A constant temperature control method and system for cooling water of a float glass production line

Abstract

The invention discloses a constant temperature control method and system for cooling water of a float glass production line, and relates to the technical field of cooling water control. The main technical scheme is as follows: based on a temperature prediction model, a temperature prediction value of cooling water in a cold water pool is obtained; first circulation data of cooling water flowing from a hot water pool to a premixing pool, second circulation data of cooling water flowing from a cooling tower to a premixing pool, and a target temperature value and a residual amount of cooling water in a cold water pool are obtained; the first circulation data, the second circulation data, the temperature prediction value and the residual amount are calculated to obtain a premixing temperature value of cooling water in a premixing pool and cooling water in a cold water pool; based on the judgment result of the premixing temperature value and the target temperature value, the ratio of the first circulation data to the second circulation data is adjusted. This eliminates the influence of ambient temperature on cooling water in a cold water pool. This allows the cooling water in a cold water pool to be maintained at a suitable temperature. This achieves the purpose of reducing the influence of ambient temperature on the water temperature of cooling water in a cold water pool, so as to improve production quality.

Description

Technical Field

[0001] The invention relates to the technical field of cooling water control, and in particular to a constant temperature control method and system for cooling water of a float glass production line. Background Art

[0002] At present, the temperature of cooling water in the cold water pool used in domestic float glass production lines varies greatly with the seasons and weather. When the cooling water flows through the float glass production line, it will cause changes in the cooling effect of the cooling equipment in the float glass production line, which will bring many negative impacts on the production of float glass: such as causing changes in the optical deformation angle of the glass, and in severe cases, causing the appearance of microbubbles in the glass; it will also cause changes in the thickness and thickness difference of the glass, affecting the quality grade of the glass and other defects.

[0003] In the existing constant temperature control device for cooling water of a float glass production line, when in use, hot water flowing out of a hot water pool and cold water flowing out of a cooling tower are mixed inside a shell, and the water temperature inside the shell is detected by a first temperature sensor, and the water temperature inside a cold water pool is detected by a second temperature sensor. Then, the flow rate of hot water entering the shell is adjusted to adjust the water temperature inside the shell, and the water temperature inside the shell is made the same as the water temperature in the cold water pool, thereby achieving the purpose of constant temperature regulation.

[0004] However, in actual use, when the ambient temperature in the area where the cold water pool is located changes dramatically, the water temperature in the cold water pool will change with the change in ambient temperature, and the water temperature inside the outer shell will be adjusted synchronously with the water temperature in the cold water pool, making it difficult to ensure that the water temperature in the cold water pool is maintained at the temperature required for cooling water for the float glass production line. Summary of the invention

[0005] The object of the present invention is to provide a constant temperature control method and system for cooling water of a float glass production line, which solves the problem that the existing constant temperature control device for cooling water of a float glass production line is difficult to ensure that the water temperature in the cold water pool is maintained at the required temperature for cooling water used in the float glass production line.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] In a first aspect, a constant temperature control method for cooling water of a float glass production line is provided, the constant temperature control method is used to control a cooling water circulation system of the float glass production line, the cooling water circulation system comprises a cooling tower, a cold water pool, a hot water pool, a cooling tower and a premixing pool, the discharge end of the cooling tower and the discharge end of the hot water pool are both connected to the feed end of the premixing pool, the discharge end of the premixing pool is connected to the feed end of the cold water pool, the discharge end of the cold water pool is used to connect to the float glass production line, and the feed end of the hot water pool is used to connect to the float glass production line, the constant temperature control method comprises the following operations:

[0008] S1. Construct a temperature prediction model for the change of the temperature of cooling water in the cold water pool with the ambient temperature;

[0009] S2. Based on the temperature prediction model, obtain the predicted value of the temperature of the cooling water in the cold water pool at the current ambient temperature;

[0010] S3. Obtain the first circulation data of cooling water from the hot water pool to the premixing pool, the second circulation data of cooling water from the cooling tower to the premixing pool, and the target temperature value and the remaining amount of cooling water in the cold water pool; wherein the first circulation data includes a first input amount and a first temperature value; the second circulation data includes a second input amount and a second temperature value; wherein the first input amount is the mass of cooling water from the hot water pool flowing into the premixing pool; the first temperature value is the temperature of cooling water in the hot water pool when it enters the premixing pool; the second input amount is the mass of cooling water from the cooling tower flowing into the premixing pool; the second temperature value is the temperature of cooling water in the cooling tower when it enters the premixing pool;

[0011] S4. The first input amount, the first temperature value, the second input amount, and the second temperature value are calculated to obtain the mixing temperature value and the mixing amount of the cooling water in the premixing tank;

[0012] S5. Calculate the mixed temperature value, mixed amount, temperature prediction value and remaining amount to obtain the premixed temperature value of the cooling water in the premixing tank and the cooling water in the cold water tank;

[0013] S6. Judge the premix temperature value and the target temperature value; if the premix temperature value is less than the target temperature value, increase the ratio of the first input amount to the second input amount, and execute S4; if the premix temperature value is equal to the target temperature value, pass the cooling water in the premix tank into the cold water tank; if the premix temperature value is greater than the target temperature value, reduce the ratio of the first input amount to the second input amount, and execute S4.

[0014] A further technical solution is: in step S1, the process of constructing the temperature prediction model includes:

[0015] S10. Obtaining several historical ambient temperature values ​​of the area where the cold water pool is located, and the historical temperature values ​​of the cooling water in the cold water pool corresponding to each historical ambient temperature value;

[0016] S11. Constructing a first data set including historical ambient temperature values ​​and historical temperature values;

[0017] S12. Cleaning the data in the first data set to obtain a second data set;

[0018] S13. Using a linear regression model to train the data in the second data set to obtain the temperature prediction model.

[0019] A further technical solution is: in step S12, the data cleaning process in the first data set includes:

[0020] S121. Calculate the correlation coefficient between the historical ambient temperature value and the corresponding historical temperature value in the second data set;

[0021] S122. Eliminate historical ambient temperature values ​​and corresponding historical temperature values ​​whose correlation coefficients do not meet preset values ​​in the second data set.

[0022] A further technical solution is: in step S2, the process of obtaining the current ambient temperature includes:

[0023] S20. Obtaining historical time data and historical weather data corresponding to the historical ambient temperature values ​​in the second data set;

[0024] S21. Constructing a third data set including historical time data, historical weather data and historical ambient temperature values ​​in the second data;

[0025] S22. Cleaning the data in the third data set to obtain a fourth data set;

[0026] S23. Training the data in the fourth data set to obtain an ambient temperature prediction model;

[0027] S24. Get current time data and weather data;

[0028] S25. Input the time data and weather data into an ambient temperature prediction model to obtain the current ambient temperature.

[0029] A further technical solution is: in step S24, the process of acquiring the weather data includes:

[0030] S240. Construct a fifth data set including the historical time data and historical weather data in the fourth data set;

[0031] S241. Clean the data in the fifth data set to obtain a sixth data set;

[0032] S422. Training the data in the sixth data set to obtain a weather prediction model;

[0033] S423. Input the time data into the weather forecast model to obtain weather data.

[0034] A further technical solution is: in step S3, the process of obtaining the first input amount includes:

[0035] Calculating a first density of cooling water in the hot water pool based on the first temperature value;

[0036] Collecting a first flow area, a first flow rate and a first flow time when cooling water in the hot water tank flows to the premixing tank;

[0037] The first density, the first flow area, the first flow rate and the first flow time are calculated to obtain a first flow rate.

[0038] A further technical solution is: in step S3, the process of obtaining the second input amount includes:

[0039] Calculating a second density of cooling water in the cooling tower based on the second temperature value;

[0040] Collect the second flow area, the second flow rate and the second flow time of cooling water in the cooling tower when it flows to the premixing tank;

[0041] The second density, the second flow area, the second flow rate and the second flow time are calculated to obtain a second flow rate.

[0042] In a second aspect, a constant temperature control system for cooling water of a float glass production line is provided, which is used to execute the constant temperature control method in the first aspect, and includes a construction module, a first acquisition module, a second acquisition module, a first calculation module, a second calculation module, a judgment module, a first execution module, a second execution module and a third execution module, wherein the construction module is used to execute S1 to construct a temperature prediction model in which the temperature of cooling water in a cold water pool changes with the ambient temperature; the first acquisition module is used to execute S2 to obtain a temperature prediction value of the cooling water in the cold water pool at the current ambient temperature based on the temperature prediction model; the second acquisition module is used to execute S3 to obtain first circulation data of cooling water flowing from a hot water pool to a premixing pool, second circulation data of cooling water flowing from a cooling tower to a premixing pool, and a target temperature value and a remaining amount of cooling water in the cold water pool; wherein the first circulation data includes a first input amount and a first temperature value; the second circulation data includes a first input amount and a first temperature value; It includes a second input amount and a second temperature value; the first calculation module is used to execute S4 to calculate the first input amount, the first temperature value, the second input amount, and the second temperature value to obtain the mixing temperature value and the mixing amount of the cooling water in the premixing tank; the second calculation module is used to execute S5 to calculate the mixing temperature value, the mixing amount, the temperature prediction value and the remaining amount to obtain the premixing temperature value of the cooling water in the premixing tank and the cooling water in the cold water tank; the judgment module is used to execute S6 to judge the premixing temperature value and the target temperature value; the first execution module is used to increase the ratio of the first input amount to the second input amount when the premixing temperature value is less than the target temperature value; the second execution module is used to pass the cooling water in the premixing tank into the cold water tank when the premixing temperature value is equal to the target temperature value; the third execution module is used to reduce the ratio of the first input amount to the second input amount when the premixing temperature value is greater than the target temperature value.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] First, the possible temperature of the cooling water in the cold water pool under the current ambient temperature is predicted, and then the temperature of the cooling water in the premixing chamber is adjusted dynamically in real time, and the cooling water in the premixing pool is passed into the cold water pool, in order to eliminate the influence of the ambient temperature on the cooling water in the cold water pool. Then, the cooling room in the cold water pool is maintained at the appropriate temperature required by the float glass production line. In this way, the influence of the ambient temperature on the cooling water temperature in the cold water pool is reduced, so as to improve the production quality of the float glass production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of a flow chart of a method for controlling the constant temperature of cooling water in a float glass production line in this embodiment;

[0046] Figure 2 This is a schematic diagram of an electrical block diagram of a constant temperature control system for cooling water of a float glass production line in this embodiment. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] Example 1

[0049] The present embodiment provides a constant temperature control method for cooling water of a float glass production line, the constant temperature control method is used to control the cooling water circulation system of the float glass production line, the cooling water circulation system comprises a cooling tower, a cold water pool, a hot water pool, a cooling tower and a premixing pool, the discharge end of the cooling tower and the discharge end of the hot water pool are both connected to the feed end of the premixing pool, the discharge end of the premixing pool is connected to the feed end of the cold water pool, the discharge end of the cold water pool is used to connect to the float glass production line, and the feed end of the hot water pool is used to connect to the float glass production line, such as Figure 1 As shown, the constant temperature control method includes the following operations:

[0050] S01. Construct a temperature prediction model for the change of the temperature of cooling water in the cold water pool with the ambient temperature;

[0051] S02. Based on the temperature prediction model, obtain the predicted value of the temperature of the cooling water in the cold water pool at the current ambient temperature;

[0052] For example, during the implementation, the current ambient temperature is collected in real time. , and the collected ambient temperature Input into the temperature prediction model to predict the temperature of the cooling water in the cooling pool at the current ambient temperature .

[0053] S03. Obtain the first circulation data of cooling water from the hot water pool to the premixing pool, the second circulation data of cooling water from the cooling tower to the premixing pool, and the target temperature value and the remaining amount of cooling water in the cold water pool; wherein the first circulation data includes the first input amount and the first temperature value; the second circulation data includes the second input amount and the second temperature value; wherein the first input amount is the mass of cooling water from the hot water pool flowing into the premixing pool; the first temperature value is the temperature of cooling water in the hot water pool when it enters the premixing pool; the second input amount is the mass of cooling water from the cooling tower flowing into the premixing pool; the second temperature value is the temperature of cooling water in the cooling tower when it enters the premixing pool;

[0054] For example, during implementation, the target temperature value It refers to the appropriate temperature of cooling water in the cold water pool required by the float glass production line. In other words, the cooling water in the cold water pool is at the target temperature. When in this state, the glass produced by the float glass production line meets the quality requirements.

[0055] In the actual production process, different types of glass (such as flat glass, architectural glass, automotive glass, etc.) and different specifications of glass (such as thickness, size, etc.) have different requirements for cooling water temperature. For example, thinner glass requires faster cooling speed and may need to use cooling water with lower temperature; while thicker glass requires slower cooling speed to avoid excessive internal stress and cracking, and may need to use cooling water with slightly higher temperature. Therefore, the target temperature value It can be determined based on the type and specifications of the glass, as well as historical production experience.

[0056] Among them, the first temperature value is the temperature of the cooling water in the hot water pool when it enters the premixing tank, that is, the temperature of the cooling water in the hot water pool when it passes through the water inlet of the premixing tank; the second temperature value is the temperature of the cooling water in the cooling tower when it enters the premixing tank, that is, the temperature of the cooling water in the cooling tower when it passes through the water inlet of the premixing tank.

[0057] S04. The first input amount, the first temperature value, the second input amount, and the second temperature value are calculated to obtain the mixing temperature value and the mixing amount of the cooling water in the premixing tank;

[0058] Exemplarily, during implementation, the calculation formula for the mixing temperature value of the cooling water in the premixing tank is as follows:

[0059]

[0060] in, is the mixing temperature of cooling water in the premixing tank; is the first input amount; is the first temperature value; is the second input amount; is the second temperature value.

[0061] The formula for calculating the mixing amount of cooling water in the premixing tank is as follows:

[0062]

[0063] in, It is the mixing amount of cooling water in the premixing tank.

[0064] S05. Calculate the mixed temperature value, mixed amount, temperature prediction value and remaining amount to obtain the premixed temperature value of the cooling water in the premixing tank and the cooling water in the cold water tank;

[0065] Exemplarily, during implementation, the mixing temperature value of the cooling water in the premixing tank is used. , the mixing amount of cooling water in the premixing tank , Predicted temperature of cooling water in cooling pool , the remaining amount of cooling water in the cold water pool , predict the premixed temperature value of the cooling water in the cold water pool after the cooling water in the premixed pool is passed into the cold water pool. The calculation formula for the premixed temperature value of the cooling water in the premixed pool and the cooling water in the cold water pool is as follows:

[0066]

[0067] in, It is the premixing temperature value of cooling water in the premixing tank and cooling water in the cold water tank; is the remaining amount of cooling water in the cold water pool.

[0068] S06. Judge the premix temperature value and the target temperature value; if the premix temperature value is less than the target temperature value, increase the ratio of the first input amount to the second input amount, and execute S04; if the premix temperature value is equal to the target temperature value, pass the cooling water in the premix tank into the cold water tank; if the premix temperature value is greater than the target temperature value, reduce the ratio of the first input amount to the second input amount, and execute S04.

[0069] First, the possible temperature of the cooling water in the cold water pool under the current ambient temperature is predicted, and then the temperature of the cooling water in the premixing chamber is adjusted dynamically in real time, and the cooling water in the premixing pool is passed into the cold water pool, in order to eliminate the influence of the ambient temperature on the cooling water in the cold water pool. Then, the cooling water in the cold water pool is maintained at the appropriate temperature required by the float glass production line. In this way, the influence of the ambient temperature on the cooling water temperature in the cold water pool is reduced, so as to improve the production quality of the float glass production line.

[0070] The change of ambient temperature is usually a continuous change with a small change amount. In this embodiment, the temperature prediction value of the cooling water in the cooling pool is obtained by real-time prediction of the change of the cooling water in the cooling pool under the ambient temperature. The target temperature of the cooling water in the cold water pool The difference between them is always maintained in a small change state, and then the temperature of the cooling water in the premixing chamber is adjusted dynamically in real time to compensate the temperature of the cooling water in the cooling pool in real time. In this way, the temperature difference between the cooling water in the premixing chamber and the cooling water in the cold water pool is reduced, and the risk of temperature fluctuation of the cooling water in the cold water pool caused by the cooling water in the premixing chamber entering the cold water pool is reduced, so as to improve the production quality of the float glass production line.

[0071] In this embodiment, in step S01, the process of constructing the temperature prediction model includes:

[0072] S010. Obtain several historical ambient temperature values ​​of the area where the cold water pool is located, and the historical temperature values ​​of the cooling water in the cold water pool corresponding to each historical ambient temperature value;

[0073] For example, during the implementation process, several historical ambient temperature values ​​of the area where the cold water pool is located in the past period of time can be collected from the factory monitoring system, weather station or other reliable data sources, and recorded as , , … , , and the historical temperature value corresponding to each historical ambient temperature value, recorded as , , … , For example, this could be data from past years to cover different seasons and weather conditions.

[0074] S011. Constructing a first data set including historical ambient temperature values ​​and historical temperature values;

[0075] Among them, the first data set Denoted as {( , )、( , )、( , )、( , )、( , )}.

[0076] S012. Cleaning the data in the first data set to obtain a second data set;

[0077] In this embodiment, in step S012, the data cleaning process of the first data set includes:

[0078] S0121. Calculate the correlation coefficient between the historical ambient temperature value and the corresponding historical temperature value in the second data set;

[0079] S0122. Eliminate historical ambient temperature values ​​and corresponding historical temperature values ​​whose correlation coefficients in the second data set do not meet preset values.

[0080] Exemplarily, during implementation, first, identify and delete the first data set For the first data set, there are obviously abnormal data points, such as data with historical ambient temperatures exceeding 40°C. The missing data in the dataset are filled by using interpolation methods (such as linear interpolation or nearest neighbor interpolation). Repeated data in .

[0081] Next, calculate the first data set The Pearson correlation coefficient between each pair of historical ambient temperature values ​​and cooling water temperature values. The value range of the Pearson correlation coefficient is [-1, 1], where 1 indicates a completely positive correlation, -1 indicates a completely negative correlation, and 0 indicates no linear correlation. Set the preset value of the correlation coefficient according to the actual situation, and set it to 0 here. Traverse the first data set , and retain the data points with correlation coefficient greater than 0. To obtain the second data set .

[0082] S013. Use a linear regression model to train the data in the second data set to obtain the temperature prediction model.

[0083] Exemplarily, during implementation, the second data set The data in is divided into a training set and a test set. With the ambient temperature as the input feature and the temperature as the output label, the linear regression model is trained on the data in the training set to obtain the temperature prediction model. The temperature prediction model is shown in the following formula:

[0084]

[0085] in, is the predicted value of the temperature of cooling water in the cooling pool; is the current ambient temperature; and is the regression coefficient; is the error term.

[0086] The data in the test set is then used to evaluate the performance of the temperature prediction model.

[0087] In this embodiment, in step S02, the process of obtaining the current ambient temperature includes:

[0088] S020. Obtaining historical time data and historical weather data corresponding to the historical ambient temperature values ​​in the second data set;

[0089] Exemplarily, during implementation, the second data set is collected from a factory monitoring system, a weather station, or other reliable data source. The historical time data corresponding to each historical ambient temperature value in is recorded as , , … , , and the historical weather data corresponding to each historical ambient temperature value, recorded as , , … , .

[0090] S021. Construct a third data set including historical time data, historical weather data and historical ambient temperature values ​​in the second data;

[0091] Among them, the third data set Denoted as {( , , )、( , , )、( , , )、( , , )、( , , )}.

[0092] S022. Cleaning the data in the third data set to obtain a fourth data set;

[0093] Exemplarily, during implementation, first, the third data set is identified and deleted. For the third data set The missing data in the dataset are filled by using interpolation methods (such as linear interpolation or nearest neighbor interpolation). Repeat the data in to get the fourth data set .

[0094] S023. Training the data in the fourth data set to obtain an ambient temperature prediction model;

[0095] Exemplarily, during implementation, the fourth data set The data in is divided into a training set and a test set. With time data (XX month XX day XX hour) and weather data (such as humidity, wind speed, precipitation) as input features and ambient temperature as output label, the data in the training set is used to train a multivariate linear regression model to obtain an ambient temperature prediction model. The ambient temperature prediction model is shown in the following formula:

[0096]

[0097] in, is time data; For weather data; , and is the regression coefficient; is the error term.

[0098] The data in the test set is then used to evaluate the performance of the ambient temperature prediction model.

[0099] S024. Get current time data and weather data;

[0100] S025. Input the time data and weather data into an ambient temperature prediction model to obtain the current ambient temperature.

[0101] In this embodiment, the ambient temperature prediction model constructed using time data, weather data and ambient temperature values ​​predicts the ambient temperature, thereby achieving the purpose of improving the accuracy of the temperature prediction model in predicting the temperature change of cooling water in the cold water pool based on time data and weather data.

[0102] In this embodiment, in step S024, the process of acquiring weather data includes:

[0103] S0240. Construct a fifth data set including the historical time data and historical weather data in the fourth data set;

[0104] Exemplarily, during implementation, a fourth data set is collected The historical time data in , , … , , and the historical weather data corresponding to each historical time data, recorded as , , … , , forming the fifth data set .

[0105] Among them, the fifth data set Denoted as {( , )、( , )、( , )、( , )、( , )}.

[0106] S0241. Clean the data in the fifth data set to obtain a sixth data set;

[0107] Exemplarily, during implementation, first, the fifth data set is identified and deleted. For the fifth data set, The missing data in the dataset are filled by using interpolation methods (such as linear interpolation or nearest neighbor interpolation). Repeat the data in to get the sixth data set .

[0108] S0422. Training the data in the sixth data set to obtain a weather prediction model;

[0109] Exemplarily, during implementation, the sixth data set The data in is divided into a training set and a test set. With time data (XX month XX day XX hour) as input features and weather data (such as humidity, wind speed, precipitation) as output labels, the data in the training set is used to train a linear regression model to obtain a weather prediction model. The weather prediction model is shown in the following formula:

[0110]

[0111] in, and is the regression coefficient; is the error term.

[0112] S0423. Input the time data into the weather forecast model to obtain weather data.

[0113] In this embodiment, the weather prediction model constructed using time data and weather data predicts weather data, thereby achieving the purpose of using time data to predict the change in cooling water temperature in the cold water pool, thereby reducing the simplicity of the prediction process.

[0114] In this embodiment, in step S03, the process of obtaining the first input amount includes:

[0115] Calculating a first density of cooling water in the hot water pool based on the first temperature value;

[0116] Collecting a first flow area, a first flow rate and a first flow time when cooling water in the hot water tank flows to the premixing tank;

[0117] The first density, the first flow area, the first flow rate and the first flow time are calculated to obtain a first flow rate.

[0118] First intake The calculation method is as follows:

[0119]

[0120] in, is the reference temperature Cooling water density under is the thermal expansion coefficient of cooling water; is the reference temperature; is the first flow area; is the first flow rate; The first circulation time.

[0121] In this embodiment, in step S03, the process of obtaining the second input amount includes:

[0122] Calculating a second density of cooling water in the cooling tower based on the second temperature value;

[0123] Collect the second flow area, the second flow rate and the second flow time of cooling water in the cooling tower when it flows to the premixing tank;

[0124] The second density, the second flow area, the second flow rate and the second flow time are calculated to obtain a second flow rate.

[0125] First intake The calculation method is as follows:

[0126]

[0127] in, is the second flow area; is the second flow rate; The second circulation time.

[0128] Example 2

[0129] This embodiment provides a constant temperature control system for cooling water of a float glass production line, which is used to execute the constant temperature control method in Embodiment 1, such as Figure 2 As shown, it includes a construction module, a first acquisition module, a second acquisition module, a first calculation module, a second calculation module, a judgment module, a first execution module, a second execution module and a third execution module.

[0130] Wherein, the construction module is used to execute S01 to construct a temperature prediction model in which the temperature of the cooling water in the cold water pool changes with the ambient temperature; the first acquisition module is used to execute S02 to obtain the temperature prediction value of the cooling water in the cold water pool under the current ambient temperature based on the temperature prediction model; the second acquisition module is used to execute S03 to obtain the first circulation data of the cooling water flowing from the hot water pool to the premixing pool, the second circulation data of the cooling water flowing from the cooling tower to the premixing pool, and the target temperature value and the remaining amount of the cooling water in the cold water pool; wherein the first circulation data includes the first input amount and the first temperature value; the second circulation data includes the second input amount and the second temperature value; the first calculation module is used to execute S04 to calculate the first input amount, the first temperature value, the second input amount, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the second temperature value, the first temperature value, the second ... The first execution module is used to increase the ratio of the first input amount to the second input amount when the premixing temperature value is less than the target temperature value; the second execution module is used to pass the cooling water in the premixing pool into the cold water pool when the premixing temperature value is equal to the target temperature value; the third execution module is used to reduce the ratio of the first input amount to the second input amount when the premixing temperature value is greater than the target temperature value.

[0131] Although the present invention is described herein with reference to a number of illustrative embodiments of the present invention, it will be appreciated that those skilled in the art may devise many other modifications and implementations that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, drawings, and claims, a variety of variations and improvements may be made to the components and / or layout of the subject combination layout. In addition to variations and improvements made to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A constant temperature control method for cooling water of a float glass production line, characterized in that: The constant temperature control method is used to control the cooling water circulation system of the float glass production line, the cooling water circulation system includes a cooling tower, a cold water pool, a hot water pool and a premixing pool, the discharge end of the cooling tower and the discharge end of the hot water pool are connected to the feed end of the premixing pool, the discharge end of the premixing pool is connected to the feed end of the cold water pool, the discharge end of the cold water pool is used to connect to the float glass production line, and the feed end of the hot water pool is used to connect to the float glass production line. The constant temperature control method includes the following operations: S1. Construct a temperature prediction model for the change of the temperature of cooling water in the cold water pool with the ambient temperature; S2. Based on the temperature prediction model, obtain the predicted value of the temperature of the cooling water in the cold water pool at the current ambient temperature; S3 obtains the first circulation data of the cooling water flowing from the hot water pool to the premixing pool, the second circulation data of the cooling water flowing from the cooling tower to the premixing pool, and the target temperature value and the remaining amount of the cooling water in the cold water pool; The first circulation data includes a first flow rate and a first temperature value; the second circulation data includes a second flow rate and a second temperature value; Among them, the first input amount is the mass of cooling water in the hot water pool flowing into the premixing pool; the first temperature value is the temperature of the cooling water in the hot water pool when it enters the premixing pool; the second input amount is the mass of cooling water in the cooling tower flowing into the premixing pool; the second temperature value is the temperature of the cooling water in the cooling tower when it enters the premixing pool; The process of obtaining the first input amount includes: Calculating a first density of cooling water in the hot water pool based on the first temperature value; Collecting a first flow area, a first flow rate and a first flow time when cooling water in the hot water tank flows to the premixing tank; Calculating the first density, the first flow area, the first flow rate and the first flow time to obtain a first flow rate; The first intake The calculation method is as follows: ； in, is the reference temperature Cooling water density under is the thermal expansion coefficient of cooling water; is the reference temperature; is the first flow area; is the first flow rate; The first circulation time; is the first temperature value; The process of obtaining the second input amount includes: Calculating a second density of cooling water in the cooling tower based on the second temperature value; Collect the second flow area, the second flow rate and the second flow time of cooling water in the cooling tower when it flows to the premixing tank; Calculating the second density, the second flow area, the second flow rate and the second flow time to obtain a second flow rate; The second input amount The calculation method is as follows: ； in, is the second flow area; is the second flow rate; The second circulation time; is the second temperature value; S4. The first input amount, the first temperature value, the second input amount, and the second temperature value are calculated to obtain the mixing temperature value and the mixing amount of the cooling water in the premixing tank; S5. Calculate the mixed temperature value, mixed amount, temperature prediction value and remaining amount to obtain the premixed temperature value of the cooling water in the premixing tank and the cooling water in the cold water tank; S6. Judge the premix temperature value and the target temperature value; if the premix temperature value is less than the target temperature value, increase the ratio of the first input amount to the second input amount, and execute S4; if the premix temperature value is equal to the target temperature value, pass the cooling water in the premix tank into the cold water tank; if the premix temperature value is greater than the target temperature value, reduce the ratio of the first input amount to the second input amount, and execute S4.

2. The constant temperature control method according to claim 1, characterized in that: In step S1, the process of constructing the temperature prediction model includes: S10. Obtaining several historical ambient temperature values ​​of the area where the cold water pool is located, and the historical temperature values ​​of the cooling water in the cold water pool corresponding to each historical ambient temperature value; S11. Constructing a first data set including historical ambient temperature values ​​and historical temperature values; S12. Cleaning the data in the first data set to obtain a second data set; S13. Using a linear regression model to train the data in the second data set to obtain the temperature prediction model.

3. The constant temperature control method according to claim 2, characterized in that: In step S12, the data cleaning process of the first data set includes: S121. Calculate the correlation coefficient between the historical ambient temperature value and the corresponding historical temperature value in the second data set; S122. Eliminate historical ambient temperature values ​​and corresponding historical temperature values ​​whose correlation coefficients do not meet preset values ​​in the second data set.

4. The constant temperature control method according to claim 2, characterized in that: In step S2, the process of obtaining the current ambient temperature includes: S20. Obtaining historical time data and historical weather data corresponding to the historical ambient temperature values ​​in the second data set; S21. Constructing a third data set including historical time data, historical weather data and historical ambient temperature values ​​in the second data; S22. Cleaning the data in the third data set to obtain a fourth data set; S23. Training the data in the fourth data set to obtain an ambient temperature prediction model; S24. Get current time data and weather data; S25. Input the time data and weather data into an ambient temperature prediction model to obtain the current ambient temperature.

5. The constant temperature control method according to claim 4, characterized in that: In step S24, the weather data acquisition process includes: S240. Construct a fifth data set including the historical time data and historical weather data in the fourth data set; S241. Clean the data in the fifth data set to obtain a sixth data set; S422. Training the data in the sixth data set to obtain a weather prediction model; S423. Input the time data into the weather forecast model to obtain weather data.

6. A constant temperature control system for cooling water of a float glass production line, characterized in that: A method for performing a constant temperature control according to any one of claims 1 to 5, comprising: A construction module, wherein the construction module is used to execute S1 to construct a temperature prediction model in which the temperature of cooling water in the cold water pool changes with the ambient temperature; A first acquisition module, the first acquisition module is used to execute S2 to obtain a temperature prediction value of cooling water in the cold water pool at the current ambient temperature based on a temperature prediction model; A second acquisition module, the second acquisition module is used to execute S3 to obtain first circulation data of cooling water from the hot water tank to the premixing tank, second circulation data of cooling water from the cooling tower to the premixing tank, and a target temperature value and a remaining amount of cooling water in the cold water tank; The first circulation data includes a first flow rate and a first temperature value; the second circulation data includes a second flow rate and a second temperature value; A first calculation module, the first calculation module is used to execute S4 to calculate the first input amount, the first temperature value, the second input amount, and the second temperature value to obtain the mixing temperature value and the mixing amount of the cooling water in the premixing tank; A second calculation module, the second calculation module is used to execute S5 to calculate the mixing temperature value, the mixing amount, the temperature prediction value and the remaining amount to obtain the premixing temperature value of the cooling water in the premixing tank and the cooling water in the cold water tank; A judgment module, the judgment module is used to execute S6 to judge the premix temperature value and the target temperature value; A first execution module, wherein the first execution module is used to increase the ratio of the first input amount to the second input amount when the premix temperature value is less than the target temperature value; A second execution module, the second execution module is used to pass the cooling water in the premixing tank into the cold water tank when the premixing temperature value is equal to the target temperature value; The third execution module is used to reduce the ratio of the first input amount to the second input amount when the premix temperature value is greater than the target temperature value.

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