An intelligent power adjustment method and device for an electric heating furnace with multiple heating tubes

Abstract

The present invention discloses a method and device for intelligent adjustment of the power of an electric heating furnace with multiple heating tubes, relating to the technical field of electric heating furnaces. The method includes: connecting the electric heating furnace with multiple heating tubes, and obtaining the basic information of the device; collecting the monitoring data inside the furnace; obtaining the operating parameter information, calculating the heating power required to reach the preset target temperature, and introducing the heat loss index of the electric heating furnace with multiple heating tubes for compensation calculation; dynamically adjusting the output power of each group of electric heating tubes to obtain the initial output power distribution set; optimizing the initial output power distribution set with the temperature change stability, and setting an elimination mechanism according to the maximization of the heating efficiency to obtain the dynamic adjustment strategy. It solves the technical problem that the power adjustment of the electric heating furnace with multiple heating tubes in the prior art is not precise enough, and achieves the technical effects of improving the heating efficiency and stability by intelligently adjusting the output power of each group of electric heating tubes.

Description

Technical Field

[0001] The present invention relates to the technical field of electric heating furnaces, and particularly to a method and device for intelligent power adjustment of an electric heating furnace with multiple heating tubes. Background Art

[0002] In the rapidly developing modern industrial system, as a key device for heat energy conversion and transfer, the power control strategy of an electric heating furnace is crucial for ensuring production efficiency and rational use of energy. However, traditional power control methods often have certain limitations. Especially in the application scenario of an electric heating furnace with multiple heating tubes, when multiple power regulators work simultaneously, if the conduction times overlap, it will cause an instantaneous increase in the grid load, forming a peak power consumption, which not only poses a threat to the stability of the power grid, but also may increase energy consumption and equipment wear. Summary of the Invention

[0003] This application provides a method and device for intelligent power adjustment of an electric heating furnace with multiple heating tubes, which solves the technical problem of inaccurate power adjustment of an electric heating furnace with multiple heating tubes in the prior art.

[0004] In view of the above problems, this application provides a method and device for intelligent power adjustment of an electric heating furnace with multiple heating tubes.

[0005] In the first aspect of this application, a method for intelligent power adjustment of an electric heating furnace with multiple heating tubes is provided. The method includes:

[0006] Obtain the basic information of the device; collect the in-furnace monitoring data including temperature monitoring data and flow monitoring data; obtain the operation parameter information of the electric heating furnace with multiple heating tubes, and calculate the heating power required to reach the preset target temperature using a mathematical model, and introduce the heat loss index of the electric heating furnace with multiple heating tubes for compensation calculation; based on the mathematical model after compensation calculation, dynamically adjust the output power of each group of electric heating tubes, use the preset target temperature as the termination condition, aim to minimize energy consumption, and combine the basic information of the device to allocate the output power of each group of electric heating tubes to obtain the initial output power allocation set corresponding to multiple groups of electric heating tubes; based on the in-furnace monitoring data, continuously monitor the parameter changes in the electric heating furnace with multiple heating tubes, optimize the initial output power allocation set with the stability of temperature change, and set an elimination mechanism according to the maximization of heating efficiency to obtain a dynamic adjustment strategy.

[0007] In the second aspect of this application, a device for intelligent power adjustment of an electric heating furnace with multiple heating tubes is provided. The device includes:

[0008] Device information acquisition module, which is used to acquire basic device information; data acquisition module, which is used to acquire in-furnace monitoring data including temperature monitoring data and flow monitoring data; calculation module, which is used to acquire operation parameter information of the multi-heating-tube electric heating furnace, calculate the heating power required to reach the preset target temperature using a mathematical model, and introduce the heat loss index of the multi-heating-tube electric heating furnace for compensation calculation; power distribution module, which is used to dynamically adjust the output power of each group of electric heating tubes based on the compensated mathematical model, use the preset target temperature as the termination condition, aim to minimize energy consumption, and combine the basic device information to distribute the output power of each group of electric heating tubes to obtain the initial output power distribution set corresponding to multiple groups of electric heating tubes; optimization module, which is used to continuously monitor the parameter changes in the multi-heating-tube electric heating furnace based on the in-furnace monitoring data, optimize the initial output power distribution set with the stability of temperature change, and set an elimination mechanism according to the maximization of heating efficiency to obtain a dynamic adjustment strategy.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, acquire basic device information. Next, acquire in-furnace monitoring data including temperature monitoring data and flow monitoring data. Then, acquire operation parameter information of the multi-heating-tube electric heating furnace, calculate the heating power required to reach the preset target temperature using a mathematical model, and introduce the heat loss index of the multi-heating-tube electric heating furnace for compensation calculation. Based on the compensated mathematical model, dynamically adjust the output power of each group of electric heating tubes, use the preset target temperature as the termination condition, aim to minimize energy consumption, and combine the basic device information to distribute the output power of each group of electric heating tubes to obtain the initial output power distribution set corresponding to multiple groups of electric heating tubes. Finally, continuously monitor the parameter changes in the multi-heating-tube electric heating furnace based on the in-furnace monitoring data, optimize the initial output power distribution set with the stability of temperature change, and set an elimination mechanism according to the maximization of heating efficiency to obtain a dynamic adjustment strategy. This solves the technical problem of inaccurate power adjustment of the multi-heating-tube electric heating furnace in the prior art, and achieves the technical effects of improving heating efficiency and stability by intelligently adjusting the output power of each group of electric heating tubes. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 Schematic flow diagram of an intelligent power adjustment method for a multi-heating-tube electric heating furnace provided by an embodiment of the present application;

[0013] Figure 2 Schematic structural diagram of an intelligent power adjustment device for a multi-heating-tube electric heating furnace provided by an embodiment of the present application.

[0014] Explanation of reference numerals: Device information acquisition module 11, data acquisition module 12, calculation module 13, power distribution module 14, optimization module 15. Detailed implementation manners

[0015] By providing an intelligent power adjustment method and device for a multi-heating-tube electric heating furnace, the present application solves the technical problem of inaccurate power adjustment of multi-heating-tube electric heating furnaces in the prior art.

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0017] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0018] Embodiment 1, as Figure 1 shown, the present application provides an intelligent power adjustment method for a multi-heating-tube electric heating furnace, where the method includes:

[0019] Obtain basic device information.

[0020] By connecting to the multi-heating-tube electric heating furnace, basic device information is obtained, and the basic device information includes the distribution position of the electric heating tubes and the rated power of each group of electric heating tubes.

[0021] Collect in-furnace monitoring data including temperature monitoring data and flow monitoring data.

[0022] Deploy high-precision temperature sensors and flow sensors inside the electric heating furnace to construct a sensor network. Among them, the temperature sensors are used to monitor the temperature distribution in different areas of the furnace, while the flow sensors are used to monitor the flow of gas or liquid in the furnace; based on the sensor network, in-furnace monitoring data of the multi-heating-tube electric heating furnace can be collected, including temperature monitoring data and flow monitoring data.

[0023] Obtain the operation parameter information of the multi-heating-tube electric heating furnace, calculate the heating power required to reach the preset target temperature using a mathematical model, and introduce the heat loss index of the multi-heating-tube electric heating furnace for compensation calculation.

[0024] Obtain the operation parameter information of the multi-heating-tube electric heating furnace through interaction, including but not limited to the initial temperature in the furnace, ambient temperature, heating time, heat capacity of the heating medium, etc.; establish a mathematical model describing the heating process of the electric heating furnace according to the basic principles of heat conduction, heat convection, and heat radiation. This model should be able to reflect the variation law of temperature with time during the heating process and the relationship between heating power and temperature; substitute parameters such as the preset target temperature, the current temperature in the furnace, and the heating time into the mathematical model for calculation to obtain the preliminary heating power required to reach the preset target temperature; analyze the possible heat loss situations during the actual heating process of the multi-heating-tube electric heating furnace, such as furnace body heat dissipation, radiant heat loss, unabsorbed heat energy, etc., determine the specific value of the heat loss index based on historical data or experimental measurements, incorporate the heat loss index into the mathematical model, and perform compensation calculation on the preliminarily calculated heating power to obtain a heating power requirement closer to the actual situation.

[0025] Based on the mathematical model after compensation calculation, dynamically adjust the output power of each group of electric heating tubes, use the preset target temperature as the termination condition, aim to minimize energy consumption, and allocate the output power of each group of electric heating tubes in combination with the equipment basic information to obtain the initial output power allocation set corresponding to multiple groups of electric heating tubes.

[0026] Based on the mathematical model after compensation calculation and the deep reinforcement learning algorithm, the output power of each group of electric heating tubes is dynamically adjusted. The deep reinforcement algorithm optimizes the power distribution strategy through continuous trial and error and learning, aiming to minimize energy consumption while ensuring that the temperature in the furnace can stably reach the preset target temperature. During the adjustment process, according to the basic information of the equipment and the real-time monitoring data, the output power of each group of electric heating tubes is reasonably allocated, and an initial output power distribution set corresponding to multiple groups of electric heating tubes is obtained. Specifically, a large amount of training data is collected, including the heating process under different initial conditions and the corresponding energy consumption data; the collected data is used to train the DRL model so that it learns how to select the optimal action (i.e., output power adjustment) according to the current state to maximize the cumulative reward (i.e., minimize energy consumption); after the training is completed, the DRL model can output an initial output power distribution set according to the current basic information and state of the equipment (such as the initial temperature in the furnace, ambient temperature, etc.), and this set specifies the initial output power of each group of electric heating tubes. This initial distribution set is obtained based on the model's comprehensive consideration of minimizing energy consumption and quickly reaching the target temperature; during the heating process, the DRL model continuously receives new state information (such as the current temperature in the furnace, the state of each group of electric heating tubes, etc.) and dynamically adjusts the output power of each group of electric heating tubes according to this information. By continuously making optimal decisions according to the current state, the DRL model can ensure that the heating process is both efficient and energy-saving.

[0027] Based on the in-furnace monitoring data, continuously monitor the parameter changes in the multi-heating-tube electric furnace, optimize the initial output power distribution set with the stability of temperature change, and set an elimination mechanism according to the maximization of heating efficiency to obtain a dynamic adjustment strategy.

[0028] Use a sensor network to collect key parameters such as temperature and flow rate in the furnace in real time. By continuously monitoring the parameter changes in the multi-heating-tube electric heating furnace, analyze the changing trend of the furnace temperature over time to evaluate the stability of temperature changes. According to the evaluation results of temperature change stability, optimize the initial output power distribution set, and use optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to assist in finding the optimal output power distribution set. At the same time, to improve heating efficiency and reduce energy consumption, an elimination mechanism is set up to eliminate or adjust poorly performing electric heating tubes or power distribution schemes. Specifically, monitor the heating efficiency of each group of electric heating tubes, including energy conversion efficiency and heat loss; set an efficiency threshold, and when the heating efficiency of a certain group of electric heating tubes is lower than this threshold, consider it in an inefficient or faulty state; introduce an elimination mechanism to reduce the power or completely turn off inefficient or faulty electric heating tubes to reduce ineffective energy consumption and improve the overall heating efficiency. Combining the above steps, a set of dynamic adjustment strategies can be formed. This strategy can automatically adjust the output power distribution set according to the real-time changes of the furnace parameters and dynamically apply the elimination mechanism to maximize the heating efficiency. Continuously optimize the heating process of the multi-heating-tube electric heating furnace based on the furnace monitoring data, improve the stability of temperature changes and maximize the heating efficiency. At the same time, the application of the dynamic adjustment strategy makes the heating process more intelligent and automated, which helps to improve production efficiency and product quality.

[0029] Furthermore, based on the furnace monitoring data, continuously monitor the parameter changes in the multi-heating-tube electric heating furnace, and optimize the initial output power distribution set with temperature change stability. The method includes:

[0030] Temperature change stability optimization formula:

[0031] ;

[0032] Wherein, is the temperature stability score, is used to characterize the monitoring period, is a weight function that changes with time, is used to characterize the preset target temperature, is used to characterize the temperature monitoring data at time t, is used to characterize the temperature change at time t, is the time interval, is the weight coefficient of the temperature change rate, which is used to balance the influence of temperature deviation and temperature change rate.

[0033] Specifically, use the temperature change stability optimization formula: to evaluate the stability of temperature changes during the heating process of the multi-heating-tube electric heating furnace, where It is the temperature stability score, which is used to quantify the stability of temperature changes. The lower the score, the more stable the temperature change is; It is used to characterize the monitoring period, that is, the time range considered for evaluating temperature stability; It is a weight function that changes with time and is used to adjust the weights of temperature deviations at different time points; It is used to characterize the preset target temperature, that is, the temperature that is expected to be reached and maintained during the heating process; It is used to characterize the temperature monitoring data at time t, that is, the actually measured temperature value; It is used to characterize the temperature change amount at time t; It is the time interval, that is, the time difference between two adjacent temperature monitors; It is the weight coefficient of the temperature change rate and is used to balance the influences of temperature deviation and temperature change rate.

[0034] Furthermore, the multi-heating-tube electric heating furnace includes multiple groups of electric heating tubes. The corresponding equipment basic information includes the distribution positions of the electric heating tubes and the rated power of each group of electric heating tubes. At the same time, each group of electric heating tubes uses a thyristor power regulator to adjust the output power; the thyristor power regulator receives the power regulation signal from the PLC; using the PLC power regulation signal, the dynamic regulation strategy is executed to allocate the conduction time of multiple thyristor power regulators with the output power distribution set.

[0035] By connecting multiple thyristor power regulators, the power of the multi-heating-tube electric heating furnace can be adjusted. The multi-heating-tube electric heating furnace includes multiple groups of electric heating tubes, and each group of electric heating tubes uses a thyristor power regulator to adjust the output power; the thyristor power regulator is a power regulation device that can adjust the voltage or current output to the electric heating tubes according to the control signal, thereby controlling its heating power.

[0036] Specifically, the adjustable range of the thyristor power regulator is 0~150KW, and the thyristor power regulator can receive the power regulation signal from the PLC (programmable logic controller). The PLC is responsible for receiving the real-time data from devices such as temperature sensors and pressure sensors, and generating the corresponding power regulation signal according to the preset algorithm and logical judgment. By changing the duty cycle or frequency of the signal, continuous adjustment of the output power can be achieved. According to the output power distribution set specified in the dynamic regulation strategy and the rated power of each thyristor power regulator (0~150KW in this scenario), the conduction time ratio of each power regulator under ideal conditions is calculated. Based on the adjusted conduction time ratio, the PLC generates the corresponding power regulation signal, and these signals may adopt the PWM (pulse width modulation) form to adjust the output power of the thyristor power regulator by changing the width of the pulse (i.e., the duty cycle).

[0037] Furthermore, the control methods of the thyristor power regulator include zero-crossing triggering and phase-shifting triggering; a conduction time overlap control interval is set, and within this conduction time overlap control interval, only M thyristor power regulators are allowed to conduct simultaneously, where M does not exceed the upper limit of simultaneous conduction.

[0038] Specifically, the control methods of the thyristor power regulator include zero-crossing triggering and phase-shifting triggering; among them, zero-crossing triggering means that in each cycle of the alternating current, when the voltage or current crosses zero (i.e., the zero point of the sine wave), the thyristor is triggered to conduct or turn off; phase-shifting triggering is to control the conduction angle of the thyristor by changing the phase difference between the trigger pulse and the voltage waveform, thereby adjusting the output power. To cope with the power peaks of the target power grid, reduce the power grid load pressure and avoid possible power grid failures, a conduction time overlap control interval is set, which corresponds to the time period with a relatively high power grid load, such as the working hours during the day or specific peak periods; specifically, according to the load curve and power consumption law of the power grid, the specific time period of the conduction time overlap control interval is determined; within the control interval, the upper limit number M of the simultaneously conducting thyristor power regulators is set, and this number is determined by comprehensively considering factors such as the carrying capacity of the power grid, the power demand of the heating furnace, and the safety margin; when entering the conduction time overlap control interval, the system will dynamically adjust the conduction time of each power regulator according to the number of currently conducting thyristor power regulators and the power demand to be adjusted, ensuring that the number of simultaneously conducting power regulators does not exceed the upper limit M at any time.

[0039] Furthermore, within the conduction time overlap control interval, only M thyristor power regulators are allowed to conduct simultaneously, and the method includes:

[0040] Based on the target power grid corresponding to the multi-heating-tube electric heating furnace, obtain the power grid load information; through the power grid load information, combined with the output power distribution set, evaluate the power grid impact index; according to the power grid impact index, add the upper limit of simultaneous conduction.

[0041] Preferably, load information is obtained from the target power grid, including key parameters such as the current voltage, current, active power, reactive power, and power factor of the power grid; based on the obtained power grid load information, combined with the output power distribution set corresponding to the dynamic adjustment strategy, the power grid impact index is evaluated. The power grid impact index is a comprehensive index used to measure the degree of impact on the power grid if multiple thyristor power regulators are turned on simultaneously during a specific time period (such as the conduction time overlap control interval); according to the evaluated power grid impact index, the upper limit M of the simultaneously turned-on thyristor power regulators can be dynamically added. This upper limit M is a dynamic value that changes with the change of the power grid load and the adjustment of the dynamic adjustment strategy; if the power grid impact index is low, it means that the power grid has sufficient capacity and stability to withstand more power input. At this time, the upper limit M of the number of simultaneously turned-on thyristor power regulators can be appropriately increased; if the power grid impact index is high, it means that the power grid has approached or exceeded its carrying capacity. At this time, the upper limit M of the number of simultaneously turned-on thyristor power regulators needs to be reduced to avoid excessive impact on the power grid.

[0042] Furthermore, the method includes:

[0043] Based on the initial output power distribution set, N thyristor power regulators are determined; compare the number of thyristor power regulators currently in the on state. If the number of thyristor power regulators in the on state is in the first segment of the simultaneous conduction upper limit, directly start the U-th thyristor power regulator, where the U-th thyristor power regulator is any one of the N thyristor power regulators, and the first segment does not exceed 80% of the simultaneous conduction upper limit.

[0044] Furthermore, compare the number of thyristor power regulators currently in the on state. The method includes:

[0045] If the number of thyristor power regulators in the on state is in the second segment of the simultaneous conduction upper limit, only allow the number of thyristor power regulators in the on state to be reduced by 1 and then start the U-th thyristor power regulator. The second segment is not less than 10% of the simultaneous conduction upper limit, where the U-th thyristor power regulator is at the head of the pending start sequence.

[0046] Specifically, based on the initial output power distribution sets corresponding to multiple groups of electric heating tubes, N silicon controlled power regulators to be used are determined. These power regulators will be responsible for adjusting the output power of each group of electric heating tubes according to the dynamic adjustment strategy. Before preparing to start the Uth silicon controlled power regulator (U is any integer between 1 and N), it is necessary to compare the number of currently conducting silicon controlled power regulators with the upper limit of simultaneous conduction. If the number of currently conducting silicon controlled power regulators is in the first segment of the upper limit of simultaneous conduction (i.e., not exceeding 80% of the upper limit), the Uth silicon controlled power regulator can be directly started without additional inspection or adjustment, which means that the power grid has sufficient capacity to accommodate more power input without causing excessive impact. If the number of currently conducting silicon controlled power regulators is in the second segment of the upper limit of simultaneous conduction (not less than 10% but less than 80% of the upper limit), more cautious measures need to be taken. Specifically, the system only allows the Uth silicon controlled power regulator to be started after one of the currently conducting silicon controlled power regulators stops working (i.e., the number decreases by 1), and the Uth silicon controlled power regulator is at the head of the waiting-to-start sequence, which helps to prioritize the start requests that have been waiting for a long time. To more effectively manage the start and stop of silicon controlled power regulators, the silicon controlled power regulators can be sorted according to certain priority rules. For example, the priority can be determined based on the urgency of the heating furnace, the heating demand, and the status of the silicon controlled power regulator (such as temperature, fault history, etc.). All the waiting-to-start silicon controlled power regulators are arranged in descending order of priority to obtain the waiting-to-start sequence. When the start condition is met, the silicon controlled power regulator will be selected from the head of the sequence for startup.

[0047] Furthermore, by setting a conduction time overlap control interval, the method further includes:

[0048] Identifying the target grid power peak and the target grid power valley, where the conduction time overlap control interval corresponds to the target grid power peak; based on the target grid power valley, a failure mark is made for the conduction time overlap control interval, and the failure mark is used to disable the conduction time overlap control interval.

[0049] When further optimizing the management strategy of the conduction time overlap control interval, considering the changes in the power consumption pattern of the target power grid, especially the periodic appearance of power consumption peaks and valleys, an analysis of the power grid's power consumption pattern can be introduced, and based on this, the effectiveness of the conduction time overlap control interval can be adjusted. Specifically, by analyzing the power consumption pattern of the target power grid corresponding to the multi-heating-tube electric heating furnace, historical power consumption data of the power grid can be collected, including key indicators such as load curves, power factors, and voltage fluctuations at different time periods (such as hours, days, weeks, months), and through data analysis, the power consumption peaks and valleys of the power grid can be identified; after identifying the power consumption valley of the target power grid, the conduction time overlap control interval can be marked as invalid. An invalid mark means that within this time period, the originally set conduction time overlap control interval no longer takes effect, that is, it no longer restricts the simultaneous conduction quantity of the thyristor power regulator; through the invalid mark, the system can more flexibly control the start and stop of the thyristor power regulator during the valley period with low power grid load to meet the heating requirements of the heating furnace while avoiding unnecessary impacts on the power grid.

[0050] In summary, the embodiments of the present application at least have the following technical effects:

[0051] First, obtain the basic information of the device. Next, collect the in-furnace monitoring data including temperature monitoring data and flow monitoring data. Then, obtain the operation parameter information of the multi-heating-tube electric heating furnace, calculate the heating power required to reach the preset target temperature using a mathematical model, and introduce the heat loss index of the multi-heating-tube electric heating furnace for compensation calculation. Based on the compensated mathematical model, use the deep reinforcement learning algorithm to dynamically adjust the output power of each group of electric heating tubes, take the preset target temperature as the termination condition, aim to minimize energy consumption, and combine the basic information of the device to allocate the output power of each group of electric heating tubes to obtain the initial output power allocation set corresponding to multiple groups of electric heating tubes. Finally, based on the in-furnace monitoring data, continuously monitor the parameter changes in the multi-heating-tube electric heating furnace, optimize the initial output power allocation set with the stability of temperature change, and set an elimination mechanism according to the maximization of heating efficiency to obtain a dynamic adjustment strategy. This solves the technical problem of inaccurate power regulation of the multi-heating-tube electric heating furnace in the prior art, and achieves the technical effects of improving heating efficiency and stability by intelligently adjusting the output power of each group of electric heating tubes.

[0052] Embodiment 2, based on the same inventive concept as the power intelligent adjustment method of a multi-heating-tube electric heating furnace in the foregoing embodiment, as Figure 2 shown, the present application provides a power intelligent adjustment device for a multi-heating-tube electric heating furnace, wherein the device includes:

[0053] Device information acquisition module 11, which is used to acquire basic device information; data acquisition module 12, which is used to acquire in-furnace monitoring data including temperature monitoring data and flow monitoring data; calculation module 13, which is used to obtain the operation parameter information of the multi-heating-tube electric heating furnace, calculate the heating power required to reach the preset target temperature using a mathematical model, and introduce the heat loss index of the multi-heating-tube electric heating furnace for compensation calculation; power distribution module 14, which is used to dynamically adjust the output power of each group of electric heating tubes based on the mathematical model after compensation calculation, use the preset target temperature as the termination condition, aim to minimize energy consumption, and combine the basic device information to distribute the output power of each group of electric heating tubes to obtain the initial output power distribution set corresponding to multiple groups of electric heating tubes; optimization module 15, which is used to continuously monitor the parameter changes in the multi-heating-tube electric heating furnace based on the in-furnace monitoring data, optimize the initial output power distribution set with temperature change stability, and set an elimination mechanism according to the maximization of heating efficiency to obtain a dynamic adjustment strategy.

[0054] Further, the device information acquisition module 11 is used to execute the following method:

[0055] The multi-heating-tube electric heating furnace includes multiple groups of electric heating tubes. The corresponding basic device information includes the distribution position of the electric heating tubes and the rated power of each group of electric heating tubes. At the same time, the output power of each group of electric heating tubes is adjusted by a silicon controlled rectifier power regulator; the silicon controlled rectifier power regulator receives the PLC power regulation signal; using the PLC power regulation signal, execute the dynamic adjustment strategy to distribute the conduction time of multiple silicon controlled rectifier power regulators with the output power distribution set.

[0056] Further, the device information acquisition module 11 is used to execute the following method:

[0057] The control modes of the silicon controlled rectifier power regulator include zero-crossing triggering and phase-shifting triggering; set a conduction time overlapping control interval, and only allow M silicon controlled rectifier power regulators to conduct simultaneously within the conduction time overlapping control interval, where M does not exceed the simultaneous conduction upper limit.

[0058] Further, the device information acquisition module 11 is used to execute the following method:

[0059] Based on the target power grid corresponding to the multi-heating-tube electric heating furnace, obtain the power grid load information; through the power grid load information, combine the output power distribution set to evaluate the power grid impact index; according to the power grid impact index, add the simultaneous conduction upper limit.

[0060] Further, the device information acquisition module 11 is used to execute the following method:

[0061] Based on the initial output power distribution set, determine N silicon controlled power regulators; compare the number of silicon controlled power regulators that are currently in the on state. If the number of silicon controlled power regulators in the on state is in the first segment of the simultaneous conduction upper limit, directly start the U-th silicon controlled power regulator, where the U-th silicon controlled power regulator is any one of the N silicon controlled power regulators, and the first segment does not exceed 80% of the simultaneous conduction upper limit.

[0062] Further, the device information acquisition module 11 is used to execute the following method:

[0063] If the number of silicon controlled power regulators in the on state is in the second segment of the simultaneous conduction upper limit, only allow the number of silicon controlled power regulators in the on state to be decreased by 1, and then start the U-th silicon controlled power regulator. The second segment is not less than 10% of the simultaneous conduction upper limit, where the U-th silicon controlled power regulator is at the head of the pending start sequence.

[0064] Further, the device information acquisition module 11 is used to execute the following method:

[0065] Identify the peak of the target grid power consumption and the valley of the target grid power consumption. The conduction time overlap control interval corresponds to the peak of the target grid power consumption; based on the valley of the target grid power consumption, perform a failure mark on the conduction time overlap control interval, and the failure mark is used to disable the conduction time overlap control interval.

[0066] Further, the optimization module 15 is used to execute the following method:

[0067] Temperature change stability optimization formula:

[0068] ; where is the temperature stability score, is used to represent the monitoring period, is a weight function that changes with time, is used to represent the preset target temperature, is used to represent the temperature monitoring data at time t, is used to represent the temperature change amount at time t, is the time interval, is the weight coefficient of the temperature change rate, which is used to balance the influence of temperature deviation and temperature change rate.

[0069] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0071] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for intelligently adjusting the power of a multi-heating tube electric heating furnace, characterized in that: The method comprises: Obtain basic equipment information, including: electric heating tubes, distribution locations, and rated power of each group of electric heating tubes; Collect furnace monitoring data including temperature monitoring data and flow monitoring data; Obtain the operating parameter information of the multi-heating tube electric heating furnace, and use the mathematical model to calculate the heating power required to reach the preset target temperature, and introduce the heat loss index of the multi-heating tube electric heating furnace for compensation calculation. The operating parameter information includes: initial temperature in the furnace, ambient temperature, heating time, and heat capacity of the heating medium; Based on the mathematical model after compensation calculation, the output power of each group of electric heating tubes is dynamically adjusted, the preset target temperature is used as the termination condition, and the output power of each group of electric heating tubes is allocated in combination with the basic information of the equipment with the goal of minimizing energy consumption, and the initial output power allocation set corresponding to the multiple groups of electric heating tubes is obtained; Based on the furnace monitoring data, the parameter changes in the multi-heating tube electric heating furnace are continuously monitored, the initial output power allocation set is optimized based on the temperature change stability, and the elimination mechanism is set according to the maximization of heating efficiency. The above steps together constitute a dynamic adjustment strategy, including: Temperature change stability optimization formula: ； in, Score the temperature stability, Used to characterize the monitoring cycle, is a time-varying weight function, Used to characterize the preset target temperature, The temperature monitoring data used to characterize time t, Used to characterize the temperature change over time t, is the time interval, is the weight coefficient of the temperature change rate, which is used to balance the influence of temperature deviation and temperature change rate; The elimination mechanism refers to downgrading or completely shutting down inefficient or faulty electric heating tubes.

2. The method for intelligently adjusting power of a multi-heating tube electric heating furnace according to claim 1, characterized in that: The multi-heating tube electric heating furnace includes multiple groups of electric heating tubes, and the corresponding basic information of the equipment includes the distribution position of the electric heating tubes and the rated power of each group of electric heating tubes. At the same time, each group of electric heating tubes uses a thyristor power regulator to adjust the output power; The thyristor power regulator receives a PLC power regulation signal; The PLC power regulation signal is used to execute the dynamic regulation strategy, and the conduction time of multiple thyristor power regulators is allocated with the optimized initial output power distribution set.

3. The method for intelligently adjusting power of a multi-heating tube electric heating furnace according to claim 2, characterized in that: The control modes of the thyristor power regulator include zero-crossing triggering and phase-shift triggering; A conduction time overlap control interval is set, in which only M thyristor power regulators are allowed to be turned on at the same time, and M does not exceed the upper limit of simultaneous conduction.

4. The method for intelligently adjusting power of a multi-heating tube electric heating furnace according to claim 3, characterized in that: Only M thyristor power regulators are allowed to be turned on at the same time within the conduction time overlap control interval, and the method includes: Based on the target power grid corresponding to the multi-heating tube electric heating furnace, obtaining power grid load information; By combining the grid load information with the output power allocation set, a grid impact index is evaluated; The upper limit of simultaneous conduction is adjusted according to the grid impact index.

5. The method for intelligently adjusting power of a multi-heating tube electric heating furnace according to claim 4, characterized in that: The method comprises: Based on the initial output power allocation set, determining N thyristor power regulators; Compare the number of thyristor power regulators currently in the on state. If the number of thyristor power regulators in the on state is in the first segment of the upper limit of simultaneous conduction, directly start the Uth thyristor power regulator, where the Uth thyristor power regulator is any one of the N thyristor power regulators, and the first segment does not exceed 80% of the upper limit of simultaneous conduction.

6. A method for intelligently adjusting power of a multi-heating tube electric heating furnace as claimed in claim 5, characterized in that: Comparing the number of thyristor power regulators currently in a conducting state, the method comprises: If the number of thyristor power regulators in the on state is in the second segment of the upper limit of simultaneous conduction, the Uth thyristor power regulator is started only after the number of thyristor power regulators in the on state is reduced by 1, and the second segment is not less than 10% of the upper limit of simultaneous conduction, wherein the Uth thyristor power regulator is at the top of the sequence to be started.

7. The method for intelligently adjusting power of a multi-heating tube electric heating furnace according to claim 3, characterized in that: Setting the on-time overlap control interval, the method further includes: Identify a target power grid power consumption peak and a target power grid power consumption valley, wherein the on-time overlap control interval corresponds to the target power grid power consumption peak; Based on the target grid power consumption valley, the on-time overlap control interval is marked as invalid, and the invalidation mark is used to disable the on-time overlap control interval.

8. An intelligent power adjustment device for a multi-heating tube electric heating furnace, characterized in that: The device is used to implement a method for intelligently adjusting power of a multi-heating tube electric heating furnace according to any one of claims 1 to 7, and comprises: A device information acquisition module, which is used to acquire basic device information; A data acquisition module, the data acquisition module is used to collect furnace monitoring data including temperature monitoring data and flow monitoring data; A calculation module, the calculation module is used to obtain the operating parameter information of the multi-heating tube electric heating furnace, and use a mathematical model to calculate the heating power required to reach a preset target temperature, and introduce a heat loss index of the multi-heating tube electric heating furnace for compensation calculation; A power allocation module, the power allocation module is used to dynamically adjust the output power of each group of electric heating tubes based on the mathematical model after compensation calculation, take the preset target temperature as the termination condition, and allocate the output power of each group of electric heating tubes in combination with the basic information of the equipment with the goal of minimizing energy consumption, and obtain the initial output power allocation set corresponding to the multiple groups of electric heating tubes; An optimization module is used to continuously monitor parameter changes in the multi-heating tube electric heating furnace based on the monitoring data in the furnace, optimize the initial output power allocation set with the stability of temperature changes, and set an elimination mechanism according to the maximization of heating efficiency to obtain a dynamic adjustment strategy.

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