Method, system, control device and heating furnace system for temperature control of a heating furnace

Abstract

The application discloses a temperature control method and system of a heating furnace, a control device and a heating furnace system. The target temperature tracking control gain is determined based on uplink channel state information, downlink channel state information and an identified heating furnace dynamic model parameter matrix. The target temperature control signal is determined based on the target temperature tracking control gain, the target temperature of the reheated slab and the temperature measurement value of the reheated slab received by the control device, so as to control the heater to adjust the temperature. In the temperature control process, the signal noise caused by the wireless network between the control device and the heating furnace is considered, so that the temperature of the heating furnace can be accurately controlled.

Description

Technical Field

[0001] This invention relates to the field of heating furnace technology, and in particular to a temperature control method, system, control device, and heating furnace system for a heating furnace. Background Technology

[0002] Heating furnaces are widely used for reheating or heat treatment of steelmaking equipment. The accuracy of heating furnace temperature control has a great influence on the quality of products manufactured in the furnace, so it is very important to control the temperature of heating furnaces accurately. At present, heating furnace temperature control methods include offline proportional-integral-derivative (PID) control methods and linear quadratic tracker (LQT) based temperature control methods: (1) The main idea of ​​PID control is to adaptively generate control signals by utilizing empirical PID coefficients and the dynamic difference between the target temperature and the real-time temperature of the reheated slab; (2) The linear quadratic tracker (LQT) based temperature control method parameterizes the heat input generated at the temperature controller by numerically solving the Riccati equation of the furnace temperature LQT problem offline or online, and by using the enhanced system state including the target temperature and the real-time temperature of the reheated slab.

[0003] However, the above methods have the following drawbacks: 1) PID temperature control is a heuristic control method that lacks robustness and is far from optimal; 2) Existing PID-based and LQT-based temperature control methods do not consider the adaptability of the control signal to the real-time wireless channel state, and when considering the wireless network between the external IoT controller and the heating furnace, the application of existing PID-based and LQT-based control methods will lead to a large mismatch between the instantaneous temperature and the target temperature of the reheated slab in the heating furnace temperature control system. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, control device, and heating furnace system for controlling the temperature of a heating furnace, which can solve the problem of difficulty in accurately controlling the temperature of a heating furnace in the prior art.

[0005] To address the aforementioned technical problems, this invention provides a temperature control method for a heating furnace. The heating furnace includes a heater and multiple temperature sensors. The heater provides heat energy to a reheated slab within the furnace, and the multiple temperature sensors are mounted on the reheated slab. Each of the multiple temperature sensors is wirelessly connected to a control device. The method is executed by the control device and includes:

[0006] Perform initialization operations;

[0007] Based on uplink channel state information, downlink channel state information, and the identified dynamic model parameter matrix of the heating furnace, the target temperature tracking control gain is determined; the identified dynamic model parameter matrix represents the parameter matrix of the identified dynamic model of the temperature of the reheated slab in the heating furnace as a function of time.

[0008] The target temperature control signal is determined based on the target temperature tracking control gain, the target temperature of the reheated slab, and the temperature measurement value of the reheated slab received by the control device.

[0009] Based on the target temperature control signal, the heater is controlled to adjust the temperature, and the process returns to the step of determining the target temperature tracking control gain based on the uplink channel state information, downlink channel state information, and the identified furnace dynamic model parameter matrix.

[0010] As a preferred embodiment, the initialization operation specifically includes:

[0011] Set the initial target temperature control signal, the initial dynamic model parameter matrix of the heating furnace, and the initial estimated variables.

[0012] As a preferred embodiment, determining the target temperature tracking control gain based on uplink channel state information, downlink channel state information, and the identified dynamic model parameter matrix of the heating furnace specifically includes:

[0013] The uplink channel state information is obtained, which includes the uplink wireless fading gain between the temperature sensor and the control device, as well as the independent identically distributed random access variables of the temperature sensor.

[0014] Acquire downlink channel state information, which includes downlink wireless fading gain between the control device and the heater, as well as the independent identically distributed random access variables of the control device;

[0015] Obtain the parameter matrix of the identified dynamic model of the heating furnace;

[0016] The target temperature tracking control gain is determined based on the uplink wireless fading gain between the temperature sensor and the control device, the independent and identically distributed random access variables of the temperature sensor, the downlink wireless fading gain between the control device and the heater, the independent and identically distributed random access variables of the control device, and the identified furnace dynamic model parameter matrix.

[0017] As a preferred embodiment, the acquisition of the identified dynamic model parameter matrix of the heating furnace specifically includes:

[0018] The parameter matrix of the identified heating furnace dynamic model is obtained using the following formula:

[0019]

[0020]

[0021]

[0022] in, For the first The parameter matrix of the dynamic model of the heating furnace identified at any time; For the first The parameter matrix of the dynamic model of the heating furnace identified at any time; For the first Normalized learning step size at any given moment; For the first Uplink wireless fading gain between the temperature sensor and the control device at any given time; For the first Uplink wireless fading gain between the temperature sensor and the control device at any given time; The temperature measurement value of the reheated slab received by the control device at time k; The temperature measurement value of the reheated slab received by the control device at time k-1; For the first Independent and identically distributed random access variables of the time-control device; For the first Downlink wireless fading gain between the timing control device and the heater; For the first The target temperature control signal at any given time; For the first The independent and identically distributed random access variables of the temperature sensor at each time step; To truncate parameters, ; For the first The independent and identically distributed random access variables of the temperature sensor at each time step; It is to satisfy and The step size sequence; The sampling period; The horizontal cross-sectional area of ​​the reheated slab; The heat transfer coefficient between the heater and the reheated slab is the heat transfer coefficient by convection. The density of the reheated slab; The heat capacity for reheating the slab; The thickness of the slab to be reheated; The thermal conductivity of the reheated slab; This refers to the total number of furnace walls and auxiliary equipment. Weighting of auxiliary equipment for heating furnaces; Specific heat of auxiliary equipment for heating furnaces.

[0023] As a preferred embodiment, determining the target temperature tracking control gain based on the uplink wireless fading gain between the temperature sensor and the control device, the independent and identically distributed random access variables of the temperature sensor, the downlink wireless fading gain between the control device and the heater, the independent and identically distributed random access variables of the control device, and the identified furnace dynamic model parameter matrix specifically includes:

[0024] The target temperature tracking control gain at the current moment is calculated using the following formula:

[0025]

[0026] in, For the first Target temperature tracking control gain at any given time; The first weighting coefficient; This is the second weighting coefficient; For the first Independent and identically distributed random access variables of the time-control device; For the first Downlink wireless fading gain between the timing control device and the heater; Discount factor; For the first The heater execution matrix is ​​aggregated at specific times; For indicator functions; , The dynamic parameter matrix represents the target temperature. For the first The independent and identically distributed random access variables of the temperature sensor at each time step; For the first Uplink wireless fading gain between the temperature sensor and the control device at any given time; For the first Estimated variables at time.

[0027] As a preferred embodiment, the first The estimated variables at time t are updated using the following formula:

[0028]

[0029]

[0030] in, For the first Estimated variables at time; ; This is the aggregated weight matrix.

[0031] As a preferred embodiment, the determination of the target temperature control signal based on the target temperature tracking control gain, the target temperature of the reheated slab, and the temperature measurement value of the reheated slab received by the control device specifically includes:

[0032] The target temperature control signal is calculated using the following formula:

[0033]

[0034] in, For the first The target temperature control signal at any given time; For the first Temperature matrix for reheating slab polymerization at all times , The target temperature for reheating the slab at time k is denoted as .

[0035] To address the same technical problem, embodiments of the present invention also provide a control device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the temperature control method for the heating furnace.

[0036] To address the same technical problem, embodiments of the present invention also provide a temperature control system for a heating furnace, including multiple temperature sensors and the aforementioned control device.

[0037] To address the same technical problem, embodiments of the present invention also provide a heating furnace system, including a heating furnace and a temperature control system for the heating furnace.

[0038] Compared with the prior art, the beneficial effects of the embodiments of the present invention are as follows: The embodiments of the present invention determine the target temperature tracking control gain based on the uplink channel state information, the downlink channel state information and the identified heating furnace dynamic model parameter matrix, and determine the target temperature control signal based on the target temperature tracking control gain, the target temperature of the reheated slab and the temperature measurement value of the reheated slab received by the control device, so as to control the heater to adjust the temperature. In this way, the signal noise brought by the wireless network between the control device and the heating furnace is taken into account during the temperature control process, thereby enabling precise control of the heating furnace temperature. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the heating furnace system in an embodiment of the present invention;

[0040] Figure 2 This is a flowchart of the temperature control method for the heating furnace in an embodiment of the present invention;

[0041] Figure 3 This is a structural block diagram of the control device in an embodiment of the present invention.

[0042] Among them, 1. heating furnace; 2. heater; 3. temperature sensor; 4. reheating slab; 5. control device; 51. processor; 52. memory; 6. moving platform. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1 The heating furnace 1 of this embodiment includes a heater 2 and multiple temperature sensors 3. The heater 2 provides heat energy to the reheated slab 4 within the heating furnace 1. The multiple temperature sensors 3 are mounted on the reheated slab 4. In practical applications, the reheated slab 4 within the heating furnace 1 is placed on a mobile platform 6. The multiple temperature sensors 3 are wirelessly connected to a control device 5. In specific implementations, the control device 5 is, for example, an Internet of Things (IoT) controller. The wireless network between the control device 5 and the heating furnace 1 can be, for example, a 5G wireless network. Wireless communication between the control device 5 and the temperature sensors 3, and between the control device 5 and the heater 2, is achieved through the wireless network. By connecting multiple temperature sensors 3 at dispersed locations on the reheated slab 4, the real-time temperature of the reheated slab 4 is detected. The temperature measurement results are transmitted to the external control device 5 via an uplink wireless network. The control device 5 identifies the dynamics of the heating furnace and generates intermittent control signals. The control signals are forwarded to the heater 2 via a downlink wireless network. The heater 2 dissipates heat energy onto the reheated slab 4 based on the received noisy control signals and dynamically adjusts the temperature of the reheated slab 4 through closed-loop feedback control.

[0045] To facilitate understanding of the temperature control method of the heating furnace in this embodiment of the invention, the dynamic model of the heating furnace in this embodiment of the invention is described below.

[0046] The reheated slab 4 inside the heating furnace 1 is modeled as a three-dimensional flat cube with a length of... and In terms of dimensions, respectively by , , Given. Since the aspect ratio of the reheated slab 4 is usually... and As a characteristic, the temperature field of the reheated slab can therefore be along the vertical spatial dimension. and time trajectory Defined as Regarding the heat flux inside the reheated slab 4 This can be expressed using Fourier's law: Using the diffusion law and the Neumann boundary condition, the heat conduction process within the reheated slab 4 can be characterized as follows:

[0047]

[0048] in, Let x(t) represent the derivative of the weight function corresponding to the Galerkin coefficients h(t). , , , Galerkin coefficient . and These represent the density, heat capacity, and thermal conductivity of the slab 4 reheated in the heating furnace 1, respectively. The Galerkin coefficient is used to express these properties. This reflects the overall average temperature of the reheated slab 4 on the temperature field of the reheated slab. The degree of influence is determined by the weighting function. The value is reflected through the Galerkin coefficient. This reflects the temperature field of the reheated slab. The influence of temperature distribution asymmetry on the overall temperature field, and the degree of influence can be determined by a weighting function. The value is reflected by the Galerkin coefficient; This reflects the temperature field of the reheated slab. Transient temperature inhomogeneity on the temperature field The impact, and the degree of impact, can be determined by the weighting function. The value is reflected in the text.

[0049] To simulate heat transfer from heater 2 to reheated slab 4, the thermal convection process between reheated slab 4 and heater 2 can be considered. Thermal convection occurs between heater 2, the upper surface of reheated slab 4, and the lower surface of reheated slab 4, following the laws of thermal convection, as shown below:

[0050] ;

[0051]

[0052] In the formula, The horizontal cross-sectional area of ​​slab 4 is for reheating. The heat transfer coefficient between heater 2 and reheated slab 4 is the heat transfer coefficient by convection. The temperature of heater 2, This indicates the temperature at which the upper surface of slab 4 is reheated. This indicates the temperature of the lower surface of the slab 4 after reheating.

[0053] The following describes the heat energy released from the input fuel in heater 2 through combustion. To the output temperature of heater 2 The transformation is modeled. To achieve this goal, heat dissipation from the heater circulating fan, heat loss from the furnace door and walls, and heat storage in the furnace walls and auxiliary equipment can be considered. The output temperature of heater 2... and the input fuel of heater 2 The relationship between them can be represented as follows:

[0054]

[0055] in, The total number of furnace walls and auxiliary equipment, including, for example, induced draft equipment (which sends the flue gas generated during the operation of heater 2 to the atmosphere through an induced draft fan and a chimney), dust removal equipment (which removes fly ash from the flue gas of heater 2), etc. and These are the weights and specific heats of the auxiliary equipment in heating furnace 1, respectively. It is a constant that characterizes the available thermal coefficient of heater 2.

[0056] Taking into account the heat conduction process inside the reheated slab 4, the heat convection process between heater 2 and the reheated slab 4, the heat conversion inside heater 2, and the furnace noise, a state representation characterizing the relationship between the input fuel of heater 2 and the temperature field of the reheated slab 4 is obtained, namely, the furnace dynamic model. A sampling rate of [missing information] is used. After sampling, the dynamic model of the heating furnace is as follows:

[0057]

[0058] in, , It is the sampling temperature of the reheated slab at time k, measured by temperature sensor 3. It is the noisy control signal containing the input fuel of heater 2 received at time k. The sampling rate is a constant, and k is a natural number representing the k-th sampling time point.

[0059] , These are the theoretical values ​​of the parameter matrix for the dynamic model of the heating furnace;

[0060] ; It follows a covariance with zero mean and finite noise. Gaussian distribution sampling of additive heating furnace noise.

[0061] The wireless communication channel between the external IoT controller and the heating furnace 1 device is modeled as a wireless fading channel. The noisy temperature measurement received at the external IoT controller is given by the following formula:

[0062]

[0063] in, It has The independent and identically distributed random access variables of temperature sensor 3, express The probability is 1, expressed as a probability value. express. It is the uplink wireless fading gain between temperature sensor 3 and control device 5. This is additive Gaussian channel noise at temperature sensor 3. Based on the received temperature measurement value containing noisy noise, control device 5 generates a control action. The control commands are forwarded to heater 2 via the downlink wireless channel. The control signals received at heater 2 contain noise. It is given by the following formula:

[0064]

[0065] in, It has The independent and identically distributed random access variables of control device 5, , Right now The probability is 1, expressed as a probability value. express. It is the downlink wireless fading gain between control device 5 and heater 2. It is the additive Gaussian channel noise of heater 2.

[0066] For example, the thermal conductivity of the reheated slab 4 The density of the reheated slab 4 The heat capacity of reheating slab 4 ; Reheat slab 4 Thickness in dimension The heat transfer coefficient between heater 2 and reheated slab 4 via heat convection ; Reheat the horizontal cross-sectional area of ​​slab 4 The usable thermal coefficient of heater 2 Total number of furnace walls The weight and specific heat of the auxiliary equipment for the heating furnace are respectively determined by... and Give; sampling period Cutoff threshold Weighting constant Discount factor ; Dynamic parameter matrix of target temperature and target temperature Celsius Noise covariance of heating furnace The activation probabilities of temperature sensor 3 and external IoT controller are respectively determined by... and Provided.

[0067] To achieve identification and temperature tracking control of the heating furnace 1 on the control device 5, the heating furnace 1 is physically characterized by combining the thermal convection process between the heater 2 and the reheated slab 4, the thermal conduction process within the reheated slab 4, and the heat conversion within the reheated slab 4. Based on this, this embodiment of the invention proposes a novel algorithm based on Normalized Stochastic Gradient Descent (NSGD) to simultaneously identify the dynamic model parameter matrix of the heating furnace, and a novel algorithm based on Stochastic Approximation (SA) to generate an online temperature tracking control solution for the heating furnace system on the control device 5.

[0068] Please see Figure 2 The temperature control method for the heating furnace in this embodiment of the invention is executed by the control device 5, and includes:

[0069] Step S101: Perform initialization operations;

[0070] Step S102: Based on the uplink channel state information, downlink channel state information, and the identified heating furnace dynamic model parameter matrix, determine the target temperature tracking control gain; the identified heating furnace dynamic model parameter matrix represents the parameter matrix of the identified dynamic model of the temperature of the reheated slab 4 in the heating furnace 1 changing with time.

[0071] Step S103: Based on the target temperature tracking control gain, the target temperature of the reheated slab, and the temperature measurement value of the reheated slab received by the control device, determine the target temperature control signal;

[0072] Step S104: Based on the target temperature control signal, control the heater 2 to adjust the temperature, and return to step S102.

[0073] In this embodiment of the invention, the target temperature tracking control gain is determined based on the uplink channel state information, the downlink channel state information, and the identified dynamic model parameter matrix of the heating furnace. The target temperature control signal is determined based on the target temperature tracking control gain, the target temperature of the reheated slab, and the temperature measurement value of the reheated slab received by the control device, so as to control the heater 2 to adjust the temperature. In this way, the signal noise brought by the wireless network between the control device 5 and the heating furnace 1 is taken into account during the temperature control process, so as to accurately control the temperature of the heating furnace.

[0074] In this embodiment of the invention, step S101, "performing an initialization operation," specifically includes:

[0075] Set the initial target temperature control signal, the initial furnace dynamic model parameter matrix, and the initial estimated variables. This is done by setting the initial target temperature control signal. Initial heating furnace dynamic model parameter matrix and initial estimated variables The initial temperature control scheme of the control device 5 is given so that temperature tracking control can be performed at various subsequent times.

[0076] In this embodiment of the invention, step S102, "determining the target temperature tracking control gain based on uplink channel state information, downlink channel state information, and the identified dynamic model parameter matrix of the heating furnace," specifically includes:

[0077] The uplink channel state information is obtained, which includes the uplink wireless fading gain between the temperature sensor 3 and the control device 5, as well as the independent identically distributed random access variables of the temperature sensor 3.

[0078] The downlink channel state information is obtained, which includes the downlink wireless fading gain between the control device 5 and the heater 2, as well as the independent identically distributed random access variables of the control device 5.

[0079] Obtain the parameter matrix of the identified dynamic model of the heating furnace;

[0080] The target temperature tracking control gain is determined based on the uplink wireless fading gain between the temperature sensor 3 and the control device 5, the independent and identically distributed random access variables of the temperature sensor 3, the downlink wireless fading gain between the control device 5 and the heater 2, the independent and identically distributed random access variables of the control device 5, and the identified dynamic model parameter matrix of the heating furnace.

[0081] For example, an initial target temperature control signal is set in the initialization operation of step S101. Initial heating furnace dynamic model parameter matrix and initial estimated variables Based on this, in each of the first Time (k=1,2,3,…), parameter matrix of the dynamic model of the heating furnace Updated. Specifically, the identified furnace dynamic model parameter matrix is ​​obtained using the following formula:

[0082]

[0083]

[0084]

[0085] in, For the first The parameter matrix of the dynamic model of the heating furnace identified at any time; For the first The parameter matrix of the dynamic model of the heating furnace identified at any time; For the first Normalized learning step size at any given moment ; For the first The uplink wireless fading gain between the temperature sensor 3 and the control device 5 at any given time; For the first The uplink wireless fading gain between the temperature sensor 3 and the control device 5 at any given time; The temperature measurement value of the reheated slab received by the control device at time k; The temperature measurement value of the reheated slab received by the control device at time k-1; For the first Independent and identically distributed random access variables of the time control device 5; For the first Downlink wireless fading gain between timing control device 5 and heater 2; For the first The target temperature control signal at any given time; For the first Independent and identically distributed random access variables of temperature sensor 3 at time 3; For the preset truncation parameters, ; For the first Independent and identically distributed random access variables of temperature sensor 3 at time 3; It is to satisfy and The step size sequence, { }={ }; The sampling period; The horizontal cross-sectional area of ​​the reheated slab 4; The heat transfer coefficient between heater 2 and reheated slab 4 is the heat transfer coefficient by heat convection. To determine the density of the reheated slab 4; The heat capacity for reheating slab 4; The thickness of the reheated slab 4; To improve the thermal conductivity of the reheated slab 4; This refers to the total number of furnace walls and auxiliary equipment. Weighting of auxiliary equipment for heating furnaces; Specific heat of auxiliary equipment for heating furnace 1.

[0086] In this embodiment of the invention, a novel algorithm for identifying the dynamic model parameter matrix of a heating furnace using noisy temperature data received from an external IoT controller is proposed. Considering the wireless network between the external IoT controller and the heating furnace 1, the proposed scheme can asymptotically learn the dynamic model parameter matrix of the heating furnace. To achieve this, the external IoT controller determines the learning step size based on the real-time wireless channel state and the temperature state of the reheated slab 4. Subsequently, the identified dynamic model parameter matrix of the heating furnace can be iteratively learned based on the proposed identification algorithm.

[0087] In one optional implementation, determining the target temperature tracking control gain based on the uplink wireless fading gain between the temperature sensor 3 and the control device 5, the independent and identically distributed random access variables of the temperature sensor 3, the downlink wireless fading gain between the control device 5 and the heater 2, the independent and identically distributed random access variables of the control device 5, and the identified furnace dynamic model parameter matrix specifically includes:

[0088] The target temperature tracking control gain at the current moment is calculated using the following formula:

[0089]

[0090] in, For the first Target temperature tracking control gain at any given time; The first weighting coefficient; This is the second weighting coefficient; For the first Independent and identically distributed random access variables of the time control device 5; For the first Downlink wireless fading gain between timing control device 5 and heater 2; Discount factor; For the first The heater execution matrix is ​​aggregated at specific times; ; This is an indicator function that returns 1 if the statement is true; Represents a 3×3 identity matrix; , The dynamic parameter matrix represents the target temperature. For the first Independent and identically distributed random access variables of temperature sensor 3 at time 3; For the first The uplink wireless fading gain between the temperature sensor 3 and the control device 5 at any given time; For the first Estimated variables at time.

[0091] For example, the first The estimated variables at time t are updated using the following formula:

[0092]

[0093]

[0094] in, For the first Estimated variables at time; ; The aggregated weight matrix, .

[0095] In this embodiment of the invention, step S103, "determining the target temperature control signal based on the target temperature tracking control gain, the target temperature of the reheated slab, and the temperature measurement value of the reheated slab received by the control device," specifically includes:

[0096] The target temperature control signal is calculated using the following formula:

[0097]

[0098] in, For the first The target temperature control signal at any given time; For the first Temperature matrix for reheating slab polymerization at all times , The target temperature for reheating the slab at time k is the temperature at which the operator inputs the target temperature according to actual needs in specific applications.

[0099] In this embodiment of the invention, an online learning algorithm is constructed using stochastic approximation theory to learn the proposed temperature control solution. Specifically, regarding the Given variables The fixed-point equation, where Given by the following formula In order to obtain the proposed control solution Obtained by applying stochastic approximation iteration The root. Specifically, given an initial feasible semidefinite matrix. and initial temperature tracking control solution For all k = 1, 2, 3, ..., the temperature tracking control solution... .

[0100] In this embodiment of the invention, a novel temperature control algorithm is proposed for a heating furnace 1 controlled by an external IoT controller, utilizing noisy temperature data received from an external IoT controller and the identified dynamic model parameter matrix of the heating furnace. Considering the wireless network between the external IoT controller and the heating furnace 1, the proposed scheme can drive the temperature of the reheated slab to the target temperature over time. To achieve this, the external IoT controller applies updates based on stochastic approximations to iteratively learn the key variables of the proposed temperature control solution online. Based on the learned key variables and the dynamic model parameter matrix of the heating furnace identified by the proposed algorithm, the external IoT controller generates the required control signal, enabling the temperature of the reheated slab 4 in the heating furnace 1 to track the target temperature over time.

[0101] Accordingly, this embodiment of the invention also provides a control device 5, including a processor 51, a memory 52, and a computer program stored in the memory 52 and configured to be executed by the processor 51. When the processor 51 executes the computer program, it implements the temperature control method for the heating furnace.

[0102] Accordingly, embodiments of the present invention also provide a temperature control system for a heating furnace, including a plurality of temperature sensors 3 and the aforementioned control device 5.

[0103] Accordingly, embodiments of the present invention also provide a heating furnace system, including a heating furnace 1 and a temperature control system for the heating furnace.

[0104] It should be noted that when a wireless network exists between the external IoT controller and the heating furnace 1, existing state-of-the-art heating furnace temperature control methods cannot drive the temperature of the reheated slab 4 to the target temperature state over time. It should be noted that the ideal controller design of the external IoT controller largely depends on an accurate understanding of the heating furnace dynamic model parameter matrix. When the external IoT controller does not fully understand the heating furnace dynamic model parameter matrix, the temperature control performance of existing solutions will be further reduced. Considering the wireless network between the external IoT controller and the heating furnace 1, this embodiment of the invention proposes a novel identification and temperature control method. Based on our proposed scheme, the external IoT controller can identify the heating furnace dynamic model parameter matrix and generate the required temperature control signal, under which the temperature of the reheated slab 4 can progressively track the target temperature.

[0105] Furthermore, over time, the solution proposed in this embodiment of the invention consumes less fuel energy at heater 2. This is because the prior art cannot accurately drive the temperature state of the reheated slab 4 to the target temperature state over time, and requires a large energy consumption at heater 2 to offset the difference between the target temperature state and the real-time temperature state of the reheated slab 4; in contrast, the solution proposed in this embodiment of the invention allows the temperature state of the reheated slab 4 to track the target temperature state over time, requiring less energy consumption at heater 2.

[0106] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A temperature control method for a heating furnace, characterized in that, The heating furnace includes a heater and multiple temperature sensors. The heater is used to provide heat energy to the reheated slab inside the heating furnace, and the multiple temperature sensors are mounted on the reheated slab. The plurality of temperature sensors are wirelessly connected to the control device, and the method is executed by the control device, including: Perform initialization operations; Based on uplink channel state information, downlink channel state information, and the identified dynamic model parameter matrix of the heating furnace, the target temperature tracking control gain is determined; the identified dynamic model parameter matrix of the heating furnace represents the parameter matrix of the dynamic model of the temperature of the reheated slab in the heating furnace changing with time. The target temperature control signal is determined based on the target temperature tracking control gain, the target temperature of the reheated slab, and the temperature measurement value of the reheated slab received by the control device. Based on the target temperature control signal, the heater is controlled to adjust the temperature, and the process returns to the step of determining the target temperature tracking control gain based on the uplink channel state information, downlink channel state information, and the identified furnace dynamic model parameter matrix. Specifically, the initialization operation includes setting the initial target temperature control signal, the initial dynamic model parameter matrix of the heating furnace, and the initial estimated variables. The determination of the target temperature tracking control gain based on uplink channel state information, downlink channel state information, and the identified dynamic model parameter matrix of the heating furnace specifically includes: The uplink channel state information is obtained, which includes the uplink wireless fading gain between the temperature sensor and the control device, as well as the independent identically distributed random access variables of the temperature sensor. Acquire downlink channel state information, which includes downlink wireless fading gain between the control device and the heater, as well as the independent identically distributed random access variables of the control device; Obtain the parameter matrix of the identified dynamic model of the heating furnace; The target temperature tracking control gain is determined based on the uplink wireless fading gain between the temperature sensor and the control device, the independent and identically distributed random access variables of the temperature sensor, the downlink wireless fading gain between the control device and the heater, the independent and identically distributed random access variables of the control device, and the identified furnace dynamic model parameter matrix. The acquisition of the identified dynamic model parameter matrix of the heating furnace specifically includes: The parameter matrix of the identified heating furnace dynamic model is obtained using the following formula: in, For the first The parameter matrix of the dynamic model of the heating furnace identified at any time; For the first The parameter matrix of the dynamic model of the heating furnace identified at any time; For the first Normalized learning step size at any given moment; For the first Uplink wireless fading gain between the temperature sensor and the control device at any given time; For the first Uplink wireless fading gain between the temperature sensor and the control device at any given time; The temperature measurement value of the reheated slab received by the control device at time k; The temperature measurement value of the reheated slab received by the control device at time k-1; For the first Independent and identically distributed random access variables of the time-control device; For the first Downlink wireless fading gain between the timing control device and the heater; For the first The target temperature control signal at any given time; For the first The independent and identically distributed random access variables of the temperature sensor at each time step; To truncate parameters, ; For the first The independent and identically distributed random access variables of the temperature sensor at each time step; It is to satisfy and The step size sequence; The sampling period; The horizontal cross-sectional area of ​​the reheated slab; The heat transfer coefficient between the heater and the reheated slab is the heat transfer coefficient by convection. The density of the reheated slab; The heat capacity for reheating the slab; The thickness of the slab to be reheated; The thermal conductivity of the reheated slab; This refers to the total number of furnace walls and auxiliary equipment. Weighting of auxiliary equipment for heating furnaces; Specific heat of auxiliary equipment for heating furnaces; The determination of the target temperature tracking control gain based on the uplink wireless fading gain between the temperature sensor and the control device, the independent and identically distributed random access variables of the temperature sensor, the downlink wireless fading gain between the control device and the heater, the independent and identically distributed random access variables of the control device, and the identified dynamic model parameter matrix of the heating furnace specifically includes: The target temperature tracking control gain at the current moment is calculated using the following formula: in, For the first Target temperature tracking control gain at any given time; The first weighting coefficient; This is the second weighting coefficient; For the first Independent and identically distributed random access variables of the time-control device; For the first Downlink wireless fading gain between the timing control device and the heater; Discount factor; For the first The heater execution matrix is ​​aggregated at specific times; For indicator functions; , The dynamic parameter matrix represents the target temperature. For the first The independent and identically distributed random access variables of the temperature sensor at each time step; For the first Uplink wireless fading gain between the temperature sensor and the control device at any given time; For the first Estimated variables at time.

2. The temperature control method for a heating furnace as described in claim 1, characterized in that, The first The estimated variables at time t are updated using the following formula: in, For the first Estimated variables at time; ; This is the aggregated weight matrix.

3. The temperature control method for a heating furnace as described in claim 1, characterized in that, The determination of the target temperature control signal based on the target temperature tracking control gain, the target temperature of the reheated slab, and the temperature measurement value of the reheated slab received by the control device specifically includes: The target temperature control signal is calculated using the following formula: in, For the first The target temperature control signal at any given time; For the first Temperature matrix for reheating slab polymerization at all times , The target temperature for reheating the slab at time k is denoted as .

4. A control device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the temperature control method for a heating furnace as described in any one of claims 1 to 3.

5. A temperature control system for a heating furnace, characterized in that, It includes multiple temperature sensors and the control device as described in claim 4.

6. A heating furnace system, characterized in that, It includes a heating furnace and a temperature control system for the heating furnace as described in claim 5.

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