CN118794269A - Intelligent optimization control method and system for temperature of aluminum oxide roasting furnace - Google Patents
Intelligent optimization control method and system for temperature of aluminum oxide roasting furnace Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000005457 optimization Methods 0.000 title claims abstract description 28
- TWNQGVIAIRXVLR-UHFFFAOYSA-N oxo(oxoalumanyloxy)alumane Chemical compound O=[Al]O[Al]=O TWNQGVIAIRXVLR-UHFFFAOYSA-N 0.000 title claims abstract description 27
- 238000005265 energy consumption Methods 0.000 claims abstract description 56
- 230000007246 mechanism Effects 0.000 claims abstract description 25
- 238000007781 pre-processing Methods 0.000 claims abstract description 22
- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 claims description 48
- 238000004364 calculation method Methods 0.000 claims description 48
- 239000007789 gas Substances 0.000 claims description 42
- 239000002737 fuel gas Substances 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000000605 extraction Methods 0.000 claims description 8
- 238000004519 manufacturing process Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 230000003044 adaptive effect Effects 0.000 description 5
- 238000010438 heat treatment Methods 0.000 description 5
- 238000002485 combustion reaction Methods 0.000 description 4
- 238000005245 sintering Methods 0.000 description 4
- ONIBWKKTOPOVIA-BYPYZUCNSA-N L-Proline Chemical compound OC(=O)[C@@H]1CCCN1 ONIBWKKTOPOVIA-BYPYZUCNSA-N 0.000 description 3
- ONIBWKKTOPOVIA-UHFFFAOYSA-N Proline Natural products OC(=O)C1CCCN1 ONIBWKKTOPOVIA-UHFFFAOYSA-N 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 239000002994 raw material Substances 0.000 description 3
- 230000033228 biological regulation Effects 0.000 description 2
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- 238000001816 cooling Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
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- 238000012544 monitoring process Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- WNROFYMDJYEPJX-UHFFFAOYSA-K aluminium hydroxide Chemical compound [OH-].[OH-].[OH-].[Al+3] WNROFYMDJYEPJX-UHFFFAOYSA-K 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001354 calcination Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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Abstract
The invention relates to the technical field of automatic temperature control, in particular to an intelligent optimization control method and system for the temperature of an aluminum oxide roasting furnace, which comprises the steps of firstly, acquiring first infrared image data of the aluminum oxide roasting furnace and the temperature of the first roasting furnace in real time, inputting a roasting furnace temperature control model, preprocessing the first infrared image data, extracting features, judging the stage state of the roasting furnace according to the extracted temperature features, and facilitating the temperature control of each stage of the roasting furnace; secondly, the self-adaptive temperature of the second roasting furnace is obtained by analyzing the temperature of the first roasting furnace and the target temperature of each stage, so that the self-adaptive control of the temperature of the roasting furnace is realized; finally, a multi-parameter self-adaptive updating mechanism based on thermal efficiency and energy consumption is established, so that temperature control parameters such as gas flow, air quantity, voltage and current are dynamically adjusted. The method intelligently realizes the temperature control of the roasting furnace by comprehensively analyzing the infrared image data and the temperature data of the roasting furnace.
Description
Technical Field
The invention relates to the technical field of automatic temperature control, in particular to an intelligent optimal control method and system for the temperature of an alumina roasting furnace.
Background
Alumina calcination is a crucial step in the alumina production process and its main purpose is to convert aluminum hydroxide to alumina by high temperature treatment. In this process, the temperature control of the roasting furnace is critical. The traditional temperature control method mainly depends on manual operation and empirical adjustment, and the mode has a plurality of defects including low control precision, slow response speed, high energy consumption and the like, and directly influences the quality and the production efficiency of the alumina product.
Along with the continuous development of scientific technology, a temperature control method for a roasting furnace is continuously proposed, for example, a Chinese patent with the technical application number of CN201910190315.X provides an alumina clinker sintering process and an alumina clinker, wherein the alumina clinker sintering process comprises the steps of spraying kiln dust into a rotary kiln through a kiln head of the rotary kiln in the sintering process, so that the temperature of a sintering zone in the rotary kiln is kept between 1140 and 1200 ℃ and the hardness of the sintered alumina clinker is moderate; in another example, chinese patent with technical application number CN202110737009.0 discloses a method for automatically controlling the temperature of an alumina roasting furnace in a fuzzy manner, firstly, an actual value F of the raw material blanking amount is collected and compared with a rated raw material blanking amount, so that the fuzzy automatic control is performed based on the gas regulating valve V as a control target or based on the raw material blanking amount F as a control target. Although the prior art solves some defects of the traditional method, on one hand, the difference of temperature changes of each stage of the roasting furnace is not fully considered, and on the other hand, the influence of temperature control parameters of the roasting furnace is not fully considered, so that the temperature control effect of the roasting furnace is poor, and the quality and the production efficiency of alumina products are influenced.
Therefore, an intelligent optimal control method and system for the temperature of the aluminum oxide roasting furnace are provided.
Disclosure of Invention
The invention aims to provide an intelligent optimal control method and system for the temperature of an alumina roasting furnace. Firstly, acquiring first infrared image data of an alumina roasting furnace and first roasting furnace temperature in real time, inputting a roasting furnace temperature control model, preprocessing the first infrared image data, extracting features, and judging the stage state of the roasting furnace according to the extracted temperature features, wherein the stage state comprises a first stage, a second stage, a third stage and a fourth stage; secondly, analyzing the temperature of the first roasting furnace and the target temperature of each stage to obtain the self-adaptive temperature of the second roasting furnace, so as to realize the self-adaptive temperature control of the roasting furnace; and finally, establishing a multi-parameter self-adaptive updating mechanism based on thermal efficiency and energy consumption, and dynamically adjusting temperature control parameters such as gas flow, air quantity, voltage, current and the like. The method intelligently realizes the temperature control of the roasting furnace by comprehensively analyzing the infrared image data and the temperature data of the roasting furnace.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent optimization control method for the temperature of an alumina roasting furnace comprises the following steps:
acquiring first data and second data of an alumina roasting furnace in real time; the first data comprise first infrared image data of the roasting furnace, which are acquired through an infrared detector, and first roasting furnace temperature, which are acquired through an in-furnace temperature sensor; inputting the first data into a roasting furnace temperature control model, and sequentially passing through a pretreatment layer, a judgment layer and a temperature control layer of the model;
The preprocessing layer is used for preprocessing the first infrared image data to obtain second infrared image data;
The judging layer is used for judging the stage state of the roasting furnace and comprises analyzing the second infrared image data to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; each stage state comprises a preset temperature threshold interval and a target temperature;
the temperature control layer is used for carrying out self-adaptive control on the temperature of the roasting furnace in each stage state, and comprises the steps of obtaining a first adjustment value through carrying out temperature deviation calculation and analysis on the temperature of the first roasting furnace and the target temperature, analyzing according to the first adjustment value and the temperature of the first roasting furnace, and outputting a second roasting furnace self-adaptive temperature;
And establishing a multi-parameter self-adaptive updating mechanism based on heat efficiency and energy consumption, wherein the multi-parameter self-adaptive updating mechanism is used for dynamically adjusting the second data, and comprises the steps of analyzing the energy consumption and heat efficiency of the roasting furnace, the temperature of the first roasting furnace and the self-adaptive temperature of the second roasting furnace to obtain an updated value of the second data.
Preferably, the second data includes gas flow, air volume, voltage and current acquired through a gas flow meter, a wind meter, a voltage sensor and a current sensor, respectively.
Preferably, the judging layer performs feature extraction on the second infrared image data to obtain temperature features, wherein the temperature features comprise maximum temperature, minimum temperature, average temperature and contrast;
Comparing the temperature characteristic with a preset temperature threshold value and a contrast threshold value of each stage, and determining the stage state of the roasting furnace, wherein the determining conditions are as follows:
；
；
；
Wherein, Denoted as the firstA preset minimum temperature threshold in a stage state; denoted as the first A preset maximum temperature threshold in a stage state; Indicated as the roasting furnace The minimum temperature at time; Indicated as the roasting furnace The maximum temperature at the moment; Indicated as the roasting furnace The contrast at time; denoted as the first A preset minimum contrast threshold in a stage state; denoted as the first A preset maximum contrast threshold in the phase state.
Preferably, the calculation formula of the temperature deviation is:
；
Wherein, Representing the roasting furnaceTemperature deviation of time; representing the target temperature; The roasting furnace The temperature of the first roasting furnace at the moment;
the calculation formula of the first adjustment value is as follows:
；
Wherein, Represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; expressed as differential gain;
the calculation formula of the self-adaptive temperature of the second roasting furnace is as follows:
；
Wherein, Indicated as the roasting furnaceThe second roasting furnace is self-adaptive to temperature at the moment.
Preferably, the updated values of the second data include a gas flow updated value, a wind volume updated value, a voltage updated value, and a current updated value;
the gas flow update value is:
；
Wherein, Indicated as the roasting furnaceA gas flow update value at a time; Indicated as the roasting furnace A fuel gas flow value at a time; representing a temperature adjustment coefficient; Representing a thermal efficiency adjustment coefficient; Representing the roasting furnace Thermal efficiency at time; Representing an energy consumption adjustment coefficient; Representing the roasting furnace The energy consumption at the moment; representing the target energy consumption of the roasting furnace;
The air quantity update value is as follows:
；
Wherein, Indicated as the roasting furnaceThe air quantity updating value at the moment; Indicated as the roasting furnace The air quantity value at the moment;
the voltage update value is:
；
Wherein, Indicated as the roasting furnaceA voltage update value at a time; Indicated as the roasting furnace A voltage value at a time;
the current update value is:
；
Wherein, Indicated as the roasting furnaceUpdating a value of current at the moment; Indicated as the roasting furnace Current value at time.
An intelligent optimization control system for the temperature of an alumina roasting furnace is applied to the intelligent optimization control method for the temperature of the alumina roasting furnace, and comprises a data acquisition module, a stage state judgment module, a temperature self-adaptive control module and a multi-parameter dynamic adjustment module;
The data acquisition module is used for acquiring first data and second data of the aluminum oxide roasting furnace in real time; the first data comprise first infrared image data of the roasting furnace, which are acquired through an infrared detector, and first roasting furnace temperature, which are acquired through an in-furnace temperature sensor; inputting the first data into a roasting furnace temperature control model, and sequentially passing through a pretreatment layer, a judgment layer and a temperature control layer of the roasting furnace temperature control model; the preprocessing layer is used for preprocessing the first infrared image data to obtain second infrared image data;
the stage state judging module is used for judging the stage state of the roasting furnace and comprises analyzing the second infrared image data through the judging layer to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; the stage state comprises a preset temperature threshold interval and a target temperature;
The temperature self-adaptive control module is used for carrying out self-adaptive control on the temperature of the roasting furnace in each stage state, and comprises the steps of carrying out temperature deviation calculation and analysis on the temperature of the first roasting furnace and the target temperature through the temperature control layer to obtain a first adjustment value, analyzing according to the first adjustment value and the temperature of the first roasting furnace, and outputting a second roasting furnace self-adaptive temperature;
The multi-parameter dynamic adjustment module is used for dynamically adjusting the second data and comprises a multi-parameter self-adaptive update mechanism based on thermal efficiency and energy consumption, and the multi-parameter self-adaptive update mechanism based on thermal efficiency and energy consumption obtains an updated value of the second data by analyzing energy consumption of a roasting furnace, thermal efficiency of the roasting furnace, temperature of the first roasting furnace and self-adaptive temperature of the second roasting furnace.
Preferably, the second data includes gas flow, air volume, voltage and current acquired through a gas flow meter, a wind meter, a voltage sensor and a current sensor, respectively.
Preferably, the judging layer performs feature extraction on the second infrared image data to obtain temperature features, wherein the temperature features comprise maximum temperature, minimum temperature, average temperature and contrast;
Preferably, the stage state of the roasting furnace is determined by comparing the temperature characteristics and the preset temperature threshold value and the contrast threshold value of each stage, and the determination conditions are as follows:
；
；
；
Wherein, Denoted as the firstA preset minimum temperature threshold in a stage state; denoted as the first A preset maximum temperature threshold in a stage state; Indicated as the roasting furnace The minimum temperature at time; Indicated as the roasting furnace The maximum temperature at the moment; Indicated as the roasting furnace The contrast at time; denoted as the first A preset minimum contrast threshold in a stage state; denoted as the first A preset maximum contrast threshold in the phase state.
Preferably, the calculation formula of the temperature deviation is:
；
Wherein, Representing the roasting furnaceTemperature deviation of time; representing the target temperature; The roasting furnace The temperature of the first roasting furnace at the moment;
the calculation formula of the first adjustment value is as follows:
；
Wherein, Represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; expressed as differential gain;
the calculation formula of the self-adaptive temperature of the second roasting furnace is as follows:
；
Wherein, Indicated as the roasting furnaceThe second roasting furnace is self-adaptive to temperature at the moment.
Preferably, the updated values of the second data include a gas flow updated value, a wind volume updated value, a voltage updated value, and a current updated value;
the gas flow update value is:
；
Wherein, Indicated as the roasting furnaceA gas flow update value at a time; Indicated as the roasting furnace A fuel gas flow value at a time; representing a temperature adjustment coefficient; Representing a thermal efficiency adjustment coefficient; Representing the roasting furnace Thermal efficiency at time; Representing an energy consumption adjustment coefficient; Representing the roasting furnace The energy consumption at the moment; representing the target energy consumption of the roasting furnace;
The air quantity update value is as follows:
；
Wherein, Indicated as the roasting furnaceThe air quantity updating value at the moment; Indicated as the roasting furnace The air quantity value at the moment;
the voltage update value is:
；
Wherein, Indicated as the roasting furnaceA voltage update value at a time; Indicated as the roasting furnace A voltage value at a time;
the current update value is:
；
Wherein, Indicated as the roasting furnaceUpdating a value of current at the moment; Indicated as the roasting furnace Current value at time.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, the infrared image data of the roasting furnace is obtained in real time by introducing the infrared detector, and the temperature characteristics (maximum temperature, minimum temperature, average temperature and contrast) are extracted for comprehensive analysis, so that the temperature control of each stage state of the roasting furnace is effectively realized, the effectiveness of the overall temperature control of the roasting furnace is improved, and the quality of the roasted product is effectively improved.
2. The invention adaptively regulates and controls the temperature of the roasting furnace according to the states of different stages of the roasting furnace. In each stage, comprehensively considering the target temperature and the real-time temperature of each stage, calculating an adjustment value and dynamically adjusting the temperature of the roasting furnace. The self-adaptive regulation and control method can ensure that the temperature of the roasting furnace in each stage is kept in a reasonable range, reduce temperature fluctuation, improve the precision and production efficiency of temperature control and effectively improve the quality of roasted products.
3. According to the invention, through a multi-parameter self-adaptive updating mechanism based on thermal efficiency and energy consumption, the thermal efficiency and the energy consumption are monitored in real time, and temperature control parameters such as gas flow, air quantity, voltage and current are dynamically adjusted. The mechanism can ensure that the temperature control of the roasting furnace under different conditions is more accurate and stable. The temperature control parameters are dynamically adjusted, so that the effectiveness of temperature control of the roasting furnace is improved, and the quality of roasted products is effectively improved.
Drawings
FIG. 1 is a flow chart of an intelligent optimization control method for the temperature of an alumina roasting furnace, which is provided by the embodiment of the invention;
FIG. 2 is a schematic diagram of an intelligent temperature optimization control system for an alumina roasting furnace according to an embodiment of the present invention;
FIG. 3 is a flow chart for determining the stage status of an alumina roasting furnace according to an embodiment of the present invention;
fig. 4 is a flow chart of an intelligent optimization control system for the temperature of an alumina roasting furnace, which is provided by the embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The accurate and effective temperature control method of the roasting furnace directly influences the quality and the production efficiency of the alumina product. In order to realize more accurate and effective temperature control of the roasting furnace, the invention will be described in detail by the following two embodiments, so as to prove that the method provided by the invention has a certain effect in actual temperature control of the roasting furnace.
Example 1
The embodiment provides an intelligent optimal control method for the temperature of an alumina roasting furnace, which is applied to a roasting step in the alumina production process, and is analyzed for the temperature control process of an alumina roasting furnace A;
referring to fig. 1, a flow chart of an intelligent optimization control method for temperature of an alumina roasting furnace according to 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, judging the stage state of the roasting furnace by utilizing a judging layer of the model, wherein the judging layer comprises analyzing the second infrared image data to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; s300, carrying out self-adaptive control on the temperature of the roasting furnace in each stage state through a temperature control layer of the model, wherein the self-adaptive control comprises the steps of obtaining a first adjustment value through carrying out temperature deviation calculation analysis on the temperature of the first roasting furnace and the target temperature, analyzing according to the first adjustment value and the temperature of the first roasting furnace, and obtaining a second roasting furnace self-adaptive temperature; s400, establishing a multi-parameter self-adaptive updating mechanism based on thermal efficiency and energy consumption, wherein the multi-parameter self-adaptive updating mechanism is used for dynamically adjusting the second data, and comprises the steps of analyzing the energy consumption of a roasting furnace, the thermal efficiency of the roasting furnace, the temperature of the first roasting furnace and the self-adaptive temperature of the second roasting furnace to obtain an updated value of the second data.
Further, according to step S100, first data and second data of the a alumina roasting furnace are obtained 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; the first data comprises first infrared image data of the roasting furnace acquired by an infrared detector (Fluke Ti 450); the first infrared image data is used for analyzing uniformity and dynamic change of temperature distribution, and the first data also comprises a first roasting furnace temperature acquired by a temperature sensor (Omega type K thermocouple) arranged at the middle position in the furnace;
The second data comprise gas flow, air volume, voltage and current acquired through a gas flow meter (Endress+Hauser Proline T-Mass 65F), a wind meter (TSI VelociCalc 9565), a voltage sensor (Hioki VT) and a current sensor (LEM LF 205-S) respectively;
And inputting the first data into a temperature control model of the roasting furnace, and sequentially passing through a pretreatment layer, a judgment layer and a temperature control layer of the model.
Further, according to step S200, the stage state of the roasting furnace is determined by using the determining layer of the model, including analyzing the second infrared image data to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; respectively correspond to the preliminary heating stage of the A alumina roasting furnace a combustion phase, a stabilization phase and a cooling phase; the specific judging flow chart is shown in fig. 3, and includes:
S201, acquiring first infrared image data of an alumina roasting furnace;
s202, preprocessing the first infrared image data to obtain second infrared image data;
s203, carrying out feature extraction on the second infrared image data to obtain temperature features;
S204, comparing the temperature characteristics with a preset temperature threshold value and a contrast threshold value of each stage, and determining the stage state of the roasting furnace, wherein the stage state comprises a first stage, a second stage, a third stage and a fourth stage;
each stage state comprises a preset temperature threshold interval and a target temperature;
The judging layer performs feature extraction on the second infrared image data to obtain temperature features, wherein the temperature features comprise maximum temperature, minimum temperature, average temperature and contrast;
the calculation formula of the maximum temperature is as follows:
；
Wherein, Representation ofMaximum temperature at time; Representation of Second infrared image data of the moment;
The calculation formula of the minimum temperature is as follows:
；
Wherein, Representation ofA minimum temperature at time; Representation of Second infrared image data of the moment;
The calculation formula of the average temperature is as follows:
；
Wherein, Representation ofAverage temperature at time; Represent the first A temperature value of each pixel; representing the total number of pixels;
The contrast formula is:
；
Wherein, Representation ofContrast at time;
comparing the temperature characteristics with preset temperature thresholds and contrast thresholds of each stage, and determining the stage state of the roasting furnace, wherein the determining conditions are as follows:
；
；
；
Wherein, Denoted as the firstA preset minimum temperature threshold in a stage state; denoted as the first A preset maximum temperature threshold in a stage state; Indicated as the roasting furnace The minimum temperature at time; Indicated as the roasting furnace The maximum temperature at the moment; Indicated as the roasting furnace The contrast at time; denoted as the first A preset minimum contrast threshold in a stage state; denoted as the first A preset maximum contrast threshold in the phase state.
According to the embodiment, the infrared image data of the roasting furnace is obtained in real time by introducing the infrared detector, the temperature characteristics (the maximum temperature, the minimum temperature, the average temperature and the contrast ratio) are extracted for comprehensive analysis, the temperature control of each stage state of the roasting furnace is effectively realized, the effectiveness of the overall temperature control of the roasting furnace is improved, and the quality of roasted products is effectively improved.
Further, according to step S300, the temperature control layer of the model is utilized to adaptively control the temperature of the roasting furnace in each stage, including obtaining a first adjustment value by performing temperature deviation calculation analysis on the first roasting furnace temperature and the target temperature, 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 as follows:
；
Wherein, Representing the roasting furnaceTemperature deviation of time; representing the target temperature; The roasting furnace The temperature of the first roasting furnace at the moment;
the calculation formula of the first adjustment value is as follows:
；
Wherein, Represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; expressed as differential gain;
the calculation formula of the self-adaptive temperature of the second roasting furnace is as follows:
；
Wherein, Indicated as the roasting furnaceThe second roasting furnace is self-adaptive to temperature at the moment.
Further, according to step S400, a multi-parameter adaptive update mechanism based on thermal efficiency and energy consumption is established, and the multi-parameter adaptive update mechanism is used for dynamically adjusting the second data, including analyzing the energy consumption of the roasting furnace, the thermal efficiency of the roasting furnace, the temperature of the first roasting furnace and the adaptive temperature of the second roasting furnace, so as to obtain an updated value of the second data.
The temperature of the roasting furnace is adaptively regulated and controlled according to different stage states of the roasting furnace. In each stage, comprehensively considering the target temperature and the real-time temperature of each stage, calculating an adjustment value and dynamically adjusting the temperature of the roasting furnace. The self-adaptive regulation and control method can ensure that the temperature of the roasting furnace in each stage is kept in a reasonable range, reduce temperature fluctuation, improve the precision and production efficiency of temperature control and effectively improve the quality of roasted products.
Further, a portion of the second data table is shown in table 1;
Table 1 part second data table
The calculation formula of the thermal efficiency of the roasting furnace is as follows:
；
Wherein, Representing the roasting furnaceThermal efficiency at time; Represents the specific heat capacity of air; Representing the outlet temperature of the roasting furnace; representing an inlet temperature of the calciner; Representing the total energy input to the calciner;
the calculation formula of the input total energy is as follows:
；
Wherein, Indicated as the roasting furnaceA fuel gas flow value at a time; Indicated as the roasting furnace The moment of time of the heating value of the fuel gas; Indicated as the roasting furnace A voltage value at a time; Indicated as the roasting furnace A current value at a time; representing a time interval;
The energy consumption calculation formula of the roasting furnace is as follows:
；
Wherein, Representing the roasting furnaceThe energy consumption at the moment; Representing the total energy input to the calciner; representing a time interval;
The updated values of the second data comprise a gas flow updated value, a wind volume updated value, a voltage updated value and a current updated value;
the gas flow update value is:
；
Wherein, Indicated as the roasting furnaceA gas flow update value at a time; Indicated as the roasting furnace A fuel gas flow value at a time; representing a temperature adjustment coefficient; Representing a thermal efficiency adjustment coefficient; Representing the roasting furnace Thermal efficiency at time; Representing an energy consumption adjustment coefficient; Representing the roasting furnace The energy consumption at the moment; representing the target energy consumption of the roasting furnace;
Further, the air volume update value is:
；
Wherein, Indicated as the roasting furnaceThe air quantity updating value at the moment; Indicated as the roasting furnace The air quantity value at the moment;
Further, the voltage update value is:
；
Wherein, Indicated as the roasting furnaceA voltage update value at a time; Indicated as the roasting furnace A voltage value at a time;
further, the current update value is:
；
Wherein, Indicated as the roasting furnaceUpdating a value of current at the moment; Indicated as the roasting furnace Current value at time.
In order to verify the effectiveness of the temperature control of the intelligent optimization control method for the temperature of the aluminum oxide roasting furnace in the actual aluminum oxide roasting process, the aluminum oxide product quality under three conditions is demonstrated by comparison, and firstly, the intelligent optimization control method for the temperature of the aluminum oxide roasting furnace is provided in the embodiment, and secondly, the temperature control is not carried out on each stage of the combustion of the roasting furnace on the basis of the first condition; thirdly, real-time adjustment of temperature parameters is not considered on the basis of the first condition; the parameters of main comparison include product purity, density and granularity of alumina, and the comparison results are shown in table 2;
table 2 comparison of quality of alumina products under different conditions
As can be seen from table 2, the intelligent optimization control method for the temperature of the alumina roasting furnace provided by the embodiment has significant advantages in terms of improving the purity and density of the product and reducing the granularity. These data demonstrate the effectiveness and superiority of the present example method in furnace temperature control, enabling significant improvement in the quality of alumina products. In industrial applications, the method of the invention can effectively ensure high quality product output.
According to the embodiment, the thermal efficiency and the energy consumption are monitored in real time through a multi-parameter self-adaptive updating mechanism based on the thermal efficiency and the energy consumption, and temperature control parameters such as gas flow, air quantity, voltage and current are dynamically adjusted. The mechanism can ensure that the temperature control of the roasting furnace under different conditions is more accurate and stable. The temperature control parameters are dynamically adjusted, so that the effectiveness of temperature control of the roasting furnace is improved, and the quality of roasted products is effectively improved.
According to the steps, a flow chart of an intelligent optimal control system for the temperature of the aluminum oxide roasting furnace can be obtained, and the flow chart is specifically shown in fig. 4; the system comprises a data acquisition unit, a preprocessing unit, a stage state judging unit, a temperature self-adaptive control unit and a multi-parameter dynamic adjustment unit, wherein the data acquisition unit comprises an infrared detector (Fluke Ti 450), a temperature sensor (Omega K-type thermocouple), a gas flowmeter (energy+Hauser Proline T-Mass 65F), a wind meter (TSI VelociCalc 9565), a voltage sensor (Hioki VT 1005) and a current sensor (LEM LF 205-S);
The temperature sensor is arranged at the middle part of the roasting furnace, the temperature of the first roasting furnace is obtained in real time, the infrared detector is used for obtaining first infrared image data of temperature distribution in the furnace through an infrared window arranged outside the roasting furnace, and the infrared window is made of a material resistant to high temperature and chemical attack; the gas flow meter is arranged on the gas supply pipeline at a position close to the roasting furnace; the air gauge is arranged at an air inlet of the roasting furnace and is used for monitoring the air quantity in the roasting furnace in real time; the voltage sensor and the current sensor are connected with a power supply system and are used for monitoring voltage and current in real time;
Acquiring first infrared image data, first roasting furnace temperature, gas flow, air quantity, voltage and current through the data;
Preprocessing the first infrared image data through a preprocessing layer to obtain second infrared image data;
Extracting temperature characteristics of the second infrared image data through a stage state judging unit, wherein the temperature characteristics comprise maximum temperature, minimum temperature, average temperature and contrast, and determining stage states of the roasting furnace according to the temperature characteristics, preset temperature thresholds and contrast thresholds of different stage states and judging conditions, wherein the stage states comprise a first state, a second state, a third state and a fourth state;
the calculation formula of the maximum temperature is as follows:
；
Wherein, Representation ofMaximum temperature at time; Representation of Second infrared image data of the moment;
The calculation formula of the minimum temperature is as follows:
；
Wherein, Representation ofA minimum temperature at time; Representation of Second infrared image data of the moment;
The calculation formula of the average temperature is as follows:
；
Wherein, Representation ofAverage temperature at time; Represent the first A temperature value of each pixel; representing the total number of pixels;
The contrast formula is:
；
Wherein, Representation ofContrast at time;
the judging conditions are as follows:
；
；
；
Wherein, Denoted as the firstA preset minimum temperature threshold in a stage state; denoted as the first A preset maximum temperature threshold in a stage state; Indicated as the roasting furnace The minimum temperature at time; Indicated as the roasting furnace The maximum temperature at the moment; Indicated as the roasting furnace The contrast at time; denoted as the first A preset minimum contrast threshold in a stage state; denoted as the first A preset maximum contrast threshold in a stage state;
The temperature self-adaptive control unit is used for carrying out self-adaptive control on the temperature of the roasting furnace in each stage state, carrying out temperature deviation calculation and analysis on the temperature of the first roasting furnace and the target temperature of each stage to obtain a first adjustment value, analyzing according to the first adjustment value and the temperature of the first roasting furnace, and outputting a second roasting furnace self-adaptive temperature;
the calculation formula of the temperature deviation is as follows:
；
Wherein, Representing the roasting furnaceTemperature deviation of time; representing the target temperature; The roasting furnace The temperature of the first roasting furnace at the moment;
the calculation formula of the first adjustment value is as follows:
；
Wherein, Represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; expressed as differential gain;
the calculation formula of the self-adaptive temperature of the second roasting furnace is as follows:
；
Wherein, Indicated as the roasting furnaceThe second roasting furnace is self-adaptive to temperature at the moment.
The multi-parameter dynamic adjustment unit is used for dynamically adjusting the second data and comprises a multi-parameter self-adaptive update mechanism based on thermal efficiency and energy consumption, and the multi-parameter self-adaptive update mechanism based on thermal efficiency and energy consumption obtains an updated value of the second data by analyzing energy consumption of a roasting furnace, thermal efficiency of the roasting furnace, temperature of the first roasting furnace and self-adaptive temperature of the second roasting furnace.
The calculation formula of the thermal efficiency of the roasting furnace is as follows:
；
Wherein, Representing the roasting furnaceThermal efficiency at time; Represents the specific heat capacity of air; Representing the outlet temperature of the roasting furnace; representing an inlet temperature of the calciner; Representing the total energy input to the calciner;
the calculation formula of the input total energy is as follows:
；
Wherein, Indicated as the roasting furnaceA fuel gas flow value at a time; Indicated as the roasting furnace The moment of time of the heating value of the fuel gas; Indicated as the roasting furnace A voltage value at a time; Indicated as the roasting furnace A current value at a time; representing a time interval;
The energy consumption calculation formula of the roasting furnace is as follows:
；
Wherein, Representing the roasting furnaceThe energy consumption at the moment; Representing the total energy input to the calciner; representing a time interval;
The updated values of the second data comprise a gas flow updated value, a wind volume updated value, a voltage updated value and a current updated value;
the gas flow update value is:
；
Wherein, Indicated as the roasting furnaceA gas flow update value at a time; Indicated as the roasting furnace A fuel gas flow value at a time; representing a temperature adjustment coefficient; Representing a thermal efficiency adjustment coefficient; Representing the roasting furnace Thermal efficiency at time; Representing an energy consumption adjustment coefficient; Representing the roasting furnace The energy consumption at the moment; representing the target energy consumption of the roasting furnace;
Further, the air volume update value is:
；
Wherein, Indicated as the roasting furnaceThe air quantity updating value at the moment; Indicated as the roasting furnace The air quantity value at the moment;
Further, the voltage update value is:
；
Wherein, Indicated as the roasting furnaceA voltage update value at a time; Indicated as the roasting furnace A voltage value at a time;
further, the current update value is:
；
Wherein, Indicated as the roasting furnaceUpdating a value of current at the moment; Indicated as the roasting furnace Current value at time.
According to the embodiment, first infrared image data of an aluminum oxide roasting furnace and first roasting furnace temperature are obtained in real time, a roasting furnace temperature control model is input, the first infrared image data is preprocessed, feature extraction is carried out, and the stage state of the roasting furnace is judged according to the extracted temperature features, wherein the stage state comprises a first stage, a second stage, a third stage and a fourth stage; secondly, analyzing the temperature of the first roasting furnace and the target temperature of each stage to obtain the self-adaptive temperature of the second roasting furnace, so as to realize the self-adaptive temperature control of the roasting furnace; and finally, establishing a multi-parameter self-adaptive updating mechanism based on thermal efficiency and energy consumption, and dynamically adjusting temperature control parameters such as gas flow, air quantity, voltage, current and the like. According to the embodiment, the temperature control of the roasting furnace is intelligently realized by comprehensively analyzing the infrared image data and the temperature data of the roasting furnace.
Example two
The embodiment provides an intelligent optimal control method for the temperature of an alumina roasting furnace, which is applied to a roasting step in the alumina production process, and is analyzed for the temperature control process of a B alumina roasting furnace;
referring to fig. 1, a flow chart of an intelligent optimization control method for temperature of an alumina roasting furnace according to 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, judging the stage state of the roasting furnace by utilizing a judging layer of the model, wherein the judging layer comprises analyzing the second infrared image data to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; s300, carrying out self-adaptive control on the temperature of the roasting furnace in each stage state through a temperature control layer of the model, wherein the self-adaptive control comprises the steps of obtaining a first adjustment value through carrying out temperature deviation calculation analysis on the temperature of the first roasting furnace and the target temperature, analyzing according to the first adjustment value and the temperature of the first roasting furnace, and obtaining a second roasting furnace self-adaptive temperature; s400, establishing a multi-parameter self-adaptive updating mechanism based on thermal efficiency and energy consumption, wherein the multi-parameter self-adaptive updating mechanism is used for dynamically adjusting the second data, and comprises the steps of analyzing the energy consumption of a roasting furnace, the thermal efficiency of the roasting furnace, the temperature of the first roasting furnace and the self-adaptive temperature of the second roasting furnace to obtain an updated value of the second data.
The steps correspond to the modules of the intelligent optimization control system for the temperature of the aluminum oxide roasting furnace provided by the figure 2; the module comprises a data acquisition module, a stage state judging module, a temperature self-adaptive control module and a multi-parameter dynamic adjustment module; respectively corresponding to the steps of the flow;
The data acquisition module acquires first data and second data of the aluminum oxide roasting furnace B 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; the first data comprises first infrared image data of the roasting furnace acquired by an infrared detector (Fluke Ti 450); the first infrared image data is used for analyzing uniformity and dynamic change of temperature distribution, and the first data also comprises a first roasting furnace temperature acquired by a temperature sensor (Omega type K thermocouple) arranged at the middle position in the furnace;
The second data comprise gas flow, air volume, voltage and current acquired through a gas flow meter (Endress+Hauser Proline T-Mass 65F), a wind meter (TSI VelociCalc 9565), a voltage sensor (Hioki VT) and a current sensor (LEM LF 205-S) respectively;
And inputting the first data into a temperature control model of the roasting furnace, and sequentially passing through a pretreatment layer, a judgment layer and a temperature control layer of the model.
Further, the stage state judging module includes: judging the stage state of the roasting furnace by utilizing a judging layer of the model, wherein the judging layer comprises analyzing the second infrared image data to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; respectively correspond to the preliminary heating stage of the B alumina roasting furnace a combustion phase, a stabilization phase and a cooling phase; the specific judgment flow chart is shown in fig. 3; each stage state comprises a preset temperature threshold interval and a target temperature;
The judging layer performs feature extraction on the second infrared image data to obtain temperature features, wherein the temperature features comprise maximum temperature, minimum temperature, average temperature and contrast;
the calculation formula of the maximum temperature is as follows:
；
Wherein, Representation ofMaximum temperature at time; Representation of Second infrared image data of the moment;
The calculation formula of the minimum temperature is as follows:
；
Wherein, Representation ofA minimum temperature at time; Representation of Second infrared image data of the moment;
The calculation formula of the average temperature is as follows:
；
Wherein, Representation ofAverage temperature at time; Represent the first A temperature value of each pixel; representing the total number of pixels;
The contrast formula is:
；
Wherein, Representation ofContrast at time;
Further, comparing the temperature characteristics with preset temperature thresholds and contrast thresholds of each stage, and determining the stage state of the roasting furnace, wherein the determining conditions are as follows:
；
；
；
Wherein, Denoted as the firstA preset minimum temperature threshold in a stage state; denoted as the first A preset maximum temperature threshold in a stage state; Indicated as the roasting furnace The minimum temperature at time; Indicated as the roasting furnace The maximum temperature at the moment; Indicated as the roasting furnace The contrast at time; denoted as the first A preset minimum contrast threshold in a stage state; denoted as the first A preset maximum contrast threshold in the phase state.
Further, the temperature adaptive control module includes: the temperature control layer of the model is utilized to carry out self-adaptive control on the temperature of the roasting furnace in each stage state, and the method comprises the steps of obtaining a first adjustment value through carrying out temperature deviation calculation and analysis on the temperature of the first roasting furnace and the target temperature, analyzing according to the first adjustment value and the temperature of the first roasting furnace, and outputting a second roasting furnace self-adaptive temperature; the calculation formula of the temperature deviation is as follows:
；
Wherein, Representing the roasting furnaceTemperature deviation of time; representing the target temperature; The roasting furnace The temperature of the first roasting furnace at the moment;
the calculation formula of the first adjustment value is as follows:
；
Wherein, Represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; expressed as differential gain;
Further, the calculation formula of the self-adaptive temperature of the second roasting furnace is as follows:
；
Wherein, Indicated as the roasting furnaceThe second roasting furnace is self-adaptive to temperature at the moment.
Further, the multi-parameter dynamic adjustment module includes: and establishing a multi-parameter self-adaptive updating mechanism based on heat efficiency and energy consumption, wherein the mechanism is used for dynamically adjusting the second data, and comprises the steps of analyzing the energy consumption of the roasting furnace, the heat efficiency of the roasting furnace, the temperature of the first roasting furnace and the self-adaptive temperature of the second roasting furnace to obtain an updated value of the second data.
Table 3 part second data sheet
Further, a portion of the second data table is shown in table 3;
The calculation formula of the thermal efficiency of the roasting furnace is as follows:
；
Wherein, Representing the roasting furnaceThermal efficiency at time; Represents the specific heat capacity of air; Representing the outlet temperature of the roasting furnace; representing an inlet temperature of the calciner; Representing the total energy input to the calciner;
the calculation formula of the input total energy is as follows:
；
Wherein, Indicated as the roasting furnaceA fuel gas flow value at a time; Indicated as the roasting furnace The moment of time of the heating value of the fuel gas; Indicated as the roasting furnace A voltage value at a time; Indicated as the roasting furnace A current value at a time; representing a time interval;
The energy consumption calculation formula of the roasting furnace is as follows:
；
Wherein, Representing the roasting furnaceThe energy consumption at the moment; Representing the total energy input to the calciner; representing a time interval;
The updated values of the second data comprise a gas flow updated value, a wind volume updated value, a voltage updated value and a current updated value;
further, the gas flow rate update value is:
；
Wherein, Indicated as the roasting furnaceA gas flow update value at a time; Indicated as the roasting furnace A fuel gas flow value at a time; representing a temperature adjustment coefficient; Representing a thermal efficiency adjustment coefficient; Representing the roasting furnace Thermal efficiency at time; Representing an energy consumption adjustment coefficient; Representing the roasting furnace The energy consumption at the moment; representing the target energy consumption of the roasting furnace;
Further, the air volume update value is:
；
Wherein, Indicated as the roasting furnaceThe air quantity updating value at the moment; Indicated as the roasting furnace The air quantity value at the moment;
Further, the voltage update value is:
；
Wherein, Indicated as the roasting furnaceA voltage update value at a time; Indicated as the roasting furnace A voltage value at a time;
further, the current update value is:
；
Wherein, Indicated as the roasting furnaceUpdating a value of current at the moment; Indicated as the roasting furnace Current value at time.
In order to verify the effectiveness of the temperature control of the intelligent optimization control method for the temperature of the aluminum oxide roasting furnace in the actual aluminum oxide roasting process, the aluminum oxide product quality under three conditions is demonstrated by comparison, and firstly, the intelligent optimization control method for the temperature of the aluminum oxide roasting furnace is provided in the embodiment, and secondly, the temperature control is not carried out on each stage of the combustion of the roasting furnace on the basis of the first condition; thirdly, real-time adjustment of temperature parameters is not considered on the basis of the first condition; the parameters of the main comparison include the product purity, density and granularity of the alumina, and the comparison results are shown in Table 4;
Table 4 comparison of quality of alumina products under different conditions
As can be seen from table 4, the intelligent optimization control method for the temperature of the alumina roasting furnace provided by the embodiment has significant advantages in terms of improving the purity and density of the product and reducing the granularity. These data demonstrate the effectiveness and superiority of the present example method in furnace temperature control, enabling significant improvement in the quality of alumina products. In industrial applications, the method of the invention can effectively ensure high quality product output.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An intelligent optimization control method for the temperature of an alumina roasting furnace is characterized by comprising the following steps:
acquiring first data and second data of an alumina roasting furnace in real time; the first data comprise first infrared image data of the roasting furnace, which are acquired through an infrared detector, and first roasting furnace temperature, which are acquired through an in-furnace temperature sensor; inputting the first data into a roasting furnace temperature control model, and sequentially passing through a pretreatment layer, a judgment layer and a temperature control layer of the model;
The preprocessing layer is used for preprocessing the first infrared image data to obtain second infrared image data;
The judging layer is used for judging the stage state of the roasting furnace and comprises analyzing the second infrared image data to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; the stage state comprises a preset temperature threshold interval and a target temperature;
the temperature control layer is used for carrying out self-adaptive control on the temperature of the roasting furnace in each stage state, and comprises the steps of obtaining a first adjustment value by carrying out temperature deviation calculation and analysis on the temperature of the first roasting furnace and the target temperature, and obtaining a second roasting furnace self-adaptive temperature according to the first adjustment value and the temperature of the first roasting furnace;
and establishing a multi-parameter self-adaptive updating mechanism based on heat efficiency and energy consumption, wherein the mechanism is used for dynamically adjusting the second data, and comprises the steps of analyzing the energy consumption of the roasting furnace, the heat efficiency of the roasting furnace, the temperature of the first roasting furnace and the self-adaptive temperature of the second roasting furnace to obtain an updated value of the second data.
2. The intelligent optimization control method for the temperature of the aluminum oxide roasting furnace according to claim 1, wherein the second data comprise gas flow, air volume, voltage and current acquired through a gas flowmeter, an air meter, a voltage sensor and a current sensor respectively.
3. The intelligent optimization control method for the temperature of the aluminum oxide roasting furnace according to claim 1, wherein the judging layer performs feature extraction on the second infrared image data to obtain temperature features, and the temperature features comprise a maximum temperature, a minimum temperature, an average temperature and a contrast;
Comparing the temperature characteristics with preset temperature thresholds and contrast thresholds of each stage, and determining the stage state of the roasting furnace, wherein the determining conditions are as follows:
；
；
；
Wherein, Denoted as the firstA preset minimum temperature threshold in a stage state; denoted as the first A preset maximum temperature threshold in a stage state; Indicated as the roasting furnace The minimum temperature at time; Indicated as the roasting furnace The maximum temperature at the moment; Indicated as the roasting furnace The contrast at time; denoted as the first A preset minimum contrast threshold in a stage state; denoted as the first A preset maximum contrast threshold in the phase state.
4. The intelligent optimization control method for the temperature of the aluminum oxide roasting furnace according to claim 1, wherein the calculation formula of the temperature deviation is as follows:
；
Wherein, Representing the roasting furnaceTemperature deviation of time; representing the target temperature; The roasting furnace The temperature of the first roasting furnace at the moment;
the calculation formula of the first adjustment value is as follows:
；
Wherein, Represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; expressed as differential gain;
the calculation formula of the self-adaptive temperature of the second roasting furnace is as follows:
；
Wherein, Indicated as the roasting furnaceThe second roasting furnace is self-adaptive to temperature at the moment.
5. The intelligent optimization control method for the temperature of the aluminum oxide roasting furnace according to claim 1, wherein the updated values of the second data comprise an updated gas flow value, an updated air volume value, an updated voltage value and an updated current value;
the gas flow update value is:
；
Wherein, Indicated as the roasting furnaceA gas flow update value at a time; Indicated as the roasting furnace A fuel gas flow value at a time; representing a temperature adjustment coefficient; Representing a thermal efficiency adjustment coefficient; Representing the roasting furnace Thermal efficiency at time; Representing an energy consumption adjustment coefficient; Representing the roasting furnace The energy consumption at the moment; representing the target energy consumption of the roasting furnace;
The air quantity update value is as follows:
；
Wherein, Indicated as the roasting furnaceThe air quantity updating value at the moment; Indicated as the roasting furnace The air quantity value at the moment;
the voltage update value is:
；
Wherein, Indicated as the roasting furnaceA voltage update value at a time; Indicated as the roasting furnace A voltage value at a time;
the current update value is:
；
Wherein, Indicated as the roasting furnaceUpdating a value of current at the moment; Indicated as the roasting furnace Current value at time.
6. An intelligent optimization control system for the temperature of an alumina roasting furnace, which is applied to the intelligent optimization control method for the temperature of the alumina roasting furnace according to any one of claims 1-5, and is characterized by comprising a data acquisition module, a stage state judgment module, a temperature self-adaptive control module and a multi-parameter dynamic adjustment module;
The data acquisition module is used for acquiring first data and second data of the aluminum oxide roasting furnace in real time; the first data comprise first infrared image data of the roasting furnace, which are acquired through an infrared detector, and first roasting furnace temperature, which are acquired through an in-furnace temperature sensor; inputting the first data into a roasting furnace temperature control model, and sequentially passing through a pretreatment layer, a judgment layer and a temperature control layer of the roasting furnace temperature control model; the preprocessing layer is used for preprocessing the first infrared image data to obtain second infrared image data;
the stage state judging module is used for judging the stage state of the roasting furnace and comprises analyzing the second infrared image data through the judging layer to obtain the stage state; the phase state comprises a first phase, a second phase, a third phase and a fourth phase; the stage state comprises a preset temperature threshold interval and a target temperature;
The temperature self-adaptive control module is used for carrying out self-adaptive control on the temperature of the roasting furnace in each stage state, and comprises the steps of carrying out temperature deviation calculation and analysis on the temperature of the first roasting furnace and the target temperature through the temperature control layer to obtain a first adjustment value, analyzing according to the first adjustment value and the temperature of the first roasting furnace, and outputting a second roasting furnace self-adaptive temperature;
The multi-parameter dynamic adjustment module is used for dynamically adjusting the second data and comprises a multi-parameter self-adaptive update mechanism based on thermal efficiency and energy consumption, and the multi-parameter self-adaptive update mechanism based on thermal efficiency and energy consumption obtains an updated value of the second data by analyzing energy consumption of a roasting furnace, thermal efficiency of the roasting furnace, temperature of the first roasting furnace and self-adaptive temperature of the second roasting furnace.
7. The intelligent optimal control system for the temperature of the aluminum oxide roasting furnace according to claim 6, wherein the second data comprises gas flow, air volume, voltage and current acquired through a gas flow meter, an air volume meter, a voltage sensor and a current sensor respectively.
8. The intelligent optimization control system for the temperature of the aluminum oxide roasting furnace according to claim 6, wherein the judging layer performs feature extraction on the second infrared image data to obtain temperature features, and the temperature features comprise a maximum temperature, a minimum temperature, an average temperature and a contrast;
Comparing the temperature characteristics and the texture characteristics with preset temperature thresholds and contrast thresholds of each stage, and determining the stage state of the roasting furnace, wherein the determining conditions are as follows:
；
；
；
Wherein, Denoted as the firstA preset minimum temperature threshold in a stage state; denoted as the first A preset maximum temperature threshold in a stage state; Indicated as the roasting furnace The minimum temperature at time; Indicated as the roasting furnace The maximum temperature at the moment; Indicated as the roasting furnace The contrast at time; denoted as the first A preset minimum contrast threshold in a stage state; denoted as the first A preset maximum contrast threshold in the phase state.
9. The intelligent optimization control system for the temperature of the aluminum oxide roasting furnace according to claim 6, wherein the calculation formula of the temperature deviation is as follows:
；
Wherein, Representing the roasting furnaceTemperature deviation of time; representing the target temperature; The roasting furnace The temperature of the first roasting furnace at the moment;
the calculation formula of the first adjustment value is as follows:
；
Wherein, Represented as the first adjustment value; Expressed as proportional gain; Expressed as integral gain; expressed as differential gain;
the calculation formula of the self-adaptive temperature of the second roasting furnace is as follows:
；
Wherein, Indicated as the roasting furnaceThe second roasting furnace is self-adaptive to temperature at the moment.
10. The intelligent optimization control system for the temperature of the aluminum oxide roasting furnace according to claim 6, wherein the updated values of the second data comprise an updated gas flow value, an updated air volume value, an updated voltage value and an updated current value;
the gas flow update value is:
；
Wherein, Indicated as the roasting furnaceA gas flow update value at a time; Indicated as the roasting furnace A fuel gas flow value at a time; representing a temperature adjustment coefficient; Representing a thermal efficiency adjustment coefficient; Representing the roasting furnace Thermal efficiency at time; Representing an energy consumption adjustment coefficient; Representing the roasting furnace The energy consumption at the moment; representing the target energy consumption of the roasting furnace;
The air quantity update value is as follows:
；
Wherein, Indicated as the roasting furnaceThe air quantity updating value at the moment; Indicated as the roasting furnace The air quantity value at the moment;
the voltage update value is:
；
Wherein, Indicated as the roasting furnaceA voltage update value at a time; Indicated as the roasting furnace A voltage value at a time;
the current update value is:
；
Wherein, Indicated as the roasting furnaceUpdating a value of current at the moment; Indicated as the roasting furnace Current value at time.
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