A furnace temperature adaptive control system and method based on MPC

Abstract

The invention relates to the technical field of temperature control, and discloses a furnace temperature adaptive control system and method based on MPC. A model building module builds a dynamic prediction model based on thermodynamic characteristics and kinetic characteristics; a data output module obtains historical operation data and outputs a future temperature change prediction table; a factor determination module analyzes the historical operation data and determines the temperature influence indirect factors and the temperature influence direct factors; a data calculation module calculates the degree value of the temperature influence factor; an update optimization module sets an adjustment coefficient based on the degree value of the temperature influence factor, updates and optimizes the future temperature change prediction table, and obtains a target future temperature change prediction table; a temperature control module performs furnace temperature adaptive control according to the target future temperature change prediction table, and can automatically adjust temperature control parameters according to changes in actual working conditions, thereby improving the adaptive ability, realizing high-precision and high-stability control of the furnace temperature, and improving product quality and production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to an MPC-based furnace temperature adaptive control system and method. Background Art

[0002] In the industrial production process, the control of furnace temperature is one of the key factors affecting product quality and production efficiency. In recent years, with the development of computer technology and control theory, model predictive control (MPC) has received widespread attention as an advanced control strategy. MPC can achieve precise control of complex systems by predicting and optimizing the future behavior of the system. Therefore, applying MPC to furnace temperature control is expected to improve control accuracy and stability, thereby improving product quality and production efficiency.

[0003] Traditional furnace temperature control methods usually use PID (proportional-integral-differential) controllers for adjustment. However, due to the complexity, nonlinearity and time-varying characteristics of the furnace temperature system, traditional PID control methods often fail to meet the control requirements of high precision and high stability. For furnace temperature control with high real-time requirements, it may not be able to meet the control requirements, and traditional furnace temperature control methods often lack adaptive capabilities and cannot automatically adjust control parameters according to changes in actual working conditions, affecting the control effect. Summary of the invention

[0004] The embodiment of the present invention provides an MPC-based furnace temperature adaptive control system and method to solve the technical problems in the prior art that the temperature control parameters cannot be automatically adjusted according to changes in actual working conditions, the requirements for high-precision and high-stability control of the furnace temperature cannot be met, and the adaptive capability is lacking.

[0005] In order to achieve the above object, the present invention provides a furnace temperature adaptive control system based on MPC, comprising:

[0006] A model building module is used to receive furnace temperature control instructions and build a dynamic prediction model of furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system;

[0007] A data output module, used for acquiring the historical operation data of the furnace, and outputting a future temperature change prediction table based on the historical operation data and the dynamic prediction model, wherein the future temperature change prediction table includes future temperature values ​​and heater control power;

[0008] A factor determination module is used to analyze the historical operation data, determine the temperature influencing factors, and determine the temperature indirect influencing factors and the temperature direct influencing factors;

[0009] A data calculation module, used for calculating the degree value of the temperature influence factor of the furnace according to the temperature influence indirect factor and the temperature influence direct factor;

[0010] An updating and optimizing module, configured to set an adjustment coefficient of the future temperature change prediction table based on the degree value of the temperature influencing factor, and to update and optimize the future temperature change prediction table based on the adjustment coefficient to obtain a target future temperature change prediction table;

[0011] The temperature control module is used to perform adaptive control of the furnace temperature of the furnace according to the target future temperature change prediction table.

[0012] Furthermore, it also includes:

[0013] A judgment module, used for matching the furnace temperature control instruction with the instruction category of the previous instruction, and judging whether the furnace temperature control instruction is the same as the previous instruction;

[0014] a collection module, configured to determine that the furnace temperature control instruction is the same as the previous instruction if the instruction category of the furnace temperature control instruction matches that of the previous instruction, collect a first time node for sending the furnace temperature control instruction, and collect a second time node for sending the previous instruction;

[0015] A second judgment module is used to calculate the time node difference between the first time node and the second time node, and judge whether the furnace temperature control instruction can be processed according to the relationship between the time node difference and a preset time node difference;

[0016] A third judgment module, configured to judge that the furnace temperature control instruction can be processed if the time node difference is greater than or equal to the preset time node difference;

[0017] A sending module, configured to determine that the furnace temperature control instruction cannot be processed if the time node difference is less than the preset time node difference, and to generate a log reminder and send the log reminder;

[0018] The fourth judgment module is used to judge that the furnace temperature control instruction is different from the previous instruction if the instruction category of the furnace temperature control instruction does not match that of the previous instruction, and process the furnace temperature control instruction.

[0019] Furthermore, the factor determination module is used to:

[0020] The factor determination module is used to construct all temperature influencing factors into the data analysis time series based on the acquisition time sequence;

[0021] The factor determination module is used to randomly determine a temperature influencing factor and a right adjacent temperature influencing factor, and obtain the corresponding historical temperature value and historical adjacent temperature value;

[0022] The factor determination module is used to calculate the absolute value of the temperature difference between the historical temperature value and the historical adjacent temperature value;

[0023] The factor determination module is used to determine the maximum temperature value and the minimum temperature value corresponding to all temperature influencing factors, and calculate the temperature difference between the maximum temperature value and the minimum temperature value;

[0024] The factor determination module is used to calculate the absolute value of the temperature difference and the temperature difference ratio of the temperature difference;

[0025] The factor determination module is used to calculate the absolute value of the influencing factor difference between the temperature influencing factor and the right adjacent temperature influencing factor;

[0026] The factor determination module is used to determine the maximum temperature influencing factor and the minimum temperature influencing factor from all temperature influencing factors, and calculate the influencing factor difference between the maximum temperature influencing factor and the minimum temperature influencing factor;

[0027] The factor determination module is used to calculate the absolute value of the influencing factor difference and the influencing factor difference ratio of the influencing factor difference;

[0028] The factor determination module is used to calculate the product value of the temperature difference ratio and the influencing factor difference ratio, and perform normalization processing to obtain the temperature instantaneous influencing factor;

[0029] The factor determination module is used to process the remaining temperature influencing factors to obtain a plurality of temperature influencing indirect factors;

[0030] The factor determination module is used to calculate the average value of all temperature instantaneous impact factors to obtain the temperature impact direct factor.

[0031] Furthermore, the data calculation module is used for:

[0032] The data calculation module is used to calculate the upper limit value and the lower limit value of the temperature indirect factor according to the following formula;

[0033] a1=(1+e)×f;

[0034] Among them, a1 is the upper limit value, e is the calculation factor, the value range is [0,1], and f is the direct factor of temperature influence;

[0035] a2=(1-e)×f;

[0036] Among them, a2 is the lower limit value;

[0037] The data calculation module is used to generate a reduction factor set according to all temperature-affecting indirect factors that are less than or equal to the lower limit value;

[0038] The data calculation module is used to generate a set of multiplication factors according to all temperature-affecting indirect factors that are greater than or equal to the upper limit value;

[0039] The data calculation module is used to calculate the subtraction difference value between each temperature influence indirect factor in the subtraction factor set and the lower limit value, and generate a subtraction difference value set;

[0040] The data calculation module is used to sort the subtraction difference value set by numerical value to obtain a sorted subtraction difference value set;

[0041] The data calculation module is used to calculate the difference between each temperature indirect factor in the increase factor set and the upper limit value, and generate a difference value set;

[0042] The data calculation module is used to sort the increase-difference value set by numerical value to obtain a sorted increase-difference value set;

[0043] The data calculation module is used to perform corresponding pairwise matching on the sorted subtraction difference value sets and the sorted increase difference value sets to obtain a plurality of increase and decrease arrays;

[0044] The data calculation module is used to calculate the degree value of the temperature influencing factor of the furnace according to all the increase and decrease arrays.

[0045] Furthermore, the data calculation module is used for:

[0046] The data calculation module is used to calculate the degree value of the temperature influencing factor of the furnace according to the following formula:

[0047]

[0048] Among them, w is the degree value of the furnace temperature influencing factor, n is the number of increase and decrease arrays, g1 i is the increase and decrease value in the i-th increase and decrease array, g2 is the decrease and decrease value in the i-th increase and decrease array, ((g1 i -g2 i ) 2 ) min For all (g1 i -g2 i ) 2 The minimum value in ((g1 i -g2 i ) 2 ) max For all (g1 i -g2 i ) 2The maximum value in r 2 For all (g1 i -g2 i ) 2 The variance of .

[0049] Furthermore, the update optimization module is used to:

[0050] The update optimization module is used to preset a first preset temperature influencing factor degree value and a second preset temperature influencing factor degree value;

[0051] The update optimization module is used to preset a first preset adjustment coefficient, a second preset adjustment coefficient and a third preset adjustment coefficient;

[0052] The updating and optimizing module is used for selecting the first preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when a first preset condition is identified;

[0053] The updating and optimizing module is used for selecting the second preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when the second preset condition is identified;

[0054] The update optimization module is used to select the third preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when a third preset condition is identified; wherein, the first preset condition includes that the temperature influence factor degree value is less than the first preset temperature influence factor degree value, the second preset condition includes that the temperature influence factor degree value is greater than or equal to the first preset temperature influence factor degree value and less than the second temperature influence factor degree value, and the third preset condition includes that the temperature influence factor degree value is greater than or equal to the second preset temperature influence factor degree value.

[0055] Furthermore, the update optimization module is used to:

[0056] The update optimization module is used to calculate the product value of the adjustment coefficient and the heater control power, and use the product value as the heater target control power;

[0057] The update optimization module is used to determine the target temperature value corresponding to the target control power of each heater according to a preset mapping table;

[0058] The update optimization module is used to obtain the target future temperature change prediction table according to the heater target control power and the target temperature value.

[0059] In order to achieve the above object, the present invention also provides a furnace temperature adaptive control method based on MPC, comprising:

[0060] Receive furnace temperature control instructions, and establish a dynamic prediction model for furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system;

[0061] Acquire historical operation data of the furnace, and output a future temperature change prediction table based on the historical operation data and the dynamic prediction model, wherein the future temperature change prediction table includes future temperature values ​​and heater control power;

[0062] Analyze the historical operation data to determine the temperature influencing factors, and determine the temperature indirect influencing factors and the temperature direct influencing factors;

[0063] Calculate the degree value of the temperature influence factor of the furnace according to the temperature influence indirect factor and the temperature influence direct factor;

[0064] Setting an adjustment coefficient of the future temperature change prediction table based on the degree value of the temperature influencing factor, and updating and optimizing the future temperature change prediction table based on the adjustment coefficient to obtain a target future temperature change prediction table;

[0065] The furnace temperature is adaptively controlled according to the target future temperature change prediction table.

[0066] Furthermore, before receiving the furnace temperature control instruction and establishing a dynamic prediction model of the furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system, it also includes:

[0067] Matching the furnace temperature control instruction with the instruction category of the previous instruction, and determining whether the furnace temperature control instruction is the same as the previous instruction;

[0068] If the furnace temperature control instruction matches the instruction category of the previous instruction, it is determined that the furnace temperature control instruction is the same as the previous instruction, and a first time node of sending the furnace temperature control instruction is collected, and a second time node of sending the previous instruction is collected;

[0069] Calculating a time node difference between the first time node and the second time node, and judging whether the furnace temperature control instruction can be processed according to a relationship between the time node difference and a preset time node difference;

[0070] If the time node difference is greater than or equal to the preset time node difference, it is determined that the furnace temperature control instruction can be processed;

[0071] If the time node difference is less than the preset time node difference, it is determined that the furnace temperature control instruction cannot be processed, and a log reminder is generated and sent;

[0072] If the furnace temperature control instruction does not match the instruction category of the previous instruction, it is determined that the furnace temperature control instruction is different from the previous instruction, and the furnace temperature control instruction is processed.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The model building module establishes a dynamic prediction model based on thermodynamic characteristics and kinetic characteristics; the data output module obtains historical operation data and outputs a future temperature change prediction table. The factor determination module analyzes the historical operation data to determine the indirect factors and direct factors affecting temperature; the data calculation module calculates the degree value of the temperature influencing factor; the update optimization module sets the adjustment coefficient based on the degree value of the temperature influencing factor, updates and optimizes the future temperature change prediction table, and obtains the target future temperature change prediction table; the temperature control module performs furnace temperature adaptive control according to the target future temperature change prediction table, and can automatically adjust the temperature control parameters according to changes in actual working conditions, improve the adaptive ability, achieve high-precision and high-stability control of the furnace temperature, and improve product quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0076] Figure 1 The structure diagram of the furnace temperature adaptive control system based on MPC in the embodiment of the present invention is shown;

[0077] Figure 2 The figure shows a flow chart of a furnace temperature adaptive control method based on MPC in an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0079] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0080] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0081] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0082] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0083] like Figure 1 As shown, an embodiment of the present invention discloses a furnace temperature adaptive control system based on MPC, comprising:

[0084] A model building module is used to receive furnace temperature control instructions and build a dynamic prediction model of furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system;

[0085] A data output module, used for acquiring the historical operation data of the furnace, and outputting a future temperature change prediction table based on the historical operation data and the dynamic prediction model, wherein the future temperature change prediction table includes future temperature values ​​and heater control power;

[0086] A factor determination module is used to analyze the historical operation data, determine the temperature influencing factors, and determine the temperature indirect influencing factors and the temperature direct influencing factors;

[0087] A data calculation module, used for calculating the degree value of the temperature influence factor of the furnace according to the temperature influence indirect factor and the temperature influence direct factor;

[0088] An updating and optimizing module, configured to set an adjustment coefficient of the future temperature change prediction table based on the degree value of the temperature influencing factor, and to update and optimize the future temperature change prediction table based on the adjustment coefficient to obtain a target future temperature change prediction table;

[0089] The temperature control module is used to perform adaptive control of the furnace temperature of the furnace according to the target future temperature change prediction table.

[0090] In this embodiment, the study of the thermodynamic characteristics in any furnace is based on the perspective of energy conservation. According to the laws of physics, the energy input in the furnace (such as the heat of combustion of the fuel) should be equal to the energy output (including the heat transferred to the material, the heat carried away by the flue gas, and the heat dissipation of the furnace body, etc.). This equilibrium relationship is the basis for establishing a temperature model. The heat transfer inside the furnace mainly includes radiation, convection and conduction. Different types of industrial furnaces may have different dominant heat transfer mechanisms. For example, in a copper refining anode furnace, the convection and radiation heat transfer of the flue gas may be particularly significant.

[0091] In this embodiment, the dynamic characteristics of the fuel supply system: The impact of fuel flow rate: The fuel supply rate has a direct impact on the furnace temperature. Changes in flow rate can lead to rapid changes in furnace temperature, so the dynamic model needs to be able to monitor and adjust the fuel supply system in real time. The role of combustion air: The flow rate and quality of combustion air directly affect the combustion efficiency and furnace temperature. An accurate model should not ignore the dynamic changes in air supply and its impact on furnace thermodynamics.

[0092] In this embodiment, the structure and parameters of the model: Structural design: The dynamic model usually involves a series of partial differential equations or algebraic equations, which describe the heat exchange process and temperature change rate of each component in the furnace. The structure of the model should be able to reflect the working principle of the actual furnace and the relationship between the various components. Parameter determination: Model parameters such as heat capacity, heat transfer coefficient, etc. need to be obtained through experiments or empirical data. Correct estimation of these parameters is the key to ensuring the accuracy of model predictions. Dynamic response: The thermal inertia of the furnace system determines its response speed to external changes such as changes in the fuel supply rate. Establishing an accurate dynamic model requires taking this thermal inertia into account so that the model can accurately predict the temperature changes in the furnace under different operating conditions.

[0093] In this example, the model was initially tested in a computer simulation environment and then verified and tuned in an actual furnace. This helps confirm the applicability of the model and make adjustments based on actual conditions. This can not only optimize the operating efficiency of the furnace, but also improve product quality and production efficiency while reducing energy consumption and environmental impact.

[0094] The beneficial effect of the above technical solution is that the present invention can automatically adjust the temperature control parameters according to the changes in actual working conditions, improve the adaptive ability, achieve high-precision and high-stability control of the furnace temperature, and improve product quality and production efficiency.

[0095] In some embodiments of the present application, it also includes:

[0096] A judgment module, used for matching the furnace temperature control instruction with the instruction category of the previous instruction, and judging whether the furnace temperature control instruction is the same as the previous instruction;

[0097] a collection module, configured to determine that the furnace temperature control instruction is the same as the previous instruction if the instruction category of the furnace temperature control instruction matches that of the previous instruction, collect a first time node for sending the furnace temperature control instruction, and collect a second time node for sending the previous instruction;

[0098] A second judgment module is used to calculate the time node difference between the first time node and the second time node, and judge whether the furnace temperature control instruction can be processed according to the relationship between the time node difference and a preset time node difference;

[0099] A third judgment module, configured to judge that the furnace temperature control instruction can be processed if the time node difference is greater than or equal to the preset time node difference;

[0100] A sending module, configured to determine that the furnace temperature control instruction cannot be processed if the time node difference is less than the preset time node difference, and to generate a log reminder and send the log reminder;

[0101] The fourth judgment module is used to judge that the furnace temperature control instruction is different from the previous instruction if the instruction category of the furnace temperature control instruction does not match that of the previous instruction, and process the furnace temperature control instruction.

[0102] In this embodiment, the previous instruction may also be a pressure control, flow control instruction, etc.

[0103] In this embodiment, the time node difference is set according to the actual situation, and is preferably 2 hours.

[0104] In this embodiment, if the time node difference is less than the preset time node difference, it may be due to staff error operation, or it may be necessary to perform temperature control again within a period of time, so a log reminder is issued.

[0105] The beneficial effect of the above technical solution is that the present invention can ensure the normal temperature control and avoid missed operations by generating log reminders.

[0106] In some embodiments of the present application, the factor determination module is used to:

[0107] The factor determination module is used to construct all temperature influencing factors into the data analysis time series based on the acquisition time sequence;

[0108] The factor determination module is used to randomly determine a temperature influencing factor and a right adjacent temperature influencing factor, and obtain the corresponding historical temperature value and historical adjacent temperature value;

[0109] The factor determination module is used to calculate the absolute value of the temperature difference between the historical temperature value and the historical adjacent temperature value;

[0110] The factor determination module is used to determine the maximum temperature value and the minimum temperature value corresponding to all temperature influencing factors, and calculate the temperature difference between the maximum temperature value and the minimum temperature value;

[0111] The factor determination module is used to calculate the absolute value of the temperature difference and the temperature difference ratio of the temperature difference;

[0112] The factor determination module is used to calculate the absolute value of the influencing factor difference between the temperature influencing factor and the right adjacent temperature influencing factor;

[0113] The factor determination module is used to determine the maximum temperature influencing factor and the minimum temperature influencing factor from all temperature influencing factors, and calculate the influencing factor difference between the maximum temperature influencing factor and the minimum temperature influencing factor;

[0114] The factor determination module is used to calculate the absolute value of the influencing factor difference and the influencing factor difference ratio of the influencing factor difference;

[0115] The factor determination module is used to calculate the product value of the temperature difference ratio and the influencing factor difference ratio, and perform normalization processing to obtain the temperature instantaneous influencing factor;

[0116] The factor determination module is used to process the remaining temperature influencing factors to obtain a plurality of temperature influencing indirect factors;

[0117] The factor determination module is used to calculate the average value of all temperature instantaneous impact factors to obtain the temperature impact direct factor.

[0118] In this embodiment, the temperature influencing factor is the combustion efficiency.

[0119] In this embodiment, the historical temperature values ​​correspond to the temperature influencing factors, and the historical adjacent temperature values ​​correspond to the right adjacent temperature influencing factors.

[0120] In this embodiment, the absolute value of the difference between the influencing factors is the absolute value of the difference between the two combustion efficiencies.

[0121] The beneficial effect of the above technical solution is that the present invention can lay a foundation for the adaptive control of furnace temperature by calculating the indirect temperature influence factor and the direct temperature influence factor, provide reliable data support, and ensure the adaptive control accuracy.

[0122] In some embodiments of the present application, the data calculation module is used to:

[0123] The data calculation module is used to calculate the upper limit value and the lower limit value of the temperature indirect factor according to the following formula;

[0124] a1=(1+e)×f;

[0125] Among them, a1 is the upper limit value, e is the calculation factor, the value range is [0,1], and f is the direct factor of temperature influence;

[0126] a2=(1-e)×f;

[0127] Among them, a2 is the lower limit value;

[0128] The data calculation module is used to generate a reduction factor set according to all temperature-affecting indirect factors that are less than or equal to the lower limit value;

[0129] The data calculation module is used to generate a set of multiplication factors according to all temperature-affecting indirect factors that are greater than or equal to the upper limit value;

[0130] The data calculation module is used to calculate the subtraction difference value between each temperature influence indirect factor in the subtraction factor set and the lower limit value, and generate a subtraction difference value set;

[0131] The data calculation module is used to sort the subtraction difference value set by numerical value to obtain a sorted subtraction difference value set;

[0132] The data calculation module is used to calculate the difference between each temperature indirect factor in the increase factor set and the upper limit value, and generate a difference value set;

[0133] The data calculation module is used to sort the increase-difference value set by numerical value to obtain a sorted increase-difference value set;

[0134] The data calculation module is used to perform corresponding pairwise matching on the sorted subtraction difference value sets and the sorted increase difference value sets to obtain a plurality of increase and decrease arrays;

[0135] The data calculation module is used to calculate the degree value of the temperature influencing factor of the furnace according to all the increase and decrease arrays.

[0136] In this embodiment, e is preferably 0.2.

[0137] In this embodiment, the increase and decrease array includes an increase difference value and a decrease difference value.

[0138] In this embodiment, if there are unmatched increase or decrease values, they are deleted.

[0139] The beneficial effect of the above technical solution is: the present invention matches the sorted subtraction difference value set and the sorted increase difference value set correspondingly in pairs to obtain multiple increase and decrease arrays; the degree value of the temperature influencing factor of the furnace is calculated according to all the increase and decrease arrays, thereby ensuring the calculation accuracy of the degree value of the temperature influencing factor and avoiding errors. It can also provide a basis for updating and optimizing the future temperature change prediction table and avoid the subjectivity of manual participation.

[0140] In some embodiments of the present application, the data calculation module is used to:

[0141] The data calculation module is used to calculate the degree value of the temperature influencing factor of the furnace according to the following formula:

[0142]

[0143] Among them, w is the degree value of the furnace temperature influencing factor, n is the number of increase and decrease arrays, g1 i is the increase and decrease value in the i-th increase and decrease array, g2 is the decrease and decrease value in the i-th increase and decrease array, ((g1 i -g2 i ) 2 ) min For all (g1 i -g2 i ) 2 The minimum value in ((g1 i -g2 i ) 2 ) max For all (g1 i -g2 i ) 2 The maximum value in r 2 For all (g1 i -g2 i ) 2 The variance of .

[0144] In some embodiments of the present application, the update optimization module is used to:

[0145] The update optimization module is used to preset a first preset temperature influencing factor degree value and a second preset temperature influencing factor degree value;

[0146] The update optimization module is used to preset a first preset adjustment coefficient, a second preset adjustment coefficient and a third preset adjustment coefficient;

[0147] The updating and optimizing module is used for selecting the first preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when a first preset condition is identified;

[0148] The updating and optimizing module is used for selecting the second preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when the second preset condition is identified;

[0149] The update optimization module is used to select the third preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when a third preset condition is identified; wherein, the first preset condition includes that the temperature influence factor degree value is less than the first preset temperature influence factor degree value, the second preset condition includes that the temperature influence factor degree value is greater than or equal to the first preset temperature influence factor degree value and less than the second temperature influence factor degree value, and the third preset condition includes that the temperature influence factor degree value is greater than or equal to the second preset temperature influence factor degree value.

[0150] In this embodiment, the first preset temperature influence factor degree value is smaller than the second preset temperature influence factor degree value, which can be specifically set according to actual conditions.

[0151] In this embodiment, the first preset adjustment coefficient is smaller than the second preset adjustment coefficient and smaller than the third preset adjustment coefficient. The first preset adjustment coefficient is preferably 0.95, the second preset adjustment coefficient is preferably 1.15, and the third preset adjustment coefficient is preferably 1.25.

[0152] The beneficial effect of the above technical solution is: the present invention sets the corresponding adjustment coefficient according to the temperature influencing factor degree value, the first preset temperature influencing factor degree value and the second preset temperature influencing factor degree value, laying the foundation for updating and optimizing the future temperature change prediction table and providing a reliable updating and optimization basis.

[0153] In some embodiments of the present application, the update optimization module is used to:

[0154] The update optimization module is used to calculate the product value of the adjustment coefficient and the heater control power, and use the product value as the heater target control power;

[0155] The update optimization module is used to determine the target temperature value corresponding to the target control power of each heater according to a preset mapping table;

[0156] The update optimization module is used to obtain the target future temperature change prediction table according to the heater target control power and the target temperature value.

[0157] In this embodiment, the mapping table is obtained based on historical temperature control records, and each control power corresponds to a temperature value.

[0158] The beneficial effect of the above technical solution is that: by determining the target future temperature change prediction table, the present invention can automatically adjust the temperature control parameters according to the changes in actual working conditions, improve the adaptability, achieve high-precision and high-stability control of the furnace temperature, and improve product quality and production efficiency.

[0159] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0160] Correspondingly, such as Figure 2 As shown, the present application also provides a furnace temperature adaptive control method based on MPC, comprising:

[0161] Receive furnace temperature control instructions, and establish a dynamic prediction model for furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system;

[0162] Acquire historical operation data of the furnace, and output a future temperature change prediction table based on the historical operation data and the dynamic prediction model, wherein the future temperature change prediction table includes future temperature values ​​and heater control power;

[0163] Analyze the historical operation data to determine the temperature influencing factors, and determine the temperature indirect influencing factors and the temperature direct influencing factors;

[0164] Calculate the degree value of the temperature influence factor of the furnace according to the temperature influence indirect factor and the temperature influence direct factor;

[0165] Setting an adjustment coefficient of the future temperature change prediction table based on the degree value of the temperature influencing factor, and updating and optimizing the future temperature change prediction table based on the adjustment coefficient to obtain a target future temperature change prediction table;

[0166] The furnace temperature is adaptively controlled according to the target future temperature change prediction table.

[0167] In some embodiments of the present application, before receiving the furnace temperature control instruction and establishing a dynamic prediction model of the furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system, the method further includes:

[0168] Matching the furnace temperature control instruction with the instruction category of the previous instruction, and determining whether the furnace temperature control instruction is the same as the previous instruction;

[0169] If the furnace temperature control instruction matches the instruction category of the previous instruction, it is determined that the furnace temperature control instruction is the same as the previous instruction, and a first time node of sending the furnace temperature control instruction is collected, and a second time node of sending the previous instruction is collected;

[0170] Calculating a time node difference between the first time node and the second time node, and judging whether the furnace temperature control instruction can be processed according to a relationship between the time node difference and a preset time node difference;

[0171] If the time node difference is greater than or equal to the preset time node difference, it is determined that the furnace temperature control instruction can be processed;

[0172] If the time node difference is less than the preset time node difference, it is determined that the furnace temperature control instruction cannot be processed, and a log reminder is generated and sent;

[0173] If the furnace temperature control instruction does not match the instruction category of the previous instruction, it is determined that the furnace temperature control instruction is different from the previous instruction, and the furnace temperature control instruction is processed.

[0174] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0175] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0176] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A furnace temperature adaptive control system based on MPC, characterized in that: include: A model building module is used to receive furnace temperature control instructions and build a dynamic prediction model of furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system; A data output module, used for acquiring the historical operation data of the furnace, and outputting a future temperature change prediction table based on the historical operation data and the dynamic prediction model, wherein the future temperature change prediction table includes future temperature values ​​and heater control power; A factor determination module is used to analyze the historical operation data, determine the temperature influencing factors, and determine the temperature indirect influencing factors and the temperature direct influencing factors; A data calculation module, used for calculating the degree value of the temperature influence factor of the furnace according to the temperature influence indirect factor and the temperature influence direct factor; An updating and optimizing module, configured to set an adjustment coefficient of the future temperature change prediction table based on the degree value of the temperature influencing factor, and to update and optimize the future temperature change prediction table based on the adjustment coefficient to obtain a target future temperature change prediction table; The temperature control module is used to perform adaptive control of the furnace temperature of the furnace according to the target future temperature change prediction table.

2. The MPC-based furnace temperature adaptive control system according to claim 1 is characterized in that: Also includes: A judgment module, used for matching the furnace temperature control instruction with the instruction category of the previous instruction, and judging whether the furnace temperature control instruction is the same as the previous instruction; a collection module, configured to determine that the furnace temperature control instruction is the same as the previous instruction if the instruction category of the furnace temperature control instruction matches that of the previous instruction, collect a first time node for sending the furnace temperature control instruction, and collect a second time node for sending the previous instruction; A second judgment module is used to calculate the time node difference between the first time node and the second time node, and judge whether the furnace temperature control instruction can be processed according to the relationship between the time node difference and a preset time node difference; A third judgment module, configured to judge that the furnace temperature control instruction can be processed if the time node difference is greater than or equal to the preset time node difference; A sending module, configured to determine that the furnace temperature control instruction cannot be processed if the time node difference is less than the preset time node difference, and to generate a log reminder and send the log reminder; The fourth judgment module is used to judge that the furnace temperature control instruction is different from the previous instruction if the instruction category of the furnace temperature control instruction does not match that of the previous instruction, and process the furnace temperature control instruction.

3. The MPC-based furnace temperature adaptive control system according to claim 1 is characterized in that: The factor determination module is used for: The factor determination module is used to construct all temperature influencing factors into the data analysis time series based on the acquisition time sequence; The factor determination module is used to randomly determine a temperature influencing factor and a right adjacent temperature influencing factor, and obtain the corresponding historical temperature value and historical adjacent temperature value; The factor determination module is used to calculate the absolute value of the temperature difference between the historical temperature value and the historical adjacent temperature value; The factor determination module is used to determine the maximum temperature value and the minimum temperature value corresponding to all temperature influencing factors, and calculate the temperature difference between the maximum temperature value and the minimum temperature value; The factor determination module is used to calculate the absolute value of the temperature difference and the temperature difference ratio of the temperature difference; The factor determination module is used to calculate the absolute value of the influencing factor difference between the temperature influencing factor and the right adjacent temperature influencing factor; The factor determination module is used to determine the maximum temperature influencing factor and the minimum temperature influencing factor from all temperature influencing factors, and calculate the influencing factor difference between the maximum temperature influencing factor and the minimum temperature influencing factor; The factor determination module is used to calculate the absolute value of the influencing factor difference and the influencing factor difference ratio of the influencing factor difference; The factor determination module is used to calculate the product value of the temperature difference ratio and the influencing factor difference ratio, and perform normalization processing to obtain the temperature instantaneous influencing factor; The factor determination module is used to process the remaining temperature influencing factors to obtain a plurality of temperature influencing indirect factors; The factor determination module is used to calculate the average value of all temperature instantaneous impact factors to obtain the temperature impact direct factor.

4. The furnace temperature adaptive control system based on MPC according to claim 1 is characterized in that: The data calculation module is used for: The data calculation module is used to calculate the upper limit value and the lower limit value of the temperature indirect factor according to the following formula; ； Among them, a1 is the upper limit value, e is the calculation factor, the value range is [0,1], and f is the direct factor of temperature influence; ； Among them, a2 is the lower limit value; The data calculation module is used to generate a reduction factor set according to all temperature-affecting indirect factors that are less than or equal to the lower limit value; The data calculation module is used to generate a set of multiplication factors according to all temperature-affecting indirect factors that are greater than or equal to the upper limit value; The data calculation module is used to calculate the subtraction difference value between each temperature influence indirect factor in the subtraction factor set and the lower limit value, and generate a subtraction difference value set; The data calculation module is used to sort the subtraction difference value set by numerical value to obtain a sorted subtraction difference value set; The data calculation module is used to calculate the difference between each temperature indirect factor in the increase factor set and the upper limit value, and generate a difference value set; The data calculation module is used to sort the increase-difference value set by numerical value to obtain a sorted increase-difference value set; The data calculation module is used to perform corresponding pairwise matching on the sorted subtraction difference value sets and the sorted increase difference value sets to obtain a plurality of increase and decrease arrays; The data calculation module is used to calculate the degree value of the temperature influencing factor of the furnace according to all the increase and decrease arrays.

5. The MPC-based furnace temperature adaptive control system according to claim 4 is characterized in that: The data calculation module is used for: The data calculation module is used to calculate the degree value of the temperature influencing factor of the furnace according to the following formula: ； Among them, w is the degree value of the furnace temperature influencing factor, n is the number of increase and decrease arrays, g1 i is the difference value in the i-th increase and decrease array, g2 i is the difference value in the i-th increase and decrease array, For all The minimum value in For all The maximum value in r 2 For all The variance of .

6. The furnace temperature adaptive control system based on MPC according to claim 1, characterized in that: The update optimization module is used to: The update optimization module is used to preset a first preset temperature influencing factor degree value and a second preset temperature influencing factor degree value; The update optimization module is used to preset a first preset adjustment coefficient, a second preset adjustment coefficient and a third preset adjustment coefficient; The updating and optimizing module is used for selecting the first preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when a first preset condition is identified; The updating and optimizing module is used for selecting the second preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when the second preset condition is identified; The update optimization module is used to select the third preset adjustment coefficient as the adjustment coefficient of the future temperature change prediction table when a third preset condition is identified; wherein, the first preset condition includes that the temperature influence factor degree value is less than the first preset temperature influence factor degree value, the second preset condition includes that the temperature influence factor degree value is greater than or equal to the first preset temperature influence factor degree value and is less than the second preset temperature influence factor degree value, and the third preset condition includes that the temperature influence factor degree value is greater than or equal to the second preset temperature influence factor degree value.

7. The furnace temperature adaptive control system based on MPC according to claim 1 is characterized in that: The update optimization module is used to: The update optimization module is used to calculate the product value of the adjustment coefficient and the heater control power, and use the product value as the heater target control power; The update optimization module is used to determine the target temperature value corresponding to the target control power of each heater according to a preset mapping table; The update optimization module is used to obtain the target future temperature change prediction table according to the heater target control power and the target temperature value.

8. A furnace temperature adaptive control method based on MPC, characterized in that: include: Receive furnace temperature control instructions, and establish a dynamic prediction model for furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system; Acquire historical operation data of the furnace, and output a future temperature change prediction table based on the historical operation data and the dynamic prediction model, wherein the future temperature change prediction table includes future temperature values ​​and heater control power; Analyze the historical operation data to determine the temperature influencing factors, and determine the temperature indirect influencing factors and the temperature direct influencing factors; Calculate the degree value of the temperature influence factor of the furnace according to the temperature influence indirect factor and the temperature influence direct factor; Setting an adjustment coefficient of the future temperature change prediction table based on the degree value of the temperature influencing factor, and updating and optimizing the future temperature change prediction table based on the adjustment coefficient to obtain a target future temperature change prediction table; The furnace temperature is adaptively controlled according to the target future temperature change prediction table.

9. The method for adaptively controlling furnace temperature based on MPC according to claim 8, characterized in that: Before receiving the furnace temperature control instruction and establishing a dynamic prediction model of the furnace temperature based on the thermodynamic characteristics of the furnace and the dynamic characteristics of the fuel supply system, it also includes: Matching the furnace temperature control instruction with the instruction category of the previous instruction, and determining whether the furnace temperature control instruction is the same as the previous instruction; If the furnace temperature control instruction matches the instruction category of the previous instruction, it is determined that the furnace temperature control instruction is the same as the previous instruction, and a first time node of sending the furnace temperature control instruction is collected, and a second time node of sending the previous instruction is collected; Calculating a time node difference between the first time node and the second time node, and judging whether the furnace temperature control instruction can be processed according to a relationship between the time node difference and a preset time node difference; If the time node difference is greater than or equal to the preset time node difference, it is determined that the furnace temperature control instruction can be processed; If the time node difference is less than the preset time node difference, it is determined that the furnace temperature control instruction cannot be processed, and a log reminder is generated and sent; If the furnace temperature control instruction does not match the instruction category of the previous instruction, it is determined that the furnace temperature control instruction is different from the previous instruction, and the furnace temperature control instruction is processed.

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