model are used to simulate and calculate each candidate combination of operating parameters to obtain the loss of raw materials and energy under specific operating parameters. The target combination of operating parameters is selected based on the two indicators, which can reduce the production loss of raw materials and reduce the energy loss in the production process, thereby achieving a comprehensive energy-saving effect.

[0176] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 in 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, it implements the steps described in each of the above embodiments of the overall energy-saving method for coke ovens, for example... Figure 1 Steps 101 to 103 are shown. Alternatively, when processor 30 executes computer program 32, it implements the functions of each module / unit in the above system embodiments, for example... Figure 2 The functions of modules / units 21 to 23 shown.

[0177] For example, computer program 32 can be divided into one or more modules / units, one or more of which are stored in memory 31 and executed by processor 30 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3. For example, computer program 32 can be divided into... Figure 2 Modules / units 21 to 23 are shown.

[0178] Electronic device 3 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0179] The processor 30 may be a Central Processing Unit (CPU), or 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. A general-purpose processor may be a microprocessor or any conventional processor.

[0180] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 31 can include both internal and external storage units 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 can also be used to temporarily store data that has been output or will be output.

[0181] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0182] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0183] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0184] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0185] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of each of the above embodiments of the overall energy-saving method for coke ovens. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed 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.

[0188] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for overall energy saving in a coke oven, characterized in that, include: Based on production targets, environmental parameters, and coke oven equipment parameters, multiple candidate combinations of operating parameters for the coke oven are generated; wherein each candidate combination of operating parameters includes values ​​for multiple types of operating parameters. Based on production targets, environmental parameters, and coke oven equipment parameters, multiple candidate combinations of operating parameters for the coke oven are generated, including: The range of the first candidate operating parameters is determined based on production targets; The range of the second candidate operating parameters is determined based on environmental parameters; The range of the third candidate operating parameters is determined based on the equipment parameters of the coke oven. In the overlapping range of the first candidate operating parameter range, the second candidate operating parameter range, and the third candidate operating parameter range, the value of each type of operating parameter is selected and combined to obtain multiple candidate operating parameter combinations for the coke oven. For each candidate combination of operating parameters, the raw material loss index of the candidate combination of operating parameters is calculated based on the raw material loss model, and the energy loss index of the candidate combination of operating parameters is calculated based on the energy loss model. Based on raw material loss indicators and energy loss indicators, a target operating parameter combination is selected from various candidate operating parameter combinations, so as to control the coke oven based on the target operating parameter combination; The selection of the target operating parameter combination from various candidate operating parameter combinations based on raw material loss indicators and energy loss indicators includes: The ratio of the average raw material loss index to the raw material loss index of the first candidate operating parameter combination is calculated and used as the first energy-saving score of the first candidate operating parameter combination; wherein, the first candidate operating parameter combination is any combination of candidate operating parameters. The ratio of the average energy loss index to the energy loss index of the first candidate operating parameter combination is calculated and used as the second energy saving score of the first candidate operating parameter combination. Calculate the average 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; The candidate operating parameter combination with the highest overall energy-saving score among all candidate operating parameter combinations is taken as the target operating parameter combination.

2. The method for overall energy saving 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, the method further includes: Obtain raw material loss indicators corresponding to multiple combinations of historical operating parameters; Based on the raw material loss index corresponding to each historical combination of operating parameters, a correlation analysis was conducted between the operating parameter category and the raw material loss index to obtain the influencing factors of the raw material loss index. By fitting the raw material loss index with the influencing factors as input variables and the raw material loss index as output variables, the raw material loss model is obtained.

3. The method for overall energy saving of a coke oven according to claim 2, characterized in that, The process of obtaining raw material loss indicators corresponding to multiple combinations of historical operating parameters includes: Obtain raw material parameters and product parameters corresponding to multiple historical operation 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; based on the raw material parameters and product parameters corresponding to each historical operation parameter combination, calculate the difference between the raw material quality and product quality corresponding to the first historical operation parameter combination, as the quality loss corresponding to the first historical operation parameter combination; wherein, the first historical operation parameter combination is any historical operation parameter combination; The difference between the proportion of the 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 is calculated as the component loss of the first element corresponding to the first historical operating parameter combination; wherein, the first element is any element. Based on preset weights, the mass loss and component loss of each element corresponding to the first historical operation parameter combination are weighted and summed to obtain the raw material loss index corresponding to the first historical operation parameter combination.

4. The method for overall energy saving 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, the method further includes: Obtain energy loss indicators corresponding to multiple combinations of historical operating parameters; Based on the energy loss index corresponding to each historical combination of operating parameters, a correlation analysis is conducted between the operating parameter category and the energy loss index to obtain the influencing factors of the energy loss index. By fitting the influencing factors of energy loss index as input variables and the energy loss index as output variable, the energy loss model is obtained.

5. The method for overall energy saving of a coke oven according to claim 4, characterized in that, The process of obtaining energy loss indices corresponding to multiple combinations of historical operating parameters includes: Obtain water consumption, electricity consumption, total heat production, waste heat recovery, total gas production, and gas utilization corresponding to multiple historical operating parameter combinations; For each historical operating parameter combination, the heat loss is calculated by the difference between the total heat production and the waste heat recovery corresponding to that historical operating parameter combination. The gas loss is calculated by the difference between the total gas production and the gas utilization. The comprehensive energy consumption is calculated based on water consumption, electricity consumption, heat loss and gas loss, and is used as the energy loss index corresponding to that historical operating parameter combination.

6. A coke oven overall energy-saving system, characterized in that, include: The combination module is used to generate multiple 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 multiple types of operating parameters; The composite module is specifically used for: The range of the first candidate operating parameters is determined based on production targets; The range of the second candidate operating parameters is determined based on environmental parameters; The range of the third candidate operating parameters is determined based on the equipment parameters of the coke oven. In the overlapping range of the first candidate operating parameter range, the second candidate operating parameter range, and the third candidate operating parameter range, the value of each type of operating parameter is selected and combined to obtain multiple candidate operating parameter combinations for the coke oven. The calculation module is used to calculate the raw material loss index of each candidate combination of operating parameters based on the raw material loss model and the energy loss index of each candidate combination of operating parameters based on the energy loss model. The selection module is used to select a target operating parameter combination from various candidate operating parameter combinations based on raw material loss index and energy loss index, so as to control the coke oven based on the target operating parameter combination; The selected module is specifically used for: The ratio of the average raw material loss index to the raw material loss index of the first candidate operating parameter combination is calculated and used as the first energy-saving score of the first candidate operating parameter combination; wherein, the first candidate operating parameter combination is any combination of candidate operating parameters. The ratio of the average energy loss index to the energy loss index of the first candidate operating parameter combination is calculated and used as the second energy saving score of the first candidate operating parameter combination. Calculate the average 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; The candidate operating parameter combination with the highest overall energy-saving score among all candidate operating parameter combinations is taken as the target operating parameter combination.

7. 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 as described in any one of claims 1 to 5 above.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5 above.

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