Reduction furnace control method and device, electronic equipment and storage medium

Abstract

The invention provides a reduction furnace control method and device, electronic equipment and a storage medium, and relates to the technical field of industrial intelligent control, and the method comprises the steps: obtaining controlled change information of controlled variables, operation change information of operation variables and an association relationship between the controlled variables and the operation variables of a plurality of production stages of a product produced by a reduction furnace; determining a target value of a controlled variable of a current production stage in the plurality of production stages according to the controlled change information; according to the operation change information, a reference value of an operation variable of the current production stage is determined; and adjusting the value of the operating variable in the current production stage based on the incidence relation of the controlled variable and the operating variable in the current production stage, the target value and the reference value so as to control the reduction furnace. In the embodiment, the value of the operation variable is automatically adjusted, so that the labor intensity of operators is reduced; control is carried out according to the production stage, the control stability can be improved, and therefore the stability of the product quality is ensured.

Description

Technical Field

[0001] This application relates to the field of industrial intelligent control technology, and particularly relates to a control method, device, electronic device, and storage medium for a reduction furnace. Background Art

[0002] A reduction furnace is a chemical reaction device that reduces trichlorosilane to polysilicon and is the core device for polysilicon production. The production raw materials are hydrogen (H2) and trichlorosilane (TCS). In the reactor, TCS is reduced by H2 to polysilicon and deposited on the surface of the silicon rod. The quality of the operation of the polysilicon reduction furnace directly affects the quality of polysilicon production and the overall energy consumption.

[0003] During the polysilicon production process, a distributed control system (DCS) is used to control the reduction furnace. However, abnormal conditions such as atomization often occur during the production process, and it is necessary for operators to constantly monitor the changes in the operation data of the DCS system. At the same time, combined with the inspection information at the observation port, it is necessary to judge whether an abnormality will occur and make timely interventions. The overall workload is large, and the operation habits of different operators are different, resulting in inconsistent quality of the produced polysilicon. Summary of the Invention

[0004] The embodiments of this application provide a control method, device, electronic device, and storage medium for a reduction furnace to solve the problems of large workload of manual monitoring of operation data and unstable product quality caused by different operation habits of operators.

[0005] In a first aspect, the embodiments of this application provide a control method for a reduction furnace. The method includes: obtaining the controlled variable change information, the manipulated variable change information, and the correlation relationship between the controlled variable and the manipulated variable in multiple production stages of the product produced by the reduction furnace; determining the target value of the controlled variable in the current production stage among the multiple production stages according to the controlled variable change information; determining the reference value of the manipulated variable in the current production stage according to the manipulated variable change information; and adjusting the value of the manipulated variable in the current production stage based on the correlation relationship, the target value, and the reference value of the controlled variable and the manipulated variable in the current production stage to achieve the control of the reduction furnace.

[0006] Second aspect, an embodiment of the present application provides a reduction furnace control device, which includes: an acquisition module for acquiring the controlled variable change information of the controlled variables in multiple production stages of the reduction furnace for producing products, the operation change information of the operation variables, and the correlation between the controlled variables and the operation variables; a target determination module for determining the target value of the controlled variable in the current production stage among the multiple production stages according to the controlled variable change information; a reference determination module for determining the reference value of the operation variable in the current production stage according to the operation change information; and a control module for adjusting the value of the operation variable in the current production stage based on the correlation, target value, and reference value of the controlled variable and the operation variable in the current production stage to achieve the control of the reduction furnace.

[0007] Third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory. The processor implements the method according to any one of the above when executing the computer program.

[0008] Fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. The computer program implements the method according to any one of the above when executed by a processor.

[0009] Compared with the prior art, the present application has the following advantages:

[0010] The present application provides a reduction furnace control method, device, electronic device, and storage medium. First, acquire the controlled variable change information of the controlled variables in multiple production stages of the reduction furnace for producing products, the operation change information of the operation variables, and the correlation between the controlled variables and the operation variables; then, determine the target value of the controlled variable in the current production stage among the multiple production stages according to the controlled variable change information; determine the reference value of the operation variable in the current production stage according to the operation change information; finally, adjust the value of the operation variable in the current production stage based on the correlation, target value, and reference value of the controlled variable and the operation variable in the current production stage to achieve the control of the reduction furnace. In this embodiment, based on the controlled variable change information and operation change information of the controlled variables in multiple production stages, as well as the correlation between the controlled variables and the operation variables, the value of the operation variable is automatically adjusted, thereby reducing the labor intensity of the operator; controlling according to the production stage can improve the stability of control, thereby ensuring the stability of product quality.

[0011] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically described below. Description of the Drawings

[0012] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the several views denote the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments in accordance with the present application and should not be regarded as limiting the scope of the present application.

[0013] Figure 1 This is a schematic diagram of the working process of the reduction furnace provided for the present application.

[0014] Figure 2 This is a schematic diagram of an application scenario of the reduction furnace control method according to an embodiment of the present application.

[0015] Figure 3 This is a schematic diagram of the operating time-exhaust gas temperature reference control curve according to an embodiment of the present application.

[0016] Figure 4 This is a flowchart of the reduction furnace control method according to an embodiment of the present application.

[0017] Figure 5 This is a flowchart of the reduction furnace control method according to an embodiment of the present application.

[0018] Figure 6 This is a schematic diagram of the reduction furnace control system according to an embodiment of the present application.

[0019] Figure 7 This is a structural block diagram of the reduction furnace control device according to an embodiment of the present application.

[0020] Figure 8 This is a block diagram of the electronic device for implementing the embodiments of the present application. Detailed implementation manners

[0021] In the following, only some exemplary embodiments are briefly described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the concept or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature and not restrictive.

[0022] To facilitate the understanding of the technical solutions of the embodiments of the present application, the related technologies of the embodiments of the present application are described below. The following related technologies can be arbitrarily combined with the technical solutions of the embodiments of the present application as optional solutions, and they all fall within the protection scope of the embodiments of the present application.

[0023] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties. Moreover, the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0024] Embodiment 1

[0025] Figure 1 It is a schematic diagram of the working process of the reduction furnace provided by the embodiment of the present application. As Figure 1 shown, the reduction furnace produces polysilicon, and the raw materials are hydrogen (H2) and trichlorosilane (TCS). In the reactor, TCS is reduced by H2 to form polysilicon and deposited on the surface of the silicon rod. The reaction heat is provided by connecting the silicon rod to the power supply, and the reaction temperature on the surface of the silicon rod can be changed by adjusting the current value. As the diameter of the silicon rod increases, the resistance of the silicon rod decreases, and the change rate of the resistance can characterize the reaction rate. There is an observation port on the reduction furnace, and a high-temperature camera can be installed to photograph the furnace condition and estimate the temperature of the silicon rod. The tail gas after the reaction is cooled by cooling water, and the high-temperature return water temperature can also reflect the reduction reaction rate. The flow rates of the raw materials (H2, TCS) and the reaction temperature change with time. The crystal growth state is indirectly reflected by the resistance, high-temperature return water temperature, etc.

[0026] Figure 2 It is a schematic diagram of an application scenario of the reduction furnace control method according to an embodiment of the present application. As Figure 2 shown, it specifically includes the following steps:

[0027] Obtain industrial data. Specifically, obtain the historical data of the industrial site where the reduction furnace produces, including: historical production data (current, voltage, high-temperature return water temperature, etc.), historical material tables (current, H2 flow rate, H2 / TCS ratio) data, etc.

[0028] Data preprocessing. Specifically, preprocess the historical data of the industrial site, including but not limited to: abnormal data processing, including processing such as removing abnormal values and filling missing values.

[0029] Optimization of the reference control curve and the material table curve. Specifically, update the operating variables (H2 flow rate, TCS flow rate, current) and controlled variables (resistance, high-temperature return water temperature, silicon rod temperature) of this batch, as well as the product quality (such as density), to the case base. Based on the production goals in dimensions such as product quality, energy consumption, and production volume, screen out the operating variables and controlled variables that meet the production goals in the case base. Use machine learning models such as support vector machines for regression calculation to obtain the controlled variable change information and operating variable change information. Among them, the controlled variable change information includes at least one of the following: three reference control curves of running time - silicon rod temperature, running time - tail gas temperature (or running time - high-temperature return water temperature), or running time - resistance. In one example, the reference control curve of running time - tail gas temperature is as Figure 3 shown. Among them, the operating variable change information includes at least one of the following: three material table curves of running time - H2 flow rate, running time - H2 / TCS ratio, or running time - current. The reference control curve and the material table curve can be updated according to a cycle. Each time a batch is produced, it can be updated once, so as to realize the optimization of the reference control curve and the material table curve.

[0030] Control model identification. Specifically, based on the historical production data of the reduction furnace, use the system identification algorithm to calculate the correlation between the operating variables and controlled variables in each production stage during multiple production stages, that is, the control model. Among them, the system identification algorithm includes: Finite Impulse Response (FIR) identification algorithm or subspace identification algorithm, etc.

[0031] Model release. Release the reference control curve, the material table curve, and the control model to the control system for real-time control of the reduction furnace production system.

[0032] Online instruction calculation. Specifically, determine the target value sequence of the controlled variables in the future time period according to the reference control curve of the controlled variables, and then determine the reference value sequence of the operating variables in the future time period based on the material table curve of the operating variables. Generate control instructions based on the target value sequence, the reference value sequence, and the control model, and send them to the control system of the reduction furnace production system to perform closed-loop control on the reduction furnace production system. At the next operating moment, repeat the control instruction calculation to achieve online real-time optimization control.

[0033] In the actual control process of the reduction furnace, since the polysilicon reduction furnace is batch-produced, according to its characteristics, it can be divided into two sections for separate control, namely the starting stage and the deposition stage. The main purpose of the starting stage is to make the diameter of the silicon rod increase steadily and raise the furnace gas temperature. The running time of the reduction furnace in the starting stage is about (0 - 30 hours). The main purpose of the deposition stage is to make the diameter of the silicon rod increase uniformly, that is, to have a stable deposition rate. In the starting stage, there is less atomization, the temperature of the silicon rod is relatively accurate, and there is a relatively large change amount. At the same time, the high-temperature return water temperature (or tail gas temperature) also has an obvious trend. In the deposition stage, the temperature of the silicon rod is in a small fluctuation state, the high-temperature return water temperature also changes within a small range, and the furnace gas temperature and the surface temperature of the silicon rod are both relatively stable. In this stage, the power, the diameter of the silicon rod, and the resistance of the silicon rod can all characterize the speed.

[0034] The operating variables and controlled variables in the two production stages are shown in the following table:

[0035]

[0036] Based on production objectives such as density, energy consumption, and output, historical data is obtained, including: density, energy consumption, output, H2 flow rate, TCS flow rate, current, silicon rod temperature, high-temperature return water temperature, and resistance. Based on the least squares regression calculation, three benchmark control curves of running time - silicon rod temperature, running time - high-temperature return water, and running time - resistance, and three feed tables curves of running time - H2 flow rate, running time - H2 / TCS ratio, and running time - current are obtained.

[0037] Two-stage model predictive control strategy: In the starting stage, when the actual temperature of the silicon rod is higher than the target value of the benchmark control curve based on the silicon rod temperature, based on the correlation between the silicon rod temperature and the current in the starting stage, the current value is reduced to make the current value lower than the benchmark value; when the silicon rod temperature is lower than the target value, based on the correlation between the silicon rod temperature and the current in the starting stage, the current value is increased to make the current value higher than the benchmark value. When the silicon rod temperature is affected by atomization and becomes abnormal, the control will be carried out with the high-temperature return water temperature as the target. In the deposition stage, when there is a deviation between the actual resistance and the target value of the benchmark control curve based on the resistance, based on the correlation between the resistance and the H2 flow rate / TCS flow rate in the deposition stage, the H2 flow rate and the TCS flow rate are preferentially adjusted. When the resistance is small, it means that the deposition is too fast, and the H2 flow rate needs to be reduced or the TCS flow rate needs to be increased, and the H2 flow rate is preferentially adjusted. When the resistance is large, it means that the deposition is too slow, and the H2 flow rate needs to be increased or the TCS flow rate needs to be reduced, and the H2 flow rate is preferentially adjusted. When the adjustment of the H2 flow rate is ineffective, the TCS flow rate is adjusted. When the high-temperature return water temperature / silicon rod temperature is abnormal, the current is adjusted for control.

[0038] Example Two

[0039] The embodiments of the present application provide a method for controlling a reduction furnace. The method in this embodiment can be applied to a computing device, which may include a server, a control terminal of the reduction furnace, etc. As Figure 4 The flowchart of the reduction furnace control method according to an embodiment of the present application is shown as follows, including:

[0040] Step S401: Obtain the controlled variable change information of the controlled variables in multiple production stages of the reduction furnace for producing products, the operating variable change information of the operating variables, and the correlation relationship between the controlled variables and the operating variables.

[0041] Step S402: Determine the target value of the controlled variable in the current production stage among multiple production stages according to the controlled variable change information.

[0042] Step S403: Determine the reference value of the operating variable in the current production stage according to the operating variable change information.

[0043] Step S404: Based on the correlation relationship, target value, and reference value of the controlled variable and the operating variable in the current production stage, adjust the value of the operating variable in the current production stage to achieve the control of the reduction furnace.

[0044] Among them, the operating variables include parameters whose values can be directly changed according to control instructions. For example, H2 flow rate, TCS flow rate, current, etc. The controlled variables include parameters whose values change as the values of the operating variables change. For example, resistance, high-temperature return water temperature, silicon rod temperature, etc.

[0045] Among them, the controlled variable change information refers to a curve in which the parameter values of the controlled variables change continuously as the production stages are different (the running time of the reduction furnace is different). The operating variable change information refers to a curve in which the parameter values of the operating variables change continuously as the production stages are different (the running time of the reduction furnace is different). The correlation relationship between the controlled variables and the operating variables may include a curve in which the values of the controlled variables change as the values of the operating variables change.

[0046] Among them, the target value can be a single value or a sequence of values composed of multiple values. The reference value can be a single value or a sequence of values composed of multiple values.

[0047] Optionally, for step S401, the controlled variable change information of the controlled variables in multiple production stages of the reduction furnace for producing products, the operating variable change information of the operating variables in multiple production stages, and the correlation relationship between the controlled variables and the operating variables in multiple production stages can be obtained in advance and stored in a preset storage space. When in use, they can be directly called from the preset storage space, and then the subsequent steps S402 - S404 can be executed.

[0048] Optionally, for step S304, an adjustment instruction may be generated based on the correlation between the controlled variable and the operating variable, the target value and the reference value in the current production stage, and the value of the operating variable in the current production stage may be adjusted using the adjustment instruction, so as to change the value of the controlled variable and achieve the control of the production process of the reduction furnace.

[0049] The reduction furnace control method provided by the embodiments of the present application first obtains the controlled change information of the controlled variables, the operating change information of the operating variables, and the correlation between the controlled variables and the operating variables in multiple production stages of the reduction furnace products; then, according to the controlled change information, determines the target value of the controlled variable in the current production stage among the multiple production stages; according to the operating change information, determines the reference value of the operating variable in the current production stage; finally, based on the correlation between the controlled variable and the operating variable, the target value and the reference value in the current production stage, adjusts the value of the operating variable in the current production stage to achieve the control of the reduction furnace. In this embodiment, based on the controlled change information of the controlled variables, the operating change information of the operating variables, and the correlation between the controlled variables and the operating variables in multiple production stages, the value of the operating variable is automatically adjusted, thereby reducing the labor intensity of the operator; controlling according to the production stage can improve the stability of the control, thereby ensuring the stability of the product quality.

[0050] The following introduces the specific implementation processes of the above steps through multiple specific implementation manners:

[0051] In one implementation manner, in step S401, obtaining the controlled change information of the controlled variables in multiple production stages of the reduction furnace products includes: screening out the values of multiple controlled variables that meet the production target from the historical production data of multiple production stages of the reduction furnace in advance, and performing regression calculation using the values of multiple controlled variables that meet the production target to obtain the controlled change information of the controlled variables.

[0052] Among them, the production target may be a target in one or more dimensions. For example, product quality, energy consumption, or product quantity, and the product quality may be the density of the silicon rod produced, etc. Screen out the numerical values of parameters such as resistance, high-temperature return water temperature, and silicon rod temperature in the production data where the density of the silicon rod meets the requirements from the historical production data of multiple production stages of the reduction furnace in advance, and perform regression calculation to obtain the change curves corresponding to each parameter.

[0053] Among them, the algorithms for regression calculation may include support vector machines, least squares method, etc.

[0054] Among them, multiple production stages can be set according to specific needs. For example, two production stages: the starting stage and the deposition stage.

[0055] In one embodiment, in step S401, the operation change information of the operation variables in multiple production stages of the reduction furnace for producing products is obtained, including: screening out the values of multiple operation variables that meet the production target from the historical production data of multiple production stages of the reduction furnace in advance, and performing regression calculation using the values of multiple operation variables that meet the production target to obtain the operation change information of the operation variables.

[0056] Among them, the production target can be a target in one or more dimensions. For example, product quality, energy consumption or product quantity, and the product quality can be the density of the silicon rod produced, etc. Screen out the numerical values of parameters such as H2 flow rate, TCS flow rate, and current in the production data that meet the energy consumption requirements from the historical production data of multiple production stages of the reduction furnace in advance, and perform regression calculation to obtain the change curves corresponding to each parameter.

[0057] Among them, the algorithms for regression calculation can include support vector machine, least squares method, etc.

[0058] Among them, multiple production stages can be set according to specific needs. For example, two production stages: the starting stage and the deposition stage.

[0059] In one embodiment, in step S401, the correlation relationship between the controlled variables and the operation variables in multiple production stages is obtained, including: obtaining the correlation relationship between the controlled variables and the operation variables in multiple production stages in advance based on the historical production data of multiple production stages of the reduction furnace by using the system identification algorithm.

[0060] Among them, the system identification algorithms include: Finite Impulse Response (FIR) identification algorithm or subspace identification algorithm, etc. The correlation relationship between the controlled variables and the operation variables can include the curve of the numerical value of the controlled variable changing with the numerical value of the operation variable.

[0061] In one embodiment, the method further includes: determining the production target in at least one of the following dimensions: product quality, energy consumption or product quantity.

[0062] In practical applications, the production target can be determined based on one or more dimensions. For example, the product quality reaches the preset standard, the energy consumption is lower than the preset energy consumption value, the product quantity reaches the preset quantity, etc.

[0063] In one embodiment, the controlled change information includes the correlation between the controlled variable and the running time of the reduction furnace; Step S402, according to the controlled change information, determine the target value of the controlled variable in the current production stage, including: query the value of the controlled variable associated with the running time of the reduction furnace in the current production stage in the controlled change information, and determine the value of the controlled variable associated with the running time of the reduction furnace in the current production stage as the target value of the controlled variable in the current production stage.

[0064] Wherein, the controlled change information is a curve of the controlled variable changing with the running time of the reduction furnace. For example, if the current production stage is the starting stage and the running time of the reduction furnace is 0 - 30 hours, then the numerical sequence of the controlled variable within the range of 0 - 30 hours of running time is used as the target value of the controlled variable in the starting stage. Each parameter among resistance, high-temperature return water temperature, and silicon rod temperature corresponds to a numerical sequence as the target value corresponding to each parameter.

[0065] In one embodiment, the operation change information includes the correlation between the operation variable and the running time of the reduction furnace; Step S303, according to the operation change information, determine the reference value of the operation variable in the current production stage among multiple production stages, including: query the value of the operation variable associated with the running time of the reduction furnace in the current production stage in the operation change information, and determine the value of the operation variable associated with the running time of the reduction furnace in the current production stage as the reference value of the operation variable in the current production stage.

[0066] Wherein, the operation change information is a curve of the operation variable changing with the running time of the reduction furnace. For example, if the current production stage is the deposition stage and the running time of the reduction furnace is more than 30 hours, then the numerical sequence of the operation variable within the time range of more than 30 hours of running time is used as the target value of the operation variable in the deposition stage. Each parameter among H2 flow rate, TCS flow rate, and current corresponds to a numerical sequence as the reference value corresponding to each parameter.

[0067] Among them, different production stages can adopt different controlled variables and operation variables. For example, the controlled variable in the starting stage is the silicon rod temperature, and the operation variable is the current; the controlled variable in the deposition stage is the resistance, and the operation variable is the H2 flow rate. Different production stages adopt different controlled variables and operation variables for corresponding control, that is, control according to the production stage, making the control more accurate, thereby ensuring the stability of product quality.

[0068] In one embodiment, step S404 adjusts the value of the manipulated variable in the current production stage based on the correlation between the controlled variable and the manipulated variable, the target value, and the reference value in the current production stage, including: comparing the value of the controlled variable in the current production stage with the target value to obtain a comparison result; generating a manipulated variable adjustment instruction according to the comparison result, the reference value, and the correlation between the controlled variable and the manipulated variable in the current production stage, and adjusting the value of the manipulated variable in the current production stage.

[0069] In practical applications, if there is a deviation between the value of the controlled variable in the current production stage and the corresponding target value, that is, it is higher or lower than the corresponding target value, then based on the correlation between the controlled variable and the manipulated variable in the current production stage, it is determined how to adjust the value of the manipulated variable, that is, adjust it higher or lower relative to the reference value, so as to achieve the automatic control of the reduction furnace.

[0070] In one embodiment, the method further includes: updating at least one of the controlled change information or the manipulated change information according to a preset update period.

[0071] In practical applications, as the production data accumulates continuously, periodically updating at least one of the controlled change information or the manipulated change information can improve the product quality.

[0072] Embodiment III

[0073] The embodiment of the present application provides a reduction furnace control method. The method in this embodiment can be applied to a computing device, and the computing device may include: a server, a control terminal of the reduction furnace, etc. As Figure 5 shown in the flowchart of the reduction furnace control method according to an embodiment of the present application, it includes:

[0074] Step S501, pre-screen the values of multiple controlled variables that meet the production target from the historical production data of multiple production stages of the reduction furnace, and perform regression calculation using the values of multiple controlled variables that meet the production target to obtain the controlled change information of the controlled variable.

[0075] Step S502, pre-screen the values of multiple manipulated variables that meet the production target from the historical production data of multiple production stages of the reduction furnace, and perform regression calculation using the values of multiple manipulated variables that meet the production target to obtain the manipulated change information of the manipulated variable.

[0076] Step S503, pre-obtain the correlation between the controlled variable and the manipulated variable in multiple production stages based on the historical production data of multiple production stages of the reduction furnace by using the system identification algorithm.

[0077] Step S504: Query the value of the controlled variable associated with the running time of the reduction furnace in the current production stage from the controlled change information, and determine the value of the controlled variable associated with the running time of the reduction furnace in the current production stage as the target value of the controlled variable in the current production stage. The controlled change information includes the association relationship between the controlled variable and the running time of the reduction furnace.

[0078] Step S505: Query the value of the operating variable associated with the running time of the reduction furnace in the current production stage from the operating change information, and determine the value of the operating variable associated with the running time of the reduction furnace in the current production stage as the reference value of the operating variable in the current production stage. The operating change information includes the association relationship between the operating variable and the running time of the reduction furnace.

[0079] Step S506: Compare the value of the controlled variable in the current production stage with the target value to obtain a comparison result.

[0080] Step S507: Generate an operating variable adjustment instruction according to the comparison result, the reference value, and the association relationship between the controlled variable and the operating variable in the current production stage, and adjust the value of the operating variable in the current production stage.

[0081] For the specific implementation manners of the above steps, please refer to the various specific implementation manners in Embodiment 2, which will not be elaborated here.

[0082] In this embodiment, based on the controlled change information of the controlled variable and the operating change information of the operating variable in multiple production stages, as well as the association relationship between the controlled variable and the operating variable, the value of the operating variable is automatically adjusted, thereby reducing the labor intensity of the operator; controlling according to the production stage can improve the stability of control, thereby ensuring the stability of product quality.

[0083] Embodiment 4

[0084] An embodiment of the present application provides a reduction furnace control system, as Figure 6 shown. The system includes a data storage subsystem, a cloud optimization and model identification subsystem, and an edge intelligent control subsystem.

[0085] The data storage subsystem is used to receive the preprocessed operation data of the reduction furnace collected from the industrial site sent by the edge intelligent control subsystem for real-time data storage; receive the optimized reference control curve (i.e., the controlled change information), the material table curve (i.e., the operating change information), and the control model (i.e., the association relationship between the controlled variable and the operating variable) sent by the cloud optimization and model identification subsystem and store them; output the target value of the controlled variable, the reference value of the operating variable, and the control model in the current production stage to the edge intelligent control subsystem.

[0086] The cloud optimization and model identification subsystem is used to query the controlled variables and manipulated variables that meet the production objectives from the operation data stored in the data storage subsystem, generate a benchmark control curve and a material table curve, perform regular update optimization, and send them to the data storage subsystem for storage; based on the operation data stored in the data storage subsystem, use the system identification algorithm for identification to obtain a control model and send it to the data storage subsystem for storage.

[0087] The edge intelligent control subsystem is used to receive the operation data of the reduction furnace collected from the industrial site from the Distributed Control System (DCS) and perform preprocessing, including but not limited to: abnormal data processing, including outlier rejection, missing value filling, etc., and send the processed data to the data storage subsystem for storage; based on the target value of the controlled variable, the benchmark value of the manipulated variable, and the control model in the current production stage, generate an adjustment instruction and send it to the DCS system, and the DCS system uses the adjustment instruction to adjust the value of the manipulated variable in the current production stage, thereby changing the value of the controlled variable and realizing the control of the production process of the reduction furnace.

[0088] Embodiment 5

[0089] Corresponding to the application scenario and method of the method provided in the embodiments of the present application, the embodiments of the present application also provide a reduction furnace control device. As Figure 7 shown in the structural block diagram of the reduction furnace control device according to an embodiment of the present application, the device includes:

[0090] An acquisition module 701, configured to acquire the controlled variable change information, the manipulated variable change information, and the association relationship between the controlled variable and the manipulated variable of multiple production stages of the products produced by the reduction furnace;

[0091] A target determination module 702, configured to determine the target value of the controlled variable in the current production stage among multiple production stages according to the controlled variable change information;

[0092] A benchmark determination module 703, configured to determine the benchmark value of the manipulated variable in the current production stage according to the manipulated variable change information;

[0093] A control module 704, configured to adjust the value of the manipulated variable in the current production stage based on the association relationship, the target value, and the benchmark value of the controlled variable and the manipulated variable in the current production stage to achieve the control of the reduction furnace.

[0094] The reduction furnace control device provided by the embodiment of the present application first obtains the controlled change information of the controlled variables in multiple production stages of the reduction furnace for producing products, the operating change information of the operating variables, and the correlation relationship between the controlled variables and the operating variables; then, according to the controlled change information, determines the target value of the controlled variable in the current production stage among the multiple production stages; according to the operating change information, determines the reference value of the operating variable in the current production stage; finally, based on the correlation relationship, target value and reference value of the controlled variable and the operating variable in the current production stage, adjusts the value of the operating variable in the current production stage to achieve the control of the reduction furnace. In this embodiment, based on the controlled change information of the controlled variables and the operating change information of the operating variables in multiple production stages, as well as the correlation relationship between the controlled variables and the operating variables, the value of the operating variable is automatically adjusted, thereby reducing the labor intensity of the operator; controlling according to the production stage can improve the stability of the control, thereby ensuring the stability of the product quality.

[0095] In one embodiment, the obtaining module 701 is configured to: pre-screen, from the historical production data of multiple production stages of the reduction furnace, multiple values of the controlled variables that meet the production target, and perform regression calculation using the multiple values of the controlled variables that meet the production target to obtain the controlled change information of the controlled variables.

[0096] In one embodiment, the obtaining module 701 is configured to: pre-screen, from the historical production data of multiple production stages of the reduction furnace, multiple values of the operating variables that meet the production target, and perform regression calculation using the multiple values of the operating variables that meet the production target to obtain the operating change information of the operating variables.

[0097] In one embodiment, the obtaining module 701 is configured to: pre-obtain, based on the historical production data of multiple production stages of the reduction furnace, the correlation relationship between the controlled variables and the operating variables in the multiple production stages by using a system identification algorithm.

[0098] In one embodiment, the obtaining module 701 is further configured to: determine the production target in at least one of the following dimensions: product quality, energy consumption, or product quantity.

[0099] In one embodiment, the controlled change information includes the correlation relationship between the controlled variable and the running time of the reduction furnace; the target determination module 702 is configured to: query, in the controlled change information, the value of the controlled variable associated with the running time of the reduction furnace in the current production stage, and determine the value of the controlled variable associated with the running time of the reduction furnace in the current production stage as the target value of the controlled variable in the current production stage.

[0100] In one embodiment, the operation change information includes the correlation between the operation variables and the running time of the reduction furnace; the reference determination module 703 is configured to: query the values of the operation variables associated with the running time of the reduction furnace in the current production stage in the operation change information, and determine the values of the operation variables associated with the running time of the reduction furnace in the current production stage as the reference values of the operation variables in the current production stage.

[0101] In one embodiment, the control module 704 is configured to: compare the value of the controlled variable in the current production stage with the target value to obtain a comparison result; generate an operation variable adjustment instruction according to the comparison result, the reference value, and the correlation between the controlled variable and the operation variable in the current production stage, and adjust the value of the operation variable in the current production stage.

[0102] In one embodiment, the device further includes an update module, configured to: update at least one of the controlled change information or the operation change information according to a preset update period.

[0103] The functions of the modules in the embodiments of the present application can be referred to the corresponding descriptions in the above methods, and have corresponding beneficial effects, which will not be elaborated here.

[0104] Figure 8 It is a block diagram of an electronic device for implementing the embodiments of the present application. As Figure 8 shown, the electronic device includes: a memory 810 and a processor 820, and a computer program that can run on the processor 820 is stored in the memory 810. When the processor 820 executes the computer program, the method in the above embodiments is implemented. The number of the memory 810 and the processor 820 can be one or more.

[0105] The electronic device further includes:

[0106] a communication interface 830, configured to communicate with external devices and perform data interaction and transmission.

[0107] If the memory 810, the processor 820, and the communication interface 830 are independently implemented, the memory 810, the processor 820, and the communication interface 830 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 8It is represented only by a thick line, but it does not mean that there is only one bus or one type of bus.

[0108] Optionally, in a specific implementation, if the memory 810, the processor 820, and the communication interface 830 are integrated on a single chip, the memory 810, the processor 820, and the communication interface 830 can communicate with each other through an internal interface.

[0109] The embodiments of the present application provide a computer-readable storage medium that stores a computer program, and when the program is executed by a processor, the methods provided in the embodiments of the present application are implemented.

[0110] The embodiments of the present application further provide a chip that includes a processor for calling and running instructions stored in a memory, so that a communication device installed with the chip executes the methods provided in the embodiments of the present application.

[0111] The embodiments of the present application further provide a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, the output interface, the processor, and the memory are connected through an internal connection path. The processor is used to execute the code in the memory, and when the code is executed, the processor is used to execute the methods provided in the embodiments of the application.

[0112] It should be understood that the above-mentioned processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor that supports the advanced reduced instruction set machine (ARM) architecture.

[0113] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synclink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0114] In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0115] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0116] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, "a plurality of" means two or more unless otherwise specifically defined.

[0117] Any process or method described in the flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed.

[0118] The logic and / or steps described in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in connection with these instruction execution systems, apparatus, or devices.

[0119] It should be understood that each part of this application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the method in the above embodiments can be completed by a program instructing relevant hardware. This program can be stored in a computer-readable storage medium. When this program is executed, it includes one or a combination of the steps of the method embodiment.

[0120] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, an optical disc, or the like.

[0121] As mentioned above, it is only an exemplary embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope recorded in the present application can easily think of various changes or substitutions thereof, and these should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for controlling a reduction furnace, characterized in that, The method includes: Obtaining the controlled variable change information of the controlled variables, the operating variable change information of the operating variables, and the correlation relationship between the controlled variables and the operating variables in multiple production stages of the reduction furnace for producing products; Determining the target value of the controlled variable in the current production stage among the multiple production stages according to the controlled variable change information; Determining the reference value of the operating variable in the current production stage according to the operating variable change information; Based on the correlation relationship between the controlled variable and the operating variable in the current production stage, the target value and the reference value, adjusting the value of the operating variable in the current production stage to achieve the control of the reduction furnace.

2. The method according to claim 1, characterized in that The obtaining of the controlled variable change information of the controlled variables in multiple production stages of the reduction furnace for producing products includes: Previously screening out the values of multiple controlled variables that meet the production target from the historical production data of multiple production stages of the reduction furnace, and performing regression calculation using the values of the multiple controlled variables that meet the production target to obtain the controlled variable change information of the controlled variables.

3. The method according to claim 1, wherein Obtaining the operating variable change information of the operating variables in multiple production stages of the reduction furnace for producing products includes: Previously screening out the values of multiple operating variables that meet the production target from the historical production data of multiple production stages of the reduction furnace, and performing regression calculation using the values of the multiple operating variables that meet the production target to obtain the operating variable change information of the operating variables.

4. The method according to claim 1, wherein Obtaining the correlation relationship between the controlled variables and the operating variables in multiple production stages includes: Previously obtaining the correlation relationship between the controlled variables and the operating variables in multiple production stages based on the historical production data of multiple production stages of the reduction furnace using a system identification algorithm.

5. The method according to claim 2 or 3, characterized in that, The method further includes: Determining the production target in at least one of the following dimensions: product quality, energy consumption, or product quantity.

6. The method according to any one of claims 1-4, characterized in that, The controlled variable change information includes the correlation relationship between the controlled variable and the running time of the reduction furnace; the determining of the target value of the controlled variable in the current production stage among the multiple production stages according to the controlled variable change information includes: Querying the value of the controlled variable associated with the running time of the reduction furnace in the current production stage in the controlled variable change information, and determining the value of the controlled variable associated with the running time of the reduction furnace in the current production stage as the target value of the controlled variable in the current production stage.

7. The method according to any one of claims 1 to 4, characterized in that, The operating variable change information includes the correlation relationship between the operating variable and the running time of the reduction furnace; the determining of the reference value of the operating variable in the current production stage according to the operating variable change information includes: Querying the value of the operating variable associated with the running time of the reduction furnace in the current production stage in the operating variable change information, and determining the value of the operating variable associated with the running time of the reduction furnace in the current production stage as the reference value of the operating variable in the current production stage.

8. The method according to any one of claims 1-4, characterized in that, The adjusting of the value of the operating variable in the current production stage based on the correlation relationship between the controlled variable and the operating variable in the current production stage, the target value, and the reference value includes: Comparing the value of the controlled variable in the current production stage with the target value to obtain a comparison result; Generate an operating variable adjustment instruction according to the comparison result, the reference value, and the correlation between the controlled variable and the operating variable in the current production stage, and adjust the value of the operating variable in the current production stage.

9. The method according to any one of claims 1-4, characterized in that, The method further includes: Updating at least one of the controlled variable change information or the operating variable change information according to a preset update period.

10. A reduction furnace control device, characterized in that, The device includes: An acquisition module, configured to acquire the controlled variable change information of the controlled variables in multiple production stages of the reduction furnace for producing products, the operating variable change information of the operating variables, and the correlation between the controlled variables and the operating variables; A target determination module, configured to determine the target value of the controlled variable in the current production stage among the multiple production stages according to the controlled variable change information; A reference determination module, configured to determine the reference value of the operating variable in the current production stage according to the operating variable change information; A control module, configured to adjust the value of the operating variable in the current production stage based on the correlation between the controlled variable and the operating variable in the current production stage, the target value, and the reference value, so as to implement the control of the reduction furnace.

11. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory. When the processor executes the computer program, the method according to any one of claims 1-9 is implemented.

12. A computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method according to any one of claims 1-9 is implemented.

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