A method and system for intelligent optimization control of temperature of an alumina roasting furnace

Abstract

The present invention relates to the technical field of automatic temperature control, and specifically to an intelligent optimization control method and system for the temperature of an alumina roasting furnace. First, the first infrared image data and the first roasting furnace temperature of the alumina roasting furnace are obtained in real time, and input into a roasting furnace temperature control model, the first infrared image data is preprocessed, and feature extraction is performed, and the stage state of the roasting furnace is judged according to the extracted temperature feature, which is conducive to temperature control of each stage of the roasting furnace; secondly, by analyzing the first roasting furnace temperature and the target temperature of each stage, the second roasting furnace adaptive temperature is obtained, and adaptive control of the roasting furnace temperature is realized; finally, a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption is established, so as to dynamically adjust temperature control parameters such as gas flow, air volume, voltage and current. The method realizes intelligent temperature control of the roasting furnace by comprehensively analyzing infrared image data and roasting furnace temperature data.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic temperature control, and in particular to an intelligent optimization control method and system for the temperature of an alumina roasting furnace. Background Art

[0002] Alumina roasting is a crucial step in the alumina production process. Its main purpose is to convert aluminum hydroxide into alumina through high-temperature treatment. In this process, the temperature control of the roasting furnace is crucial. Traditional temperature control methods mainly rely on manual operation and experience adjustment. This method has many shortcomings, including low control accuracy, slow response speed, high energy consumption and other problems, which directly affect the quality and production efficiency of alumina products.

[0003] With the continuous development of science and technology, temperature control methods for roasting furnaces are constantly proposed. For example, a Chinese patent with technical application number CN201910190315.X provides an alumina clinker sintering process and alumina clinker, including an alumina clinker sintering process. During the sintering process, kiln ash is sprayed into the rotary kiln through the kiln head of the rotary kiln, so that the temperature of the burning zone in the rotary kiln is maintained at 1140-1200°C, so that the sintered alumina clinker is moderately soft and hard; for example, a Chinese patent with technical application number CN202110737009.0 discloses a method for fuzzy automatic control of the temperature of an alumina roasting furnace. First, the actual value f of the raw material feeding amount is collected, and its value is compared with the rated amount of the raw material feeding, so as to select fuzzy automatic control based on the gas regulating valve V as the control target, or select fuzzy automatic control based on the raw material feeding amount F as the control target. Although the above existing technologies have solved some shortcomings of the traditional methods, on the one hand, they have not fully considered the differences in temperature changes in each stage of the roasting furnace, and on the other hand, they have not fully considered the influence of the temperature control parameters of the roasting furnace, resulting in poor temperature control effect of the roasting furnace, thereby affecting the quality and production efficiency of alumina products.

[0004] Therefore, an intelligent optimization control method and system for the temperature of an alumina roasting furnace are proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent optimization control method and system for the temperature of an alumina roasting furnace. First, the first infrared image data and the first roasting furnace temperature of the alumina roasting furnace are obtained in real time, and input into the roasting furnace temperature control model, the first infrared image data is preprocessed, and feature extraction is performed, and the stage state of the roasting furnace is judged according to the extracted temperature features, including the first stage, the second stage, the third stage and the fourth stage; secondly, by analyzing the temperature of the first roasting furnace and the target temperature of each stage, the adaptive temperature of the second roasting furnace is obtained to realize the adaptive temperature control of the roasting furnace; finally, a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption is established to dynamically adjust the temperature control parameters such as gas flow, air volume, voltage and current. This method realizes the temperature control of the roasting furnace intelligently by comprehensively analyzing the infrared image data and the roasting furnace temperature data.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An intelligent optimization control method for temperature of an alumina roasting furnace, comprising:

[0008] Acquire first data and second data of an alumina roasting furnace in real time; the first data includes first infrared image data of the roasting furnace acquired by an infrared detector and first roasting furnace temperature acquired by a temperature sensor in the furnace; input the first data into a roasting furnace temperature control model, and sequentially pass through a preprocessing layer, a judgment layer and a temperature control layer of the model;

[0009] The preprocessing layer is used to preprocess the first infrared image data to obtain second infrared image data;

[0010] The judgment layer is used to judge the stage state of the roasting furnace, including analyzing the second infrared image data to obtain the stage state; the stage state includes the first stage, the second stage, the third stage and the fourth stage; each stage state includes a preset temperature threshold range and a target temperature;

[0011] The temperature control layer is used to adaptively control the temperature of the roasting furnace in each stage state, including obtaining a first adjustment value by calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature, and analyzing the first adjustment value and the first roasting furnace temperature, and outputting a second roasting furnace adaptive temperature;

[0012] A multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption is established to dynamically adjust the second data, including analyzing the energy consumption and thermal efficiency of the roasting furnace, the temperature of the first roasting furnace and the adaptive temperature of the second roasting furnace to obtain an updated value of the second data.

[0013] Preferably, the second data includes gas flow, air volume, voltage and current respectively obtained by a gas flow meter, an air volume meter, a voltage sensor and a current sensor.

[0014] Preferably, the judgment layer performs feature extraction on the second infrared image data to obtain temperature features, wherein the temperature features include maximum temperature, minimum temperature, average temperature and contrast;

[0015] According to the temperature characteristics and the preset temperature thresholds of each stage and the contrast threshold, the stage state of the roasting furnace is determined, and the determination conditions are:

[0016] ;

[0017] ;

[0018] ;

[0019] in, Expressed as Preset minimum temperature thresholds in stage states; Expressed as Preset maximum temperature thresholds in stage states; Represented as the roasting furnace The minimum temperature at the time; Represented as the roasting furnace the maximum temperature at the time; Represented as the roasting furnace the contrast at the moment; Expressed as Preset minimum contrast threshold in phase state; Expressed as Preset maximum contrast threshold in phase state.

[0020] Preferably, the calculation formula of the temperature deviation is:

[0021] ;

[0022] in, Indicates the roasting furnace Temperature deviation at all times; represents the target temperature; The roasting furnace The first roasting furnace temperature at the moment;

[0023] The calculation formula of the first adjustment value is:

[0024] ;

[0025] in, is represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; Expressed as differential gain;

[0026] The calculation formula of the adaptive temperature of the second roasting furnace is:

[0027] ;

[0028] in, Represented as the roasting furnace The second roasting furnace adapts to the temperature at this moment.

[0029] Preferably, the updated value of the second data includes an updated value of gas flow, an updated value of air volume, an updated value of voltage and an updated value of current;

[0030] The gas flow update value is:

[0031] ;

[0032] in, Represented as the roasting furnace The updated value of gas flow at the moment; Represented as the roasting furnace Gas flow value at the moment; represents the temperature adjustment factor; It represents the thermal efficiency adjustment factor; Indicates the roasting furnace Thermal efficiency at all times; Indicates the energy consumption adjustment factor; Indicates the roasting furnace Energy consumption at each moment; represents the target energy consumption of the roasting furnace;

[0033] The air volume update value is:

[0034] ;

[0035] in, Represented as the roasting furnace The updated value of wind volume at the moment; Represented as the roasting furnace The wind volume value at the moment;

[0036] The voltage update value is:

[0037] ;

[0038] in, Represented as the roasting furnace Voltage update value at the moment; Represented as the roasting furnace Voltage value at the moment;

[0039] The current update value is:

[0040] ;

[0041] in, Represented as the roasting furnace Current update value at the moment; Represented as the roasting furnace Current value at the moment.

[0042] An intelligent optimization control system for the temperature of an alumina roasting furnace, applied to the intelligent optimization control method for the temperature of an alumina roasting furnace described above, the system comprising a data acquisition module, a stage state judgment module, a temperature adaptive control module and a multi-parameter dynamic adjustment module;

[0043] The data acquisition module is used to acquire first data and second data of the alumina roasting furnace in real time; the first data includes first infrared image data of the roasting furnace acquired by an infrared detector and first roasting furnace temperature acquired by a temperature sensor in the furnace; the first data is input into a roasting furnace temperature control model, and sequentially passes through a preprocessing layer, a judgment layer and a temperature control layer of the roasting furnace temperature control model; the preprocessing layer is used to preprocess the first infrared image data to obtain second infrared image data;

[0044] The stage state judgment module is used to judge the stage state of the roasting furnace, including analyzing the second infrared image data through the judgment layer to obtain the stage state; the stage state includes the first stage, the second stage, the third stage and the fourth stage; the stage state includes a preset temperature threshold range and a target temperature;

[0045] The temperature adaptive control module is used to adaptively control the temperature of the roasting furnace in each stage state, including calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature through the temperature control layer to obtain a first adjustment value, and analyzing according to the first adjustment value and the first roasting furnace temperature, and outputting a second roasting furnace adaptive temperature;

[0046] The multi-parameter dynamic adjustment module is used to dynamically adjust the second data, including a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption. The multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption obtains the updated value of the second data by analyzing the roasting furnace energy consumption, roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature.

[0047] Preferably, the second data includes gas flow, air volume, voltage and current respectively obtained by a gas flow meter, an air volume meter, a voltage sensor and a current sensor.

[0048] Preferably, the judgment layer performs feature extraction on the second infrared image data to obtain temperature features, wherein the temperature features include maximum temperature, minimum temperature, average temperature and contrast;

[0049] Preferably, the stage state of the roasting furnace is determined according to the temperature characteristics and the comparison between the preset temperature threshold and the contrast threshold of each stage, and the determination condition is:

[0050] ;

[0051] ;

[0052] ;

[0053] in, Expressed as Preset minimum temperature thresholds in stage states; Expressed as Preset maximum temperature thresholds in stage states; Represented as the roasting furnace The minimum temperature at the time; Represented as the roasting furnace the maximum temperature at the time; Represented as the roasting furnace the contrast at the moment; Expressed as Preset minimum contrast threshold in phase state; Expressed as Preset maximum contrast threshold in phase state.

[0054] Preferably, the calculation formula of the temperature deviation is:

[0055] ;

[0056] in, Indicates the roasting furnace Temperature deviation at all times; represents the target temperature; The roasting furnace The first roasting furnace temperature at the moment;

[0057] The calculation formula of the first adjustment value is:

[0058] ;

[0059] in, is represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; Expressed as differential gain;

[0060] The calculation formula of the adaptive temperature of the second roasting furnace is:

[0061] ;

[0062] in, Represented as the roasting furnace The second roasting furnace adapts to the temperature at this moment.

[0063] Preferably, the updated value of the second data includes an updated value of gas flow, an updated value of air volume, an updated value of voltage and an updated value of current;

[0064] The gas flow update value is:

[0065] ;

[0066] in, Represented as the roasting furnace The updated value of gas flow at the moment; Represented as the roasting furnace Gas flow value at the moment; represents the temperature adjustment factor; It represents the thermal efficiency adjustment factor; Indicates the roasting furnace Thermal efficiency at all times; Indicates the energy consumption adjustment factor; Indicates the roasting furnace Energy consumption at each moment; represents the target energy consumption of the roasting furnace;

[0067] The air volume update value is:

[0068] ;

[0069] in, Represented as the roasting furnace The updated value of wind volume at the moment; Represented as the roasting furnace The wind volume value at the moment;

[0070] The voltage update value is:

[0071] ;

[0072] in, Represented as the roasting furnace Voltage update value at the moment; Represented as the roasting furnace Voltage value at the moment;

[0073] The current update value is:

[0074] ;

[0075] in, Represented as the roasting furnace Current update value at the moment; Represented as the roasting furnace Current value at the moment.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] 1. The present invention introduces an infrared detector to obtain infrared image data of the roasting furnace in real time, and extracts temperature characteristics (maximum temperature, minimum temperature, average temperature and contrast) for comprehensive analysis, thereby effectively realizing temperature control of each stage of the roasting furnace, which is beneficial to improving the effectiveness of the overall temperature control of the roasting furnace, thereby effectively improving the quality of the roasted products.

[0078] 2. The present invention adaptively controls the temperature of the roasting furnace according to the different stage states of the roasting furnace. At each stage, the target temperature and real-time temperature of each stage are comprehensively considered, the adjustment value is calculated, and the temperature of the roasting furnace is dynamically adjusted. This adaptive control method can ensure that the temperature of the roasting furnace at each stage is kept within a reasonable range, reduce temperature fluctuations, improve the accuracy of temperature control and production efficiency, and effectively improve the quality of roasted products.

[0079] 3. The present invention uses a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption to monitor thermal efficiency and energy consumption in real time and dynamically adjust temperature control parameters such as gas flow, air volume, voltage and current. This mechanism can ensure that the temperature control of the roasting furnace is more accurate and stable under different conditions. By dynamically adjusting the temperature control parameters, it is beneficial to improve the effectiveness of the temperature control of the roasting furnace, thereby effectively improving the quality of the roasted products. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A flow chart of an intelligent optimization control method for temperature of an alumina roasting furnace provided by an embodiment of the present invention;

[0081] Figure 2 A schematic diagram of the structure of an intelligent optimization control system for temperature of an alumina roasting furnace provided in an embodiment of the present invention;

[0082] Figure 3A flow chart of stage state judgment of an alumina roasting furnace provided by an embodiment of the present invention;

[0083] Figure 4 A flow chart of an intelligent optimization control system for temperature of an alumina roasting furnace provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] The accurate and effective roasting furnace temperature control method directly affects the quality and production efficiency of alumina products. There are still some shortcomings in the existing roasting furnace temperature control method. In order to achieve more accurate and effective roasting furnace temperature control, the present invention will be described in detail by relying on the following two embodiments to prove that the method proposed by the present invention has certain effectiveness in actual roasting furnace temperature control.

[0086] Embodiment 1

[0087] This embodiment proposes an intelligent optimization control method for the temperature of an alumina roasting furnace, which is applied to the roasting process in the alumina production process. The temperature control process of an alumina roasting furnace A will be analyzed below;

[0088] Reference Figure 1A flow chart of an intelligent optimization control method for the temperature of an alumina roasting furnace provided in an embodiment of the present invention includes S100. acquiring first data and second data of the alumina roasting furnace in real time; inputting the first data into a roasting furnace temperature control model, and preprocessing the first infrared image data through a preprocessing layer of the model to obtain second infrared image data; S200. using a judgment layer of the model to judge the stage state of the roasting furnace, including analyzing the second infrared image data to obtain the stage state; the stage state includes a first stage, a second stage, a third stage and a fourth stage; S300. The temperature of the roasting furnace in each stage state is adaptively controlled through the temperature control layer of the model, including obtaining a first adjustment value by calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature, and obtaining a second roasting furnace adaptive temperature based on the first adjustment value and the first roasting furnace temperature; S400. Establish a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption, which is used to dynamically adjust the second data, including analyzing the roasting furnace energy consumption, the roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature to obtain an updated value of the second data.

[0089] Further, according to step S100, first data and second data of A alumina roasting furnace are obtained in real time; the first data is input into the roasting furnace temperature control model, and the first infrared image data is preprocessed by the preprocessing layer of the model to obtain second infrared image data; the first data includes first infrared image data of the roasting furnace obtained by an infrared detector (Fluke Ti450); the first infrared image data is used to analyze the uniformity and dynamic changes of temperature distribution, and the first data also includes the first roasting furnace temperature obtained by a temperature sensor (Omega K-type thermocouple) arranged in the middle of the furnace;

[0090] The second data includes gas flow, air volume, voltage and current respectively obtained by a gas flow meter (Endress+Hauser Proline T-Mass65F), an air volume meter (TSI VelociCalc 9565), a voltage sensor (Hioki VT1005) and a current sensor (LEM LF205-S);

[0091] The first data is input into the roasting furnace temperature control model, and passes through the pre-processing layer, the judgment layer and the temperature control layer of the model in sequence.

[0092] Further, according to step S200, the stage state of the roasting furnace is judged by using the judgment layer of the model, including analyzing the second infrared image data to obtain the stage state; the stage state includes the first stage, the second stage, the third stage and the fourth stage; which respectively correspond to the initial heating stage, the combustion stage, the stabilization stage and the cooling stage of the A alumina roasting furnace; the specific judgment flow chart is as follows Figure 3 As shown, including:

[0093] S201. Acquire the first infrared image data of the alumina roasting furnace;

[0094] S202. Preprocessing the first infrared image data to obtain second infrared image data;

[0095] S203. Extracting features from the second infrared image data to obtain temperature features;

[0096] S204. According to the temperature characteristics and the preset temperature threshold of each stage and the contrast threshold, the stage state of the roasting furnace is determined, and the stage state includes the first stage, the second stage, the third stage and the fourth stage;

[0097] Each stage status includes a preset temperature threshold range and a target temperature;

[0098] The judgment layer extracts features from the second infrared image data to obtain temperature features, wherein the temperature features include maximum temperature, minimum temperature, average temperature and contrast;

[0099] The maximum temperature is calculated as follows:

[0100] ;

[0101] in, express Maximum temperature at the moment; express The second infrared image data at the time;

[0102] The calculation formula of the minimum temperature is:

[0103] ;

[0104] in, express The minimum temperature at the time; express The second infrared image data at the time;

[0105] The calculation formula of the average temperature is:

[0106] ;

[0107] in, express Average temperature at the time; Indicates The temperature value of each pixel; Indicates the total number of pixels;

[0108] The contrast formula is:

[0109] ;

[0110] in, express The contrast of moments;

[0111] The stage state of the roasting furnace is determined by comparing the temperature characteristics with the preset temperature thresholds and contrast thresholds of each stage, and the determination conditions are:

[0112] ;

[0113] ;

[0114] ;

[0115] in, Expressed as Preset minimum temperature thresholds in stage states; Expressed as Preset maximum temperature thresholds in stage states; Represented as the roasting furnace The minimum temperature at the time; Represented as the roasting furnace the maximum temperature at the time; Represented as the roasting furnace the contrast at the moment; Expressed as Preset minimum contrast threshold in phase state; Expressed as Preset maximum contrast threshold in phase state.

[0116] This embodiment introduces an infrared detector to acquire infrared image data of the roasting furnace in real time, and extracts temperature characteristics (maximum temperature, minimum temperature, average temperature and contrast) for comprehensive analysis, thereby effectively realizing temperature control of each stage of the roasting furnace, which is beneficial to improving the effectiveness of the overall temperature control of the roasting furnace, thereby effectively improving the quality of the roasted products.

[0117] Further, according to step S300, the temperature control layer of the model is used to adaptively control the roasting furnace temperature in each stage state, including obtaining a first adjustment value by calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature, and analyzing the first adjustment value and the first roasting furnace temperature, and outputting a second roasting furnace adaptive temperature;

[0118] The calculation formula of the temperature deviation is:

[0119] ;

[0120] in, Indicates the roasting furnace Temperature deviation at all times; represents the target temperature; The roasting furnace The first roasting furnace temperature at the moment;

[0121] The calculation formula of the first adjustment value is:

[0122] ;

[0123] in, is represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; Expressed as differential gain;

[0124] The calculation formula of the adaptive temperature of the second roasting furnace is:

[0125] ;

[0126] in, Represented as the roasting furnace The second roasting furnace adapts to the temperature at this moment.

[0127] Further, according to step S400, a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption is established to dynamically adjust the second data, including analyzing the roasting furnace energy consumption, roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature to obtain an updated value of the second data.

[0128] This embodiment adaptively controls the temperature of the roasting furnace according to the different stage states of the roasting furnace. At each stage, the target temperature and real-time temperature of each stage are comprehensively considered, the adjustment value is calculated, and the temperature of the roasting furnace is dynamically adjusted. This adaptive control method can ensure that the temperature of the roasting furnace at each stage is kept within a reasonable range, reduce temperature fluctuations, improve the accuracy of temperature control and production efficiency, and effectively improve the quality of roasted products.

[0129] Further, part of the second data table is shown in Table 1;

[0130] Table 1 Part 2 Data Table

[0131]

[0132] The calculation formula of the thermal efficiency of the roasting furnace is:

[0133] ;

[0134] in, Indicates the roasting furnace Thermal efficiency at all times; represents the specific heat capacity of air; Indicates the outlet temperature of the roasting furnace; represents the inlet temperature of the roasting furnace; represents the total energy input of the roasting furnace;

[0135] The calculation formula of the total input energy is:

[0136] ;

[0137] in, Represented as the roasting furnace Gas flow value at the moment; Represented as the roasting furnace The calorific value of the gas at the time; Represented as the roasting furnace Voltage value at the moment; Represented as the roasting furnace The current value at the moment; Indicates a time interval;

[0138] The calculation formula of the energy consumption of the roasting furnace is:

[0139] ;

[0140] in, Indicates the roasting furnace Energy consumption at each moment; represents the total energy input of the roasting furnace; Indicates a time interval;

[0141] The updated value of the second data includes an updated value of gas flow, an updated value of air volume, an updated value of voltage and an updated value of current;

[0142] The gas flow update value is:

[0143] ;

[0144] in, Represented as the roasting furnace The updated value of gas flow at the moment; Represented as the roasting furnace Gas flow value at the moment; represents the temperature adjustment factor; It represents the thermal efficiency adjustment factor; Indicates the roasting furnace Thermal efficiency at all times; Indicates the energy consumption adjustment factor; Indicates the roasting furnace Energy consumption at each moment; represents the target energy consumption of the roasting furnace;

[0145] Furthermore, the wind volume update value is:

[0146] ;

[0147] in, Represented as the roasting furnace The updated value of wind volume at the moment; Represented as the roasting furnace The wind volume value at the moment;

[0148] Furthermore, the voltage update value is:

[0149] ;

[0150] in, Represented as the roasting furnace Voltage update value at the moment; Represented as the roasting furnace Voltage value at the moment;

[0151] Furthermore, the current update value is:

[0152] ;

[0153] in, Represented as the roasting furnace Current update value at the moment; Represented as the roasting furnace Current value at the moment.

[0154] In order to verify the effectiveness of temperature control of an alumina roasting furnace temperature intelligent optimization control method provided in this embodiment in the actual alumina roasting process, this embodiment will demonstrate by comparing the quality of alumina products under three conditions. First, condition one is an alumina roasting furnace temperature intelligent optimization control method provided in this embodiment, condition two is that on the basis of condition one, temperature control is not performed at each stage of roasting furnace combustion; condition three is that on the basis of condition one, real-time adjustment of temperature parameters is not considered; the main compared parameters include product purity, density and particle size of alumina, and the comparison results are shown in Table 2;

[0155] Table 2 Comparison of the quality of alumina products under different conditions

[0156]

[0157] As can be seen from Table 2, the intelligent optimization control method for the temperature of an alumina roasting furnace provided in this embodiment has significant advantages in improving product purity, density and reducing particle size. These data prove the effectiveness and superiority of the method of this embodiment in controlling the temperature of the roasting furnace, and can significantly improve the quality of alumina products. In industrial applications, the method of the present invention can effectively ensure high-quality product output.

[0158] This embodiment uses a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption to monitor thermal efficiency and energy consumption in real time and dynamically adjust temperature control parameters such as gas flow, air volume, voltage and current. This mechanism can ensure that the temperature control of the roasting furnace is more accurate and stable under different conditions. By dynamically adjusting the temperature control parameters, it is beneficial to improve the effectiveness of the temperature control of the roasting furnace, thereby effectively improving the quality of the roasted products.

[0159] According to the above steps, a flow chart of an intelligent optimization control system for the temperature of an alumina roasting furnace can be obtained, as shown in the following figure: Figure 4 As shown; the system includes a data acquisition unit, a preprocessing unit, a stage state judgment unit, a temperature adaptive control unit and a multi-parameter dynamic adjustment unit, and the data acquisition unit includes an infrared detector (Fluke Ti450), a temperature sensor (Omega K-type thermocouple), a gas flow meter (Endress+Hauser Proline T-Mass 65F), an air volume meter (TSIVelociCalc 9565), a voltage sensor (Hioki VT1005) and a current sensor (LEM LF 205-S);

[0160] The temperature sensor is placed in the middle of the roasting furnace to obtain the first roasting furnace temperature in real time. The infrared detector is used to obtain the first infrared image data of the temperature distribution in the furnace through the infrared window installed outside the roasting furnace. The infrared window is made of a material resistant to high temperature and chemical corrosion. The gas flow meter is installed on the gas supply pipeline near the roasting furnace. The air volume meter is placed at the air inlet of the roasting furnace to monitor the air volume in the roasting furnace in real time. The voltage sensor and the current sensor are connected to the power supply system to monitor the voltage and current in real time.

[0161] The first infrared image data, the first roasting furnace temperature, the gas flow rate, the air volume, the voltage and the current are obtained by acquiring the above data;

[0162] Preprocessing the first infrared image data through a preprocessing layer to obtain second infrared image data;

[0163] Extracting temperature features from the second infrared image data by a stage state judgment unit, wherein the temperature features include a maximum temperature, a minimum temperature, an average temperature, and a contrast ratio, and then determining the stage state of the roasting furnace according to the temperature features and preset temperature thresholds and contrast thresholds of different stage states, and according to judgment conditions, wherein the stage states include a first state, a second state, a third state, and a fourth state;

[0164] The maximum temperature is calculated as follows:

[0165] ;

[0166] in, express Maximum temperature at the moment; express The second infrared image data at the time;

[0167] The calculation formula of the minimum temperature is:

[0168] ;

[0169] in, express The minimum temperature at the time; express The second infrared image data at the time;

[0170] The calculation formula of the average temperature is:

[0171] ;

[0172] in, express Average temperature at the time; Indicates The temperature value of each pixel; Indicates the total number of pixels;

[0173] The contrast formula is:

[0174] ;

[0175] in, express The contrast of moments;

[0176] The determination conditions are:

[0177] ;

[0178] ;

[0179] ;

[0180] in, Expressed as Preset minimum temperature thresholds in stage states; Expressed as Preset maximum temperature thresholds in stage states; Represented as the roasting furnace The minimum temperature at the time; Represented as the roasting furnace the maximum temperature at the time; Represented as the roasting furnace the contrast at the moment; Expressed as Preset minimum contrast threshold in phase state; Expressed as Preset maximum contrast threshold in phase state;

[0181] The temperature adaptive control unit is used to adaptively control the roasting furnace temperature in each stage state, calculate and analyze the temperature deviation between the first roasting furnace temperature and the target temperature in each stage to obtain a first adjustment value, and analyze according to the first adjustment value and the first roasting furnace temperature, and output a second roasting furnace adaptive temperature;

[0182] The calculation formula of the temperature deviation is:

[0183] ;

[0184] in, Indicates the roasting furnace Temperature deviation at all times; represents the target temperature; The roasting furnace The first roasting furnace temperature at the moment;

[0185] The calculation formula of the first adjustment value is:

[0186] ;

[0187] in, is represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; Expressed as differential gain;

[0188] The calculation formula of the adaptive temperature of the second roasting furnace is:

[0189] ;

[0190] in, Represented as the roasting furnace The second roasting furnace adapts to the temperature at this moment.

[0191] The multi-parameter dynamic adjustment unit is used to dynamically adjust the second data, including a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption. The multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption obtains the updated value of the second data by analyzing the roasting furnace energy consumption, roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature.

[0192] The calculation formula of the thermal efficiency of the roasting furnace is:

[0193] ;

[0194] in, Indicates the roasting furnace Thermal efficiency at all times; represents the specific heat capacity of air; Indicates the outlet temperature of the roasting furnace; represents the inlet temperature of the roasting furnace; represents the total energy input of the roasting furnace;

[0195] The calculation formula of the total input energy is:

[0196] ;

[0197] in, Represented as the roasting furnace Gas flow value at the moment; Represented as the roasting furnace The calorific value of the gas at the time; Represented as the roasting furnace Voltage value at the moment; Represented as the roasting furnace The current value at the moment; Indicates a time interval;

[0198] The calculation formula of the energy consumption of the roasting furnace is:

[0199] ;

[0200] in, Indicates the roasting furnace Energy consumption at each moment; represents the total energy input of the roasting furnace; Indicates a time interval;

[0201] The updated value of the second data includes an updated value of gas flow, an updated value of air volume, an updated value of voltage and an updated value of current;

[0202] The gas flow update value is:

[0203] ;

[0204] in, Represented as the roasting furnace The updated value of gas flow at the moment; Represented as the roasting furnace Gas flow value at the moment; represents the temperature adjustment factor; It represents the thermal efficiency adjustment factor; Indicates the roasting furnace Thermal efficiency at all times; Indicates the energy consumption adjustment factor; Indicates the roasting furnace Energy consumption at each moment; represents the target energy consumption of the roasting furnace;

[0205] Furthermore, the wind volume update value is:

[0206] ;

[0207] in, Represented as the roasting furnace The updated value of wind volume at the moment; Represented as the roasting furnace The wind volume value at the moment;

[0208] Furthermore, the voltage update value is:

[0209] ;

[0210] in, Represented as the roasting furnace Voltage update value at the moment; Represented as the roasting furnace Voltage value at the moment;

[0211] Furthermore, the current update value is:

[0212] ;

[0213] in, Represented as the roasting furnace Current update value at the moment; Represented as the roasting furnace Current value at the moment.

[0214] This embodiment obtains the first infrared image data and the first roasting furnace temperature of the alumina roasting furnace in real time, and inputs them into the roasting furnace temperature control model, pre-processes the first infrared image data, and extracts features, and judges the stage state of the roasting furnace according to the extracted temperature features, including the first stage, the second stage, the third stage and the fourth stage; secondly, by analyzing the first roasting furnace temperature and the target temperature of each stage, the second roasting furnace adaptive temperature is obtained to realize the roasting furnace adaptive temperature control; finally, a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption is established to dynamically adjust the temperature control parameters such as gas flow, air volume, voltage and current. This embodiment realizes the temperature control of the roasting furnace intelligently by comprehensively analyzing the infrared image data and the roasting furnace temperature data.

[0215] Embodiment 2

[0216] This embodiment proposes an intelligent optimization control method for the temperature of an alumina roasting furnace, which is applied to the roasting process in the alumina production process. The temperature control process of the B alumina roasting furnace will be analyzed below;

[0217] Reference Figure 1A flow chart of an intelligent optimization control method for the temperature of an alumina roasting furnace provided in an embodiment of the present invention includes S100. acquiring first data and second data of the alumina roasting furnace in real time; inputting the first data into a roasting furnace temperature control model, and preprocessing the first infrared image data through a preprocessing layer of the model to obtain second infrared image data; S200. using a judgment layer of the model to judge the stage state of the roasting furnace, including analyzing the second infrared image data to obtain the stage state; the stage state includes a first stage, a second stage, a third stage and a fourth stage; S300. The temperature of the roasting furnace in each stage state is adaptively controlled through the temperature control layer of the model, including obtaining a first adjustment value by calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature, and obtaining a second roasting furnace adaptive temperature based on the first adjustment value and the first roasting furnace temperature; S400. Establish a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption, which is used to dynamically adjust the second data, including analyzing the roasting furnace energy consumption, the roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature to obtain an updated value of the second data.

[0218] The above steps correspond to Figure 2 The modules of the intelligent optimization control system for the temperature of an alumina roasting furnace provided correspond to each other; the modules include a data acquisition module, a stage state judgment module, a temperature adaptive control module and a multi-parameter dynamic adjustment module; they correspond to the above process steps respectively;

[0219] The data acquisition module acquires the first data and the second data of the B alumina roasting furnace in real time; inputs the first data into the roasting furnace temperature control model, and preprocesses the first infrared image data through the preprocessing layer of the model to obtain the second infrared image data; the first data includes the first infrared image data of the roasting furnace obtained by an infrared detector (Fluke Ti450); the first infrared image data is used to analyze the uniformity and dynamic changes of temperature distribution, and the first data also includes the first roasting furnace temperature obtained by a temperature sensor (Omega K-type thermocouple) arranged in the middle of the furnace;

[0220] The second data includes gas flow, air volume, voltage and current respectively obtained by a gas flow meter (Endress+Hauser Proline T-Mass65F), an air volume meter (TSI VelociCalc 9565), a voltage sensor (Hioki VT1005) and a current sensor (LEM LF205-S);

[0221] The first data is input into the roasting furnace temperature control model, and passes through the pre-processing layer, the judgment layer and the temperature control layer of the model in sequence.

[0222] Furthermore, the stage state judgment module includes: using the judgment layer of the model to judge the stage state of the roasting furnace, including analyzing the second infrared image data to obtain the stage state; the stage state includes the first stage, the second stage, the third stage and the fourth stage; respectively corresponding to the initial heating stage, the combustion stage, the stabilization stage and the cooling stage of the B alumina roasting furnace; the specific judgment flow chart is as follows Figure 3 As shown; each stage status includes a preset temperature threshold range and a target temperature;

[0223] The judgment layer extracts features from the second infrared image data to obtain temperature features, wherein the temperature features include maximum temperature, minimum temperature, average temperature and contrast;

[0224] The maximum temperature is calculated as follows:

[0225] ;

[0226] in, express Maximum temperature at the moment; express The second infrared image data at the time;

[0227] The calculation formula of the minimum temperature is:

[0228] ;

[0229] in, express The minimum temperature at the time; express The second infrared image data at the time;

[0230] The calculation formula of the average temperature is:

[0231] ;

[0232] in, express Average temperature at the time; Indicates The temperature value of each pixel; Indicates the total number of pixels;

[0233] The contrast formula is:

[0234] ;

[0235] in, express The contrast of moments;

[0236] Furthermore, the stage state of the roasting furnace is determined by comparing the temperature characteristics with the preset temperature thresholds and contrast thresholds of each stage, and the determination conditions are:

[0237] ;

[0238] ;

[0239] ;

[0240] in, Expressed as Preset minimum temperature thresholds for stage states; Expressed as Preset maximum temperature thresholds in stage states; Represented as the roasting furnace The minimum temperature at the time; Represented as the roasting furnace the maximum temperature at the time; Represented as the roasting furnace the contrast at the moment; Expressed as Preset minimum contrast threshold in phase state; Expressed as Preset maximum contrast threshold in phase state.

[0241] Further, the temperature adaptive control module includes: using the temperature control layer of the model to adaptively control the roasting furnace temperature in each stage state, including calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature to obtain a first adjustment value, and analyzing according to the first adjustment value and the first roasting furnace temperature, and outputting a second roasting furnace adaptive temperature; the calculation formula of the temperature deviation is:

[0242] ;

[0243] in, Indicates the roasting furnace Temperature deviation at all times; represents the target temperature; The roasting furnace The first roasting furnace temperature at the moment;

[0244] The calculation formula of the first adjustment value is:

[0245] ;

[0246] in, is represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; Expressed as differential gain;

[0247] Furthermore, the calculation formula of the adaptive temperature of the second roasting furnace is:

[0248] ;

[0249] in, Represented as the roasting furnace The second roasting furnace adapts to the temperature at this moment.

[0250] Furthermore, the multi-parameter dynamic adjustment module includes: establishing a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption, for dynamically adjusting the second data, including analyzing the roasting furnace energy consumption, roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature to obtain an updated value of the second data.

[0251] Table 3 Part 2 Data Table

[0252]

[0253] Further, part of the second data table is shown in Table 3;

[0254] The calculation formula of the thermal efficiency of the roasting furnace is:

[0255] ;

[0256] in, Indicates the roasting furnace Thermal efficiency at all times; represents the specific heat capacity of air; Indicates the outlet temperature of the roasting furnace; represents the inlet temperature of the roasting furnace; represents the total energy input of the roasting furnace;

[0257] The calculation formula of the total input energy is:

[0258] ;

[0259] in, Represented as the roasting furnace Gas flow value at the moment; Represented as the roasting furnace The calorific value of the gas at the time; Represented as the roasting furnace Voltage value at the moment; Represented as the roasting furnace The current value at the moment; Indicates a time interval;

[0260] The calculation formula of the energy consumption of the roasting furnace is:

[0261] ;

[0262] in, Indicates the roasting furnace Energy consumption at each moment; represents the total energy input of the roasting furnace; Indicates a time interval;

[0263] The updated value of the second data includes an updated value of gas flow, an updated value of air volume, an updated value of voltage and an updated value of current;

[0264] Furthermore, the gas flow update value is:

[0265] ;

[0266] in, Represented as the roasting furnace The updated value of gas flow at the moment; Represented as the roasting furnace Gas flow value at the moment; represents the temperature adjustment factor; It represents the thermal efficiency adjustment factor; Indicates the roasting furnace Thermal efficiency at all times; Indicates the energy consumption adjustment factor; Indicates the roasting furnace Energy consumption at each moment; represents the target energy consumption of the roasting furnace;

[0267] Furthermore, the wind volume update value is:

[0268] ;

[0269] in, Represented as the roasting furnace The updated value of wind volume at the moment; Represented as the roasting furnace The wind volume value at the moment;

[0270] Furthermore, the voltage update value is:

[0271] ;

[0272] in, Represented as the roasting furnace Voltage update value at the moment; Represented as the roasting furnace Voltage value at the moment;

[0273] Furthermore, the current update value is:

[0274] ;

[0275] in, Represented as the roasting furnace Current update value at the moment; Represented as the roasting furnace Current value at the moment.

[0276] In order to verify the effectiveness of temperature control of an alumina roasting furnace temperature intelligent optimization control method provided in this embodiment in the actual alumina roasting process, this embodiment will demonstrate by comparing the quality of alumina products under three conditions. First, condition one is an alumina roasting furnace temperature intelligent optimization control method provided in this embodiment, condition two is that on the basis of condition one, temperature control is not performed at each stage of roasting furnace combustion; condition three is that on the basis of condition one, real-time adjustment of temperature parameters is not considered; the main compared parameters include product purity, density and particle size of alumina, and the comparison results are shown in Table 4;

[0277] Table 4 Quality comparison of alumina products under different conditions

[0278]

[0279] As shown in Table 4, the intelligent optimization control method for the temperature of an alumina roasting furnace provided in this embodiment has significant advantages in improving product purity, density and reducing particle size. These data prove the effectiveness and superiority of the method of this embodiment in controlling the temperature of the roasting furnace, and can significantly improve the quality of alumina products. In industrial applications, the method of the present invention can effectively ensure high-quality product output.

[0280] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent optimization control method for the temperature of an alumina roasting furnace, characterized in that: include: Acquire first data and second data of the alumina roasting furnace in real time; the first data includes first infrared image data of the roasting furnace acquired by an infrared detector and first roasting furnace temperature acquired by a temperature sensor in the furnace; the second data includes gas flow, air volume, voltage and current acquired by a gas flow meter, an air volume meter, a voltage sensor and a current sensor respectively; input the first data into a roasting furnace temperature control model, and sequentially pass through a preprocessing layer, a judgment layer and a temperature control layer of the model; The preprocessing layer is used to preprocess the first infrared image data to obtain second infrared image data; The judgment layer is used to judge the stage state of the roasting furnace, including analyzing the second infrared image data to obtain the stage state; the stage state includes the first stage, the second stage, the third stage and the fourth stage; the stage state includes a preset temperature threshold range and a target temperature; The temperature control layer is used to adaptively control the temperature of the roasting furnace in each stage state, including obtaining a first adjustment value by calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature, and obtaining a second roasting furnace adaptive temperature based on the first adjustment value and the first roasting furnace temperature; A multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption is established to dynamically adjust the second data, including analyzing the roasting furnace energy consumption, roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature to obtain an updated value of the second data.

2. The method for intelligent optimization control of temperature of an alumina roasting furnace according to claim 1, characterized in that: The judgment layer extracts features from the second infrared image data to obtain temperature features, where the temperature features include maximum temperature, minimum temperature, average temperature and contrast; The stage state of the roasting furnace is determined by comparing the temperature characteristics with the preset temperature thresholds and contrast thresholds of each stage, and the determination conditions are: T i,min ≤T min,t <T i,max ; T i,min ≤T max,t <T i,max ; C i,min ≤C t <C i,max ; Among them, T i,min It is represented as the preset minimum temperature threshold in the i-th stage state; T i,max It is represented as the preset maximum temperature threshold in the i-th stage state; T min,t T is the minimum temperature of the roasting furnace at time t; max,t Expressed as the maximum temperature of the roasting furnace at time t; C t Expressed as the contrast ratio of the roasting furnace at time t; C i,min It is represented as the preset minimum contrast threshold in the i-th stage state; C i,max It is represented as the preset maximum contrast threshold in the i-th stage state.

3. The method for intelligent optimization control of temperature of an alumina roasting furnace according to claim 1, characterized in that: The calculation formula of the temperature deviation is: ε(t)=T target -T t,1 ; Wherein, ε(t) represents the temperature deviation of the roasting furnace at time t; T target represents the target temperature; T t,1 represents the first roasting furnace temperature of the roasting furnace at time t; The calculation formula of the first adjustment value is: Wherein, ΔT1 represents the first adjustment value; K p Expressed as proportional gain; K i Expressed as integral gain; K d It is expressed as differential gain; ε(0) represents the initial temperature deviation; The calculation formula of the adaptive temperature of the second roasting furnace is: T t,2 =ΔT1+T t,1 ; Among them, T t,2 It is represented as the adaptive temperature of the second roasting furnace at time t of the roasting furnace.

4. The method for intelligent optimization control of temperature of an alumina roasting furnace according to claim 1, characterized in that: The update value of the second data includes a gas flow update value, an air volume update value, a voltage update value and a current update value; The gas flow update value is: Among them, Q g,adjusted,t It is represented by the updated value of the gas flow rate of the roasting furnace at time t; Q g,t It is represented by the gas flow value of the roasting furnace at time t; α represents the temperature adjustment coefficient; β represents the thermal efficiency adjustment coefficient; η t represents the thermal efficiency of the roasting furnace at time t; γ represents the energy consumption adjustment coefficient; E t represents the energy consumption of the roasting furnace at time t; E target represents the target energy consumption of the roasting furnace; The air volume update value is: Among them, Q a,adjusted,t It is represented as the updated value of the air volume of the roasting furnace at time t; Q a,t It is expressed as the air volume value of the roasting furnace at time t; The voltage update value is: Among them, Q v,adjusted,t It is represented by the voltage update value of the roasting furnace at time t; Q v,t It is represented as the voltage value of the roasting furnace at time t; The current update value is: Among them, Q i,adjusted,t It is represented by the current update value of the roasting furnace at time t; Q i,t It is expressed as the current value of the roasting furnace at time t.

5. An intelligent optimization control system for the temperature of an alumina roasting furnace, applied to an intelligent optimization control method for the temperature of an alumina roasting furnace as claimed in any one of claims 1 to 4, characterized in that: The system includes a data acquisition module, a stage state judgment module, a temperature adaptive control module and a multi-parameter dynamic adjustment module; The data acquisition module is used to acquire the first data and the second data of the alumina roasting furnace in real time; the first data includes the first infrared image data of the roasting furnace acquired by the infrared detector and the first roasting furnace temperature acquired by the temperature sensor in the furnace; the second data includes the gas flow, air volume, voltage and current acquired by the gas flow meter, air volume meter, voltage sensor and current sensor respectively; the first data is input into the roasting furnace temperature control model, and passes through the preprocessing layer, judgment layer and temperature control layer of the roasting furnace temperature control model in sequence; the preprocessing layer is used to preprocess the first infrared image data to obtain the second infrared image data; The stage state judgment module is used to judge the stage state of the roasting furnace, including analyzing the second infrared image data through the judgment layer to obtain the stage state; the stage state includes the first stage, the second stage, the third stage and the fourth stage; the stage state includes a preset temperature threshold range and a target temperature; The temperature adaptive control module is used to adaptively control the temperature of the roasting furnace in each stage state, including calculating and analyzing the temperature deviation between the first roasting furnace temperature and the target temperature through the temperature control layer to obtain a first adjustment value, and analyzing according to the first adjustment value and the first roasting furnace temperature, and outputting a second roasting furnace adaptive temperature; The multi-parameter dynamic adjustment module is used to dynamically adjust the second data, including a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption. The adaptive update mechanism obtains the updated value of the second data by analyzing the roasting furnace energy consumption, roasting furnace thermal efficiency, the first roasting furnace temperature and the second roasting furnace adaptive temperature.

6. The intelligent optimization control system for temperature of an alumina roasting furnace according to claim 5, characterized in that: The judgment layer extracts features from the second infrared image data to obtain temperature features, where the temperature features include maximum temperature, minimum temperature, average temperature and contrast; The stage state of the roasting furnace is determined by comparing the temperature characteristics with the preset temperature thresholds and contrast thresholds of each stage, and the determination conditions are: T i,min ≤T min,t <T i,max ; T i,min ≤T max,t <T i,max ; C i,min ≤C t <C i,max ; Among them, T i,min It is represented as the preset minimum temperature threshold in the i-th stage state; T i,max It is represented as the preset maximum temperature threshold in the i-th stage state; T min,t T is the minimum temperature of the roasting furnace at time t; max,t Expressed as the maximum temperature of the roasting furnace at time t; C t Expressed as the contrast ratio of the roasting furnace at time t; C i,min It is represented as the preset minimum contrast threshold in the i-th stage state; C i,max It is represented as the preset maximum contrast threshold in the i-th stage state.

7. The intelligent optimization control system for temperature of an alumina roasting furnace according to claim 5, characterized in that: The calculation formula of the temperature deviation is: ε(t)=T target -T t,1 ; Wherein, ε(t) represents the temperature deviation of the roasting furnace at time t; T target represents the target temperature; T t,1 represents the first roasting furnace temperature of the roasting furnace at time t; The calculation formula of the first adjustment value is: Wherein, ΔT1 represents the first adjustment value; K p Expressed as proportional gain; K i Expressed as integral gain; K d It is expressed as differential gain; ε(0) represents the initial temperature deviation; The calculation formula of the adaptive temperature of the second roasting furnace is: T t,2 =ΔT1+T t,1 ; Among them, T t,2 It is represented as the adaptive temperature of the second roasting furnace at time t of the roasting furnace.

8. The intelligent optimization control system for temperature of an alumina roasting furnace according to claim 5, characterized in that: The update value of the second data includes a gas flow update value, an air volume update value, a voltage update value and a current update value; The gas flow update value is: Among them, Q g,adjusted,t It is represented by the updated value of the gas flow rate of the roasting furnace at time t; Q g,t It is represented by the gas flow value of the roasting furnace at time t; α represents the temperature adjustment coefficient; β represents the thermal efficiency adjustment coefficient; η t represents the thermal efficiency of the roasting furnace at time t; γ represents the energy consumption adjustment coefficient; E t represents the energy consumption of the roasting furnace at time t; E target represents the target energy consumption of the roasting furnace; The air volume update value is: Among them, Q a,adjusted,t It is represented as the updated value of the air volume of the roasting furnace at time t; Q a,t It is expressed as the air volume value of the roasting furnace at time t; The voltage update value is: Among them, Q v,adjusted,t It is represented by the voltage update value of the roasting furnace at time t; Q v,t It is represented as the voltage value of the roasting furnace at time t; The current update value is: Among them, Q i,adjusted,t It is represented by the current update value of the roasting furnace at time t; Q i,t It is expressed as the current value of the roasting furnace at time t.

Images