Intelligent control system for energy-saving continuous upward casting furnace based on machine learning

Abstract

The present invention discloses an energy-saving continuous upward-drawing melting and casting furnace intelligent control system based on machine learning, which specifically relates to the field of artificial intelligence technology, including a data acquisition and processing module, an intelligent control module, a composite heating module, a waste heat recovery module, and a safety monitoring and protection module. The data acquisition and processing module collects temperature, flow rate, pressure, and gas composition data and processes them, and stores them in a database. The intelligent control module dynamically adjusts the heating power and traction speed, and generates control instructions to send to the composite heating module. The composite heating module allocates the ratio of resistance heating and gas heating according to the instructions of the intelligent control module. The waste heat recovery module recovers the waste heat of the furnace exhaust gas and uses the waste heat to drive the ORC power generation unit to convert the waste heat into electrical energy. The safety monitoring and protection module configures a multiple sensor network at the key links of the melting and casting furnace to monitor the safe operation status.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and more specifically, to an energy-saving continuous upward casting furnace intelligent control system based on machine learning. Background Art

[0002] In industrial production, the melting and casting process is a key link in metal processing, especially in the production of aluminum alloys and other metals. As a highly efficient melting and casting equipment, the continuous upward casting furnace is widely used due to its high production efficiency, low quality fluctuation and good energy utilization.

[0003] Traditional casting furnace control systems typically rely on traditional PID control or empirical models. These control methods struggle to achieve precise energy efficiency control and adaptive optimization in complex production environments. With the advancement of industrial automation and information technology, machine learning, a technology capable of processing large amounts of data and extracting underlying patterns, is increasingly being applied to the optimization and intelligent control of industrial processes. By constructing complex mathematical models, machine learning can monitor the operating status of casting furnaces in real time, accurately predict key parameters such as furnace temperature and fuel consumption, and implement more efficient and energy-saving control strategies. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an energy-saving continuous upward casting furnace intelligent control system based on machine learning to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: an energy-saving, continuous upward casting furnace intelligent control system based on machine learning, comprising a data acquisition and processing module, an intelligent control module, a composite heating module, a waste heat recovery module, and a safety monitoring and protection module;

[0006] The data acquisition and processing module arranges multi-parameter sensors at key positions of the melting furnace to collect temperature, flow rate, pressure, and gas composition data, processes the collected data, and stores the processed data in a database;

[0007] The intelligent control module uses a deep learning model to analyze the collected data, build a relationship model between temperature, flow rate and pressure, and dynamically adjust the heating power and traction speed based on the temperature, flow rate and pressure data input. It also introduces a feedback adjustment mechanism to generate control instructions and send them to the composite heating module.

[0008] The composite heating module allocates the ratio of resistance heating and gas heating according to the instructions of the intelligent control module to achieve the goals of rapid heating and temperature uniformity;

[0009] The waste heat recovery module uses a heat pipe heat exchanger to recover the waste heat from the furnace exhaust gas, and uses the medium and low temperature waste heat to drive the ORC power generation unit to convert the waste heat into electrical energy, reducing the equipment's dependence on external power supply;

[0010] The safety monitoring and protection module configures a multi-sensor network at key links of the melting furnace, including thermocouples, pressure sensors and high-sensitivity gas leak detectors, to monitor the safe operating status and set safety thresholds. When the monitoring data exceeds the safety limit, an alarm is immediately issued and the alarm event is recorded.

[0011] In a preferred embodiment, the data acquisition and processing module arranges multi-parameter sensors at key locations of the melting furnace to collect temperature, flow rate, pressure, and gas composition data, processes the collected data, and stores the processed data in a database. The specific steps are as follows:

[0012] Step A1, data acquisition: various types of sensors are arranged in the melting zone, holding zone and flow zone of the casting furnace, including high-precision thermocouples, flow rate sensors, pressure sensors and gas composition analyzers, to collect melt temperature T, melt flow rate V, furnace pressure P, and gas composition data in the exhaust gas, including oxygen C1, nitrogen C2 and carbon monoxide C3. For each time point n, the collected data is expressed as D(n) = {T(n), V(n), P(n), C1(n), C2(n), C3(n)}, where D(n) represents the multidimensional data obtained at the nth sampling moment, T(n) represents the melt temperature collected at time point n, V(n) represents the melt flow rate collected at time point n, P(n) represents the furnace pressure collected at time point n, and C1(n), C2(n) and C3(n) respectively represent the gas component concentrations collected at time point n;

[0013] Step A2, data processing: Based on signal processing and data preprocessing algorithms, remove noise and perform real-time filtering and outlier detection on the collected multidimensional data, integrate them through multi-channel data fusion technology, and store them in the database according to time series.

[0014] In a preferred embodiment, the intelligent control module uses a deep learning model to analyze the collected data, construct a relationship model between temperature, flow rate, and pressure, dynamically adjusts the heating power and traction speed based on the temperature, flow rate, and pressure data input, and introduces a feedback adjustment mechanism to generate control instructions and send them to the composite heating module. The specific steps are as follows:

[0015] Step B1, deep learning model training: extracting historical data sets from the database as input to the deep learning model, using a multilayer perceptron to predict control outputs, including heating power and traction speed, further including the following steps:

[0016] Step B101: Extract historical data sets from the database and segment them according to time series. Each time point n corresponds to a data vector {T(n), V(n), P(n), C1(n), C2(n), C3(n)}, which is used as the input of the deep learning model.

[0017] Step B102: Construct a multilayer perceptron model, including an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is equal to the number of input features. The output layer has two neurons, which are used to predict heating power and traction speed respectively. For an L-layer perceptron network, the input layer is represented as the 0th layer, the output layer is represented as the Lth layer, and the input and output of the lth layer are represented as h respectively. l and w l , the input from the input layer to the first layer is h1=f(W1x(n)+b1), and the input from the lth layer to the l+1th layer is h l+1 =f(W l+1 h l +b l+1 ), the output of the output layer is y(n)=W L h L +b L =[P heat (n),V traction (n)], where W L is the weight matrix of the last layer, b L is the bias term of the last layer, h L is the output of the last layer, W l+1 is the weight matrix of the l+1th layer, h l+1 is the output of the l+1th layer, b l+1 is the bias term of the l+1th layer, W1 is the weight matrix of the first layer, b1 is the bias term of the first layer, f is the activation function, x(n) is the input vector, is the predicted heating power and traction speed;

[0018] Step B103: Use the mean square error loss function to calculate the difference between the predicted value and the true value. The formula is: Among them, [P heat (n),V traction (n)] is the actual control value, are the heating power and pulling speed predicted by the model, and N is the total number of training samples;

[0019] Step B2: Feedback adjustment: Generate preliminary control instructions based on the predicted output of the deep learning model and The heating power is adjusted according to the difference between the real-time temperature and the target temperature, and the adjusted heating power is obtained as follows: The traction speed is adjusted according to the difference between the real-time flow rate and the target flow rate. The adjusted traction speed is Among them, K heat is the feedback gain of the heating power, K traction is the feedback gain of the traction speed, T target and V target are the target temperature and target flow rate, T actual (n) and V actual (n) are the actual temperature and flow rate respectively, and the heating power and traction speed after feedback adjustment are sent to the composite heating module as the final control instruction. The final control instruction is: u(n) = {P′ heat (n),V′ traction (n)}, ensuring that the temperature and flow rate are within the set range during the casting process.

[0020] In a preferred embodiment, the composite heating module allocates the ratio of resistance heating and gas heating according to the instructions of the intelligent control module to achieve the goals of rapid heating and temperature uniformity. The specific steps are as follows:

[0021] Step C1, setting the proportional factor: receiving the control instruction u(n) output from the intelligent control module = {P′ heat (n),V′ traction (n)}, according to the required heating power P′ heat (n), set the target heating power, set a proportional factor α, which is used to control the distribution ratio between resistance heating and gas heating. The proportional factor α is in the range of [0,1], which represents the power ratio of resistance heating to gas heating. When α=1, it means that all heating power is provided by resistance heating, and α=0, it means that all heating power is provided by gas heating;

[0022] Step C2: Allocate heating power: According to the target heating power P' heat (n) and proportional factor α, calculate the power distribution of resistance heating and gas heating. The specific calculation formula is: P res (n) = α·P′ heat (n), P gas (n) = (1-α)·P′ heat (n), where P res (n) is the power of the resistance heating, P gas (n) is the power of gas heating. According to the allocated power, the power of the resistance heater and the gas heater is adjusted respectively, and during the heating process, the temperature and flow rate are monitored in real time to achieve the desired heating effect.

[0023] In a preferred embodiment, the waste heat recovery module uses a heat pipe heat exchanger to recover the waste heat from the furnace exhaust gas, and uses the medium and low temperature waste heat to drive the ORC power generation unit to convert the waste heat into electrical energy. The specific steps are as follows:

[0024] Step D1: Use a heat pipe heat exchanger to transfer the waste heat in the exhaust gas to the working medium through the heat pipe to recover the heat. Set the effective heat transfer efficiency of the heat exchanger to η heat , calculate the heat power recovered from the exhaust gas, the specific formula is Among them, P recover (n) is the heat power recovered from the exhaust gas, is the exhaust gas flow rate, C p is the specific heat capacity of the exhaust gas, T flue,in 、T flue,out are the temperature of the exhaust gas when it enters the heat exchanger and the temperature of the exhaust gas when it leaves the heat exchanger;

[0025] Step D2: Input the recovered thermal power into the ORC power generation unit and calculate the electrical energy output from the ORC power generation unit. The specific formula is: E output (n) = [η ORC ·P recover (n)]·t, where E output (n) is the conversion efficiency of the ORC power generation unit, t is the time duration, η ORC is the conversion efficiency of the ORC power generation unit.

[0026] In a preferred embodiment, the safety monitoring and protection module configures a multi-sensor network at key links of the melting furnace, including thermocouples, pressure sensors, and highly sensitive gas leak detectors, to monitor safe operating conditions and set safety thresholds. When the monitoring data exceeds the safety limit, an alarm is immediately issued and the alarm event is recorded. The specific steps are as follows:

[0027] Step E1, sensor data monitoring: set the safety threshold of the temperature sensor to T max and T min , the safety threshold of the pressure sensor is P max and P min , the safety threshold of gas concentration is C max ,According to the real-time data obtained by each sensor, it is compared with its preset safety threshold;

[0028] Step E2, alarm triggering and response: When the monitoring data of any sensor exceeds the set safety threshold, the alarm is immediately triggered and safety protection measures are initiated. When the alarm is triggered, the alarm event is automatically recorded, including the alarm type, occurrence time, and specific threshold value of the trigger. Different response measures are taken according to the level and type of the alarm event. When the temperature or pressure exceeds the limit, the casting furnace is automatically shut down and an alarm information is sent to the operator via SMS or email.

[0029] The beneficial effects of the present invention are as follows: multi-parameter sensors are arranged at key positions of the melting furnace to collect temperature, flow rate, pressure, and gas composition data, and the collected data are processed and stored in a database. The collected data are analyzed by a deep learning model, and a relationship model between temperature, flow rate, and pressure is constructed. According to the temperature, flow rate, and pressure data input, the heating power and traction speed are dynamically adjusted, and a feedback adjustment mechanism is introduced to generate control instructions. According to the generated control instructions, the ratio of resistance heating and gas heating is allocated to achieve the goal of rapid heating and temperature uniformity. A heat pipe heat exchanger is used to recover the waste heat of the furnace exhaust gas, and the heat pipe heat exchanger is used to recover the waste heat of the furnace exhaust gas. The ORC power generation unit is driven by medium and low temperature waste heat, which is converted into electrical energy, reducing the equipment's dependence on external power sources. A multi-sensor network, including thermocouples, pressure sensors, and high-sensitivity gas leak detectors, is configured at key links in the melting furnace to monitor safe operating conditions and set safety thresholds. When the monitoring data exceeds the safety limit, an alarm is immediately issued and the alarm event is recorded, which can reduce the risk of accidents and ensure the safety of personnel and equipment. The present invention can improve energy utilization efficiency by real-time monitoring and dynamic adjustment of heating power. By optimizing thermal management, temperature fluctuations and uneven heating are reduced, the consistency and quality of casting products are improved, and the defect rate is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0032] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0033] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0034] Example 1

[0035] This embodiment provides Figure 1 The energy-saving, continuous upward melting and casting furnace intelligent control system based on machine learning is shown, which specifically includes a data acquisition and processing module, an intelligent control module, a composite heating module, a waste heat recovery module, and a safety monitoring and protection module;

[0036] The data acquisition and processing module arranges multi-parameter sensors at key positions of the melting furnace to collect temperature, flow rate, pressure, and gas composition data, processes the collected data, and stores the processed data in a database;

[0037] The intelligent control module uses a deep learning model to analyze the collected data, build a relationship model between temperature, flow rate and pressure, and dynamically adjust the heating power and traction speed based on the temperature, flow rate and pressure data input. It also introduces a feedback adjustment mechanism to generate control instructions and send them to the composite heating module.

[0038] The composite heating module allocates the ratio of resistance heating and gas heating according to the instructions of the intelligent control module to achieve the goals of rapid heating and temperature uniformity;

[0039] The waste heat recovery module uses a heat pipe heat exchanger to recover the waste heat from the furnace exhaust gas, and uses the medium and low temperature waste heat to drive the ORC power generation unit to convert the waste heat into electrical energy, reducing the equipment's dependence on external power supply;

[0040] The safety monitoring and protection module configures a multi-sensor network at key links of the melting furnace, including thermocouples, pressure sensors and high-sensitivity gas leak detectors, to monitor the safe operating status and set safety thresholds. When the monitoring data exceeds the safety limit, an alarm is immediately issued and the alarm event is recorded.

[0041] In this embodiment, the data acquisition and processing module specifically needs to be explained. The data acquisition and processing module arranges multi-parameter sensors at key positions of the melting furnace to collect temperature, flow rate, pressure, and gas composition data, processes the collected data, and stores the processed data in a database. The specific steps are as follows:

[0042] Step A1, data acquisition: various types of sensors are arranged in the melting zone, holding zone and flow zone of the casting furnace, including high-precision thermocouples, flow rate sensors, pressure sensors and gas composition analyzers, to collect melt temperature T, melt flow rate V, furnace pressure P, and gas composition data in the exhaust gas, including oxygen C1, nitrogen C2 and carbon monoxide C3. For each time point n, the collected data is expressed as D(n) = {T(n), V(n), P(n), C1(n), C2(n), C3(n)}, where D(n) represents the multidimensional data obtained at the nth sampling moment, T(n) represents the melt temperature collected at time point n, V(n) represents the melt flow rate collected at time point n, P(n) represents the furnace pressure collected at time point n, and C1(n), C2(n) and C3(n) respectively represent the gas component concentrations collected at time point n;

[0043] Step A2, data processing: Based on signal processing and data preprocessing algorithms, remove noise and perform real-time filtering and outlier detection on the collected multidimensional data, integrate them through multi-channel data fusion technology, and store them in the database according to time series.

[0044] In this embodiment, the intelligent control module specifically needs to be explained. The intelligent control module uses a deep learning model to analyze the collected data, constructs a relationship model between temperature, flow rate, and pressure, dynamically adjusts the heating power and traction speed according to the temperature, flow rate, and pressure data input, and introduces a feedback adjustment mechanism to generate control instructions and send them to the composite heating module. The specific steps are as follows:

[0045] Step B1, deep learning model training: extracting historical data sets from the database as input to the deep learning model, using a multilayer perceptron to predict control outputs, including heating power and traction speed, further including the following steps:

[0046] Step B101: Extract historical data sets from the database and segment them according to time series. Each time point n corresponds to a data vector {T(n), V(n), P(n), C1(n), C2(n), C3(n)}, which is used as the input of the deep learning model.

[0047] Step B102: Construct a multilayer perceptron model, including an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is equal to the number of input features. The output layer has two neurons, which are used to predict heating power and traction speed respectively. For an L-layer perceptron network, the input layer is represented as the 0th layer, the output layer is represented as the Lth layer, and the input and output of the lth layer are represented as h respectively. l and w l , the input from the input layer to the first layer is h1=f(W1x(n)+b1), and the input from the lth layer to the l+1th layer is h l+1 =f(W l+1 h l +b l+1 ), the output of the output layer is y(n)=W L h L +b L =[P heat (n),V traction (n)], where W L is the weight matrix of the last layer, b L is the bias term of the last layer, h L is the output of the last layer, W l+1 is the weight matrix of the l+1th layer, h l+1 is the output of the l+1th layer, b l+1 is the bias term of the l+1th layer, W1 is the weight matrix of the first layer, b1 is the bias term of the first layer, f is the activation function, x(n) is the input vector, is the predicted heating power and traction speed;

[0048] Step B103: Use the mean square error loss function to calculate the difference between the predicted value and the true value. The formula is: Among them, [P heat (n),V traction (n)] is the actual control value, are the heating power and pulling speed predicted by the model, and N is the total number of training samples;

[0049] Step B2: Feedback adjustment: Generate preliminary control instructions based on the predicted output of the deep learning model and The heating power is adjusted according to the difference between the real-time temperature and the target temperature, and the adjusted heating power is obtained as follows: The traction speed is adjusted according to the difference between the real-time flow rate and the target flow rate. The adjusted traction speed is Among them, K heat is the feedback gain of the heating power, K traction is the feedback gain of the traction speed, T target and V target are the target temperature and target flow rate, T actual (n) and V actual (n) are the actual temperature and flow rate respectively, and the heating power and traction speed after feedback adjustment are sent to the composite heating module as the final control instruction. The final control instruction is: u(n) = {P′ heat (n),V′ traction (n)}, ensuring that the temperature and flow rate are within the set range during the casting process.

[0050] In this embodiment, the composite heating module is specifically described. The composite heating module allocates the ratio of resistance heating and gas heating according to the instructions of the intelligent control module to achieve the goals of rapid heating and temperature uniformity. The specific steps are as follows:

[0051] Step C1, setting the proportional factor: receiving the control instruction u(n) output from the intelligent control module = {P′ heat (n),V′ traction (n)}, according to the required heating power P′ heat (n), set the target heating power and a proportional factor α to control the distribution ratio between resistance heating and gas heating. The proportional factor α is in the range of [0,1] and represents the power ratio of resistance heating to gas heating. When α = 1, it means that all heating power is provided by resistance heating, and α = 0, it means that all heating power is provided by gas heating. By adjusting α, the heating rate and temperature uniformity can be flexibly adjusted;

[0052] Step C2: Allocate heating power: According to the target heating power P' heat (n) and proportional factor α, calculate the power distribution of resistance heating and gas heating. The specific calculation formula is: P res (n) = α·P′ heat (n), P gas (n) = (1-α)·P′ heat (n), where P res (n) is the power of the resistance heating, P gas (n) is the power of gas heating. According to the allocated power, the power of the resistance heater and the gas heater is adjusted respectively, and during the heating process, the temperature and flow rate are monitored in real time to achieve the desired heating effect.

[0053] In this embodiment, the waste heat recovery module is specifically described. The waste heat recovery module uses a heat pipe heat exchanger to recover waste heat from the furnace exhaust gas, and uses the medium and low temperature waste heat to drive the ORC power generation unit to convert the waste heat into electrical energy. The ORC power generation unit is a key component for converting waste heat from exhaust gas into electrical energy. By improving energy efficiency and utilizing waste heat, energy consumption and waste gas emissions are reduced, which helps to reduce the environmental impact of the production process. The specific steps are as follows:

[0054] Step D1: Use a heat pipe heat exchanger to transfer the waste heat in the exhaust gas to the working medium through the heat pipe to recover the heat. Set the effective heat transfer efficiency of the heat exchanger to η heat , calculate the heat power recovered from the exhaust gas, the specific formula is Among them, P recover (n) is the heat power recovered from the exhaust gas, is the exhaust gas flow rate, C p is the specific heat capacity of the exhaust gas, T flue,in 、T flue,out are the temperature of the exhaust gas when it enters the heat exchanger and the temperature of the exhaust gas when it leaves the heat exchanger;

[0055] Step D2: Input the recovered thermal power into the ORC power generation unit and calculate the electrical energy output from the ORC power generation unit. The specific formula is: E output (n) = [η ORC ·P recover (n)]·t, where E output (n) is the conversion efficiency of the ORC power generation unit, t is the time duration, η ORC is the conversion efficiency of the ORC power generation unit.

[0056] In this embodiment, the safety monitoring and protection module specifically needs to be explained. The safety monitoring and protection module configures a multi-sensor network at the key links of the melting furnace, including thermocouples, pressure sensors, and high-sensitivity gas leak detectors, to monitor the safe operating status and set safety thresholds. When the monitoring data exceeds the safety limit, an alarm is immediately issued and the alarm event is recorded. The specific steps are as follows:

[0057] Step E1, sensor data monitoring: set the safety threshold of the temperature sensor to T max and T min , the safety threshold of the pressure sensor is P max and P min , the safety threshold of gas concentration is C max ,According to the real-time data obtained by each sensor, it is compared with its preset safety threshold;

[0058] Step E2, alarm triggering and response: When the monitoring data of any sensor exceeds the set safety threshold, the alarm is immediately triggered and safety protection measures are initiated, including shutting down the casting furnace and starting the cooling system. When the alarm is triggered, the alarm event is automatically recorded, including the alarm type, occurrence time, and specific triggering threshold. Different response measures are taken according to the level and type of the alarm event. When the temperature or pressure exceeds the limit, the casting furnace is automatically shut down and an alarm message is sent to the operator via SMS or email.

[0059] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0060] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0065] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An energy-saving, continuous upward casting furnace intelligent control system based on machine learning, characterized by: It includes data acquisition and processing module, intelligent control module, composite heating module, waste heat recovery module, and safety monitoring and protection module; The data acquisition and processing module arranges multi-parameter sensors at key positions of the melting furnace to collect temperature, flow rate, pressure, and gas composition data, processes the collected data, and stores the processed data in a database; The intelligent control module uses a deep learning model to analyze the collected data, build a relationship model between temperature, flow rate and pressure, and dynamically adjust the heating power and traction speed based on the temperature, flow rate and pressure data input. It also introduces a feedback adjustment mechanism to generate control instructions and send them to the composite heating module. The composite heating module allocates the ratio of resistance heating and gas heating according to the instructions of the intelligent control module to achieve the goals of rapid heating and temperature uniformity; The waste heat recovery module uses a heat pipe heat exchanger to recover the waste heat of the furnace exhaust gas, and uses the medium and low temperature waste heat to drive the ORC power generation unit to convert the waste heat into electrical energy; The safety monitoring and protection module configures a multi-sensor network at key links of the melting furnace, including thermocouples, pressure sensors and high-sensitivity gas leak detectors, to monitor the safe operating status and set safety thresholds. When the monitoring data exceeds the safety limit, an alarm is immediately issued and the alarm event is recorded; The intelligent control module uses a deep learning model to analyze the collected data, build a relationship model between temperature, flow rate, and pressure, and dynamically adjusts the heating power and traction speed based on the temperature, flow rate, and pressure data input. It also introduces a feedback adjustment mechanism to generate control instructions and send them to the composite heating module. The specific steps are as follows: Step B1, deep learning model training: extracting historical data sets from the database as input to the deep learning model, using a multilayer perceptron to predict control outputs, including heating power and traction speed, further including the following steps: Step B101: Extract historical data sets from the database and segment them according to time series. Each time point n corresponds to a data vector. , which is used as input to the deep learning model; Step B102: Construct a multi-layer perceptron model, including an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is equal to the number of input features. The output layer has two neurons, which are used to predict heating power and traction speed respectively. For an L-layer perceptron network, the input layer is represented as the 0th layer, the output layer is represented as the Lth layer, and the Lth layer is represented as the Lth layer. The input and output of the layer are represented as and , the input from the input layer to the first layer is , No. Layer to The input of the layer is , the output of the output layer is ,in, is the weight matrix of the last layer, is the bias term of the last layer, is the output of the last layer, It is The weight matrix of the layer, It is The output of the layer, It is The bias term of the layer, is the weight matrix of layer 1, is the bias term of the first layer, f is the activation function, is the input vector, is the predicted heating power and traction speed; Step B103: Use the mean square error loss function to calculate the difference between the predicted value and the true value. The formula is: ,in, is the actual control value, are the heating power and pulling speed predicted by the model, and N is the total number of training samples; Step B2: Feedback adjustment: Generate preliminary control instructions based on the predicted output of the deep learning model and , the heating power is adjusted according to the difference between the real-time temperature and the target temperature, and the adjusted heating power is ; According to the difference between the real-time flow rate and the target flow rate, the traction speed is adjusted. The adjusted traction speed is ,in, is the feedback gain of the heating power, is the feedback gain of the traction speed, and are the target temperature and target flow rate, and are the actual temperature and flow rate respectively. The heating power and traction speed after feedback adjustment are sent to the composite heating module as the final control instruction. The final control instruction is: .

2. The energy-saving, continuous upward casting furnace intelligent control system based on machine learning according to claim 1 is characterized in that: The data acquisition and processing module arranges multi-parameter sensors at key locations of the melting furnace to collect temperature, flow rate, pressure, and gas composition data, processes the collected data, and stores the processed data in the database. The specific steps are as follows: Step A1, data acquisition: Various types of sensors are arranged in the melting zone, holding zone and flow zone of the casting furnace, including high-precision thermocouples, flow rate sensors, pressure sensors and gas composition analyzers, to collect data on melt temperature T, melt flow rate V, furnace pressure P, and gas composition data in the exhaust gas, including oxygen , nitrogen and carbon monoxide , for each time point n, the collected data is expressed as ,in, Represents the multidimensional data obtained at the nth sampling moment, represents the melt temperature collected at time point n, represents the melt flow rate collected at time point n, represents the gas pressure in the furnace collected at time point n, 、 、 They represent the concentrations of gas components collected at time point n; Step A2, data processing: Based on signal processing and data preprocessing algorithms, remove noise and perform real-time filtering and outlier detection on the collected multidimensional data, integrate them through multi-channel data fusion technology, and store them in the database according to time series.

3. The energy-saving, continuous upward casting furnace intelligent control system based on machine learning according to claim 1 is characterized in that: The composite heating module distributes the ratio of resistance heating and gas heating according to the instructions of the intelligent control module to achieve the goals of rapid heating and temperature uniformity. The specific steps are as follows: Step C1, setting the proportional factor: receiving the output control instruction from the intelligent control module , according to the required heating power , set the target heating power, set a proportional factor , used to control the distribution ratio between resistance heating and gas heating, the proportional factor exist In the range, it indicates the power ratio of resistance heating to gas heating. Indicates that all heating power is provided by resistance heating, Indicates that all heating power is provided by gas heating; Step C2: Allocate heating power: according to the target heating power and scale factor , calculate the power distribution of resistance heating and gas heating, the specific calculation formula is: , ,in, is the power of the resistance heating, It is the power of gas heating. According to the allocated power, the power of the resistance heater and the gas heater is adjusted respectively, and during the heating process, the temperature and flow rate are monitored in real time to achieve the desired heating effect.

4. The energy-saving, continuous upward casting furnace intelligent control system based on machine learning according to claim 1 is characterized in that: The waste heat recovery module uses a heat pipe heat exchanger to recover the waste heat from the furnace exhaust gas, and uses the medium and low temperature waste heat to drive the ORC power generation unit to convert the waste heat into electrical energy. The specific steps are as follows: Step D1: Use a heat pipe heat exchanger to transfer the waste heat in the exhaust gas to the working medium through the heat pipe to recover the heat. Set the effective heat transfer efficiency of the heat exchanger to , calculate the heat power recovered from the exhaust gas, the specific formula is ,in, is the heat power recovered from the exhaust gas, is the exhaust gas flow rate, is the specific heat capacity of the exhaust gas, 、 are the temperature of the exhaust gas when it enters the heat exchanger and the temperature of the exhaust gas when it leaves the heat exchanger; Step D2: Input the recovered thermal power into the ORC power generation unit and calculate the electrical energy output from the ORC power generation unit. The specific formula is: ,in, is the conversion efficiency of the ORC power generation unit, t is the duration of time, is the conversion efficiency of the ORC power generation unit.

5. The energy-saving, continuous upward casting furnace intelligent control system based on machine learning according to claim 1 is characterized in that: The safety monitoring and protection module configures a multi-sensor network at key links of the melting furnace, including thermocouples, pressure sensors, and highly sensitive gas leak detectors, to monitor safe operating conditions and set safety thresholds. When the monitoring data exceeds the safety limit, an alarm is immediately issued and the alarm event is recorded. The specific steps are as follows: Step E1, sensor data monitoring: set the safety threshold of the temperature sensor to and , the safety threshold of the pressure sensor is and , the safety threshold of gas concentration is ,According to the real-time data obtained by each sensor, it is compared with its preset safety threshold; Step E2, alarm triggering and response: When the monitoring data of any sensor exceeds the set safety threshold, the alarm is immediately triggered and safety protection measures are initiated. When the alarm is triggered, the alarm event is automatically recorded, including the alarm type, occurrence time, and specific threshold value of the trigger. Different response measures are taken according to the level and type of the alarm event. When the temperature or pressure exceeds the limit, the casting furnace is automatically shut down and an alarm information is sent to the operator via SMS or email.

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