Ozone generation control method based on dynamic updating mathematical model technology

Abstract

The invention discloses an ozone generation control method based on a dynamic updating mathematical model technology, and relates to the technical field of ozone generation and preparation. An ozone generation control method based on a dynamic updating mathematical model technology comprises the following steps that S11, historical operation data of an ozone generator are obtained, and the historical operation data comprise driving voltage, ozone concentration corresponding to the driving voltage and zero gas flow matched with a time sequence; and S12, fitting a polynomial mathematical model according to the data relationship between the driving voltage and the ozone concentration corresponding to the driving voltage, wherein the polynomial mathematical model is used for predicting the driving voltage and controlling the ozone generator to generate ozone with the target concentration. According to the ozone generation control method based on the dynamic updating mathematical model technology, the adjustment control mode of an existing ozone generation calibration system is optimized, the time for adjusting the ozone generator to the target concentration is shortened, and the efficiency of the ozone generation system is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ozone generation equipment, and particularly relates to an ozone generation control method based on dynamic update mathematical model technology. Background Art

[0002] During the use of an ozone analyzer, it needs to be calibrated regularly to ensure the accuracy of the analyzer. However, due to the active nature of ozone, it cannot be made into a standard gas and stored in a standard gas cylinder. Instead, it needs to be generated and used simultaneously by an ozone generator. The ultraviolet lamp in the ozone generator emits ultraviolet light with a wavelength of 185 nm, and oxygen in the zero air can be converted into ozone under the ultraviolet light of this wavelength.

[0003] However, the light intensity of the ultraviolet light is not linearly related to ozone generation and is affected by the flow rate of the zero air. In order to flexibly obtain ozone gas with different concentrations at different zero air flow rates, an ozone generator and an ozone photometer are installed in the ozone generation system. The ozone photometer monitors the generated ozone concentration in real time and feeds the concentration signal back to the ozone generator, which adjusts the light intensity of the 185 nm ultraviolet lamp to make the ozone generation system generate ozone with the target concentration.

[0004] When the ozone photometer identifies the ozone concentration, there are large fluctuations, and it takes a long time to stabilize to obtain an accurate ozone concentration. In the ozone generator part, the light intensity of the mercury lamp is not linearly related to ozone generation; research shows that only when the zero air flow rate is fixed, the light intensity is positively correlated with the concentration. Therefore, the ozone generator cannot quickly generate ozone with the target concentration according to a fixed rule and can only perform multiple approximation adjustments according to the feedback signal of the photometer. This adjustment control method will cause the ozone generation system to have a slow response to the target concentration and reduce the ozone generation efficiency.

[0005] Therefore, how to optimize the adjustment control method of the existing ozone generation calibration system, shorten the time for the ozone generator to adjust to the target concentration, and improve the ozone generation efficiency of the ozone generation system is an urgent technical problem that needs to be solved by those skilled in the art at present.

[0006] The information disclosed in this background art section is only intended to enhance the overall understanding of the present invention and should not be regarded as an admission or any form of suggestion that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0007] In view of the above technical problems, an embodiment of the present invention provides an ozone generation control method based on dynamic update mathematical model technology to solve the problems proposed in the above background art.

[0008] The present invention provides the following technical solutions: An ozone generation control method based on dynamic update mathematical model technology, comprising the following steps:

[0009] S11: Obtain the historical operation data of the ozone generator, where the historical operation data includes the driving voltage, the ozone concentration corresponding to the driving voltage, and the zero gas flow rate matched with the time series;

[0010] S12: Fit a polynomial mathematical model according to the data relationship between the driving voltage and the ozone concentration corresponding to the driving voltage. The polynomial mathematical model is used to predict the driving voltage and control the ozone generator to generate ozone with a target concentration;

[0011] S13: During the ozone generation operation, if it is determined that the current zero gas flow rate is a learned zero gas flow rate; the controller will select the polynomial mathematical model matched with the current zero gas flow rate to predict the driving voltage and control the ozone generator to generate ozone.

[0012] During the process of the ozone generator generating ozone, if it is determined that the error threshold between the actual generated ozone concentration and the target concentration meets the requirements, continue to generate ozone with the target concentration until a stop instruction is received;

[0013] S14: During the ozone generation operation, if it is determined that the current zero gas flow rate is an unlearned zero gas flow rate; the controller will select the zero gas flow rate with the smallest difference from the current zero gas flow rate and for which a mathematical model has been established, and use the polynomial mathematical model corresponding to this zero gas flow rate to predict the driving voltage and control the ozone generator to generate ozone;

[0014] During the process of the ozone generator generating ozone, the controller uses the PID algorithm to dynamically adjust the driving voltage of the ozone generator. If it is determined that the error threshold between the actual generated ozone concentration and the target concentration meets the requirements, continue to generate ozone with the target concentration until a stop instruction is received;

[0015] S15: During the process of the controller executing step S14, the controller will re - establish a new polynomial mathematical model for this flow rate for subsequent prediction of other driving voltages according to the data relationship between the driving voltage and the corresponding ozone concentration newly obtained during the dynamic adjustment of the driving voltage by the PID algorithm.

[0016] It should be noted that the zero gas flow rate matched with the time series can be understood as: in the historical usage data of the ozone generator, the zero gas flow rate corresponds to the driving voltage and the ozone concentration according to the corresponding time series data; in other words, the zero gas flow rate data is gradually recorded over time and synchronized with the driving voltage and the ozone concentration.

[0017] The PID algorithm is a feedback control method used in control systems to automatically adjust the output of the system to make it reach the desired target value; the PID controller continuously corrects the deviation of the system by adjusting the driving voltage, making the response of the system more accurate and stable.

[0018] Preferably, in the step of obtaining the historical operation data of the ozone generator, the obtaining step of the historical operation data includes:

[0019] S21: Input the initial target concentration of ozone generated by the ozone generator, and record the zero-air flow rate at this target concentration; the controller controls the ozone generator to generate ozone by controlling the driving voltage of the ozone generator.

[0020] S22: According to the generated ozone concentration, the controller dynamically adjusts the driving voltage of the ozone generator using the PID algorithm until the error threshold between the generated ozone concentration and the initial target concentration meets the requirements, and completes the generation operation of the initial target concentration of ozone at the current zero-air flow rate.

[0021] S23: During the process of dynamically adjusting the driving voltage by the PID algorithm, several groups of driving voltage, corresponding ozone concentration, and zero-air flow rate data will be dynamically generated; these data are the historical operation data.

[0022] Preferably, during the process of the controller executing step S13, if the error threshold between the actual generated ozone concentration and the target concentration does not meet the requirements, the following steps will be continued:

[0023] S31: The controller continues to dynamically adjust the driving voltage of the ozone generator using the PID algorithm based on the predicted driving voltage. If it is determined that the error threshold between the actual generated ozone concentration and the target concentration meets the requirements, continuously generate ozone at the target concentration until a stop instruction is received.

[0024] S32: The controller synchronously obtains the latest recorded driving voltage and ozone concentration data during the dynamic adjustment process of the PID algorithm, and adds the newly obtained data to the previous polynomial mathematical model and updates the polynomial mathematical model for subsequent prediction of other driving voltages.

[0025] Preferably, the concentration of generated ozone is fed back in real time by an ozone photometer.

[0026] Preferably, the error threshold between the actual generated ozone concentration and the target concentration means that the error of the generated ozone concentration shall not exceed 2% of the target concentration.

[0027] An ozone generation control method based on the technology of dynamically updating the mathematical model provided by the embodiments of the present invention has the following

[0028] Beneficial effects:

[0029] 1. By using the historical operation data of the ozone generation equipment, the present invention constructs a polynomial mathematical model to predict the driving voltage, and the driving voltage is the control instruction for controlling the generation of ozone concentration.

[0030] 2. During the control process, the polynomial mathematical model in the ozone generator can be dynamically calibrated and updated, and the new polynomial mathematical model can continue to be applied to the control of the next target concentration;

[0031] 3. During the process of dynamically calibrating the ozone generator, the control model of the ozone generator can be continuously modified, the adjustment range can be narrowed, the adjustment trend can be predicted, the number of adjustment judgments of the controller can be reduced, and the ozone generation can be made faster;

[0032] 4. Moreover, the more times of ozone generation, the more perfect the calibration curve is, the adjustment control mode of the existing ozone generation calibration system is optimized, the time for the ozone generator to be adjusted to the target concentration is shortened, and the efficiency of the ozone generation system is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flowchart of an ozone generation control method based on the technology of dynamically updating the mathematical model of the present invention;

[0034] Figure 2 is a result graph of the first fitting of the polynomial prediction curve in the present invention;

[0035] Figure 3 is a result graph of the second fitting of the polynomial prediction curve in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0037] In view of the problems mentioned in the above background technology, the embodiments of the present invention provide an ozone generation control method based on the technology of dynamically updating the mathematical model to solve the above technical problems, and the technical solutions are as follows:

[0038] I. The design idea and optimization steps of an ozone generation control method based on the technology of dynamically updating the mathematical model provided by the present invention are as follows:

[0039] Figure 1 As shown; this technical solution adopts the method of intelligent identification of the mathematical model to establish the relationship between the driving voltage of the ozone generator and the target ozone concentration under different zero-gas output flows; according to the identified mathematical model, the driving voltage corresponding to the target ozone concentration is determined, and the operation of the ozone generator is controlled. The control method provided by this technical solution specifically includes:

[0040] 1. Initially set the target concentration, the CPU records the zero gas flow, controls the maximum driving voltage allowed by the output system, drives the ozone generator, and reads the concentration fed back by the ozone photometer. According to the feedback of the ozone concentration, PID adjusts the driving voltage to make the actual concentration close to the target concentration, and stores the driving voltage and the corresponding ozone concentration during the adjustment process.

[0041] 2. The CPU intelligently identifies the n sets of data recorded in the mediation process of step 1, matches the functional relationship with high correlation, and establishes the mathematical model f(x1) under the flow;

[0042] 3. When other target concentrations occur at the same flow rate, the corresponding theoretical driving voltage value is predicted according to the mathematical model obtained in step 2 above, and the CPU outputs and controls the ozone generator. The ozone photometer reads the concentration in real time and feeds it back to the CPU for comparison and adjustment;

[0043] 4. If the actual concentration is equal to the target concentration, or within the allowable error range, the CPU stops adjusting and continues to output ozone until a stop command is issued manually;

[0044] If the predicted driving voltage causes the ozone concentration generated by the ozone generator to exceed the target concentration error allowable range, since the voltage is positively correlated with the ozone concentration, the error between the current predicted voltage and the voltage corresponding to the target concentration is relatively small, and the PID continues to adjust the driving voltage based on this predicted voltage until the target concentration is reached. The latest recorded relationship between the driving voltage and the ozone concentration during the adjustment process is added to the previous mathematical model, and a new mathematical model is identified and established to predict and determine the CPU control value corresponding to the next target concentration.

[0045] 5. If ozone concentration at different flow rates occurs again, it is necessary to first determine and query the flow rate that is closest to the flow rate and has established a mathematical model, use its mathematical model to predict the driving voltage, and continue to adjust the driving voltage by PID based on this predicted voltage until the target concentration is reached. During the adjustment process, re-record the relationship between n groups of driving voltage and ozone concentration, identify and establish a new mathematical model f(x i＝1，2，3，4…… ); 2. Specific embodiments

[0047] (1) The output flow rate of the ozone generator is set to 5L, and the target ozone concentration is 400ppb;

[0048] During the PID approach adjustment process, the light intensity voltage and the actual ozone concentration are sorted in order to obtain the following corresponding relationships (1000mv, 45ppb) (1200mv, 140ppb) (1500mv, 310ppb) (2000mv, 730ppb);

[0049] Figure 2As shown; a polynomial mathematical model is established based on this mathematical relationship as the mathematical prediction model of this occurrence curve. The mathematical relationship between ozone concentration and light intensity voltage is: y = 0.0003x 2 - 0.2224x - 31.944, R 2 = 0.9999

[0050] Through R 2 It can be judged that the polynomial model is basically reliable, and this mathematical model can be stored. When calculating 400 ppb, the CPU should adjust the light intensity to 1626.7 mv, and the CPU controls the output of 1626.7 mv voltage. Assuming the actual concentration of the feedback is 401 ppb, which meets the error requirement of 2%, then continuously output 1626.7 mv; assuming the feedback concentration is 410 ppb, which does not meet the ±2% error requirement, then continue to perform PID fine-tuning on the basis of 1626.7 mv

[0051] During the fine-tuning process, a new corresponding relationship between light intensity voltage and actual ozone concentration is generated: (1626.7 mv, 410 ppb)(1610 mv, 392 ppb)(1618 mv, 398 ppb);

[0052] Figure 3 As shown; during this process, 398 ppb meets 400 ppb ± 2%. The CPU continuously controls the output of 1618 mv driving voltage and adds the new corresponding relationship. Then, a polynomial mathematical model is established again: y = 0.0003x 2 - 0.2035x - 45.393, R 2 = 0.9998

[0053] While generating the target concentration, the parameters of the mathematical prediction model - the polynomial model at a flow rate of 5 L / min are updated. When generating other concentrations at this flow rate again, the next target control voltage can be calculated according to this prediction model. After stably judging whether the target concentration meets the error range, select to directly output this prediction model or obtain more samples to update the model

[0054] 2. Output 400 ppb at 2.5 L / min

[0055] If systematic errors are not considered, the flow rate ratio is inversely proportional to the concentration ratio. That is, 400 ppb at 2.5 L / min is 200 ppb at 5 L / min. When there is no mathematical model for other similar flow rates in the system, borrow the mathematical model of 5 L: y = 0.0003x 2-0.2035x - 45.393, the driving voltage calculated for 200 ppb is 1305.1 mV; based on 1305.1 mV, PID approach adjustment is carried out to obtain the corresponding relationship between n groups of driving voltages and actual concentrations, and a mathematical prediction model at 2.5 L / min is established.

[0056] 3. After multiple occurrences, predictions, and PID approach adjustments, a polynomial mathematical model library at different flow rates is finally formed, achieving the goal of increasing the speed of generating the target ozone concentration and improving the working efficiency of the instrument.

[0057] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that based on the technical solutions of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. An ozone generation control method based on dynamic update mathematical model technology, characterized in that It includes the following steps: S11: Obtain the historical operation data of the ozone generator, where the historical operation data includes the driving voltage, the ozone concentration corresponding to the driving voltage, and the zero gas flow rate matched with the time series; S12: Fit a polynomial mathematical model according to the data relationship between the driving voltage and the ozone concentration corresponding to the driving voltage. The polynomial mathematical model is used to predict the driving voltage and control the ozone generator to generate ozone with a target concentration; During the ozone generation operation, if it is determined that the current zero gas flow rate is the learned zero gas flow rate; the controller will select the polynomial mathematical model matched with the current zero gas flow rate to predict the driving voltage and control the ozone generator to generate ozone; During the process of the ozone generator generating ozone, if it is determined that the error threshold between the actual generated ozone concentration and the target concentration meets the requirements, continue to generate ozone with the target concentration until a stop instruction is received; During the ozone generation operation, if it is determined that the current zero gas flow rate is the unlearned zero gas flow rate; the controller will select the zero gas flow rate with the smallest difference from the current zero gas flow rate and for which a mathematical model has been established, and use the polynomial mathematical model corresponding to this zero gas flow rate to predict the driving voltage and control the ozone generator to generate ozone; During the process of the ozone generator generating ozone, the controller uses the PID algorithm to dynamically adjust the driving voltage of the ozone generator. If it is determined that the error threshold between the actual generated ozone concentration and the target concentration meets the requirements, continue to generate ozone with the target concentration until a stop instruction is received; During the process of the controller executing step S14, the controller will re - establish a new polynomial mathematical model for this flow rate based on the data relationship between the driving voltage and the corresponding ozone concentration newly obtained during the dynamic adjustment of the driving voltage according to the PID algorithm, for use in predicting other subsequent driving voltages.

2. The ozone generation control method based on the dynamic update mathematical model technology according to claim 1, characterized in that In the step of obtaining the historical operation data of the ozone generator, the steps for obtaining the historical operation data include: S21: Input the initial target concentration of ozone generated by the ozone generator and record the zero gas flow rate at this target concentration; the controller controls the ozone generator to generate ozone by controlling the driving voltage of the ozone generator; S22: According to the generated ozone concentration, the controller uses the PID algorithm to dynamically adjust the driving voltage of the ozone generator until the error threshold between the generated ozone concentration and the initial target concentration meets the requirements, completing the generation operation of the initial target concentration of ozone at the current zero gas flow rate; During the process of the PID algorithm dynamically adjusting the driving voltage, several groups of driving voltage, corresponding ozone concentration, and zero gas flow rate data will be dynamically generated; these data are the historical operation data.

3. The ozone generation control method based on the dynamic update mathematical model technology according to claim 1, characterized in that During the process of the controller executing step S13, if the error threshold between the actual generated ozone concentration and the target concentration does not meet the requirements, the following steps will continue to be executed: S31: The controller continues to use the PID algorithm to dynamically adjust the driving voltage of the ozone generator based on the predicted driving voltage. If it is determined that the error threshold between the actual generated ozone concentration and the target concentration meets the requirements, continue to generate ozone with the target concentration until a stop instruction is received; S32: The controller synchronously obtains the latest recorded drive voltage and ozone concentration data during the dynamic adjustment process of the PID algorithm, adds the newly obtained data to the previous polynomial mathematical model and updates the polynomial mathematical model for predicting other drive voltages in the future.

4. The ozone generation control method based on the dynamic update mathematical model technology according to claim 1, characterized in that, The concentration of ozone generation is fed back in real time by an ozone photometer.

5. The ozone generation control method based on the dynamic update mathematical model technology according to claim 1, wherein, The error threshold between the actual ozone generation concentration and the target concentration means that the error in the ozone generation concentration shall not exceed 2% of the target concentration.

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