CN116736907A - Intelligent regulation and control method for production temperature of low borosilicate glass - Google Patents
Intelligent regulation and control method for production temperature of low borosilicate glass Download PDFInfo
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- CN116736907A CN116736907A CN202311020321.3A CN202311020321A CN116736907A CN 116736907 A CN116736907 A CN 116736907A CN 202311020321 A CN202311020321 A CN 202311020321A CN 116736907 A CN116736907 A CN 116736907A
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- 230000033228 biological regulation Effects 0.000 title claims abstract description 144
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 116
- 239000005388 borosilicate glass Substances 0.000 title claims abstract description 48
- 239000000446 fuel Substances 0.000 claims abstract description 97
- 239000000498 cooling water Substances 0.000 claims abstract description 72
- 238000000034 method Methods 0.000 claims abstract description 24
- 238000013528 artificial neural network Methods 0.000 claims abstract description 13
- 238000001816 cooling Methods 0.000 claims description 11
- 230000007423 decrease Effects 0.000 claims description 4
- 230000001105 regulatory effect Effects 0.000 abstract description 5
- 230000001276 controlling effect Effects 0.000 abstract description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 239000011521 glass Substances 0.000 description 5
- 238000002485 combustion reaction Methods 0.000 description 3
- 239000002994 raw material Substances 0.000 description 3
- 238000002844 melting Methods 0.000 description 2
- 230000008018 melting Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000002834 transmittance Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
- G05D23/22—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature the sensing element being a thermocouple
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Abstract
The application relates to the field of data processing, and provides an intelligent regulation and control method for low borosilicate glass production temperature, which comprises the following steps: acquiring historical production data of the low borosilicate glass, wherein the historical production data comprises temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production moment; calculating a temperature regulation characteristic value based on temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production time; training the RNN neural network by using the temperature regulation characteristic value to obtain a fuzzy PID algorithm model; and adjusting the production temperature of the low borosilicate glass based on the target temperature and the current temperature by using a fuzzy PID algorithm model. The method has higher stability in a complex system for regulating and controlling the production temperature of the low borosilicate glass, and can realize accurate regulation of the temperature.
Description
Technical Field
The application relates to the field of data processing, in particular to an intelligent regulation and control method for low borosilicate glass production temperature.
Background
The low borosilicate glass is a special glass material, and has the characteristics of high temperature stability, high chemical corrosion resistance, high light transmittance and the like compared with common glass. When the low borosilicate glass is subjected to temperature regulation, due to various reasons such as complex structural organization of the low borosilicate glass, changeable environmental factors and the like, the problem that the regulation and control are inaccurate when the production temperature of the low borosilicate glass is regulated and controlled can occur, the traditional regulation and control of the production temperature of the low borosilicate glass is mostly manually observed, but the temperature of the low borosilicate glass during production is higher, and related protective clothing is required to be worn by staff, so that the task is heavy. The traditional intelligent regulation algorithm such as a support vector machine control algorithm can effectively cope with high-dimensional data, but has higher data requirement and larger calculated amount; the PID control algorithm achieves the aim of temperature regulation and control by carrying out real-time monitoring on data in the production process of the low borosilicate glass, is simple and easy to realize, and has poor stability for complex systems.
Disclosure of Invention
The application provides an intelligent regulation and control method for low borosilicate glass production temperature, which has higher stability in a complex system for regulating and controlling low borosilicate glass production temperature and can realize accurate temperature regulation.
In a first aspect, the application provides an intelligent regulation and control method for low borosilicate glass production temperature, comprising the following steps:
acquiring historical production data of the low borosilicate glass, wherein the historical production data comprises temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production moment;
calculating a temperature regulation characteristic value based on temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production time;
training the RNN neural network by using the temperature regulation characteristic value to obtain a fuzzy PID algorithm model;
and adjusting the production temperature of the low borosilicate glass based on the target temperature and the current temperature by using a fuzzy PID algorithm model.
In one embodiment, calculating the temperature regulation characteristic value based on the temperature data, the fuel supply rate, the fuel-air ratio, and the cooling water flow rate corresponding to each production time includes:
constructing a temperature regulation and control feature descriptor corresponding to each production time based on temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production time;
and calculating a temperature regulation characteristic value based on the temperature regulation characteristic descriptor.
In an embodiment, constructing a temperature regulation feature descriptor corresponding to each production time based on temperature data, fuel supply rate, fuel-air ratio, and cooling water flow corresponding to each production time, including:
calculating a temperature regulation hysteresis factor of each production time based on temperature data corresponding to each production time;
and constructing a temperature regulation characteristic descriptor corresponding to each production time based on the temperature regulation hysteresis factor, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time.
In one embodiment, calculating the temperature regulation hysteresis factor for each production time based on the temperature data corresponding to each production time includes:
calculating the temperature regulation hysteresis factor at each production time by using the following formula:
；
wherein ,temperature control hysteresis factor indicating t production time, < ->Time difference between time representing time of t production time and time of next local extremum, ++>Indicating the temperature difference between the current temperature and the temperature change, < >>Representing a time difference coordination coefficient; the temperature regulation hysteresis factor is used for reflecting the emergency degree of temperature regulation, < >>An exponential function representing a natural constant.
In one embodiment, calculating the temperature regulation feature value based on the temperature regulation feature descriptor includes:
determining a fuel thermal energy release intensity based on the fuel-air ratio in the temperature regulation feature descriptor;
calculating cooling water flow cooling rate based on the cooling water flow in the temperature regulation and control feature descriptor;
and calculating a temperature regulation characteristic value based on the temperature regulation hysteresis factor, the fuel heat energy release intensity, the cooling water flow cooling rate and the fuel supply rate.
In one embodiment, determining the fuel thermal energy release intensity based on the fuel-air ratio in the temperature regulation feature descriptor includes:
if the fuel-air ratio is less than or equal to the optimal fuel-air ratio, the fuel thermal energy release strength increases with an increase in the fuel-air ratio;
if the fuel-air ratio is greater than the optimal fuel-air ratio, the fuel thermal energy release strength decreases as the fuel-air ratio increases.
In one embodiment, determining the fuel thermal energy release intensity based on the fuel-air ratio in the temperature regulation feature descriptor includes:
if the fuel-air ratio is less than or equal to the optimal fuel-air ratio, calculating to obtain the fuel heat energy release strength based on a difference value between the fuel-air ratio and the optimal fuel-air ratio;
if the fuel air ratio is greater than the optimal fuel air ratio, a fuel thermal energy release strength is calculated based on a quotient of the fuel air ratio and the optimal fuel air ratio.
In one embodiment, calculating the temperature regulation characteristic value based on the temperature regulation hysteresis factor, the fuel thermal energy release intensity, the cooling water flow rate, the fuel supply rate comprises:
calculating a temperature regulation characteristic value by using the following formula:
；
wherein ,indicating the fuel heat release strength->Fuel-air ratio at time t>Indicating cooling water flow rate of cooling water>The cooling water flow at the time of t production is represented, Q (t) represents the temperature regulation characteristic value at the time of t production,/->Fuel feed rate at time t production, +.>Temperature control hysteresis factor indicating t production time, < ->Representing coordination factors->Representing the coordination constant.
In one embodiment, calculating the cooling water flow rate based on the cooling water flow in the temperature regulation feature descriptor includes:
the cooling water flow rate is calculated by the following steps:
；
indicating cooling water flow rate of cooling water>Cooling water flow at time t +.>And the difference between the temperature of cooling water at the time point t and the temperature of the hearth at the time point t is shown.
In one embodiment, training the RNN neural network using the temperature regulation feature values to obtain a fuzzy PID algorithm model includes:
forming a temperature regulation characteristic sequence according to the time sequence by the temperature regulation characteristic values corresponding to all production moments;
and processing the temperature regulation and control characteristic sequence and the historical production data by utilizing the RNN neural network, so as to obtain a fuzzy PID algorithm model.
The intelligent regulation and control method for the production temperature of the low borosilicate glass has the beneficial effects that the intelligent regulation and control method is different from the prior art, and comprises the following steps: acquiring historical production data of the low borosilicate glass, wherein the historical production data comprises temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production moment; calculating a temperature regulation characteristic value based on temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production time; training the RNN neural network by using the temperature regulation characteristic value to obtain a fuzzy PID algorithm model; and adjusting the production temperature of the low borosilicate glass based on the target temperature and the current temperature by using a fuzzy PID algorithm model. The method has higher stability in a complex system for regulating and controlling the production temperature of the low borosilicate glass, and can realize accurate regulation of the temperature.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the intelligent control method for low borosilicate glass production temperature;
FIG. 2 is a flowchart illustrating an embodiment of the step S12 in FIG. 1;
fig. 3 is a flowchart of an embodiment of step S22 in fig. 2.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The present application will be described in detail with reference to the accompanying drawings and examples.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a method for intelligently controlling a production temperature of low borosilicate glass, which specifically includes:
step S11: historical production data of the low borosilicate glass is obtained, wherein the historical production data comprise temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production time.
Historical production data of the low borosilicate glass is obtained from manufacturers, and the historical production data comprises production temperature data corresponding to the low borosilicate glass at different production moments.
Because the temperature of the glass is higher in the process of producing the low borosilicate glass, the thermocouple temperature sensor is required to collect the temperature data of the low borosilicate glass in real time in the production process; the method comprises the steps of collecting water temperature data of cooling water in real time through a temperature sensor, collecting water flow data of the cooling water in real time through a flowmeter, obtaining fuel supply rate through a fuel flowmeter, obtaining air flow data through an air flowmeter, calculating fuel-air ratio data according to the fuel flow data and the air flow data, collecting time interval is t, usually taking an empirical value of 10 seconds, collecting a group of data of the same batch of raw materials at each time point, such as water temperature, water flow and the like of a first batch of raw materials at a first time point, and collecting the water temperature, water flow and the like of the first batch of raw materials at a second time point after 10 seconds.
The temperature data, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time are obtained through the mode.
Step S12: and calculating a temperature regulation characteristic value based on the temperature data, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time.
Specifically, referring to fig. 2, step S12 includes:
step S21: and constructing a temperature regulation and control feature descriptor corresponding to each production time based on the temperature data, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time.
Specifically, calculating a temperature regulation hysteresis factor at each production time based on temperature data corresponding to each production time; and constructing a temperature regulation characteristic descriptor corresponding to each production time based on the temperature regulation hysteresis factor, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time.
In the production process of the low borosilicate glass, factors influencing temperature regulation mainly comprise a fuel supply rate, a fuel-air ratio and a cooling water flow, wherein the higher the fuel supply rate is, the higher the corresponding production temperature is, but with the increase of the fuel supply rate, the time is required for full combustion of fuel, so that the temperature rising rate is reduced; too high a fuel-air ratio means that too much fuel is supplied, which may result in insufficient fuel combustion, reduced combustion efficiency, wasted energy, and too low a fuel-air ratio means that too much air is supplied, which may result in reduced heat output in the fuel region, which may result in failure of the temperature in the furnace to reach the desired value; the increase of the cooling water flow can improve the cooling rate, more heat can be taken away when the cooling water flows into the hearth in the same time, so that the heat is reduced more quickly, excessive cooling can be caused, the quality of glass is affected or the hearth structure is damaged, and the cooling water flow is reduced, so that the cooling water cannot be cooled to the target temperature.
Because the temperature regulation has hysteresis, namely, when the temperature is raised or lowered, a certain time is needed to be raised or lowered to the target temperature, the temperature regulation hysteresis factor is constructed according to the application, the larger the temperature regulation hysteresis factor is, the temperature regulation is required to be performed as soon as possible, the smaller the temperature regulation hysteresis factor is, the temperature regulation can be performed slowly, and the calculation formula is as follows:
；
wherein Indicating the temperature regulation hysteresis factor at time t +.>Time difference between the time representing the current moment and the moment of the next local extremum, ++>Indicating the temperature difference between the current temperature and the temperature change, < >>The time difference coordination coefficient is expressed, the size of the time difference coordination coefficient is usually 30, and the aim is to avoid the phenomenon of index explosion caused by overlarge index, if the current time point is 10min in the melting stage and the time required for cooling in the next stage is 120min, the time is->120, the temperature in the current melting stage is 1600 ℃ and the temperature in the next stage is 1000 ℃ when the temperature is reduced to form glass>The size of (2) is 600 ℃.The smaller the time difference between the current time and the temperature change is, the larger the temperature difference between the current temperature and the temperature change is, the worse the hysteresis of the temperature regulation is, and the smaller the corresponding temperature regulation hysteresis factor is; the larger the time difference between the current time and the temperature change is, the smaller the temperature difference between the current temperature and the temperature change is, the stronger the hysteresis of the temperature regulation is, and the larger the corresponding temperature regulation hysteresis factor is. />An exponential function representing a natural constant.
According to the method, the temperature regulation characteristic descriptors corresponding to each production time can be constructed based on the temperature regulation hysteresis factors, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time. Specifically, a temperature regulation and control characteristic descriptor at the time of t production is constructed and recorded asThen->, wherein />Fuel feed rate at time t, +.>Fuel-air ratio at time t +.>Cooling water flow at time t +.>The temperature control hysteresis factor at time t is shown.
Step S22: and calculating a temperature regulation characteristic value based on the temperature regulation characteristic descriptor.
Specifically, referring to fig. 3, step S22 includes:
step S31: the fuel thermal energy release intensity is determined based on the fuel-air ratio in the temperature regulation feature descriptor.
In one embodiment, if the fuel-air ratio is less than or equal to the optimal fuel-air ratio, the fuel thermal energy release intensity increases with increasing fuel-air ratio; if the fuel-air ratio is greater than the optimal fuel-air ratio, the fuel thermal energy release strength decreases as the fuel-air ratio increases. Specifically, if the fuel-air ratio is less than or equal to the optimal fuel-air ratio, calculating to obtain the fuel heat energy release strength based on a difference between the fuel-air ratio and the optimal fuel-air ratio; if the fuel air ratio is greater than the optimal fuel air ratio, a fuel thermal energy release strength is calculated based on a quotient of the fuel air ratio and the optimal fuel air ratio.
In one embodiment, the fuel thermal energy release intensity is calculated by:
；
wherein ,indicating the fuel heat release strength->Fuel-air ratio at time t +.>The fuel heat energy release coefficient is expressed, the empirical value 256 is generally taken, b is expressed as an optimal fuel air ratio, and the empirical value 0.0625 is generally taken, when the fuel air ratio is equal to or smaller than the optimal fuel air ratio, the fuel heat energy release strength increases with an increase in the fuel air ratio, and when the fuel air ratio is greater than the optimal fuel air ratio, the fuel heat energy release strength decreases with an increase in the fuel air ratio.
Step S32: and calculating the cooling water flow cooling rate based on the cooling water flow in the temperature regulation and control feature descriptor.
In one embodiment, the cooling water flow rate is calculated by:
；
indicating cooling water flow rate of cooling water>Cooling water flow at time t +.>And the difference between the temperature of cooling water at the time point t and the temperature of the hearth at the time point t is shown.
Step S33: and calculating a temperature regulation characteristic value based on the temperature regulation hysteresis factor, the fuel heat energy release intensity, the cooling water flow cooling rate and the fuel supply rate.
Calculating a temperature regulation characteristic value by using the following formula:
；
wherein ,indicating the fuel heat release strength->Fuel-air ratio at time t>Indicating cooling water flow rate of cooling water>The cooling water flow at the time of t production is represented, Q (t) represents the temperature regulation characteristic value at the time of t production,/->Fuel feed rate at time t production, +.>Temperature regulation indicating time of t productionControl lag factor(s)>Representing a coordination factor, typically taking an empirical value of 0.001, for reducing the cooling water cooling rate to the same order of magnitude as the logarithmic fuel supply rate, +.>The coordination constant is expressed, so that the situation that the denominator is 0 is avoided, and the empirical value is usually 0.0001 in order to avoid great influence on the result.
Specifically, the larger the temperature regulation hysteresis factor is, the larger the fuel heat energy release intensity is, the larger the difference between the cooling water flow rate and the fuel supply rate is, the larger the corresponding temperature regulation characteristic value is, which indicates that the temperature regulation is needed; the smaller the temperature regulation hysteresis factor, the smaller the fuel heat energy release intensity, the smaller the difference between the cooling water flow cooling rate and the fuel supply rate, and the smaller the corresponding temperature regulation characteristic value, which indicates that the temperature regulation is not needed.
Step S13: and training the RNN neural network by using the temperature regulation characteristic value to obtain a fuzzy PID algorithm model.
Forming a temperature regulation characteristic sequence according to the time sequence by the temperature regulation characteristic values corresponding to all production moments; and processing the temperature regulation and control characteristic sequence and the historical production data by utilizing the RNN neural network, so as to obtain a fuzzy PID algorithm model.
Through the steps, the temperature regulation characteristic value of each time point can be calculated, a sequence formed by the temperature regulation characteristic values from the starting time point to the t time point is recorded as a temperature regulation characteristic sequence of the t time point, the temperature regulation characteristic sequence of each time point and related data acquired by each time point are taken as the input of the RNN neural network, the optimization algorithm is a random gradient descent method, and the output of the network is fuzzy to three parameters of the PID algorithm, namely proportional gain, integral gain and differential gain.
In the production process of the low borosilicate glass, the temperature regulation and control directly influence the quality of the produced low borosilicate glass, so that the temperature regulation and control are vital in the production process of the low borosilicate glass.
Step S14: and adjusting the production temperature of the low borosilicate glass based on the target temperature and the current temperature by using a fuzzy PID algorithm model.
And taking three parameters of a fuzzy PID algorithm, a target temperature and a current temperature obtained by the neural network as inputs of the fuzzy PID algorithm, and performing intelligent regulation and control on the production temperature of the low borosilicate glass according to the output of the fuzzy PID algorithm.
According to the application, factors influencing the production temperature of the low borosilicate glass are analyzed, the characteristic of hysteresis of temperature regulation is combined, a temperature regulation hysteresis factor is constructed, a temperature regulation characteristic descriptor is constructed based on the influencing factors and the temperature regulation hysteresis factor, the temperature regulation characteristic value is calculated according to the characteristics of different influencing factors and the temperature regulation hysteresis factor, the degree of the required regulation of the production temperature of the low borosilicate glass is reflected, the temperature regulation characteristic value from the starting time point to each time point is taken as a temperature regulation characteristic sequence of each time point and is sent into an RNN neural network, and parameters of a fuzzy PID algorithm are obtained, so that the fuzzy PID algorithm can have higher stability in a complex system for regulating the production temperature of the low borosilicate glass.
The foregoing is only the embodiments of the present application, and therefore, the scope of the present application is not limited by the above embodiments, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or direct or indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. An intelligent regulation and control method for the production temperature of low borosilicate glass is characterized by comprising the following steps:
acquiring historical production data of the low borosilicate glass, wherein the historical production data comprises temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production moment;
calculating a temperature regulation characteristic value based on temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production time;
training the RNN neural network by using the temperature regulation characteristic value to obtain a fuzzy PID algorithm model;
and adjusting the production temperature of the low borosilicate glass based on the target temperature and the current temperature by using the fuzzy PID algorithm model.
2. The intelligent regulation method of low borosilicate glass production temperature according to claim 1, wherein calculating the temperature regulation characteristic value based on the temperature data, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time comprises:
constructing a temperature regulation and control feature descriptor corresponding to each production time based on temperature data, fuel supply rate, fuel-air ratio and cooling water flow corresponding to each production time;
and calculating the temperature regulation characteristic value based on the temperature regulation characteristic descriptor.
3. The intelligent regulation method of low borosilicate glass production temperature according to claim 2, wherein constructing the temperature regulation feature descriptors corresponding to each production time based on the temperature data, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time comprises:
calculating a temperature regulation hysteresis factor of each production time based on the temperature data corresponding to each production time;
and constructing a temperature regulation characteristic descriptor corresponding to each production time based on the temperature regulation hysteresis factor, the fuel supply rate, the fuel-air ratio and the cooling water flow corresponding to each production time.
4. The intelligent regulation and control method of low borosilicate glass production temperature according to claim 3, wherein calculating the temperature regulation and control hysteresis factor at each production time based on the temperature data corresponding to each production time comprises:
calculating the temperature regulation hysteresis factor at each production time by using the following formula:
；
wherein ,temperature control hysteresis factor indicating t production time, < ->Time difference between time representing time of t production time and time of next local extremum, ++>Indicating the temperature difference between the current temperature and the temperature change, < >>Representing a time difference coordination coefficient; the temperature regulation hysteresis factor is used for reflecting the emergency degree of temperature regulation,/and/or>An exponential function representing a natural constant.
5. The intelligent regulation method of low borosilicate glass production temperature according to claim 2, wherein calculating the temperature regulation feature value based on the temperature regulation feature descriptor comprises:
determining a fuel thermal energy release intensity based on the fuel-air ratio in the temperature regulation feature descriptor;
calculating cooling water flow cooling rate based on the cooling water flow in the temperature regulation and control feature descriptor;
and calculating the temperature regulation characteristic value based on the temperature regulation hysteresis factor, the fuel heat energy release intensity, the cooling water flow cooling rate and the fuel supply rate.
6. The method of claim 5, wherein determining the fuel heat release intensity based on the fuel-air ratio in the temperature regulation feature descriptor comprises:
if the fuel-air ratio is less than or equal to the optimal fuel-air ratio, the fuel thermal energy release strength increases with an increase in the fuel-air ratio;
if the fuel-air ratio is greater than the optimal fuel-air ratio, the fuel thermal energy release strength decreases as the fuel-air ratio increases.
7. The method of claim 5, wherein determining the fuel heat release intensity based on the fuel-air ratio in the temperature regulation feature descriptor comprises:
if the fuel-air ratio is less than or equal to the optimal fuel-air ratio, calculating to obtain the fuel heat energy release strength based on a difference value between the fuel-air ratio and the optimal fuel-air ratio;
if the fuel air ratio is greater than the optimal fuel air ratio, a fuel thermal energy release strength is calculated based on a quotient of the fuel air ratio and the optimal fuel air ratio.
8. The method according to claim 5, wherein calculating the temperature regulation characteristic value based on a temperature regulation hysteresis factor, a fuel heat energy release intensity, a cooling water flow rate, a fuel supply rate, comprises:
calculating a temperature regulation characteristic value by using the following formula:
；
wherein ,indicating the fuel heat release strength->Fuel-air ratio at time t>Indicating cooling water flow rate of cooling water>The flow of cooling water at the time of t production is represented, Q (t) represents a temperature regulation characteristic value at the time of t production,fuel feed rate at time t production, +.>Temperature control hysteresis factor indicating t production time, < ->Representing coordination factors->Representing the coordination constant.
9. The intelligent regulation and control method of low borosilicate glass production temperature according to claim 5, wherein calculating the cooling water flow rate based on the cooling water flow rate in the temperature regulation and control feature descriptor comprises:
the cooling water flow rate is calculated by the following steps:
；
indicating cooling water flow rate of cooling water>Cooling water flow at time t +.>And the difference between the temperature of cooling water at the time point t and the temperature of the hearth at the time point t is shown.
10. The intelligent regulation and control method for the production temperature of low borosilicate glass according to claim 1, wherein the training of the RNN neural network by using the temperature regulation and control characteristic value to obtain a fuzzy PID algorithm model comprises the following steps:
forming a temperature regulation characteristic sequence according to the time sequence by the temperature regulation characteristic values corresponding to all production moments;
and processing the temperature regulation and control characteristic sequence and the historical production data by utilizing the RNN neural network, so as to obtain a fuzzy PID algorithm model.
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| CN202311020321.3A CN116736907A (en) | 2023-08-15 | 2023-08-15 | Intelligent regulation and control method for production temperature of low borosilicate glass |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117492490A (en) * | 2023-12-29 | 2024-02-02 | 山东兴诺工贸股份有限公司 | Intelligent temperature control system for glass processing based on data analysis |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117492490A (en) * | 2023-12-29 | 2024-02-02 | 山东兴诺工贸股份有限公司 | Intelligent temperature control system for glass processing based on data analysis |
| CN117492490B (en) * | 2023-12-29 | 2024-04-16 | 山东兴诺工贸股份有限公司 | Intelligent temperature control system for glass processing based on data analysis |
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