Multi-stage ion exchange resin tank control method and system, and storage medium

Abstract

The present invention relates to a multi-stage ion exchange resin tank control method and system, and a storage medium. The method comprises: H1, starting a production line, on the basis of a flow sensor on each stage of ion exchange resin tank, obtaining flow data information of a sodium silicate solution, and on the basis of a timer on each stage of ion exchange resin tank, obtaining filtering time data information of a current resin tank; H2, inputting, into a system identification model of silica sol liquid conductivity in a mixed ion exchange resin tank established by a neural network, the flow data information of the sodium silicate solution and the filtering time data information of the current resin tank for training and learning, and outputting conductivity data information of the current resin tank; and H3, inputting, into a proportional valve model, the conductivity data information of the current resin tank for simulation training. The present invention can not only achieve production of a high-quality desalted silica sol with conductivity lower than 20 μs / cm and a neutral pH value, but also improve the production efficiency of the silica sol.

Description

A control method, system and storage medium for a multi-stage ion exchange resin tank Technical Field

[0001] The present invention relates to the technical field of silica sol production, and in particular to a control method, system and storage medium for a multi-stage ion exchange resin tank. Background Art

[0002] Silica sol is a colloid produced by the hydrolysis of silicates, its primary component being silicon dioxide (SiO2). Silica sol exhibits high specific surface area, high porosity, high chemical stability, and a low thermal expansion coefficient, making it widely used in catalysts, adsorbents, separation materials, optical materials, and electronic materials. Silica sol's performance can also be manipulated by controlling its preparation conditions, thereby adjusting its pore size and distribution.

[0003] There are various methods for preparing silica sol, including the sol-gel method, hydrothermal method, microemulsion method, and vapor phase method. Among them, the sol-gel method is one of the most commonly used preparation methods. The basic steps of this method include dissolving silicate in water to form a sol, adding a catalyst and a regulator to gelate it, and then drying and calcining it to obtain the silica sol.

[0004] Silica sol has a high specific surface area and porosity, making it widely used in catalysts, adsorbents, separation materials, and other fields. For example, silica sol can be used as a catalyst carrier, loading active components onto its surface, thereby improving the activity and stability of the catalyst. Silica sol can also be used as an adsorbent to remove pollutants such as heavy metal ions and organic matter from water. In addition, silica sol can be used to prepare separation materials such as molecular sieves and ion exchange resins.

[0005] In recent years, with the development of nanotechnology, the application range of silica sol has continued to expand. For example, silica sol can be used to prepare nanocomposites and nanocatalysts. It can also be used in high-tech fields such as optical and electronic materials. As a result, the demand for silica sol is constantly increasing, and its application fields are also constantly expanding. How to prepare high-quality silica sol has become a pressing issue.

[0006] Summary of the Invention

[0007] In view of the above problems, the present invention provides a control method, system and storage medium for a multi-stage ion exchange resin tank, which can not only produce high-quality desalinated silica sol with a conductivity of less than 20μs / cm and a neutral pH value, but also improve the production efficiency of silica sol.

[0008] In order to achieve the above-mentioned and other related purposes, the present invention provides the following technical solutions:

[0009] A method for controlling a multi-stage ion exchange resin tank, the method comprising:

[0010] H1. The production line is started, and the flow rate data of the sodium silicate solution is obtained based on the flow sensors on the ion exchange resin tanks at all levels. The filtration time data of the current resin tank is obtained based on the timers on the ion exchange resin tanks at all levels.

[0011] H2. The flow data of the sodium silicate solution and the filtration time data of the current resin tank are input into a system identification model of the conductivity of the silica sol liquid in the mixed ion exchange resin tank established by a neural network for training and learning, and outputting the conductivity data of the current resin tank;

[0012] H3. Input the conductivity data of the current resin tank into the proportional valve model for simulation training, and output the conductivity error value and error change value of the resin tank at a certain moment;

[0013] H4. Based on the conductivity error value and error change value of the resin tank at a certain moment, an adaptive fuzzy PID control algorithm is used to output control data information of the current resin tank.

[0014] Furthermore, the control data information of the current resin tank includes the switching of the resin tank regeneration valve and the switching of the resin tank filter valve. A preset threshold is set. If the conductivity error value of the resin tank at a certain moment exceeds the preset threshold, the resin tank regeneration valve is switched to regenerate the exchange resin. If the conductivity error value of the resin tank at a certain moment is less than the preset threshold, the resin tank filter valve is kept open.

[0015] Furthermore, in step H2, the system identification model of the conductivity of the silica sol liquid in the mixed ion exchange resin tank established by the neural network includes:

[0016] H21. Based on the flow data information of the sodium silicate solution and the filtration time data information of the current resin tank, establish a characteristic matrix function f i , f i =α(x i1 , x i2 ,...,x in ) T +β(t i1 , t i2 ,...,t in ) T ,

[0017] Among them, x in is the flow rate of sodium silicate solution sampled n times in the i-th resin tank, t in is the filtration time of n samplings of the i-th level resin tank, α is the flow characteristic factor, and β is the time characteristic factor;

[0018] H22. The characteristic matrix function f i The output function F of the hidden unit of the input neural network i ,

[0019] Among them, k ji represents the weight from the jth hidden unit to the ith output unit, ε represents the transformation function of the hidden unit of the neural network, r j represents the center vector of the jth hidden unit, ‖f i -r j ‖ represents f i and r j the distance between them;

[0020] H23. Output function F of the hidden unit based on the neural network i , output the conductivity data information of the i-th level resin tank.

[0021] Furthermore, in step H22, the transformation function ε of the hidden unit of the neural network is,

[0022] Here, λ is the expansion constant.

[0023] Furthermore, in step H3, the proportional valve model is

[0024] Among them, β is the flow gain of the proportional valve, ω is the natural frequency, and γ is the damping coefficient of the proportional valve.

[0025] Furthermore, in step H4, the adaptive fuzzy PID control algorithm includes:

[0026] H41. Obtain the conductivity error value d(k) of the resin tank at different times k, and obtain the conductivity error change value Δ(k) of the resin tank at different times k;

[0027] H42. Input the conductivity error value d(k) of the resin tank and the conductivity error change value Δ(k) of the resin tank into the fuzzy PID control function.

[0028] Among them, l p is the proportional coefficient, l i is the integral coefficient, l d is the differential coefficient, P(k) is the output value at the kth moment;

[0029] H43. Based on the fuzzy PID control function, according to the PID parameter self-tuning algorithm, the tuned system parameters l p 、l i and ld , thereby outputting the fuzzy control data information of the current resin tank;

[0030] H44. Convert the fuzzy control data information of the current resin tank into precise output data information of the control execution component, and output the control data information of the current resin tank according to the defuzzification algorithm.

[0031] Furthermore, the PID parameter self-determination algorithm is any one of the Z-N method, the optimal tuning method, the engineering tuning method or the expanded critical proportional band method.

[0032] Furthermore, the defuzzification algorithm selects any one of the maximum membership method, the median method or the center of gravity method.

[0033] To achieve the above objectives and other related objectives, the present invention also provides a control system for a multi-stage ion exchange resin tank, comprising a computer device programmed or configured to execute any one of the steps of the control method for a multi-stage ion exchange resin tank.

[0034] In order to achieve the above-mentioned and other related objects, the present invention further provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the control methods for the multi-stage ion exchange resin tank.

[0035] The present invention has the following positive effects:

[0036] 1. The present invention adopts multi-stage ion exchange resin tanks to produce silica sol, which not only improves the quality of silica sol, but also eliminates the need to stop the production process to replace the filter material, further improving the production efficiency of silica sol.

[0037] 2. The present invention adopts an adaptive fuzzy PID control algorithm to control the conductivity of the silica sol, which can accurately adjust the conductivity of the silica sol, thereby improving the stable production of the silica sol.

[0038] 3. The present invention monitors each mixed ion exchange resin tank and calculates the optimal filtration efficiency through an algorithm, thereby ensuring high efficiency and quality of desalination of the silica sol. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] FIG1 is a schematic flow chart of the method of the present invention;

[0040] FIG2 is a schematic diagram of a system identification model flow chart of the silica sol liquid conductivity of the present invention;

[0041] FIG3 is a schematic diagram of the flow of the adaptive fuzzy PID control algorithm of the present invention;

[0042] FIG4 is a connection diagram of ion exchange resin tanks at various stages of the present invention. DETAILED DESCRIPTION

[0043] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0044] Example 1: As shown in FIG1 or FIG4 , a control method for a multi-stage ion exchange resin tank comprises:

[0045] H1. The production line is started, and the flow rate data of the sodium silicate solution is obtained based on the flow sensor on each level of the ion exchange resin tank. The filtration time data of the current resin tank is obtained based on the timer on each level of the ion exchange resin tank.

[0046] H2. The flow data of the sodium silicate solution and the filtration time data of the current resin tank are input into a system identification model of the conductivity of the silica sol liquid in the mixed ion exchange resin tank established by a neural network for training and learning, and outputting the conductivity data of the current resin tank;

[0047] H3. Input the conductivity data of the current resin tank into the proportional valve model for simulation training, and output the conductivity error value and error change value of the resin tank at a certain moment;

[0048] H4. Based on the conductivity error value and error change value of the resin tank at a certain moment, an adaptive fuzzy PID control algorithm is used to output control data information of the current resin tank.

[0049] In this embodiment, the control data information of the current resin tank includes the switching of the resin tank regeneration valve and the switching of the resin tank filter valve. A preset threshold is set. If the conductivity error value of the resin tank at a certain moment exceeds the preset threshold, the resin tank regeneration valve is switched to regenerate the exchange resin. If the conductivity error value of the resin tank at a certain moment is less than the preset threshold, the resin tank filter valve is kept open.

[0050] In this embodiment, as shown in FIG2 , in step H2 , the system identification model of the conductivity of the silica sol liquid in the mixed ion exchange resin tank established by the neural network includes:

[0051] H21. Based on the flow data information of the sodium silicate solution and the filtration time data information of the current resin tank, establish a characteristic matrix function f i , f i =α(x i1, x i2 ,...,x in ) T +β(t i1 , t i2 ,...,t in ) T ,

[0052] Among them, x in is the flow rate of sodium silicate solution sampled n times in the i-th resin tank, t in is the filtration time of n samplings of the i-th level resin tank, α is the flow characteristic factor, and β is the time characteristic factor;

[0053] H22. The characteristic matrix function f i The output function F of the hidden unit of the input neural network i ,

[0054] Among them, k ji represents the weight from the jth hidden unit to the ith output unit, ε represents the transformation function of the hidden unit of the neural network, r j represents the center vector of the jth hidden unit, ‖f i -r j ‖ represents f i and r j the distance between them;

[0055] H23. Output function F of the hidden unit based on the neural network i , output the conductivity data information of the i-th level resin tank.

[0056] In this embodiment, in step H22, the transformation function ε of the hidden unit of the neural network is,

[0057] Here, λ is the expansion constant.

[0058] In this embodiment, in step H3, the proportional valve model is

[0059] Among them, β is the flow gain of the proportional valve, ω is the natural frequency, and γ is the damping coefficient of the proportional valve.

[0060] Mixed ion exchange resins require regeneration after a certain period of use. This patented method utilizes multi-stage, alternating mixed ion exchange resin tanks for filtration. Under uninterrupted production (valve switching controls regeneration of only two saturated resin tanks, allowing the remaining tanks within the effective range to continue filtering), it can produce high-quality demineralized silica sol with a conductivity below 300 LS / cm and a neutral pH. This involves monitoring each mixed ion exchange resin tank and calculating the optimal filtration efficiency through an algorithm.

[0061] Example 2: Based on the control method of a multi-stage ion exchange resin tank in Example 1, the present invention is further illustrated and described below.

[0062] As shown in FIG3 , in step H4, the adaptive fuzzy PID control algorithm includes:

[0063] H41. Obtain the conductivity error value d(k) of the resin tank at different times k, and obtain the conductivity error change value Δ(k) of the resin tank at different times k;

[0064] H42. Input the conductivity error value d(k) of the resin tank and the conductivity error change value Δ(k) of the resin tank into the fuzzy PID control function.

[0065] Among them, l p is the proportional coefficient, l i is the integral coefficient, l d is the differential coefficient, P(k) is the output value at the kth moment;

[0066] H43. Based on the fuzzy PID control function, according to the PID parameter self-tuning algorithm, the tuned system parameters l p 、l i and l d , thereby outputting the fuzzy control data information of the current resin tank;

[0067] H44. Convert the fuzzy control data information of the current resin tank into precise output data information of the control execution component, and output the control data information of the current resin tank according to the defuzzification algorithm.

[0068] In this embodiment, the PID parameter self-determination algorithm is any one of the Z-N method, the optimal tuning method, the engineering tuning method or the expanded critical proportional band method.

[0069] In this embodiment, the defuzzification algorithm is selected from any one of the maximum membership method, the median method or the centroid method.

[0070] To achieve the above objectives and other related objectives, the present invention further provides a control system for a multi-stage ion exchange resin tank, comprising a computer device programmed or configured to execute any one of the steps of the control method for a multi-stage ion exchange resin tank.

[0071] In order to achieve the above-mentioned and other related objects, the present invention further provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the control methods for the multi-stage ion exchange resin tank.

[0072] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0073] In summary, the present invention can not only produce high-quality desalted silica sol with a conductivity of less than 20 μs / cm and a neutral pH value, but also improve the production efficiency of silica sol.

[0074] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A control method for a multi-stage ion exchange resin tank, characterized in that: The method comprises: H1. The production line is started, and the flow data information of the sodium silicate solution is obtained based on the flow sensors on the ion exchange resin tanks at all levels, and the filtration time data information of the current resin tank is obtained based on the timers on the ion exchange resin tanks at all levels; H2. The flow data information of the sodium silicate solution and the filtration time data information of the current resin tank are input into the system identification model of the conductivity of the silica sol liquid in the mixed ion exchange resin tank established by the neural network for training and learning, and the conductivity data information of the current resin tank is output; H3. Input the conductivity data information of the current resin tank into the proportional valve model for simulation training, and output the conductivity error value and error change value of the resin tank at a certain moment; H4. Based on the conductivity error value and error change value of the resin tank at a certain moment, an adaptive fuzzy PID control algorithm is used to output the control data information of the current resin tank.

2. The control method of the multi-stage ion exchange resin tank according to claim 1, characterized in that: The control data information of the current resin tank includes the switching of the resin tank regeneration valve and the switching of the resin tank filter valve. A preset threshold is set. If the conductivity error value of the resin tank at a certain moment exceeds the preset threshold, the resin tank regeneration valve is switched to regenerate the exchange resin. If the conductivity error value of the resin tank at a certain moment is less than the preset threshold, the resin tank filter valve is kept open.

3. The control method of the multi-stage ion exchange resin tank according to claim 1, characterized in that: In step H2, the system identification model of the conductivity of the silica sol liquid in the mixed ion exchange resin tank established by the neural network includes: H21. Based on the flow data information of the sodium silicate solution and the filtration time data information of the current resin tank, establish a characteristic matrix function f i , f i =α(x i1 ,x i2 ,…,x in ) T +β(t i1 ,t i2 ,…,t in ) T , Among them, x in is the flow rate of sodium silicate solution sampled n times in the i-th resin tank, t in is the filtration time of the nth sampling of the i-th level resin tank, α is the flow characteristic factor, and β is the time characteristic factor; H22. The characteristic matrix function f i The output function F of the hidden unit of the input neural network i , Among them, k ji represents the weight from the jth hidden unit to the ith output unit, ε represents the transformation function of the hidden unit of the neural network, r j represents the center vector of the jth hidden unit, ‖f i -r j ‖ represents f i and r j The distance between H23. Output function F of the hidden unit based on the neural network i , output the conductivity data information of the i-th level resin tank.

4. The control method of the multi-stage ion exchange resin tank according to claim 3, characterized in that: In step H22, the transformation function ε of the hidden unit of the neural network is, Here, λ is the expansion constant.

5. The control method of the multi-stage ion exchange resin tank according to claim 1, characterized in that: In step H3, the proportional valve model is Among them, β is the flow gain of the proportional valve, ω is the natural frequency, and γ is the damping coefficient of the proportional valve.

6. The control method of the multi-stage ion exchange resin tank according to claim 1, characterized in that: In step H4, the adaptive fuzzy PID control algorithm includes: H41. Obtain the conductivity error value d(k) of the resin tank at different times k, and obtain the conductivity error change value Δ(k) of the resin tank at different times k; H42. Input the conductivity error value d(k) of the resin tank and the conductivity error change value Δ(k) of the resin tank into the fuzzy PID control function, Among them, l p is the proportionality coefficient, l i is the integral coefficient, l d is the differential coefficient, P(k) is the output value at the kth moment; H43. Based on the fuzzy PID control function, according to the PID parameter self-tuning algorithm, the tuned system parameters l are obtained. p , l i and l d , thereby outputting the fuzzy control data information of the current resin tank; H44. Convert the fuzzy control data information of the current resin tank into precise output data information of the control execution component, and output the control data information of the current resin tank according to the defuzzification algorithm.

7. The control method of the multi-stage ion exchange resin tank according to claim 6, characterized in that: The PID parameter self-determination algorithm is any one of the Z-N method, the optimal setting method, the engineering setting method or the expanded critical proportional band method.

8. The control method of the multi-stage ion exchange resin tank according to claim 6, characterized in that: The defuzzification algorithm selects any one of the maximum membership method, the median method or the centroid method.

9. A control system for a multi-stage ion exchange resin tank, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the control method of the multi-stage ion exchange resin tank according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program programmed or configured to execute the control method of the multi-stage ion exchange resin tank according to any one of claims 1 to 8.

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