A digital calculation method for determining the endpoint of continuous purification of TiO2 sol

Abstract

The present application relates to the technical field of TiO2 sol purification, and particularly relates to a digital judgment and calculation method for a TiO2 sol continuous purification end point, which calculates the TiO2 sol purification end point, thereby improving the production quality of the TiO2 sol, and reducing the failure rate of the TiO2 sol purification equipment; the method comprises the following steps: S1, detecting: detecting the temperature of the TiO2 sol through a temperature sensor, detecting the pressure received by the TiO2 sol through a pressure sensor, detecting the PH value of the TiO2 sol through a PH value sensor, and detecting the flow rate of the TiO2 sol through a flow rate sensor; S2, judging: comparing the detection result with previous detection results through an analysis processor; S3, calculating: substituting the detection result into a calculation formula through the analysis processor to obtain a Z value; S4, predicting: predicting the TiO2 sol purification qualified end point; and S5, data recording: storing the detection data, calculation process and Z value of each time through a storage module.

Description

Technical Field

[0001] This invention relates to the technical field of TiO2 sol purification, and in particular to a digital method for determining and calculating the endpoint of continuous purification of TiO2 sol. Background Technology

[0002] Titanium dioxide sol is a liquid solution containing titanium dioxide (TiO2) particles. It is usually composed of a titanium source and a solvent and is prepared through appropriate treatment. Preparation methods for titanium dioxide sol include a method for preparing titanium dioxide sol disclosed in invention patent CN107913692B and a transparent and stable titanium dioxide sol disclosed in invention patent CN101827650B. Both methods improve the quality of titanium dioxide sol during preparation.

[0003] However, titanium dioxide purification generally employs dialysis, which is further divided into continuous membrane separation and intermittent bag membrane separation. Regardless of the method, determining the dialysis endpoint is challenging. If the endpoint is reached prematurely, the sol contains more impurities; if the endpoint is reached later, the sol may gel (in the gelation method, the product will solidify in the pipeline, leading to equipment failure). The endpoint of sol purification is affected by factors such as temperature, pressure, pH, flow rate, and pipe diameter (in the case of continuous methods, the pipe transporting the colloid). This makes determining the endpoint of titanium dioxide sol purification difficult. For safety, laboratories typically end the purification process as early as possible based on one or two of the aforementioned parameters and experience. This significantly impacts the performance of the titanium dioxide sol and results in coarser control in production compared to laboratory settings, leading to lower batch product quality and poor reproducibility. Therefore, a digital method for determining the endpoint of continuous TiO2 sol purification is urgently needed to address these issues. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a digital method for determining and calculating the continuous purification endpoint of TiO2 sol. This method reduces residual impurities in TiO2 sol and decreases gelation, thereby improving the production quality of TiO2 sol, reducing the generation of substandard TiO2 sol, and lowering the failure rate of TiO2 sol purification equipment.

[0005] The present invention provides a digital method for determining the endpoint of continuous purification of TiO2 sol, comprising the following steps:

[0006] S1, Detection

[0007] The temperature of the TiO2 sol is detected by a temperature sensor to determine if it is between 2℃ and 45℃. The pressure of the TiO2 sol is detected by a pressure sensor to determine if it is between 0.07 MPa and 0.20 MPa. The pH value of the TiO2 sol is detected by a pH sensor to determine if it is between 1 and 14. The flow rate of the TiO2 sol is detected by a flow rate sensor to determine if it is greater than 0.5 m / s. The inner diameter of the delivery tube is kept between 0.01 and 0.05 m. The detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor are then transmitted to the analysis processor.

[0008] S2, Judgment

[0009] The processor analyzes and compares the current detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor with previous detection results. If the difference is within the set range and meets the standard, the process proceeds to the next step. If the difference exceeds the set range, an alarm is issued to remind the staff, and the temperature sensor, pressure sensor, pH sensor, and flow rate sensor are re-detected to obtain data.

[0010] S3, Calculation

[0011] The processor analyzes the detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor and substitutes them into the following calculation formula:

[0012]

[0013] Calculate the Z value. If the Z value is between 0.2 and 0.4, it indicates that the TiO2 sol purification is qualified, and the purification is ended.

[0014] S4, Prediction

[0015] When the Z value is below 0.2, it indicates that the TiO2 sol purification is unqualified. The TiO2 sol purification should continue. At the same time, the analysis processor predicts the endpoint of TiO2 sol purification based on the detection results of temperature sensor, pressure sensor, pH sensor and flow rate sensor, thus reducing the need for secondary calculation of Z value.

[0016] S5, Data Recording

[0017] The storage module stores the detection data, calculation process, and Z-value for each batch, and deletes batches with the same detection data, calculation process, and Z-value. This makes it easy to compare the detection data with the stored data next time, and directly obtain the Z-value when the detection data and the stored data are the same.

[0018] Preferably, the temperature of the TiO2 sol in S1 needs to be between 4℃ and 40℃, the pressure of the TiO2 sol needs to be between 0.09Mpa and 0.15Mpa, and the pH value of the TiO2 sol needs to be between 1 and 13.

[0019] Preferably, in the calculation formula of S3, Z is the endpoint digital parameter, Y value is related to TiO2 sol content, T is the temperature of TiO2 sol, P is the pressure of TiO2 sol, PH is the acidity or alkalinity of TiO2 sol, V is the average flow rate of TiO2 sol in the pipe, and R is the inner diameter of the delivery pipe.

[0020] Preferably, the Y value is 0.7 when the TiO2 sol content is between 85.0-90.0%, 0.6 when the TiO2 sol content is between 90.1-93%, 0.4 when the TiO2 sol content is between 93.1-94.5%, 0.2 when the TiO2 sol content is between 94.6-95.2%, 0.3 when the TiO2 sol content is between 95.3-96.8%, 0.4 when the TiO2 sol content is between 96.7-98.5%, and 0.6 when the TiO2 sol content is between 98.5-100.0%.

[0021] Preferably, the analysis processor includes:

[0022] Data comparison module: It compares the data obtained from previous tests with the data stored in the storage module to determine whether the data obtained from the current test is normal. If the difference between the comparison results is within the set range, it meets the standard. If the difference between the comparison results exceeds the set range, an alarm is issued. The system then re-detects and obtains data through temperature sensors, pressure sensors, pH sensors, and flow rate sensors for a second data comparison until the data meets the standard or the operator stops the equipment.

[0023] Calculation module: Calculates the Z value based on the calculation formula and the data detected by the temperature sensor, pressure sensor, pH sensor and flow rate sensor;

[0024] Deep learning module: The model is repeatedly trained using a large amount of qualified data and a large amount of unqualified data in the database. The model weights are repeatedly adjusted until a qualified model is trained. The qualified model is then used to predict the endpoint of qualified purification of TiO2 sol.

[0025] Database: Stores a large amount of qualified data and a large amount of unqualified data;

[0026] Monitoring module: Records the data comparison process, calculation process, and processing results of the deep learning module, and also records the information of logged-in users.

[0027] Preferably, the monitoring module includes:

[0028] Login monitoring unit: Records the information of logged-in personnel, login time, and operation steps;

[0029] Operation monitoring unit: Records the data comparison process, calculation process, and processing results of the deep learning module.

[0030] Preferably, the storage module includes:

[0031] Data storage unit: Stores the detection data, calculation process, and Z-value for each batch, and deletes those batches that have the same detection data, calculation process, and Z-value;

[0032] Data deletion unit: Deletes useless data;

[0033] Time-limited recovery unit: Recovers deleted data within a set time period;

[0034] Data sharing unit: Shares data with other production lines, enabling multiple production lines to store the same data.

[0035] Preferably, the data sharing unit includes:

[0036] Data transfer unit: Transfers data from the storage unit to storage units on other production lines;

[0037] Periodic comparison unit: Regularly compares the data in the storage units on multiple production lines to ensure that the data in the storage units on multiple production lines is the same. When the data is different, an alarm is issued to remind the staff.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] 1. The purification endpoint of TiO2 sol is calculated using a formula to reduce residual impurities in TiO2 sol and reduce gelation, thereby improving the production quality of TiO2 sol, reducing the generation of unqualified TiO2 sol, and lowering the failure rate of TiO2 sol purification equipment.

[0040] 2. By comparing data, the accuracy of the obtained detection data is judged, thereby improving the accuracy of the purification endpoint calculation of TiO2 sol;

[0041] 3. Deep learning is used to predict the purification endpoint of unqualified TiO2 sol, reducing the burden on the computation module;

[0042] 4. Improve the security of testing data, production parameters, and calculation formulas by sharing data with other production lines. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the present invention;

[0044] Figure 2 This is a schematic diagram of the calculation steps of the present invention;

[0045] Figure 3 This is a schematic diagram of the monitoring module of the present invention;

[0046] Figure 4 This is a schematic diagram of the storage module of the present invention;

[0047] Figure 5 This is a schematic diagram of the structure of the data sharing unit of the present invention;

[0048] Figure 6 This is a schematic diagram showing the relationship between the Y value and the TiO2 sol content of the present invention. Detailed Implementation

[0049] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0050] like Figures 1 to 6 As shown, it includes the following steps:

[0051] S1, Detection

[0052] The temperature of the TiO2 sol is detected by a temperature sensor to determine if it is between 2℃ and 45℃. The pressure of the TiO2 sol is detected by a pressure sensor to determine if it is between 0.07 MPa and 0.20 MPa. The pH value of the TiO2 sol is detected by a pH sensor to determine if it is between 1 and 14. The flow rate of the TiO2 sol is detected by a flow rate sensor to determine if it is greater than 0.5 m / s. The inner diameter of the delivery tube is kept between 0.01 and 0.05 m. The detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor are then transmitted to the analysis processor.

[0053] S2, Judgment

[0054] The processor analyzes and compares the current detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor with previous detection results. If the difference is within the set range and meets the standard, the process proceeds to the next step. If the difference exceeds the set range, an alarm is issued to remind the staff, and the temperature sensor, pressure sensor, pH sensor, and flow rate sensor are re-detected to obtain data.

[0055] S3, Calculation

[0056] The processor analyzes the detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor and substitutes them into the following calculation formula:

[0057]

[0058] Calculate the Z value. If the Z value is between 0.2 and 0.4, it indicates that the TiO2 sol purification is qualified, and the purification is ended.

[0059] S4, Prediction

[0060] When the Z value is below 0.2, it indicates that the TiO2 sol purification is unqualified. The TiO2 sol purification should continue. At the same time, the analysis processor predicts the endpoint of TiO2 sol purification based on the detection results of temperature sensor, pressure sensor, pH sensor and flow rate sensor, thus reducing the need for secondary calculation of Z value.

[0061] S5, Data Recording

[0062] The storage module stores the detection data, calculation process, and Z-value for each batch, and deletes those batches that have the same detection data, calculation process, and Z-value. This makes it convenient to compare the detection data with the stored data next time, and directly obtain the Z-value when the detection data and the stored data are the same.

[0063] The temperature of the TiO2 sol in S1 needs to be between 4℃ and 40℃, the pressure of the TiO2 sol needs to be between 0.09Mpa and 0.15Mpa, and the pH value of the TiO2 sol needs to be between 1 and 13.

[0064] In the calculation formula of S3, Z is the endpoint digital parameter, Y value is related to TiO2 sol content, T is the temperature of TiO2 sol, P is the pressure of TiO2 sol, PH is the acidity or alkalinity of TiO2 sol, V is the average flow rate of TiO2 sol in the pipe, and R is the inner diameter of the delivery pipe.

[0065] The Y value is 0.7 when the TiO2 sol content is between 85.0-90.0%, 0.6 when the TiO2 sol content is between 90.1-93%, 0.4 when the TiO2 sol content is between 93.1-94.5%, 0.2 when the TiO2 sol content is between 94.6-95.2%, 0.3 when the TiO2 sol content is between 95.3-96.8%, 0.4 when the TiO2 sol content is between 96.7-98.5%, and 0.6 when the TiO2 sol content is between 98.5-100.0%.

[0066] The analysis processor includes:

[0067] Data comparison module: It compares the data obtained from previous tests with the data stored in the storage module to determine whether the data obtained from the current test is normal. If the difference between the comparison results is within the set range, it meets the standard. If the difference between the comparison results exceeds the set range, an alarm is issued. The system then re-detects and obtains data through temperature sensors, pressure sensors, pH sensors, and flow rate sensors for a second data comparison until the data meets the standard or the operator stops the equipment.

[0068] Calculation module: Calculates the Z value based on the calculation formula and the data detected by the temperature sensor, pressure sensor, pH sensor and flow rate sensor;

[0069] Deep learning module: The model is repeatedly trained using a large amount of qualified data and a large amount of unqualified data in the database. The model weights are repeatedly adjusted until a qualified model is trained. The qualified model is then used to predict the endpoint of qualified purification of TiO2 sol.

[0070] Database: Stores a large amount of qualified data and a large amount of unqualified data;

[0071] Monitoring module: Records the data comparison process, calculation process, and processing results of the deep learning module, and also records the information of logged-in users;

[0072] The monitoring module includes:

[0073] Login monitoring unit: Records the information of logged-in personnel, login time, and operation steps;

[0074] Operation monitoring unit: Records the data comparison process, calculation process, and processing results of the deep learning module;

[0075] The storage module includes:

[0076] Data storage unit: Stores the detection data, calculation process, and Z-value for each batch, and deletes those batches that have the same detection data, calculation process, and Z-value;

[0077] Data deletion unit: Deletes useless data;

[0078] Time-limited recovery unit: Recovers deleted data within a set time period;

[0079] Data sharing unit: Shares data with other production lines, enabling multiple production lines to store the same data;

[0080] The data sharing unit includes:

[0081] Data transfer unit: Transfers data from the storage unit to storage units on other production lines;

[0082] Periodic comparison unit: Regularly compares the data in the storage units on multiple production lines to ensure that the data in the storage units on multiple production lines is the same. When the data is different, an alarm is issued to remind the staff.

[0083] Example 1

[0084] When the composition is 95 (Y = 0.2), the temperature is 20℃, the gauge pressure is 0.1, the pH is 4, the flow rate is V = 0.5m / s and the pipe diameter is 0.2m, Z = 0.23, which is within the allowable range, and purification can be terminated.

[0085] To ensure that the purification effect of TiO2 sol is within the required range and to reduce the residue left in the equipment pipeline.

[0086] Example 2

[0087] When the composition is 92 (Y = 0.6), temperature is 40℃, gauge pressure is 0.1, pH = 7, flow rate V = 1.5 m / s, and pipe diameter is 0.25 m, Z = 0.36. Within this allowable range, purification can be terminated.

[0088] To ensure that the purification effect of TiO2 sol is within the required range and to reduce the residue left in the equipment pipeline.

[0089] The main functions achieved by this invention are: calculating the purification endpoint of TiO2 sol, reducing computational burden, and improving the security of stored data;

[0090] 1. Calculate the purification endpoint of TiO2 sol: Calculate the purification endpoint of TiO2 sol using a formula to reduce residual impurities in TiO2 sol and reduce gelation.

[0091] 2. Reduce computational burden: By using deep learning to predict the purification endpoint of unqualified TiO2 sol, the computational burden on the module is reduced.

[0092] 3. Improve the security of stored data: Enhance the security of testing data, production parameters, and calculation formulas by sharing data with other production lines.

[0093] Technical personnel in this industry only need to operate it according to the accompanying instruction manual, without requiring any creative effort from those skilled in the art.

[0094] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A digital method for determining and calculating the endpoint of continuous purification of TiO2 sol, characterized in that, Includes the following steps: S1, Detection The temperature of the TiO2 sol is detected by a temperature sensor to determine if it is between 2℃ and 45℃. The pressure of the TiO2 sol is detected by a pressure sensor to determine if it is between 0.07 MPa and 0.20 MPa. The pH value of the TiO2 sol is detected by a pH sensor to determine if it is between 1 and 14. The flow rate of the TiO2 sol is detected by a flow rate sensor to determine if it is greater than 0.5 m / s. The inner diameter of the delivery tube is kept between 0.01 and 0.05 m. The detection results from the temperature, pressure, pH, and flow rate sensors are then transmitted to the analysis processor. S2, Judgment The processor analyzes and compares the current detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor with previous detection results. If the difference is within the set range and meets the standard, the process proceeds to the next step. If the difference exceeds the set range, an alarm is issued to remind the staff, and the temperature sensor, pressure sensor, pH sensor, and flow rate sensor are re-detected to obtain data. S3, Calculation The processor analyzes the detection results from the temperature sensor, pressure sensor, pH sensor, and flow rate sensor and substitutes them into the following calculation formula: ， Calculate the Z value. If the Z value is between 0.2 and 0.4, it indicates that the TiO2 sol purification is qualified, and the purification is ended. S4, Prediction When the Z value is below 0.2, it indicates that the TiO2 sol purification is unqualified. The TiO2 sol purification should continue. At the same time, the analysis processor predicts the endpoint of TiO2 sol purification based on the detection results of temperature sensor, pressure sensor, pH sensor and flow rate sensor, thus reducing the need for secondary calculation of Z value. S5, Data Recording The storage module stores the detection data, calculation process, and Z-value for each batch, and deletes those batches that have the same detection data, calculation process, and Z-value. This makes it convenient to compare the detection data with the stored data next time, and directly obtain the Z-value when the detection data and the stored data are the same. In the calculation formula of S3, Z is the endpoint digital parameter, Y value is related to TiO2 sol content, T is the temperature of TiO2 sol, P is the pressure of TiO2 sol, PH is the acidity or alkalinity of TiO2 sol, V is the average flow rate of TiO2 sol in the pipe, and R is the inner diameter of the delivery pipe.

2. The digital determination and calculation method for the endpoint of continuous purification of TiO2 sol as described in claim 1, characterized in that, The analysis processor includes: Data comparison module: It compares the data obtained from previous tests with the data stored in the storage module to determine whether the data obtained from the current test is normal. If the difference between the comparison results is within the set range, it meets the standard. If the difference between the comparison results exceeds the set range, an alarm is issued. The system then re-detects and obtains data through temperature sensors, pressure sensors, pH sensors, and flow rate sensors for a second data comparison until the data meets the standard or the operator stops the equipment. Calculation module: Calculates the Z value based on the calculation formula and the data detected by the temperature sensor, pressure sensor, pH sensor and flow rate sensor; Deep learning module: The model is repeatedly trained using a large amount of qualified data and a large amount of unqualified data in the database. The model weights are repeatedly adjusted until a qualified model is trained. The qualified model is then used to predict the endpoint of qualified purification of TiO2 sol. Database: Stores a large amount of qualified data and a large amount of unqualified data; Monitoring module: Records the data comparison process, calculation process, and processing results of the deep learning module, and also records the information of logged-in users.

3. The digital determination and calculation method for the endpoint of continuous purification of TiO2 sol as described in claim 2, characterized in that, The monitoring module includes: Login monitoring unit: Records the information of logged-in personnel, login time, and operation steps; Operation monitoring unit: Records the data comparison process, calculation process, and processing results of the deep learning module.

4. The digital determination and calculation method for the endpoint of continuous purification of TiO2 sol as described in claim 1, characterized in that, The storage module includes: Data storage unit: Stores the detection data, calculation process and Z value for each batch, and deletes those batches that are different but have the same detection data, calculation process and Z value; Data deletion unit: Deletes useless data; Time-limited recovery unit: Recovers deleted data within a set time period; Data sharing unit: Shares data with other production lines, enabling multiple production lines to store the same data.

5. The digital determination and calculation method for the endpoint of continuous purification of TiO2 sol as described in claim 4, characterized in that, The data sharing unit includes: Data transfer unit: Transfers data from the storage unit to storage units on other production lines; Periodic comparison unit: Regularly compares the data in the storage units on multiple production lines to ensure that the data in the storage units on multiple production lines is the same. When the data is different, an alarm is issued to remind the staff.

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