A converter gas system regulation method and device, electronic equipment and storage medium

Abstract

The present application provides a kind of converter gas system regulation method, device, electronic equipment and storage medium, by obtaining converter gas system information, the production plan of future time period is input to converter gas recovery and consumption prediction model, obtain the coal gas recovery amount prediction value and gas consumption amount prediction value of future time period, establish converter gas tank collaborative optimization regulation model, the coal gas recovery amount prediction value and gas consumption amount prediction value are input converter gas tank collaborative optimization regulation model, obtain the opening of each converter gas tank import and export regulating valve and the optimization regulation amount of the operating frequency of each converter gas pressurizer in preset period, obtain regulation instruction according to optimization regulation amount, based on regulation instruction control the opening of each converter gas tank import and export regulating valve and the operating frequency of each converter gas pressurizer in preset period, to regulate converter gas system;More practical, more accurate converter gas system regulation method is provided by the above method, so that converter gas system is safely and efficiently operated.

Description

A method, device, electronic equipment and storage medium for regulating a converter gas system Technical Field

[0001] This invention relates to the field of metallurgical industry, specifically to a method, device, electronic equipment, and storage medium for regulating a converter gas system. Background Technology

[0002] Converter gas is a byproduct produced during the production process of iron and steel metallurgical converters. Its main components are hydrogen, carbon monoxide, and carbon dioxide. It has the characteristics of high calorific value and low pollution, and is mostly used as fuel for power generation, heating, and industrial production.

[0003] The converter gas system is a complex energy network system consisting of converters, gas pipelines and valves, gas holders, pressurization stations, and various converter gas users. Due to the dynamic nature of production at the converter and its users, the converter gas system requires dynamic adjustment to balance gas supply and demand based on actual changes at both the production and consumption ends. With the increasing scale of enterprise production, most companies have built two or more converter gas holders to buffer gas supply and demand. The capacity of each gas holder is controlled by adjusting the valve openings and the frequency of the pressurization units to achieve optimal buffering and regulation capabilities. However, in actual production, due to the complexity of the system, adjustments rely mainly on personnel monitoring key parameters and personal experience, which can easily lead to untimely and inaccurate adjustments, resulting in gas pressure fluctuations and venting.

[0004] It should be noted that the above content only provides background information related to the present invention and does not necessarily constitute prior art. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the present invention provides a converter gas system control method, device, electronic device and storage medium to solve at least one of the above technical problems.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to one aspect of the present invention, a method for regulating a converter gas system is provided, comprising: acquiring converter gas system information, wherein the converter gas system information includes smelting records of each converter, gas recovery volume of each converter, gas consumption of each converter gas user, production plans of each converter and each converter gas user, capacity of each converter gas holder, opening degree of inlet and outlet regulating valves of each converter gas holder, operating frequency of each converter gas compressor, and a simulation model of the converter gas pipeline system; inputting the production plan for a future time period into a converter gas recovery and consumption prediction model to obtain predicted values ​​of gas recovery volume and gas consumption for the future time period, wherein the converter gas recovery and consumption prediction model is trained from smelting records, historical data of gas recovery volume, and historical data of gas consumption for past time periods, wherein the production plan for a future time period is... The production plan corresponds to the smelting plan in the smelting record; based on the simulation model of the converter gas pipeline system, using the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, and aiming to minimize the relative tolerance of each converter gas holder, a converter gas holder collaborative optimization control model is established. The predicted values ​​of gas recovery and gas consumption are input into the converter gas holder collaborative optimization control model to obtain the optimized control amount of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period; control instructions are obtained according to the optimized control amount, and the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the preset period are controlled based on the control instructions to regulate the converter gas system.

[0008] In one embodiment of the present invention, based on the aforementioned scheme, the converter gas recovery and consumption prediction model is trained from smelting records, historical data of gas recovery, and historical data of gas consumption over a past period, including: acquiring smelting records, historical data of gas recovery, and historical data of gas consumption over a past period; training a preset converter gas recovery and consumption prediction model based on the smelting records, historical data of gas recovery, and historical data of gas consumption over the past period to obtain the converter gas recovery and consumption prediction model.

[0009] In one embodiment of the present invention, after obtaining the predicted values ​​of gas recovery and gas consumption for the future time period based on the aforementioned scheme, the method further includes: randomly varying the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset control range to obtain an initial scheme set with a preset number of schemes; iteratively calculating the initial scheme set according to the relative cabinet tolerance and intelligent optimization algorithm; if the preset iterative calculation exit condition is met, outputting the control scheme with the minimum relative cabinet tolerance; and obtaining the optimized control amount of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the preset period according to the control scheme.

[0010] In one embodiment of the present invention, based on the aforementioned scheme, the initial scheme set is iteratively calculated according to the relative cabinet tolerance and the intelligent optimization algorithm, including: using the predicted values ​​of gas recovery and gas consumption in the future time period as boundary conditions, and calculating the predicted cabinet capacity of each converter gas holder corresponding to each initial scheme in the initial scheme set in the future time period through the converter gas pipeline network system simulation model; calculating the relative cabinet tolerance of each converter gas holder corresponding to each initial scheme in the future time period according to the objective function of the converter gas holder collaborative optimization control model; and iteratively calculating the initial scheme set corresponding to the relative cabinet tolerance according to the intelligent optimization algorithm.

[0011] In one embodiment of the present invention, based on the aforementioned scheme, the initial scheme set is iteratively calculated according to the relative cabinet tolerance and the intelligent optimization algorithm. If the preset iterative calculation exit condition is met, the control scheme with the smallest relative cabinet tolerance is output, including at least one of the following: counting the number of iterations; if the number of iterations is greater than the preset number of iterations, stopping the iterative calculation and outputting the control scheme with the smallest relative cabinet tolerance in the iterative calculation; calculating the absolute deviation between the minimum relative cabinet tolerance of the current iteration and the minimum relative cabinet tolerance of the previous iteration; if the absolute deviation is less than the preset residual, stopping the iterative calculation and outputting the control scheme with the smallest relative cabinet tolerance in all iterations.

[0012] In one embodiment of the present invention, based on the aforementioned scheme, the initial scheme set is iteratively calculated according to the relative cabinet tolerance and the intelligent optimization algorithm, and further includes: if the number of iterations is less than or equal to the preset number of iterations and the absolute deviation is greater than or equal to the preset residual, then the initial scheme set is iteratively calculated according to the intelligent optimization algorithm; the target achievement degree of each scheme is calculated, and the optimal scheme set is updated according to the iterative calculation mechanism of the optimization algorithm based on the target achievement degree, and the optimal scheme set is iteratively calculated according to the intelligent optimization algorithm until the preset iterative calculation exit condition is met.

[0013] In one embodiment of the present invention, based on the foregoing scheme, the converter gas system control method further includes: acquiring configuration information of the converter gas pipeline network system; establishing a simulation model of the converter gas pipeline network system based on the configuration information of the converter gas pipeline network system, wherein the configuration information of the converter gas pipeline network system includes information on each section of the pipeline in the gas pipeline network, the pipeline information including the length, outer diameter, wall thickness, and absolute roughness of the pipeline, the resistance characteristic curve of the valve, the operating characteristic curve of the compressor, the equipment size, operating pressure limit and piston speed limit of the gas holder, and the connection relationship between each gas pipeline, valve, gas holder, compressor, converter and converter gas user.

[0014] According to one aspect of the present invention, a converter gas system control device is provided, comprising: an acquisition module configured to acquire converter gas system information, the converter gas system information including smelting records of each converter, gas recovery amount of each converter, gas consumption of each converter gas user, production plans of each converter and each converter gas user, capacity of each converter gas holder, opening degree of inlet and outlet regulating valves of each converter gas holder, operating frequency of each converter gas compressor, and a converter gas pipeline system simulation model; and a first processing module configured to input the production plan for a future time period into a converter gas recovery and consumption prediction model to obtain predicted values ​​of gas recovery amount and gas consumption for the future time period, the converter gas recovery and consumption prediction model being trained from smelting records, historical data of gas recovery amount, and historical data of gas consumption for past time periods. The production plan corresponds to the smelting plan in the smelting record; the second processing module is configured to establish a converter gas holder collaborative optimization control model based on the converter gas pipeline network system simulation model, using the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, with the goal of minimizing the relative tolerance of each converter gas holder, and inputting the predicted value of gas recovery and the predicted value of gas consumption into the converter gas holder collaborative optimization control model to obtain the optimized control amount of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period; the control module is configured to obtain control instructions based on the optimized control amount, and control the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the preset period based on the control instructions, so as to control the converter gas system.

[0015] According to one aspect of the present invention, an electronic device is provided, the electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the converter gas system control method as described in any of the above embodiments.

[0016] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform the converter gas system control method as described in any of the above embodiments.

[0017] The beneficial effects of this invention are as follows: This invention provides a method, device, electronic equipment, and storage medium for regulating a converter gas system. Based on the actual dynamic operation of the converter gas generation and consumption ends, this invention optimizes and adjusts in real time the opening degree of the inlet and outlet valves of each gas holder in the converter gas pipeline network and the operating frequency of the gas compressor. This makes the regulation of the converter gas system more timely and precise, achieving feedforward optimization regulation to balance the supply and demand of the converter gas system. It also fully utilizes the dynamic buffering and regulation capacity of the converter gas holders, ensuring the safe and efficient operation of the converter gas system.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0020] Figure 1 is a schematic diagram illustrating an exemplary system architecture of an exemplary embodiment of this application;

[0021] Figure 2 is a schematic flowchart illustrating a converter gas system control method in an exemplary embodiment of this application;

[0022] Figure 3 is a schematic diagram of the calculation flow of a converter gas system control method according to an exemplary embodiment of this application;

[0023] Figure 4 is a schematic diagram of the system structure of the converter gas system control system shown in an exemplary embodiment of this application;

[0024] Figure 5 is a schematic diagram of the converter gas pipeline network system of the converter gas system control system shown in an exemplary embodiment of this application;

[0025] Figure 6 is a block diagram of a converter gas system control device illustrating an exemplary embodiment of this application;

[0026] Figure 7 shows a schematic diagram of the structure of a computer system suitable for implementing an electronic device according to embodiments of the present invention. Detailed Implementation

[0027] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0028] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0029] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0030] First, it should be noted that a gas holder is a steel container for storing industrial and domestic coal gas, and there are two types: wet gas holders and dry gas holders. A wet gas holder is a cylindrical, tubular structure sealed with water; a dry gas holder is a piston-type structure sealed with thin oil, dry oil, or a flexible diaphragm. The bell, tower, and piston of the gas holder are movable structures, bearing gas pressure and ensuring good sealing performance, requiring high installation precision.

[0031] A PLC (Programmable Logic Controller) network interface refers to the port through which a PLC exchanges data with a computer or other network devices. Through this interface, the PLC can acquire and send various information and communicate and collaborate with other devices. The advent of network communication technology has greatly improved the functionality and efficiency of PLCs, making them indispensable in industrial automation.

[0032] The BP (back propagation) neural network algorithm is a multilayer feedforward neural network trained according to the error back propagation algorithm, and it is one of the most widely used neural network models.

[0033] Figure 1 is a schematic diagram illustrating an exemplary system architecture in an exemplary embodiment of this application.

[0034] Referring to Figure 1, the system architecture may include a data acquisition device 101 and a computer device 102. The computer device 102 may be at least one of a desktop graphics processing unit (GPU) computer, a GPU computing cluster, a neural network computer, etc. Technical personnel can use the computer device 102 to input the production plan for a future time period into the converter gas recovery and consumption prediction model to obtain the predicted values ​​of gas recovery and gas consumption for the future time period. Based on the simulation model of the converter gas pipeline system, and using the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, and with the goal of minimizing the relative tolerance of each converter gas holder, a converter gas holder collaborative optimization control model is established. The predicted values ​​of gas recovery and gas consumption are input into the converter gas holder collaborative optimization control model to obtain the optimized control values ​​of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period. Based on the optimized control values, control instructions are obtained, and based on the control instructions, the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period are controlled to regulate the converter gas system. Data acquisition device 101 is used to acquire the required images. In this embodiment, after acquiring converter gas system information, data acquisition device 101 provides the converter gas system information to computer device 102 for processing. The converter gas system information includes the smelting records of each converter, the gas recovery amount of each converter, the gas consumption of each converter gas user, the production plans of each converter and each converter gas user, the capacity of each converter gas holder, the opening degree of the inlet and outlet regulating valves of each converter gas holder, the operating frequency of each converter gas compressor, and the simulation model of the converter gas pipeline system. It should be noted that the data acquisition device 101 and computer device 102 provided in this embodiment are only examples and should not impose any limitations on the function and scope of use of the embodiments of the present invention.

[0035] It should be noted that the converter gas system control method provided in this application embodiment is generally executed by computer equipment 102, and correspondingly, the converter gas system control device is generally installed in computer equipment 102.

[0036] Figure 2 is a schematic flowchart illustrating an exemplary embodiment of a converter gas system control method according to this application. This converter gas system control method can be executed by a computing processing device, which can be the computer device 102 shown in Figure 1. Referring to Figure 2, the converter gas system control method includes at least steps S210 to S240, which are described in detail below:

[0037] In step S210, converter gas system information is obtained.

[0038] The converter gas system information includes the smelting records of each converter, the gas recovery volume of each converter, the gas consumption of each converter gas user, the production plans of each converter and each converter gas user, the capacity of each converter gas holder, the opening degree of the inlet and outlet regulating valves of each converter gas holder, the operating frequency of each converter gas compressor, and the simulation model of the converter gas pipeline system.

[0039] In one embodiment of this application, the operation of the converter gas system is monitored and recorded to obtain converter gas system information. This information includes the smelting records of each converter, the gas recovery rate of each converter, the gas consumption of each converter gas user, the production plans of each converter and its gas users, the capacity and piston speed of each converter gas holder, the opening degree of the inlet and outlet regulating valves of each converter gas holder, the inlet and outlet pressures of each converter gas compressor, the flow rate and operating frequency of each converter gas compressor, and a simulation model of the converter gas pipeline system. The smelting records of each converter include the temperature of the molten iron entering the furnace, the composition of the molten iron, the steel grade to be produced, and the blowing cycle time. The production plans of each converter and its gas users include the production plans of each converter and its gas users.

[0040] In one embodiment of this application, configuration information of the converter gas pipeline network system is obtained; a simulation model of the converter gas pipeline network system is established based on the configuration information of the converter gas pipeline network system. The configuration information of the converter gas pipeline network system includes information on each section of the pipeline in the gas pipeline network. The pipeline information includes the length, outer diameter, wall thickness, and absolute roughness of the pipeline, the resistance characteristic curve of the valve, the operating characteristic curve of the compressor, the equipment size, operating pressure limit and piston speed limit of the gas holder, and the connection relationship between each gas pipeline, valve, gas holder, compressor, converter and converter gas user.

[0041] In this embodiment, the simulation model of the converter gas pipeline system is established based on the configuration conditions, i.e., configuration information, of the converter gas pipeline system, and on the mass, momentum, and energy conservation equations for pipeline flow, as well as the connection relationship equations of gas pipelines, valves, gas holders, compressors, converters, and converter gas users. The configuration conditions of the converter gas pipeline system include the length, outer diameter, wall thickness, and absolute roughness of each section of the gas pipeline, the resistance characteristic curves of the valves, the operating characteristic curves of the compressors, the equipment dimensions, operating pressure, and piston speed limits of the gas holders, and the connection relationships between each gas pipeline, valve, gas holder, compressor, converter, and converter gas user.

[0042] In step S220, the production plan for the future time period is input into the converter gas recovery and consumption prediction model to obtain the predicted value of gas recovery and gas consumption for the future time period.

[0043] The converter gas recovery and consumption prediction model is trained from smelting records, historical data of gas recovery, and historical data of gas consumption over past time periods. The production plan corresponds to the smelting plan in the smelting records, meaning that the production plan for the same time period corresponds to the smelting plan in the smelting records.

[0044] In one embodiment of this application, a converter gas recovery and consumption prediction model is established using the smelting records of each converter, historical data on converter gas recovery, and historical data on gas consumption of each converter gas user. The predicted values ​​of gas recovery of each converter and gas consumption of each converter gas user are obtained by combining the production plans of each converter and converter gas user in the future.

[0045] In one embodiment of this application, smelting records, historical data of gas recovery volume, and historical data of gas consumption over past time periods are obtained. Based on the smelting records, historical data of gas recovery volume, and historical data of gas consumption over past time periods, a preset converter gas recovery and consumption prediction model is trained to obtain the converter gas recovery and consumption prediction model, and the production plan corresponds to the smelting plan in the smelting records.

[0046] In this embodiment, smelting records of each converter and historical data on gas recovery over past time periods are acquired. A converter gas recovery prediction model is trained using data analysis methods. The production plans of each converter in the future are input into the converter gas recovery prediction model to obtain predicted gas recovery values ​​for each converter in the future. Historical data on gas consumption by each converter gas user within the same past time period are acquired and analyzed to train a converter gas consumption prediction model. The production plans of each converter gas user in the future are input into the converter gas consumption prediction model to predict gas consumption values ​​for each converter gas user in the future. Smelting records include smelting plans for each furnace smelting session, and production plans include future converter production furnace smelting plans. The smelting plans in the furnace smelting records correspond to the furnace smelting plans, meaning the smelting plans in the smelting records correspond to the smelting plans in the production plans. It should be noted that the converter gas recovery and consumption prediction model includes both a converter gas recovery prediction model and a converter gas consumption prediction model.

[0047] In step S230, based on the simulation model of the converter gas pipeline system, a converter gas holder collaborative optimization control model is established with the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, and with the goal of minimizing the relative tolerance of each converter gas holder. The predicted values ​​of gas recovery and gas consumption are input into the converter gas holder collaborative optimization control model to obtain the optimized control values ​​of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period.

[0048] In one embodiment of this application, with the goal of minimizing the relative tolerance of each converter gas holder, a collaborative optimization control model for converter gas holders is established based on a simulation model of the converter gas pipeline system. The model uses the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters. Based on the predicted values ​​of gas recovery and gas consumption, an intelligent optimization algorithm is applied to obtain the optimized control values ​​for the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period. These optimized control values ​​include the optimized control values ​​for the opening degree of the inlet and outlet regulating valves of each converter gas holder and the optimized control values ​​for the operating frequency of each converter gas compressor.

[0049] In one embodiment of this application, the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor are randomly varied within a preset control range to obtain an initial set of preset schemes. The initial set of schemes is iteratively calculated based on the relative cabinet tolerance and an intelligent optimization algorithm. If the preset iterative calculation exit condition is met, the control scheme with the smallest relative cabinet tolerance is output. The optimized control amount of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period is obtained based on the control scheme.

[0050] In this embodiment, based on the current real-time values ​​of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor, random changes are made within their control limits to form an initial set of preset schemes. Then, an iterative optimization calculation is performed. The iterative optimization calculation includes the following steps: 1) Based on the predicted value of converter gas recovery and the predicted value of gas consumption of each converter gas user, the simulation model of the converter gas pipeline system is called to calculate the expected capacity of each converter gas holder after the implementation of each scheme in the scheme set; 2) Based on the above expected capacity, the relative capacity difference of each converter gas holder after the implementation of each scheme is further calculated; 3) It is determined whether the optimization target has been achieved, that is, whether the preset iterative calculation exit condition is met; if the preset iterative calculation exit condition is not met, the iterative calculation is continued according to steps 1) to 3) until the preset iterative calculation exit condition is met; if the preset iterative calculation exit condition is met, the control scheme with the smallest relative capacity difference is output; the optimized control amount of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the preset period is obtained according to the control scheme.

[0051] In one embodiment of this application, the predicted values ​​of gas recovery and gas consumption in the future time period are used as boundary conditions. The predicted capacity of each converter gas holder corresponding to each initial scheme in the initial scheme set is calculated in the future time period through the converter gas pipeline network system simulation model. The relative capacity difference of each converter gas holder corresponding to each initial scheme in the future time period is calculated according to the objective function of the converter gas holder collaborative optimization control model. The initial scheme set corresponding to the relative capacity difference is iteratively calculated according to the intelligent optimization algorithm.

[0052] In this embodiment, the predicted recovery amount of each converter gas and the predicted gas consumption of each converter gas user within a future time period are used as boundary conditions. The predicted capacity of each converter gas holder in the future time period under each optimization scheme is calculated using the converter gas pipeline network system simulation model, thus obtaining the capacity change. The relative capacity difference of each converter gas holder in the corresponding time period under each optimization scheme is calculated according to the objective function of the converter gas holder collaborative optimization control model. The relative capacity difference of each converter gas holder under each optimization scheme is updated according to the intelligent optimization algorithm to update the optimization scheme set.

[0053] In one embodiment of this application, the number of iterations is counted. If the number of iterations exceeds a preset number of iterations, the iteration calculation is stopped, and the control scheme with the smallest relative cabinet tolerance in the iteration calculation is output. Alternatively, the absolute deviation between the minimum relative cabinet tolerance of the current iteration and the minimum relative cabinet tolerance of the previous iteration is calculated. If the absolute deviation is less than a preset residual, the iteration calculation is stopped, and the control scheme with the smallest relative cabinet tolerance in all iterations is output. The minimum relative cabinet tolerance is determined by sorting the relative cabinet tolerances from smallest to largest, and determining the relative cabinet tolerance ranked first as the minimum relative cabinet tolerance.

[0054] In this embodiment, whether the optimization objective has been achieved can be determined as follows: (1) the number of iterations exceeds the preset maximum number of iterations, i.e., the preset number of iterations; (2) the absolute deviation between the minimum relative cabinet tolerance obtained in this iteration and the minimum relative cabinet tolerance obtained in the previous iteration is less than the preset residual. If the iterative calculation meets any of the above conditions, the iterative calculation is stopped, and the control scheme with the minimum relative cabinet tolerance in each iteration is output.

[0055] In one embodiment of this application, if the number of iterations is less than or equal to the preset number of iterations and the absolute deviation is greater than or equal to the preset residual, the initial solution set is iteratively calculated according to the intelligent optimization algorithm; the target achievement degree of each solution is calculated, and the optimization algorithm iteratively calculates based on the target achievement degree to update the preferred solution set, and the preferred solution set is iteratively calculated according to the intelligent optimization algorithm until the preset iterative calculation exit condition is met.

[0056] In this embodiment, if the number of iterations is less than or equal to the preset number of iterations and the absolute deviation is greater than or equal to the preset residual, the initial scheme set is iteratively calculated according to the intelligent optimization algorithm; the target achievement degree of each scheme is calculated, and a preset proportion of schemes are eliminated based on the target achievement degree to obtain the preferred scheme; the preferred scheme set is obtained according to the preferred scheme, and the preferred scheme set is iteratively calculated according to the intelligent optimization algorithm until the preset iterative calculation exit condition is met.

[0057] In this embodiment, whether the optimization goal has been achieved can be determined as follows: (1) the number of iterations exceeds the preset maximum number of iterations, i.e., the preset number of iterations; (2) the absolute deviation between the minimum relative cabinet tolerance obtained in this round of iteration and the minimum relative cabinet tolerance obtained in the previous round of iteration is less than the preset residual. If the calculation does not meet one of the above conditions, the scheme set is iterated according to the genetic algorithm. Preferably, a certain proportion of schemes can be eliminated according to the goal achievement degree of each scheme. The preferred schemes are obtained by copying and crossover genetic calculation to obtain a new generation of scheme set, and a new round of iterative calculation is performed according to steps 1) to 3) above, until the iteration optimization stopping condition is reached, i.e., the optimization goal is achieved. It should be noted that the genetic algorithm in this embodiment is only an example. This application does not limit the iteration method, and this embodiment should not impose any limitations on the function and scope of use of the present invention.

[0058] In one embodiment of this application, an initial set of solutions for model solving is formed based on the real-time values ​​of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor; iterative optimization calculations are performed according to the following steps:

[0059] Step S231: Using the predicted values ​​of gas recovery volume of each converter and gas consumption of each converter gas user in the future period as boundary conditions, the simulation model of the converter gas pipeline system is used to calculate the changes in the capacity of each converter gas holder in the corresponding period under each optimization scheme.

[0060] Step S232: Based on the changes in the capacity of each converter gas holder under each optimization scheme calculated in step S231, calculate the relative capacity difference of each converter gas holder in the corresponding time period under each optimization scheme according to the objective function of the converter gas holder collaborative optimization control model.

[0061] Step S233: Based on the relative tolerance of each converter gas holder under each optimization scheme calculated in step S232, update the optimization scheme set according to the intelligent optimization algorithm.

[0062] Repeat steps S231 to S233 for iterative calculation.

[0063] When the preset exit condition for iterative calculation is met, the control scheme of the iterative optimization calculation is output, that is, the control scheme with the minimum relative cabinet tolerance is output.

[0064] Among them, the collaborative optimization control model for converter gas holders uses the opening degree V of the inlet and outlet regulating valves of each converter gas holder as the basis. o,i (i represents the valve code) and the operating frequency f of each converter gas compressor j (j is the valve code) is the variable to be optimized. The objective function is to minimize the relative tolerance of each converter gas holder. The calculation method for minimizing the relative tolerance is as follows:

[0065]

[0066]

[0067]

[0068] Where obj is the objective function, φ k (k is the gas holder code) represents the relative capacity of each converter gas holder, and is the actual capacity V. k With maximum cabinet capacity V 0,k The ratio, where n is the total number of gas holders in the system. As a relative average gas holder capacity, the model's constraints include the upper and lower limits of each gas holder capacity, the limit of the gas holder piston speed, the adjustment range of each regulating valve opening and the limit of the regulating speed, and the adjustment range and the limit of the regulating speed of each converter gas compressor operating frequency.

[0069] In one embodiment of this application, an enterprise operates five 180t converters, equipped with three 100,000 m³ converter gas holders, each with its own inlet and outlet regulating valves. Two converter gas pressurization stations are built, each with five converter gas pressurizers. Simultaneously, six converter gas users, including sintering machines, lime kilns, heating furnaces, and gas boilers, are connected via a converter gas pipeline network. It should be noted that this embodiment is merely illustrative and does not limit the quantity or specifications of equipment within the converter gas system of this application, nor should it impose any limitations on the function or scope of application of this invention.

[0070] In this embodiment, based on the real-time values ​​of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor, random variations are made within a preset control range of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor to form an initial scheme set of a preset number of schemes, for example, forming an initial scheme set of 30 schemes. In the formula, 0 represents the iteration number of the scheme set, and m = 1 to 30 represents the scheme number of a certain generation of the scheme set. The random change is generated according to the rules of random number generation, generally using a random number generation algorithm. For example, if the current real-time operating frequency of a converter gas compressor is 50Hz, and the adjustment range is 45 to 55Hz, random numbers will generate 30 values ​​within this range, such as 46.2Hz, 53.4Hz, etc.

[0071] Then, an iterative optimization calculation is performed:

[0072] 1) Based on the recovery and consumption of converter gas, the simulation model of the converter gas pipeline system is called to calculate the expected capacity of the three converter gas holders after the implementation of each scheme in the scheme set.

[0073] 2) Based on the above calculation results, the relative cabinet tolerance of the three converter gas holders after the implementation of each scheme is further calculated using formula (4).

[0074]

[0075] Where, φ k (k is the gas holder code) represents the relative capacity of each converter gas holder. is the relative cabinet capacity average, n is the iteration number, and m is the scheme number.

[0076] 3) Based on the second round of calculation, determine whether the optimization objective has been achieved, i.e., whether the preset iterative calculation exit condition has been met. This can be determined in the following way:

[0077] (1) Whether the number of iterations n exceeds the preset maximum number of iterations N, i.e., the preset number of iterations;

[0078] (2) The absolute deviation between the minimum relative cabinet tolerance obtained in this iteration and the minimum relative cabinet tolerance obtained in the previous iteration is less than the preset residual.

[0079] If the calculation meets at least one of the above conditions, the iterative calculation is stopped, and the control scheme with the smallest relative cabinet tolerance in all iterations is output.

[0080] If the calculation fails to meet any of the above conditions, the scheme set is iterated according to the genetic algorithm. Preferably, a certain proportion of schemes can be eliminated according to the target achievement of each scheme. The preferred schemes are obtained by copying and crossover genetic calculation to obtain a new generation of scheme set, and a new round of iterative calculation is performed according to steps 1) to 3) until the preset iterative calculation exit condition is met.

[0081] In step S240, a control command is obtained based on the optimized control amount. Based on the control command, the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor are controlled within a preset period to regulate the converter gas system.

[0082] In one embodiment of this application, the above-mentioned optimized control quantity is converted into a control instruction plan and issued to the corresponding control system to control the opening degree of the inlet and outlet control valves of each gas holder and the operating frequency of each converter gas compressor within a preset period.

[0083] In this embodiment, the control command for the opening of the inlet and outlet regulating valves of each gas holder is obtained based on the optimized control amount of the opening of the inlet and outlet regulating valves of each gas holder. The control command for the start and stop of each converter gas compressor and the control command for the operating frequency of each converter gas compressor are obtained based on the optimized control amount of the operating frequency of each converter gas compressor. The opening of the inlet and outlet regulating valves of each converter gas holder, the start and stop of each converter gas compressor, and the operating frequency of each converter gas compressor are controlled within a preset period according to the above control commands, so as to regulate the converter gas system.

[0084] Figure 3 is a schematic diagram of the calculation flow of a converter gas system control method according to an exemplary embodiment of this application.

[0085] Referring to Figure 3, in one embodiment of this application, a converter gas recovery prediction model is trained based on the smelting records of each converter and historical data on converter gas recovery to obtain a converter gas recovery prediction model. The production plans of each converter are input into the converter gas recovery prediction model to predict the amount of converter gas recovered, resulting in a predicted value for the recovered gas. A converter gas consumption prediction model is trained based on historical data on the gas consumption of each converter gas user to obtain a converter gas consumption prediction model. The production plans of each converter gas user are input into the converter gas consumption prediction model to predict the amount of converter gas consumed, resulting in a predicted value for the converted gas consumption. An initial optimization scheme set, i.e., an initial scheme set, is obtained based on real-time data of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of the converter gas compressor. The initial optimization scheme set is then used to calculate the objective function for optimization. Finally, a converter gas pipeline system simulation model is obtained based on the converter gas pipeline system configuration information. Using the predicted values ​​of gas recovery and gas consumption as boundary conditions, the cabinet capacity changes of each scheme within a preset period are calculated through a converter gas pipeline system simulation model. The relative cabinet capacity difference of each converter gas holder is calculated based on the objective function of the converter gas holder collaborative optimization control model. It is then determined whether the initial schemes in each initial scheme set meet the optimization iteration requirements (i.e., the iteration exit condition) after the objective function calculation. If the optimization iteration requirements are not met, the scheme set is iteratively optimized according to the optimization algorithm. The predicted values ​​of gas recovery and gas consumption are again used as boundary conditions. The cabinet capacity changes of each scheme within a preset period are calculated through a converter gas pipeline system simulation model. The relative cabinet capacity difference of each converter gas holder is calculated based on the objective function of the converter gas holder collaborative optimization control model. It is then determined whether the initial schemes in each initial scheme set meet the optimization iteration requirements after the objective function calculation. If the optimization iteration requirements are met, the optimized control scheme is output.

[0086] Figure 4 is a schematic diagram of the system structure of a converter gas system control system, illustrating an exemplary embodiment of this application.

[0087] Referring to FIG4, in one embodiment of this application, a converter gas system control system, namely a multi-converter gas holder collaborative optimization control system, includes: a converter gas pipeline control module, a converter gas operation data monitoring module, a data acquisition and transmission module, and an optimization control module. The converter gas pipeline control module receives and executes control commands to control the operation of the inlet and outlet regulating valves of the converter gas holder and the compressor. The converter gas operation data monitoring module monitors relevant production parameters at each gas generation and consumption end of the converter gas system, as well as the operation data of the converter gas holder, pipeline valves, and compressor. The data acquisition and transmission module collects monitoring data from the operation data monitoring module and transmits it to the optimization and control module. It also transmits control commands from the optimization and control module to the converter gas pipeline control module. The optimization and control module calculates corresponding control commands based on the monitoring data and the optimization model, and sends them to the converter gas pipeline control module. The control commands include, but are not limited to, control commands for the opening degree of each converter gas holder inlet and outlet regulating valve, start and stop control commands for each converter gas compressor, and control commands for the operating frequency of each converter gas compressor.

[0088] In this embodiment, the converter gas pipeline control module receives control commands to control the opening degree of the corresponding converter gas holder inlet and outlet regulating valves, as well as the start-up, shutdown, and operating frequency of the corresponding converter gas compressor.

[0089] In this embodiment, the converter gas operation data monitoring module is used to acquire the smelting records of each converter in the converter gas system. The smelting records include the temperature, composition, steel grade, and blowing cycle of the molten iron entering the furnace, the gas recovery amount of each converter, the gas consumption of each converter gas user, and the production plans of each converter and each converter gas user. At the same time, it also monitors the capacity and piston speed of each converter gas holder, the opening degree of the inlet and outlet regulating valves of each converter gas holder, and the inlet and outlet pressure, flow rate, and operating frequency of each converter gas compressor.

[0090] In this embodiment, Figure 5 is a schematic diagram of the converter gas pipeline network system of the converter gas system control system shown in an exemplary embodiment of this application. A schematic diagram of the converter gas pipeline network system of a steel enterprise is shown in Figure 5. The enterprise operates five 180t converters (1-1 to 1-5), and has three 100,000 m³ converter gas holders (2-1 to 2-3), with corresponding inlet and outlet regulating valves (3-1 to 3-6). Two converter gas pressurization stations (4-1 to 4-2) are built, each equipped with five converter gas pressurizers. Simultaneously, six converter gas users (5-1 to 5-6) are connected through the converter gas pipeline network, including sintering machines, lime kilns, heating furnaces, and gas boilers.

[0091] In this embodiment, the converter gas operation data monitoring module obtains the following information through the enterprise's production management system: smelting records for each converter, including the temperature, composition, steel grade, and blowing cycle of the molten iron fed into the furnace; production plans for each converter and converter gas users, including production scheduling, shutdown plans, and maintenance plans; monitoring and recording of gas recovery from each converter, gas consumption and pressure of each converter gas user, inlet and outlet gas flow and pressure of each gas holder, and inlet and outlet flow and pressure of each converter gas compressor by gas flow meters, pressure gauges, and thermometers; operation status data such as the start-up and shutdown of each compressor and its operating frequency by the control system of each converter gas compressor; and operation parameters such as the capacity of each converter gas holder, piston speed, and opening degree of the inlet and outlet regulating valves of each gas holder by the control system of each converter gas holder.

[0092] In this embodiment, the data acquisition and transmission module communicates with the enterprise production management system via a communication network, collecting and transmitting smelting records of each converter and production plans of each converter and converter gas user to the optimization and control module. Monitoring data from flow meters, pressure gauges, and thermometers are collected through the instrument network interface and transmitted to the optimization and control module via the network by the data acquisition and transmission module. The control systems of each converter gas compressor and each converter gas holder are collected by the corresponding control system PLC network interface and transmitted to the optimization and control module via the network by the data acquisition and transmission module. Historical gas consumption data of each converter gas user is analyzed to train a converter gas consumption prediction model. Combined with the production plans of each converter gas user, the gas consumption of each converter gas user in the future is predicted, i.e., the predicted gas consumption value for the future period.

[0093] In this embodiment, the optimization and control module is divided into a converter gas production and consumption prediction submodule and an optimization control analysis and calculation submodule. The optimization control analysis and calculation submodule, based on the predicted data of converter gas recovery and consumption by each converter gas user, aims to minimize the relative tolerance of each converter gas holder. It uses the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters. Combining this with a converter gas pipeline system simulation model, it establishes a collaborative optimization and control model for the converter gas holders. Then, it applies an intelligent optimization algorithm to solve for the control command plan for the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period, and transmits this plan to the converter gas pipeline control module.

[0094] In this embodiment, the converter gas production and consumption prediction submodule uses the smelting records of each converter and historical data on gas recovery to perform data analysis. It then uses time-series prediction algorithms, such as the BP neural network algorithm, to train and generate a converter gas recovery prediction model. Combined with the production plans of each converter, it predicts the amount of gas recovered by each converter in the future. Simultaneously, it uses historical data on gas consumption of each converter gas user to perform data analysis. It then uses time-series prediction algorithms, such as the BP neural network algorithm, to train a converter gas consumption prediction model. Combined with the production plans of each converter gas user, it predicts the gas consumption of each converter gas user in the future. It should be noted that the converter gas recovery and consumption prediction model includes both a converter gas recovery prediction model and a converter gas consumption prediction model.

[0095] In this embodiment, the system predicts the recovery and consumption of converter gas according to a certain period and frequency, and inputs the prediction results into the optimization control analysis and calculation submodule. The intelligent optimization algorithm is combined with the simulation model of the converter gas pipeline system. With the goal of minimizing the relative tolerance of each converter gas holder, and with the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, a collaborative optimization control model for converter gas holders is established to obtain the control commands for the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the corresponding period.

[0096] The simulation model of the converter gas pipeline system is established based on the configuration information of the converter gas pipeline system. This model is based on the mass, momentum, and energy conservation equations for pipeline flow, as well as the connection equations for gas pipelines, valves, gas holders, compressors, converters, and converter gas users. The pipeline configuration information includes the length, outer diameter, wall thickness, and absolute roughness of each section of the gas pipeline; the resistance characteristic curves of the valves; the operating characteristic curves of the compressors; the equipment dimensions, operating pressure limits, and piston speed limits of the gas holders; and the connection relationships between the gas pipelines, valves, gas holders, compressors, converters, and converter gas users.

[0097] This application, based on monitoring the production and actual operating parameters of the converter gas system in a production enterprise from the gas generation, storage, and consumption ends, and based on converter gas production and consumption prediction and gas pipeline system simulation, establishes a collaborative optimization and control model for converter gas holders. This model is used for feedforward optimization and regulation of the converter gas system's supply and demand balance, fully leveraging the dynamic buffering and regulation capabilities of the converter gas holders. This application can optimize and adjust the opening degree of the inlet and outlet valves of each gas holder in the converter gas pipeline network and the operating frequency of the gas compressor in real time according to the actual dynamic operating conditions at the converter gas generation and consumption ends. This achieves feedforward optimization and regulation of the converter gas system's supply and demand balance, fully leveraging the dynamic buffering and regulation capabilities of the converter gas holders, and ensuring the safe and efficient operation of the converter gas system.

[0098] Figure 6 is a block diagram illustrating a converter gas system control device according to an exemplary embodiment of this application. This device can be applied to the implementation environment shown in Figure 1 and is specifically configured in computer device 102. This device can also be applied to other exemplary implementation environments and specifically configured in other devices; this embodiment does not limit the implementation environment to which the device is applicable.

[0099] As shown in Figure 6, the exemplary converter gas system control device includes: an acquisition module 610, a first processing module 620, a second processing module 630, and a control module 640.

[0100] The acquisition module 610 is configured to acquire converter gas system information, including smelting records of each converter, gas recovery volume of each converter, gas consumption of each converter gas user, production plans of each converter and each converter gas user, capacity of each converter gas holder, opening degree of inlet and outlet regulating valves of each converter gas holder, operating frequency of each converter gas compressor, and simulation model of the converter gas pipeline system. The first processing module 620 is configured to input the production plan for a future time period into the converter gas recovery and consumption prediction model to obtain predicted values ​​for gas recovery volume and gas consumption for the future time period. The converter gas recovery and consumption prediction model is trained using smelting records, historical data on gas recovery volume, and historical data on gas consumption from past time periods. The production plan corresponds to the smelting... The system includes: a smelting plan; a second processing module 630 configured to establish a converter gas holder collaborative optimization control model based on a converter gas pipeline network system simulation model, using the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, with the goal of minimizing the relative tolerance of each converter gas holder; inputting the predicted values ​​of gas recovery and gas consumption into the converter gas holder collaborative optimization control model to obtain the optimized control values ​​of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period; and a control module 640 configured to obtain control instructions based on the optimized control values, and control the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period based on the control instructions, so as to regulate the converter gas system.

[0101] It should be noted that the converter gas system control device and the converter gas system control method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the converter gas system control device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0102] Embodiments of the present invention also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the converter gas system control method provided in the above embodiments.

[0103] Figure 7 shows a schematic diagram of a computer system suitable for implementing embodiments of the present invention. It should be noted that the computer system 700 of the electronic device shown in Figure 7 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0104] As shown in Figure 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes, such as executing the methods provided in the various embodiments described above, based on programs stored in Read-Only Memory (ROM) 702 or programs loaded from storage portion 708 into Random Access Memory (RAM) 703. The RAM 703 also stores various programs and data required for system operation. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An Input / Output (I / O) interface 705 is also connected to the bus 704.

[0105] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0106] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs various functions defined in the system of the present invention.

[0107] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0108] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0109] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0110] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform the converter gas system control method as provided in the various embodiments described above. This computer-readable storage medium may be included in the electronic devices described in the above embodiments, or it may exist independently and not incorporated into the electronic devices.

[0111] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0112] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the converter gas system control method provided in the above embodiments.

[0113] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0114] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0115] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for regulating a converter gas system, characterized in that, include: The process involves acquiring converter gas system information, including smelting records for each converter, gas recovery volume for each converter, gas consumption for each converter gas user, production plans for each converter and each converter gas user, capacity of each converter gas holder, opening degree of inlet and outlet regulating valves for each converter gas holder, operating frequency of each converter gas compressor, and a simulation model of the converter gas pipeline system. The production plans for future time periods are then input into a converter gas recovery and consumption prediction model to obtain predicted values ​​for gas recovery and gas consumption for those future time periods. This model is trained using smelting records, historical data on gas recovery volume, and historical data on gas consumption from past time periods. The production plans correspond to the smelting plans recorded in the smelting records. Based on the simulation model of the converter gas pipeline system, using the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, and aiming to minimize the relative tolerance of each converter gas holder, a collaborative optimization control model for converter gas holders is established. The predicted values ​​of gas recovery and gas consumption are input into the collaborative optimization control model to obtain the optimized control values ​​of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period. Control commands are obtained based on these optimized control values, and the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the preset period are controlled based on these control commands to regulate the converter gas system. The calculation method for minimizing the relative tolerance is as follows: Official (1) Official (2) Formula (3) where obj is the objective function, These are the relative capacities of the gas holders in each converter, representing the actual capacities. With maximum cabinet capacity The ratio, where k is the gas holder code and n is the total number of gas holders in the system. This represents the average relative cabinet capacity.

2. The converter gas system control method according to claim 1, characterized in that, The converter gas recovery and consumption prediction model is trained using smelting records, historical data on gas recovery, and historical data on gas consumption over a past period. The model includes: acquiring smelting records, historical data on gas recovery, and historical data on gas consumption over a past period; and training a preset converter gas recovery and consumption prediction model based on the smelting records, historical data on gas recovery, and historical data on gas consumption over the past period to obtain the converter gas recovery and consumption prediction model.

3. The converter gas system control method according to any one of claims 1 or 2, characterized in that, After obtaining the predicted values ​​of gas recovery and gas consumption for the future time period, the method further includes: randomly varying the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset control range to obtain an initial set of preset schemes; iteratively calculating the initial set of schemes based on the relative cabinet tolerance and intelligent optimization algorithm; and outputting the control scheme with the minimum relative cabinet tolerance if the preset iteration calculation exit condition is met; and obtaining the optimized control amount of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the preset period based on the control scheme.

4. The converter gas system control method according to claim 3, characterized in that, The initial scheme set is iteratively calculated based on the relative cabinet tolerance and the intelligent optimization algorithm, including: using the predicted values ​​of gas recovery and gas consumption in the future time period as boundary conditions, and calculating the predicted cabinet capacity of each converter gas holder corresponding to each initial scheme in the initial scheme set in the future time period through the converter gas pipeline network system simulation model; calculating the relative cabinet tolerance of each converter gas holder corresponding to each initial scheme in the future time period according to the objective function of the converter gas holder collaborative optimization control model; and iteratively calculating the initial scheme set corresponding to the relative cabinet tolerance based on the intelligent optimization algorithm.

5. The converter gas system control method according to claim 3, characterized in that, The initial scheme set is iteratively calculated based on the relative cabinet tolerance and the intelligent optimization algorithm. If the preset iterative calculation exit condition is met, the control scheme with the smallest relative cabinet tolerance is output, including at least one of the following: count the number of iterations; if the number of iterations is greater than the preset number of iterations, stop the iterative calculation and output the control scheme with the smallest relative cabinet tolerance in the iterative calculation; calculate the absolute deviation between the minimum relative cabinet tolerance in this iteration and the minimum relative cabinet tolerance in the previous iteration; if the absolute deviation is less than the preset residual, stop the iterative calculation and output the control scheme with the smallest relative cabinet tolerance in all iterations.

6. The converter gas system control method according to claim 5, characterized in that, The initial scheme set is iteratively calculated based on the relative cabinet tolerance and the intelligent optimization algorithm, and the calculation further includes: if the number of iterations is less than or equal to the preset number of iterations and the absolute deviation is greater than or equal to the preset residual, the initial scheme set is iteratively calculated again based on the intelligent optimization algorithm; the target achievement degree of each scheme is calculated, and iterative calculation is performed based on the target achievement degree through the iterative mechanism of the optimization algorithm to update the preferred scheme set, and the preferred scheme set is iteratively calculated again based on the intelligent optimization algorithm until the preset iterative calculation exit condition is met.

7. The converter gas system control method according to any one of claims 1 or 2, characterized in that, The converter gas system control method further includes: acquiring the configuration information of the converter gas pipeline network system; establishing a simulation model of the converter gas pipeline network system based on the configuration information of the converter gas pipeline network system, wherein the configuration information of the converter gas pipeline network system includes the pipeline information of each section in the gas pipeline network, the pipeline information including the pipeline length, outer diameter, wall thickness, absolute roughness, resistance characteristic curve of the valve, operating characteristic curve of the compressor, equipment size, operating pressure limit and piston speed limit of the gas holder, and the connection relationship between each gas pipeline, valve, gas holder, compressor, converter and converter gas user.

8. A converter gas system control device, characterized in that, include: The acquisition module is used to acquire converter gas system information, which includes smelting records of each converter, gas recovery volume of each converter, gas consumption of each converter gas user, production plans of each converter and each converter gas user, capacity of each converter gas holder, opening degree of inlet and outlet regulating valves of each converter gas holder, operating frequency of each converter gas compressor, and simulation model of converter gas pipeline system. The first processing module is used to input the production plan for a future time period into the converter gas recovery and consumption prediction model to obtain the predicted value of gas recovery volume and gas consumption for the future time period. The converter gas recovery and consumption prediction model is trained from smelting records, historical data of gas recovery volume, and historical data of gas consumption for past time periods. The production plan corresponds to the smelting plan in the smelting records. The second processing module is used to establish a converter gas holder collaborative optimization control model based on the simulation model of the converter gas pipeline system, using the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor as adjustable parameters, and aiming to minimize the relative tolerance of each converter gas holder. The module inputs the predicted value of gas recovery and the predicted value of gas consumption into the converter gas holder collaborative optimization control model to obtain the optimized control amount of the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within a preset period. The control module is used to obtain control instructions based on the optimized control amount, and control the opening degree of the inlet and outlet regulating valves of each converter gas holder and the operating frequency of each converter gas compressor within the preset period based on the control instructions, so as to regulate the converter gas system; wherein, the calculation method for the minimum relative holder tolerance is as follows: Official (1) Official (2) Formula (3) where obj is the objective function, These are the relative capacities of the gas holders in each converter, representing the actual capacities. With maximum cabinet capacity The ratio, where k is the gas holder code and n is the total number of gas holders in the system. This represents the average relative cabinet capacity.

9. An electronic device, characterized in that, The electronic device includes: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the converter gas system control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by the computer's processor, causes the computer to perform the converter gas system control method according to any one of claims 1 to 7.

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