Industrial furnace energy efficiency optimization control method and system based on digital twinning
Patent Information
- Application Number
- CN202611018998.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-09
- Publication Date
- 2026-09-15
Smart Images
Figure CN122755899A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of industrial furnace control, and particularly relates to a method and system for optimizing the energy efficiency of industrial furnaces based on digital twins. Background Technology
[0002] Industrial furnaces are key equipment in die casting, non-ferrous metal smelting, and casting processes. They are typically used for melting, holding, and supplying molten metals such as aluminum alloys and zinc alloys. Equipment types include centralized melting furnaces, pool-type holding furnaces, tower-type machine-side melting and holding furnaces, crucible furnaces, and associated aluminum molten metal conveying and tapping devices. In actual production, the furnace needs to continuously operate in coordination with the die casting machine's production cycle, tapping schedule, feeding status, and holding requirements. Energy consumption depends not only on the efficiency of the burner or electric heater itself but also on whether heat energy can be applied to the appropriate aluminum molten metal area at the right time. Related technologies often use furnace temperature, power, gas flow rate, electricity consumption, or a single liquid level as the main control basis. This results in control commands primarily generated around temperature deviations or fixed process parameters, failing to effectively suppress inefficient heat energy input in practical engineering scenarios such as tapping delays, die casting machine waiting times, and long-term holding times. Consequently, it is difficult to further reduce the overall energy consumption per unit of effective aluminum molten metal output. Therefore, how to achieve optimized energy efficiency control of industrial furnaces and reduce overall energy consumption has become an urgent technical problem to be solved. Summary of the Invention
[0003] The purpose of this invention is to provide a method and system for optimizing the energy efficiency of industrial furnaces based on digital twins, which can achieve optimized control of the energy efficiency of industrial furnaces and reduce overall energy consumption.
[0004] To achieve the above objectives, a first aspect of the present invention provides a method for optimizing and controlling the energy efficiency of an industrial furnace based on digital twins, the method comprising:
[0005] Obtain the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace during the current control cycle;
[0006] Obtain the demand matching factor and waiting penalty factor corresponding to the thermal energy unit, and calculate the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value, importance weight, demand matching factor and waiting penalty factor of all the thermal energy units.
[0007] Obtain the maximum comprehensive control capability and control inhibition coefficient of the industrial furnace, and calculate the energy efficiency optimization control command based on the thermal energy utilization efficiency evaluation results, the maximum comprehensive control capability and the control inhibition coefficient;
[0008] The industrial furnace is subjected to energy efficiency optimization control according to the energy efficiency optimization control command.
[0009] Furthermore, the industrial furnace includes multiple execution objects, and the step of performing energy efficiency optimization control on the industrial furnace according to the energy efficiency optimization control command includes:
[0010] Obtain the control allocation coefficient, minimum stable control quantity, and maximum safe control quantity for each execution object;
[0011] The actual control quantity issued to each execution object is calculated based on the energy efficiency optimization control command, control allocation coefficient, minimum stable control quantity, and maximum safe control quantity.
[0012] The energy efficiency optimization control of the execution object is performed based on the actual control quantity issued.
[0013] Further, the step of calculating the actual control quantity issued to each execution object based on the energy efficiency optimization control command, control allocation coefficient, minimum stable control quantity, and maximum safe control quantity includes:
[0014] Multiply the energy efficiency optimization control command by the control allocation coefficient to obtain the first data, and select the largest number from the first data and the minimum stable control quantity as the second data;
[0015] The smaller number between the second data and the maximum security control amount is selected as the actual control amount issued.
[0016] Furthermore, after performing energy efficiency optimization control on the execution object based on the actual issued control quantity, the method further includes:
[0017] Obtain the receipt status for each of the execution objects;
[0018] Deviation data is obtained by calculating the deviation based on the receipt status and the actual control quantity issued;
[0019] If the deviation data is greater than the preset allowable deviation range, an execution anomaly is recorded and the corresponding control allocation coefficient is reduced according to the preset fault strategy in the next control cycle.
[0020] Furthermore, obtaining the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace includes:
[0021] Obtain the temperature status, energy consumption status, liquid level status, and production demand status of each thermal energy unit in the industrial furnace during the current control cycle;
[0022] The temperature state is linearly mapped according to the preset lower temperature limit and upper temperature limit to obtain the updated temperature state. The energy consumption state is linearly mapped according to the ratio of unit power to total power to obtain the updated energy consumption state.
[0023] The liquid level status is linearly mapped according to the proportion of unit liquid volume to total liquid volume to obtain the updated liquid level status. The temperature status, energy consumption status, liquid level status and production demand status are summarized according to the preset process proportion to obtain the comprehensive status value.
[0024] Further, the step of calculating the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value of all the thermal energy units, the importance weight, the demand matching factor, and the waiting penalty factor includes:
[0025] The third data is obtained by multiplying the comprehensive status value, the importance weight, the demand matching factor, and the waiting penalty factor together;
[0026] The thermal energy utilization efficiency evaluation result is obtained by summing all the third data corresponding to all the thermal energy units.
[0027] Further, the step of calculating the energy efficiency optimization control command based on the thermal energy utilization efficiency assessment result, the maximum comprehensive control capability, and the control inhibition coefficient includes:
[0028] Subtract the preset number from the control inhibition coefficient to obtain the fourth data, and multiply the fourth data by the square of the fourth data to obtain the fifth data;
[0029] The energy efficiency optimization control command is obtained by multiplying the fifth data, the thermal energy utilization efficiency evaluation result, and the maximum comprehensive control capability.
[0030] In a second aspect, the present invention provides an industrial furnace energy efficiency optimization control system based on digital twins, the system comprising:
[0031] The acquisition unit is used to acquire the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace during the current control cycle.
[0032] The first calculation unit is used to obtain the demand matching factor and waiting penalty factor corresponding to the thermal energy unit, and calculate the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value, importance weight, demand matching factor and waiting penalty factor of all the thermal energy units.
[0033] The second calculation unit is used to obtain the maximum comprehensive control capability and control inhibition coefficient of the industrial furnace, and to calculate the energy efficiency optimization control command based on the thermal energy utilization efficiency evaluation result, the maximum comprehensive control capability and the control inhibition coefficient;
[0034] The control unit is used to perform energy efficiency optimization control on the industrial furnace according to the energy efficiency optimization control command.
[0035] In a third aspect of the invention, an electronic device is provided, the electronic device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the method described in the first aspect above.
[0036] In a fourth aspect of the invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.
[0037] The beneficial technical effects of the present invention are at least as follows:
[0038] To address the aforementioned problems, this invention provides a digital twin-based method and system for optimizing energy efficiency control of industrial furnaces. Its core lies in separating the internal thermal energy of the furnace from a single furnace temperature or power state. Through a digital twin, different virtual regions such as the melting zone, holding zone, and molten aluminum discharge zone are represented as thermal energy units. Temperature, energy consumption, liquid level, and production demand are unified into a thermal energy state set, allowing the system to preserve the differences in thermal energy between different regions, rather than simplifying the entire furnace to an average temperature or average energy consumption. This invention solves the technical conversion problem between on-site sensor data of industrial furnaces, digital twin thermal state representation, production cycle constraints, and PLC execution control. The controlled objects include burners, electric heaters, holding control loops, and feeding or molten aluminum discharge mechanisms, rather than simply production scheduling, energy consumption statistics, or management evaluation methods. Based on this, the system further generates a thermal energy utilization efficiency assessment result according to the importance of the thermal energy units, the degree of matching of liquid supply demand, and the efficiency reduction caused by waiting and holding, thereby determining whether the current thermal energy can be effectively converted into aluminum liquid supply capacity in subsequent production cycles. Subsequently, the system converts the evaluation result into energy efficiency optimization control commands. Through the allocation coefficients and limiting relationships of the execution objects, the comprehensive control commands are distributed to the burner, electric heater, heat preservation control loop, feeding mechanism, or discharging mechanism. This prioritizes high-efficiency thermal energy areas and reduces ineffective heating and excessive heat preservation in low-efficiency thermal energy areas. Through this technical solution, the present invention moves beyond simply mapping equipment states to support continuous conversion between unitized thermal energy expression, thermal energy utilization efficiency evaluation, and on-site control execution. It can address the actual operating characteristics of industrial furnaces such as centralized melting furnaces, pool-type heat preservation furnaces, tower-type machine-side melting and heat preservation furnaces, and crucible furnaces. While ensuring stable liquid supply and process temperature requirements, it reduces energy waste caused by waiting, delayed discharging, and long-term heat preservation, thereby achieving energy efficiency optimization control of industrial furnaces that better suits the die-casting production cycle. Attached Figure Description
[0039] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.
[0040] Figure 1 This is a flowchart of an energy efficiency optimization control method for industrial furnaces based on digital twins, provided in an embodiment of this application.
[0041] Figure 2 This is a schematic diagram of the structure of an industrial furnace energy efficiency optimization control system based on digital twin provided in an embodiment of this application. Detailed Implementation
[0042] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0043] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an energy efficiency optimization control method for industrial furnaces based on digital twins, as provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.
[0044] Step S101: Obtain the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace during the current control cycle;
[0045] Step S102: Obtain the demand matching factor and waiting penalty factor corresponding to the thermal energy unit, and calculate the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value, importance weight, demand matching factor and waiting penalty factor of all thermal energy units.
[0046] Step S103: Obtain the maximum comprehensive control capability and control inhibition coefficient of the industrial furnace, and calculate the energy efficiency optimization control command based on the thermal energy utilization efficiency evaluation results, the maximum comprehensive control capability and control inhibition coefficient;
[0047] Step S104: Perform energy efficiency optimization control on the industrial furnace according to the energy efficiency optimization control command.
[0048] In step S101 of some embodiments, a thermal energy state set is generated in a pre-configured digital twin of the industrial furnace. During the operation of industrial furnaces, including centralized melting furnaces, pool-type holding furnaces, and tower-type machine-side melting and holding furnaces, data such as the internal temperature distribution of the furnace, the output power of burners or electric heaters, the aluminum liquid level, and the molten aluminum discharge plan are usually distributed in different equipment modules, making it difficult to directly reflect the state of each part of the thermal energy and its subsequent utilization potential. Therefore, this step uses a digital twin to uniformly map the operating data from different sources to multiple thermal energy units. Each thermal energy unit represents a virtual area inside the furnace, such as the melting zone, transition zone, holding zone, and molten aluminum discharge zone, and corresponds to the actual furnace structure. The division of thermal energy units is determined by the furnace body structure model, the arrangement of burners or electric heaters, the location of temperature measuring points, the flow path of molten aluminum, and the location of the outlet. For furnaces that have already been put into production, thermal energy unit numbers can be established during the commissioning phase based on the furnace CAD structure, PLC location table, and the installation location of field sensors. The correspondence between these numbers and the actual equipment areas can be saved in the digital twin.
[0049] Temperature data is collected by thermocouples or temperature sensors located in each thermal energy unit. The temperature status of each thermal energy unit is obtained by weighted averaging of multiple measuring points in the melting zone, holding zone, and pouring zone. When there are multiple temperature measuring points in a thermal energy unit, the weight of each measuring point can be determined based on the distance between the measuring point and the geometric center of the thermal energy unit, the reliability of the measuring point, and the furnace type debugging results. Heating energy consumption data is read from the heating power of each unit through the burner controller or electric heating controller. If an execution object acts on multiple thermal energy units simultaneously, the energy consumption is allocated to the corresponding units according to the area of action of the execution object, the range of heat radiation coverage, or the proportion of heat response obtained during the debugging phase. The aluminum liquid level is obtained through a level gauge and mapped to the liquid volume ratio of each unit. The pouring record is obtained through the PLC execution record and is used to identify which units' heat energy is about to be consumed. Production plan information is obtained from the die-casting machine control system interface, reflecting the liquid supply demand of each unit in the future. Before entering the digital twin, the above data is time-aligned according to the same control cycle. If the data of a certain sensor lags behind the preset sampling cycle or the difference with the adjacent measuring point exceeds the allowable range of the device, the data is marked as abnormal and the data of the previous valid cycle, the interpolated data of the adjacent thermal energy unit, or the safety default value are used in the calculation. At the same time, the abnormal state is written into the record field of the thermal energy state set.
[0050] After mapping the above data to the thermal energy units, each thermal energy unit generates a comprehensive state value. . The evaluation is jointly formed by temperature status, energy consumption status, liquid level status, and production demand status. Since these data types differ, they need to be normalized and mapped to a unified evaluation range. For example, temperature status is linearly mapped to a 0-1 range using the lower and upper temperature limits; energy consumption status is mapped to a 0-1 range using the unit power as a percentage of total power; liquid level status is mapped to a 0-1 range using the unit liquid volume as a percentage of total liquid volume; and production demand status is represented by a Boolean value of 0 or 1, indicating whether the unit has a liquid supply task in the near future. The lower and upper temperature limits can be determined by the furnace process temperature window; the power normalization benchmark can be determined by the burner's rated power or the electric heater's rated power; the liquid level normalization benchmark can be determined by the effective aluminum liquid capacity; and the time window for near-term liquid supply tasks can be determined by the die-casting machine cycle time, single-mold liquid consumption, and pouring advance. If a normalized input is below the lower limit, it is treated as 0; if it is above the upper limit, it is treated as 1. If data is marked as abnormal, the input is not directly included in the normal aggregation, but instead uses a safe default value or the previous valid value, and the evaluation credibility of the corresponding thermal unit is reduced in subsequent steps. After normalization, the digital twin aggregates the above states according to the process proportions set during the equipment commissioning phase. Each process parameter has a proportion between 0 and 1, and the sum of the proportions used for aggregation within the same thermal energy unit is 1. When there is no special process bias, proportional aggregation can be used. This results in... It is the first The comprehensive state value of each thermal energy unit within the current control cycle, rather than a single temperature value.
[0051] Thermal state set The overall state value and importance weight of all thermal energy units are stored, and are expressed as follows:
[0052] ;
[0053] in, Represents the set of thermal states; The number of thermal energy units divided within the digital twin; For the first The comprehensive state value of a thermal energy unit is formed by the temperature state, energy consumption state, liquid level state and production demand state corresponding to that thermal energy unit. For the first The importance weight of each thermal energy unit. The value is determined based on the spatial distance between the unit and the pouring area, as well as the urgency of the corresponding molten aluminum in the current production plan. For example, heat energy units near the pouring outlet and about to be consumed by the die-casting machine have a higher weight, while heat energy units that require long-term heat preservation and have no pouring task in the short term have a lower weight. To avoid inconsistent weighting sources, Within each control cycle, the weights of the dispensing distance, estimated liquid supply time, and equipment debugging are comprehensively determined and normalized, allowing the weights of all thermal energy units to be compared within the same evaluation scale. When the production planning interface is temporarily unavailable, the system can generate temporary weights based on the default furnace type weight and the current dispensing zone position, and mark the missing planning data in the control log. This expression belongs to the set representation method in mathematics. This step uses it to save the status records of each thermal energy unit within the digital twin, enabling subsequent steps to continue reading the data of each thermal energy unit. and In practical systems, It can also simultaneously record the thermal energy unit number, furnace type area name, data timestamp, anomaly marker, and sensor validity status. These records are used for traceability and anomaly handling, but do not change the data. The state values and weights serve as inputs for subsequent calculations.
[0054] For example, in a centralized melting furnace, if the comprehensive state value of the thermal energy unit in the melting zone... The overall state value of the thermal energy unit in the insulation zone The comprehensive state value of the thermal energy unit in the soup outlet area Corresponding importance weight , , Then the thermal energy state set is represented as This result preserves the comprehensive state value and importance weight of each thermal energy unit, enabling the next step to determine the utilization efficiency of different thermal energy units in subsequent production, rather than simplifying the entire furnace to an average state. If a temperature measuring point malfunctions during the cycle, such as a lost temperature sensor in the insulation zone, the system does not directly delete the thermal energy unit in the insulation zone. Instead, it temporarily sets its comprehensive state value according to the interpolation method of the previous effective cycle or adjacent units, and then... Anomaly markers are recorded in the data so that subsequent efficiency assessments and control execution can identify the credibility of the unit.
[0055] The thermal state set output in this step The comprehensive state values and importance weights of all thermal energy units in the furnace are directly used in the next step to generate the thermal energy utilization efficiency assessment results. Through this step, the digital twin can integrate scattered data into a computable thermal state record, preserving the impact of temperature, power, liquid level, and production requirements on the thermal state, as well as the differences between different thermal units.
[0056] In step S102 of some embodiments, thermal energy state set As input, generate thermal energy utilization efficiency assessment results. In actual operation of industrial furnaces, although the various thermal energy units within the furnace may have similar temperature and power states, their subsequent effective utilization varies significantly due to differences in the timing of molten aluminum supply, tapping tasks, and holding periods. Therefore, this step focuses on the thermal energy state. The overall state values of each thermal energy unit stored in the database And importance weight Based on this, and combined with the production planning interface and soup dispensing task records, the comprehensive contribution of each thermal energy unit to the subsequent effective liquid supply process is calculated. If a thermal energy unit has an anomaly flag, this step retains the anomaly status when calculating demand matching and waiting penalty, and can reduce the credibility of the thermal energy unit's participation in the evaluation, avoiding sensor anomalies being mistakenly identified as true inefficiency or high efficiency.
[0057] First, from Read the comprehensive state value of each thermal energy unit. And importance weight .in Indicates the first The overall state value of each thermal energy unit during the current control cycle. This indicates the importance of the thermal energy unit in the current liquid supply process. Subsequently, the demand matching factor is determined by combining the production planning interface and the soup output task records. . This reflects whether the corresponding molten aluminum in the thermal energy unit will be consumed in the near future and the urgency of consumption, such as the thermal energy unit located in the pouring area and corresponding to the upcoming pouring task. Choose the higher value for the thermal energy unit located in the insulation zone and with a long future liquid supply interval. Take the lower value. The value ranges from 0 to 1, where 0 indicates that there is no corresponding liquid supply demand within the current evaluation time window, and 1 indicates that the aluminum liquid corresponding to this unit will soon be supplied to the die-casting machine or the pouring mechanism; the intermediate value can be determined based on the ratio between the expected liquid supply time and the evaluation time window. The waiting penalty factor is then determined based on the expected waiting state and heat preservation state of the aluminum liquid corresponding to this thermal energy unit. This is used to indicate the reduction in the effectiveness of heat energy utilization due to waiting, delaying the serving of soup, or keeping it warm for a long time. The value ranges from 0 to 1. The longer the waiting time, the higher the heat preservation loss, or the more uncertain the waiting time of the die-casting machine, the better. The lower the value; when the molten aluminum is at a normal supply cycle and long-term heat preservation is not required. Approximately 1. If the production planning interface does not provide a clear future task, this step can determine a temporary task based on the most recent casting record, the current cycle time of the die-casting machine, and the default insulation loss model. and And write the temporary calculation state into the control log.
[0058] In this invention, the classic weighted summation evaluation model is applied to the evaluation of the thermal energy unit of a digital twin furnace, and a demand matching factor is introduced on the basis of the original model. and waiting penalty factor The product term allows the evaluation results to simultaneously reflect the current thermal state, the spatial importance of the thermal unit, future liquid supply requirements, and the risk of waiting losses. Thermal energy utilization efficiency assessment results. The calculation method involves multiplying the comprehensive state value, importance weight, demand matching factor, and waiting penalty factor to obtain the third data; then, all the third data corresponding to all thermal energy units are summed to obtain the thermal energy utilization efficiency evaluation result. The formula is shown below:
[0059] ;
[0060] in, The thermal energy utilization efficiency assessment result represents a comprehensive evaluation result indicating that the current thermal energy of an industrial furnace is effectively utilized in subsequent production processes. The number of thermal energy units to be divided for a digital twin; For the first The importance weight of each thermal energy unit; For the first The overall state value of each thermal energy unit; The demand matching factor is determined by the production planning interface and the soup production task record; The waiting penalty factor is determined by the expected waiting state and holding state of the aluminum liquid corresponding to the thermal energy unit. This formula originates from the weighted summation model in mathematics, and this invention introduces [the following formula] into the formula. and As an improvement, the future utilization efficiency of the digital twin thermal energy unit will be assessed. The higher the value, the more likely the current heat energy in the furnace is to form an effective supply of molten aluminum in subsequent production cycles. The lower the value, the higher the risk of waiting, insulation, or demand mismatch.
[0061] In one embodiment, a centralized melting furnace is divided into three thermal energy units: the first thermal energy unit is located in the melting zone, , , , The second thermal energy unit is located in the insulation zone. , , , The third thermal energy unit is located in the soup outlet area. , , , Substituting into the formula, the contribution of the first thermal energy unit is calculated. The second thermal energy unit contributes The third thermal unit contributes approximately The final thermal energy utilization efficiency assessment result is approximately This calculation method clearly reflects the differences in the actual contribution of different thermal energy units during future production, giving higher evaluations to units with high thermal energy and imminent liquid supply, while reducing the contribution of units that are waiting for a long time or have low demand. This results in a quantitative assessment of the actual operating scenarios of industrial furnaces. In this embodiment, the contribution value of each unit is determined by... In and And the determination in this step , The calculation yields the final output. It serves directly as input for generating energy efficiency optimization control commands in the next step, rather than being an intermediate indicator used only for display or statistics.
[0062] This step outputs the thermal energy utilization efficiency evaluation results. This result will directly serve as the input for the next step of generating energy efficiency optimization control commands for industrial furnaces. In actual operation, and Can be derived from thermal energy state set The current state of the thermal energy unit is determined jointly by the demand status in the digital twin, the regional status in the digital twin, the production planning interface, and the soup production task records. This step closely integrates the current state of the thermal energy unit with future production needs, realizing a comprehensive assessment from a simple thermal status evaluation to one that focuses on future production utilization efficiency.
[0063] In some embodiments, steps S103 to S104 are evaluated based on thermal energy utilization efficiency. As input, generate energy efficiency optimization control commands. The previous step has already converted the current thermal state, liquid supply demand matching degree, and waiting loss risk of each thermal energy unit in the industrial furnace digital twin into thermal energy utilization efficiency assessment results. Therefore, this step will... This is further converted into a comprehensive control intensity that can be allocated to subsequent execution steps. For centralized melting furnaces, pool-type holding furnaces, tower-type machine-side melting and holding furnaces, and crucible furnaces, the control system cannot act solely based on furnace temperature deviations; instead, it should determine the heat input intensity based on whether effective molten aluminum supply is imminent. This step involves... Mapped to energy efficiency optimization control commands This prioritizes heat input for states with clear liquid supply needs and low waiting losses, while reducing control intensity under long-term waiting or low-demand insulation conditions. In the generation... Before, check Whether it is within the allowed evaluation range, if it is due to abnormal input data. If the data is missing or exceeds the limit, a normal control command will not be generated directly. Instead, the original temperature closed-loop control or safety insulation control will be used as a temporary control method, and the user will be prompted to re-acquire or verify the digital twin input data.
[0064] The control calculations in this step are derived from the proportional control concept in industrial control. The basic idea of proportional control is to map the evaluation result to the execution intensity: the higher the evaluation result, the higher the execution intensity; the lower the evaluation result, the lower the execution intensity. Based on this idea, this step replaces the traditional temperature deviation with the thermal energy utilization efficiency assessment result. Furthermore, a waiting suppression term is added, ensuring that the control command is not only positively correlated with thermal energy utilization efficiency but also suppresses operating states with significant waiting losses. Because... The conditions, demand matching, and waiting penalty calculations for each thermal energy unit have already been obtained, and this step directly uses them. As the basis for control decisions, the thermal state is no longer calculated repeatedly. Energy efficiency optimization control commands The calculation method involves subtracting the preset number from the control suppression coefficient to obtain the fourth data point; multiplying the square of the fourth data point by the second data point to obtain the fifth data point; and multiplying the fifth data point by the thermal energy utilization efficiency assessment result and the maximum comprehensive control capability to obtain the energy efficiency optimization control command. The formula is shown below:
[0065] ;
[0066] in, This indicates an energy efficiency optimization control command, used to specify the overall control intensity that the furnace should execute within the current control cycle; The thermal energy utilization efficiency assessment result is obtained by matching the liquid supply demand and assessing the waiting loss of each thermal energy unit in the thermal energy state concentration. This indicates the normalized maximum integrated control capability of the current furnace, which can be read from the burner controller, electric heating controller, or PLC parameter table. This represents the control inhibition coefficient, which is set during the furnace commissioning stage based on the furnace type and process stability, and is used to limit overheating under low utilization efficiency conditions. The value can be set between 0 and 1. A larger value indicates stronger suppression of heat input under low utilization efficiency conditions; when the equipment does not allow strong suppression or requires priority insulation, A smaller value can be taken. In this formula... and All are adjustment factors within a unified evaluation interval, therefore the final result is... and If the control variables belong to the same type, they can be further assigned to specific execution objects in the next step. If the calculated adjustment factor within the parentheses is lower than the equipment's allowable lower limit, the PLC sets the minimum control intensity according to the safety insulation strategy to prevent the furnace temperature from falling below the process safety range.
[0067] The above formula is derived from the proportional control relationship. The basic proportional control relationship can be expressed as the control command equals the thermal energy utilization efficiency assessment result multiplied by the maximum comprehensive control capability, i.e. This fundamental relationship allows the control intensity to vary with thermal energy utilization efficiency, but in the die-casting furnace scenario, when... At lower values, it often indicates a waiting period, delayed pouring, or prolonged heat preservation. Simple proportional adjustment may still maintain a certain heat input. To adapt to the operating conditions in industrial furnaces where low-efficiency heat energy should be suppressed, this step adds to the basic proportional adjustment relationship... .when When the value is close to high, the square term is relatively small, and the control command mainly maintains a stable liquid supply with high thermal energy utilization; when At lower efficiency levels, the squared term increases, further suppressing the control command and thus reducing ineffective heating in low-efficiency states. The squared form is used to enhance the suppression effect in low-efficiency regions while preventing sudden drops in the control command under high-efficiency states.
[0068] For example, the thermal energy utilization efficiency assessment results of a certain pool-type gas-fired insulation furnace within the current control cycle are obtained. The current normalized maximum integrated control capability in the PLC parameter table. During the furnace commissioning phase, the control inhibition coefficient is set. Substituting into the formula, we get:
[0069] ;
[0070] First calculate , then calculate Then calculate Therefore, the result in parentheses is Finally obtained This result indicates that the overall control intensity of the furnace within the current control cycle is approximately 60%. If the same furnace has an upcoming molten metal removal task in the next production cycle, the result will be... ,exist , When unchanged, the result inside the parentheses is ,final The control intensity is significantly improved. If the die-casting machine is waiting... The result in parentheses is ,final The control intensity decreased significantly. This demonstrates that this step can differentiate between high-demand liquid supply and low-demand insulation states based on subsequent heat energy utilization efficiency.
[0071] Upon receiving energy efficiency optimization control instructions Then, energy efficiency optimization control is performed on the industrial furnace according to the energy efficiency optimization control instructions. Specifically, this will... As a comprehensive control intensity output, it is used for the next step to allocate and limit the output according to the execution capacity of the burner, electric heater, heat preservation control circuit, feeding mechanism, or soup dispensing mechanism. For different furnace types, This indicates the overall adjustment intention within the same control cycle, while the specific control quantities for each execution object are further generated in the next step based on equipment parameters. To facilitate control traceability, the system outputs... Time-synchronous recording of input , , The control cycle number and whether the default safety control is used enable the next step to determine whether the control command can be issued directly.
[0072] This step outputs energy efficiency optimization control commands. It is used to generate actual control quantities, so that the thermal energy utilization efficiency assessment results can be transformed into a control basis that can be executed by field equipment.
[0073] Furthermore, optimize control commands based on energy efficiency. For input, This is converted into controllable quantities that can be executed on-site in the industrial furnace and then sent to the burner, electric heater, insulation controller, feeding mechanism, or discharging mechanism. The previous step has already generated energy efficiency optimization control instructions based on the thermal energy utilization efficiency assessment results. Therefore, the focus of this step is to execute the conversion, that is, to assign an energy efficiency optimization control command to a specific execution object according to the actual furnace type and the control capability of the execution equipment, so that the digital twin evaluation and control calculation can truly play a role in the operation of the industrial furnace. Before executing the conversion, the PLC or furnace controller first reads the equipment's allowed action status, including furnace door status, liquid level status, gas pressure, electric heater availability status, burner ignition status, soup dispensing mechanism interlock status, and safety alarm status. When any safety interlock is not met, this step does not issue a normal optimization control quantity, but maintains the original safety control or alarm shutdown status, and writes the reason for non-execution into the control record.
[0074] In actual equipment, the execution targets differ depending on the furnace type. For gas-fired centralized melting furnaces, the main execution targets are the burner, combustion fan, and gas valve assembly; for pool-type electric holding furnaces, the main execution targets are the electric heater and the holding control circuit; for tower-type machine-side melting and holding furnaces, the main execution targets include the heating control components of the feeding preheating zone, melting zone, and holding liquid supply zone; and for crucible furnaces, the main execution targets are the crucible heater and the holding control circuit. Therefore, this step pre-sets the control allocation coefficients for each execution target in the PLC or furnace controller and uses them during operation. The conversion is performed. This control allocation coefficient is derived from the furnace configuration parameters during the equipment commissioning phase, such as the maximum output capacity of the burner, the rated adjustment range of the heater, the allowable adjustment range of the insulation zone, and the allowable operating conditions of the soup dispensing mechanism. For the same execution object, the control allocation coefficient can be switched according to the furnace type area, the position of the soup dispensing side, and the current production task status, but the switching rules are pre-configured by the PLC parameter table and cannot be arbitrarily changed without a source during operation.
[0075] This step employs a limiting proportional execution relationship commonly used in industrial control. Its foundation lies in the execution quantity mapping method within proportional control, which converts the higher-level control command into a control quantity for the specific execution object based on the equipment allocation coefficient. Considering that industrial furnace execution equipment has a minimum stable output and a maximum safe output, this invention adds an execution limit to the proportional mapping, ensuring that the control quantity always falls within the allowable range of the corresponding equipment. The actual issued control quantity is calculated by multiplying the energy efficiency optimization control command by the control allocation coefficient to obtain the first data. The larger number between the first data and the minimum stable control quantity is selected as the second data; the smaller number between the second data and the maximum safe control quantity is selected as the actual issued control quantity. As shown in the following formula:
[0076] ;
[0077] in, Indicates the first The actual amount of control issued to each execution object; This indicates an energy efficiency optimization control command; Indicates the first The control allocation coefficients for each execution object are pre-configured by the PLC parameter table or the furnace controller parameter table. Indicates the first The minimum stable control quantity allowed for each execution object; Indicates the first The maximum amount of security controls allowed for each execution object. Here... It is the upper limit of a single execution object. This represents the upper limit of the overall control capability of the furnace; the two are at different control levels. The formula first calculates... , obtained the The target control quantity of each execution object, and then through Ensure the control quantity is not lower than the stable operating lower limit, and finally pass Ensure that the control quantity does not exceed the safe operating limit. If a certain execution object is allowed to be completely shut down under the current operating condition, then the corresponding... It can be configured to 0; if the execution object must maintain flame stability, thermal safety, or the lower limit of the liquid metal temperature, then... Configure the equipment according to its safety minimum. This prevents unreasonable forced heating under low utilization efficiency and also prevents necessary insulation from being accidentally shut off.
[0078] For example, the energy efficiency optimization control command of a certain pool-type electric heating furnace. The furnace is equipped with two electric heating control loops: the first loop is located on the soup outlet side, and the second loop is located on the liquid storage side. Since the soup outlet side directly affects the stability of the liquid supply, the control allocation coefficient for the first loop is set in the PLC parameter table. Control allocation coefficient of the second loop The minimum stable control quantity of the first loop. Maximum safety control quantity Minimum stable control quantity of the second loop Maximum safety control quantity Substituting into the formula, the target control quantity for the first loop is... The result is within the acceptable range, therefore The target control quantity for the second loop is The result is within the acceptable range, therefore After execution, the dispensing side gains higher control intensity, while the storage side maintains lower insulation intensity, allowing heat energy to be more concentrated for the upcoming liquid supply process.
[0079] For gas-fired centralized melting furnaces, if Control allocation coefficient of burner control loop Control allocation coefficient of combustion fan control loop The burner's permissible control range is to The allowable control range of the combustion fan is to The actual control quantity issued by the burner is The actual control quantity issued by the combustion fan is The PLC writes the results into the burner power setting register and the fan frequency setting register, respectively, to ensure that the combustion intensity and thermal efficiency control results are consistent. If the target control quantity calculated in a certain instance exceeds the allowable range of the equipment, the limiting relationship will restrict it within the corresponding equipment safety boundary, thereby preventing the control command from directly causing burner overshoot or electric heater over-limit.
[0080] The three thermal energy unit examples described above can be used again during a complete control cycle. The cycle calculation yields... Approximately If in the PLC parameter table , The energy efficiency optimization control command is approximately If this cycle corresponds to two electric heating circuits in a pool-type electric heating furnace, the control allocation coefficient for the first circuit is: The control allocation coefficient for the second loop is Since neither of them touched the amplitude limit boundary, the actual control quantity issued by the first circuit is approximately The actual control quantity sent by the second circuit is approximately This shows that the thermal energy state set First, generate the thermal energy utilization efficiency assessment results. , Re-form energy efficiency optimization control commands , Ultimately converted into the values of each execution object The variables before and after can be continuously closed.
[0081] When issuing control commands, the PLC converts them according to the type of the object being executed. For burners, This is converted to the gas valve opening degree, burner output level, or combustion power setting value; for electric heaters, Convert to heating power setpoint; for the heat preservation control circuit, Converted to thermal insulation output strength; for the feeding mechanism, This can serve as a control basis for allowing or delaying the addition of ingredients; for the soup dispensing mechanism, This can serve as the basis for controlling the soup dispensing rhythm after the liquid supply conditions are met. During execution, the PLC judges the furnace door status, liquid level status, and equipment permissible action status according to the existing safety interlock logic. After the equipment action conditions are met, a control quantity is issued. After the control quantity is issued, the PLC reads the acknowledgment status of the executed object, such as the actual output level of the burner, the actual power of the electric heater, the valve opening feedback, the fan frequency feedback, and the action status of the feeding mechanism or the soup dispensing mechanism. If the deviation between the acknowledgment status and the actual issued control quantity exceeds the allowable deviation range, an execution anomaly is recorded, and in the next control cycle, the control allocation coefficient of the executed object is reduced, the executed object is set to unavailable, or switched to safety control mode according to the preset fault strategy in the PLC parameter table.
[0082] This step outputs the actual control quantity sent. When multiple execution objects exist, each execution object... Together, these factors constitute the actual execution control result of the industrial furnace in this control cycle. This result directly affects the furnace heating, holding, charging, and discharging processes, enabling energy efficiency optimization control commands to be executed. This translates into actions on-site, thereby achieving energy efficiency optimization control of industrial furnaces based on digital twin thermal energy utilization efficiency assessment. In the next control cycle, the system re-collects temperature, power, liquid level, dispensing records, and production plan data, and regenerates the thermal state set. If the temperature of the molten aluminum outlet zone, the stability of the molten aluminum supply, and the energy consumption per unit of molten aluminum improve towards the preset target after execution, the current control strategy is maintained. If insufficient molten aluminum supply, excessively rapid temperature drop, or no improvement in energy consumption occurs for several consecutive cycles, the weights, suppression coefficients, or control allocation coefficients of the executed object are adjusted according to the parameter group switching rules preset during the commissioning phase, and the operator is prompted to verify the furnace parameters and sensor status. Through this feedback method, the present invention forms a closed-loop control process from digital twin thermal energy status expression, efficiency assessment, control command generation, execution and issuance to the status update of the next cycle.
[0083] Steps S101 to S105, as illustrated in this embodiment, involve obtaining the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace during the current control cycle. The demand matching factor and waiting penalty factor corresponding to each thermal energy unit are obtained. Based on the comprehensive state value, importance weight, demand matching factor, and waiting penalty factor of all thermal energy units, the thermal energy utilization efficiency evaluation result of the industrial furnace is calculated. The maximum comprehensive control capability and control inhibition coefficient of the industrial furnace are obtained. Based on the thermal energy utilization efficiency evaluation result, the maximum comprehensive control capability, and the control inhibition coefficient, an energy efficiency optimization control command is calculated. Energy efficiency optimization control of the industrial furnace is performed according to the energy efficiency optimization control command, thereby achieving energy efficiency optimization control of the industrial furnace and reducing overall energy consumption.
[0084] Please see Figure 2 This application also provides a digital twin-based industrial furnace energy efficiency optimization control system, which can implement the above-mentioned digital twin-based industrial furnace energy efficiency optimization control method. The system includes:
[0085] The acquisition unit 201 is used to acquire the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace during the current control cycle.
[0086] The first calculation unit 202 is used to obtain the demand matching factor and waiting penalty factor corresponding to the thermal energy unit, and calculate the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value, importance weight, demand matching factor and waiting penalty factor of all thermal energy units.
[0087] The second calculation unit 203 is used to obtain the maximum comprehensive control capability and control inhibition coefficient of the industrial furnace, and to calculate the energy efficiency optimization control command based on the thermal energy utilization efficiency evaluation results, the maximum comprehensive control capability and control inhibition coefficient.
[0088] The control unit 204 is used to perform energy efficiency optimization control on the industrial furnace according to the energy efficiency optimization control command.
[0089] The specific implementation of this digital twin-based industrial furnace energy efficiency optimization control system is basically the same as the specific embodiment of the digital twin-based industrial furnace energy efficiency optimization control method described above, and will not be repeated here.
[0090] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect above.
[0091] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.
[0092] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. A method for optimizing energy efficiency control of industrial furnaces based on digital twins, characterized in that, The method includes: Obtain the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace during the current control cycle; Obtain the demand matching factor and waiting penalty factor corresponding to the thermal energy unit, and calculate the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value, importance weight, demand matching factor and waiting penalty factor of all the thermal energy units. Obtain the maximum comprehensive control capability and control inhibition coefficient of the industrial furnace, and calculate the energy efficiency optimization control command based on the thermal energy utilization efficiency evaluation results, the maximum comprehensive control capability and the control inhibition coefficient; The industrial furnace is subjected to energy efficiency optimization control according to the energy efficiency optimization control command.
2. The method for optimizing energy efficiency control of an industrial furnace based on digital twins according to claim 1, characterized in that, The industrial furnace includes multiple execution objects, and the step of performing energy efficiency optimization control on the industrial furnace according to the energy efficiency optimization control command includes: Obtain the control allocation coefficient, minimum stable control quantity, and maximum safe control quantity for each execution object; The actual control quantity issued to each execution object is calculated based on the energy efficiency optimization control command, control allocation coefficient, minimum stable control quantity, and maximum safe control quantity. The energy efficiency optimization control of the execution object is performed based on the actual control quantity issued.
3. The method for optimizing and controlling the energy efficiency of an industrial furnace based on digital twins according to claim 2, characterized in that, The calculation of the actual control quantity issued to each execution object based on the energy efficiency optimization control command, control allocation coefficient, minimum stable control quantity, and maximum safe control quantity includes: Multiply the energy efficiency optimization control command by the control allocation coefficient to obtain the first data, and select the largest number from the first data and the minimum stable control quantity as the second data; The smaller number between the second data and the maximum security control amount is selected as the actual control amount issued.
4. The method for optimizing and controlling the energy efficiency of an industrial furnace based on digital twins according to claim 2, characterized in that, After performing energy efficiency optimization control on the execution object based on the actual issued control quantity, the method further includes: Obtain the receipt status for each of the execution objects; Deviation data is obtained by calculating the deviation based on the receipt status and the actual control quantity issued; If the deviation data is greater than the preset allowable deviation range, an execution anomaly is recorded and the corresponding control allocation coefficient is reduced according to the preset fault strategy in the next control cycle.
5. The method for optimizing and controlling the energy efficiency of an industrial furnace based on digital twins according to claim 1, characterized in that, The acquisition of the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace includes: Obtain the temperature status, energy consumption status, liquid level status, and production demand status of each thermal energy unit in the industrial furnace during the current control cycle; The temperature state is linearly mapped according to the preset lower temperature limit and upper temperature limit to obtain the updated temperature state. The energy consumption state is linearly mapped according to the ratio of unit power to total power to obtain the updated energy consumption state. The liquid level status is linearly mapped according to the proportion of unit liquid volume to total liquid volume to obtain the updated liquid level status. The temperature status, energy consumption status, liquid level status and production demand status are summarized according to the preset process proportion to obtain the comprehensive status value.
6. The method for optimizing energy efficiency control of an industrial furnace based on digital twins according to claim 1, characterized in that, The calculation of the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value of all the thermal energy units, the importance weight, the demand matching factor, and the waiting penalty factor includes: The third data is obtained by multiplying the comprehensive status value, the importance weight, the demand matching factor, and the waiting penalty factor together; The thermal energy utilization efficiency evaluation result is obtained by summing all the third data corresponding to all the thermal energy units.
7. The method for optimizing energy efficiency control of an industrial furnace based on digital twins according to claim 1, characterized in that, The step of calculating the energy efficiency optimization control command based on the thermal energy utilization efficiency assessment result, the maximum comprehensive control capability, and the control inhibition coefficient includes: Subtract the preset number from the control inhibition coefficient to obtain the fourth data, and multiply the fourth data by the square of the fourth data to obtain the fifth data; The energy efficiency optimization control command is obtained by multiplying the fifth data, the thermal energy utilization efficiency evaluation result, and the maximum comprehensive control capability.
8. A digital twin-based energy efficiency optimization control system for industrial furnaces, characterized in that, The system includes: The acquisition unit is used to acquire the comprehensive state value and importance weight of each thermal energy unit in the industrial furnace during the current control cycle. The first calculation unit is used to obtain the demand matching factor and waiting penalty factor corresponding to the thermal energy unit, and calculate the thermal energy utilization efficiency evaluation result of the industrial furnace based on the comprehensive state value, importance weight, demand matching factor and waiting penalty factor of all the thermal energy units. The second calculation unit is used to obtain the maximum comprehensive control capability and control inhibition coefficient of the industrial furnace, and to calculate the energy efficiency optimization control command based on the thermal energy utilization efficiency evaluation result, the maximum comprehensive control capability and the control inhibition coefficient; The control unit is used to perform energy efficiency optimization control on the industrial furnace according to the energy efficiency optimization control command.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the energy efficiency optimization control method for an industrial furnace based on digital twins as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the energy efficiency optimization control method for industrial furnaces based on digital twins as described in any one of claims 1 to 7.