Method for adjusting pH value of three-phase catalytic oxidation Fenton process

Abstract

The invention discloses a three-phase catalytic oxidation Fenton process pH value adjusting method, which utilizes the Bernoulli's law to analyze the process that fluid flows from a high-level liquid trap to an adding point, a medicament is lifted to the high-level liquid trap and automatically flows to a tail end, and a set of linear opening adjusting valve is additionally arranged at the tail end to adjust the adding flow rate. The accurate and stable control of the pH value is realized, and the error is controllable; historical data and real-time data are fully utilized, the water quality change is predicted by utilizing an algorithm, and advanced intervention is realized; an adaptive adjustment algorithm is added to automatically adapt to different working conditions; multiple parameters such as flow, water quality and temperature are comprehensively considered, and cooperative control is achieved; and finally, through accurate dosing control, the dosage of chemicals is remarkably reduced, the pH value fluctuation is reduced, the stability of the whole treatment system is improved, the effluent quality is improved, and the environmental influence is reduced.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a method for adjusting the pH value of a three-phase catalytic oxidation Fenton process. Background Technology

[0002] The three-phase catalytic oxidation Fenton process is an advanced wastewater treatment technology that excels in treating recalcitrant organic pollutants. The core of this process lies in precisely controlling pH, catalyst concentration, and oxidant dosage to generate highly reactive hydroxyl radicals in a three-phase reaction environment consisting of gas (oxygen), liquid (water), and solid (catalyst), thereby achieving efficient degradation of pollutants.

[0003] Data shows that the current process has significant shortcomings in practical applications, especially in the precise control of pH value, i.e., the addition of sulfuric acid and sodium hydroxide. It cannot effectively cope with sudden changes in influent water quality and flow rate; its predictive ability is insufficient, lacking the ability to anticipate changes in the system; its chemical consumption is high, with excessive or insufficient addition of chemicals leading to energy waste; its maintenance costs are high, requiring frequent manual intervention and adjustments; and its treatment effect is unstable, with pH fluctuations affecting the efficiency of the entire treatment system.

[0004] Chinese patent document CN106517476A discloses an "acid-base neutralization device and wastewater treatment system for wastewater treatment." It includes a pH adjustment tank, a water supply pipeline, and a chemical dosing pipeline. The outlet of the water supply pipeline is connected to the pH adjustment tank, and an opening is provided on the water supply pipeline. The chemical dosing pipeline has a chemical outlet end, which is connected to and communicates with the water supply pipeline through the opening. However, this technical solution lacks the ability to effectively handle sudden changes in influent flow, resulting in unstable treatment performance. Summary of the Invention

[0005] This invention primarily addresses the technical problems of existing solutions lacking the ability to effectively handle sudden changes in influent and resulting in unstable treatment effects. It provides a method for pH adjustment in a three-phase catalytic oxidation Fenton process. Utilizing Bernoulli's law to analyze the fluid flow from the high-level collector to the dosing point, the reagent is raised to the high-level collector and flows by gravity to the end point. A linear opening regulating valve is added at the end point to adjust the dosing flow rate, achieving precise and stable pH control with manageable error. It fully utilizes historical and real-time data, employing algorithms to predict water quality changes and enable early intervention. An adaptive adjustment algorithm is added to automatically adapt to different operating conditions. It comprehensively considers multiple parameters such as flow rate, water quality, and temperature to achieve coordinated control. Ultimately, through precise dosing control, it significantly reduces reagent usage, minimizes pH fluctuations, improves the stability of the entire treatment system, enhances effluent quality, and reduces environmental impact.

[0006] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions: The present invention includes the following steps: S1. Collecting influent parameters and mixed parameters and setting system operating conditions; S2. Real-time rolling updates of pH values ​​and acid / alkali reagent dosing flow rates; S3. Perform pH prediction calculations to reduce the error caused by the lag in pH changes; S4. Calculate the PID output based on the predicted pH value to control the dosing flow rate of acid and alkali reagents; S5. Based on the PID output, perform nonlinear compensation and feedforward control to adjust the acid and alkali reagent dosing flow rate.

[0007] Preferably, the influent parameters collected in step S1 include influent flow rate, influent temperature and influent pH, and the parameters after mixing include mixed temperature and pH.

[0008] Preferably, step S2 specifically includes automatically recording specific data values ​​according to the time interval triggering conditions set for the system operating conditions, transmitting the data to the historical data array, and updating the recorded pH value and dosing flow rate in real time.

[0009] Preferably, step S3 specifically includes establishing a linear relationship between the target pH value y and the acid / base agent dosing flow rate x1 and the initial pH value x2, and solving for the parameter β0, the parameter β1 of the acid / base agent dosing flow rate x1, and the parameter β2 of the initial pH value x2.

[0010] As a preferred approach, the least squares method is used to approximate the fit, and the parameters to be determined are calculated in order to achieve the best fit to the data: for n sets of historical data, the sum of squared errors is minimized, and then the partial derivatives of β0, β1 and β2 are calculated and set to 0. The partial derivatives of β0, β1 and β2 are then arranged into matrix form and solved.

[0011] As a preferred approach, after obtaining parameter β0, parameter β1 of acid / base reagent dosing flow rate x1, and parameter β2 of initial pH value x2, the future pH value is predicted under the current acid / base reagent dosing flow rate x1_new and the current initial pH value x2_new.

[0012] Preferably, step S5, nonlinear compensation, specifically includes establishing a nonlinear relationship between pH value change and acid / alkali agent dosage using a modified hyperbolic tangent function tanh; inputting the midpoint position v0 of the curve, the difference between the acid / alkali agent dosage v, and the steepness coefficient k of the curve into the hyperbolic tangent function tanh; increasing the amplitude coefficient A of the curve; and then calculating the pH value after adding v ml of acid / alkali agent based on the midpoint pH value of the curve.

[0013] As a preferred approach, the nonlinear compensation coefficient η is defined, the steepness coefficient α and the compensation intensity coefficient β of the compensation curve are determined, the PID output of the nonlinear compensation is calculated, and the compensation formula is substituted into the discretized PID equation to obtain the complete control equation.

[0014] Preferably, step S5, feedforward control, specifically includes calculation-based feedforward control based on influent pH and flow rate, inputting the difference between the target pH value pH_target and the current influent pH value pH_inlet(k), and the feedforward gain K. gain Multiply by these to obtain the current feedforward pH adjustment value u_pH(k) for the influent.

[0015] Preferably, step S5, feedforward control, specifically includes feedforward control based on influent flow rate, inputting the ratio of the current flow rate Q(k) to the nominal flow rate Q_nominal, and the base dosing rate K. rate Multiply by each product to obtain the current feedforward adjustment value of the influent flow rate, u_flow(k).

[0016] The beneficial effects of this invention are: it can ensure faster and more precise flow regulation, achieve precise and stable pH control with controllable error; it makes full use of historical and real-time data, uses algorithms to predict water quality changes, and achieves early intervention; it adds an adaptive adjustment algorithm to automatically adapt to different operating conditions; it comprehensively considers multiple parameters such as flow rate, water quality, and temperature to achieve coordinated control; it develops standardized control modules, which are convenient for rapid deployment and optimization in different projects; and finally, through precise dosing control, it significantly reduces the amount of chemicals used, reduces pH fluctuations, improves the stability of the entire treatment system, improves effluent quality, and reduces environmental impact. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention.

[0018] Figure 2 This is a diagram of an adjustment system according to the present invention.

[0019] Figure 3 This is a diagram of the automatic control system architecture for acid and alkali dosing according to the present invention.

[0020] Figure 4 This is a conventional PID control curve diagram of the present invention.

[0021] Figure 5 This is a nonlinear compensation coefficient curve diagram of the present invention.

[0022] Figure 6 This is a nonlinear compensation PID control curve diagram of the present invention.

[0023] In the diagram, 1 is the storage tank, 2 is the booster pump, 3 is the inlet pipe, 4 is the overflow pipe, 5 is the bypass pipe, 6 is the collector, 7 is the outlet pipe, 8 is the regulating valve, and 9 is the dosing point. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this application will be further described in detail below through embodiments and in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0025] Existing technology Traditional pesticide dosing control systems commonly use frequency converters to adjust the frequency and speed of the pesticide dosing pump to control the dosage. However, in practical applications, this method has many problems, affecting the stability, accuracy, and reliability of pesticide dosing. The following is a detailed analysis of these problems: Nonlinearity caused by changes in the liquid level in the upstream storage tank: As the liquid level in the upstream storage tank changes, it affects the pump's suction pressure, which in turn affects the pump's performance curve. Therefore, changes in the same pump frequency converter will cause different flow rate changes, resulting in a nonlinear relationship between frequency and flow rate.

[0026] The inertial effect of fluids in long pipes is significant. Pressure waves propagate, and pressure changes require time to propagate within the pipe. Adjusting the pump speed results in a noticeable lag in flow rate changes at the injection point. The dynamic changes in flow rate at the upstream and downstream points are asynchronous. This increases control complexity: traditional PID control struggles to handle these complex dynamic characteristics.

[0027] Each pump has a minimum flow rate limit; below this limit, turbulence or cavitation may occur inside the pump. At low frequencies, motor efficiency decreases significantly, potentially failing to provide sufficient torque. At low flow rates, the static head of the piping system may exceed the pump's output pressure. This severely limits the system's adjustability within low flow ranges. Precise small-dose dosing is impossible, potentially leading to intermittent or complete cessation of dosing.

[0028] As the pump ages and its performance curve shifts, the deviation between the actual and theoretical flow rates increases. Flow output at the same frequency decreases. Changes in equipment performance can be gradual or sudden.

[0029] There are two main traditional methods for controlling drug dosing.

[0030] 1) Set proportional parameters based on the influent flow rate for proportional dosing: Where Ft1 is the target dosage of the chemical, Ft2 is the influent flow rate, and ppm is the set value of the target concentration.

[0031] The target agent flow rate is calculated by proportional dosing, and then the equipment is controlled using a conventional PID method (target flow rate and actual flow rate). 2) Directly set the target pH value and use the conventional PID method (target pH and actual pH) to control the equipment.

[0032] The first method ignores changes in influent water quality, cannot adapt to water quality fluctuations, is difficult to accurately control pH value, results in waste of chemicals, has unstable treatment effect, and may lead to excessive discharge.

[0033] The second method has a slow response time, making it difficult to respond quickly. The parameters are complex to adjust, making it difficult to adapt to different working conditions. It is prone to overshoot and oscillation, resulting in large pH fluctuations, which affect subsequent treatment processes. The system has poor stability, low control accuracy, and difficulty in meeting strict emission standards.

[0034] Neither of the two dosing control methods can effectively cope with sudden changes in influent water quality and flow rate; the predictive ability is insufficient, and there is a lack of prediction of system change trends; the chemical consumption is high, and excessive or insufficient addition of chemicals leads to energy waste; the maintenance cost is high, requiring frequent manual intervention and adjustment; the treatment effect is unstable, and pH fluctuations affect the efficiency of the entire treatment system.

[0035] The existing three-phase catalytic oxidation Fenton process has the following main problems: The lack of precise automatic dosing control methods and the crude dosing methods lead to excessive use of chemicals, resulting in chemical waste and pH fluctuations.

[0036] The system has poor stability and is difficult to cope with sudden changes in influent water quality and flow rate. Parameter changes can cause uncontrollable changes in the acid-base environment.

[0037] After the addition of acid and alkali reagents, the pH value changes with a long lag time and sensitive fluctuations. This effect makes it difficult to adjust in a timely manner using conventional control methods, which can easily lead to excessive or insufficient reagent addition.

[0038] Unstable pH levels directly affect the catalytic oxidation effect, making it difficult to accurately control water quality targets and impacting subsequent process steps.

[0039] Over-reliance on experienced technicians for manual analysis and prediction.

[0040] With increasingly stringent environmental regulations, more precise wastewater treatment control systems are needed. Improving economic efficiency by reducing chemical waste and lowering operating costs through precise control; enhancing treatment efficiency, as stable pH control contributes to improved overall system efficiency; adapting to complex operating conditions, as modern industrial wastewater has a complex composition requiring more intelligent control systems; reducing human intervention and dependence on manual operation, thus increasing automation levels; and promoting the intelligent development of the wastewater treatment field. This application provides a pH control and acid / base dosing control method to achieve the following objectives: Improve the precision of drug dosing and the stability of pH control.

[0041] Design predictive control systems to significantly reduce response lag.

[0042] Optimize system response speed and reduce the impact of parameter mutations.

[0043] To achieve precise control of water quality targets.

[0044] Precise drug dosage can save on drug costs.

[0045] Improve the stability, smoothness and precision of the overall process.

[0046] This dosing control algorithm significantly improves the efficiency, stability, and economy of the three-phase catalytic oxidation Fenton wastewater treatment process, providing an innovative solution for this field.

[0047] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the figures; the process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0048] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0049] Example: This example describes a method for adjusting the pH value of a three-phase catalytic oxidation Fenton process, such as... Figure 1 As shown, it includes the following steps: S1. Collect influent parameters and mixed parameters and set system operating conditions; S2. Real-time rolling updates of pH values ​​and acid / alkali reagent dosing flow rates; S3. Perform pH prediction calculations to reduce the error caused by the lag in pH changes; S4. Calculate the PID output based on the predicted pH value to control the dosing flow rate of acid and alkali reagents; S5. Based on the PID output, perform nonlinear compensation and feedforward control to adjust the acid and alkali reagent dosing flow rate.

[0050] This application is designed in two aspects: 1. Adding an optimization scheme for the underlying control equipment; 2. PLC control algorithm for controlling the dosage of the reagent.

[0051] 1. Add optimization scheme for underlying control equipment. Regarding the problems of traditional variable frequency controlled chemical dosing systems mentioned in Article 2, such as... Figure 2 As shown, an optimized design scheme is proposed, in which the reagent is raised to a high-level collector and allowed to flow by gravity to the end, where a linear regulating valve is added to adjust the dosing flow rate. Theoretical analysis and experimental verification demonstrate that this scheme effectively improves the stability, accuracy, and reliability of reagent dosing.

[0052] Bernoulli's principle, a fundamental principle in fluid mechanics, describes the conservation of energy during fluid flow. In a gravity-flow system, we can use Bernoulli's principle to analyze the process of fluid flowing from the high-level collector to the dosing point 9. The storage tank 1 is connected to the collector 6 via the booster pump 2 and the inlet pipe 3. The storage tank 1 is also connected to the collector 6 via the overflow pipe 4. The collector 6 is connected to the dosing point 9 via the outlet pipe 7 and the regulating valve 8. A bypass pipe 5 connects the outlet pipe 7 and the inlet pipe 3. When the fluid flows in the pipe, the sum of its kinetic energy, pressure energy, and gravitational potential energy remains constant. In a gravity-flow pipe, after the system releases the liquid and the level drops, the energy of the static pressure above the liquid surface is converted into velocity energy, thus generating a flow velocity.

[0053] Where h1, V1, and P1 are the liquid level, flow velocity, and atmospheric pressure of the liquid surface in the collector 6; h2, V2, and P2 are the liquid level, flow velocity, and pressure at the dosing point; g is the acceleration due to gravity; ρ is the fluid density; and h L The head loss is from h1 to h2.

[0054] After simplification, we get: This equation clearly shows that the height difference (h1-h2) is converted into the kinetic energy of the fluid. and overcome losses along the way L .

[0055] The velocity of a fluid in a pipe can be expressed as: V = Q / A (V is the flow velocity, Q is the fluid flow rate, and A is the cross-sectional area of ​​the pipe). Considering head loss h L It can usually be expressed as a function of velocity: The formula for calculating the flow rate of a gravity-flow pipe can be obtained: Therefore, under a given system design (i.e., A and K are fixed), the flow rate Q mainly depends on the height difference (h1-h2). When the reagent is lifted to the collector, the change in the liquid level in the collector is the change in the height difference of gravity flow (h1-h2), which is less than 1% relative to the overall height difference. The change is extremely small, and the maximum flow rate Q at the dosing point at the end of the pipeline remains basically constant. This provides a basis for the precise control of the reagent dosing flow rate.

[0056] At this point, a linear regulating valve 8 with good linear characteristics is added to adjust the dosing flow rate, further enhancing the system's controllability. Through this theoretical analysis, we can see the advantages of this invention in terms of stability, controllability, and low-flow-rate regulation. It can provide rapid and accurate flow rate regulation response, unaffected by other variable factors; it can provide precise control in certain low-flow-rate scenarios; and the stable and accurate drug dosing provides a good foundation for subsequent dosing control algorithms.

[0057] 2. Methods and algorithms for controlling the dosage of pesticides like Figure 3 As shown: This algorithm is based on a PLC controller. It uses an I / O module to collect and output control data from field instruments and the equipment's operating status. Through the development of a logic program, it achieves precise dosing of acid and alkali reagents, resulting in stable pH control. The algorithm and method for acid and alkali dosing are consistent; taking a sulfuric acid dosing system as an example: System input module: includes the observation dimensions required by the current system, such as the influent flow rate, influent temperature, influent pH, current pH after mixing, and system operating conditions (manually set), etc. Historical data update: The PLC controller automatically records specific data values ​​according to preset time intervals and triggers conditions. The data is then transmitted to the historical data array and the pH value and dosing flow rate are updated in real time. This is mainly used to assist in the pH value prediction function. pH Prediction: Due to the significant lag in pH changes, a prediction algorithm is introduced during calculation. This algorithm uses the predicted pH value to calculate the error, rather than the current pH value. This method can proactively address pH changes and reduce the impact of system lag. This scheme uses linear regression to predict future pH values ​​based on historical data, capturing pH trends and facilitating timely adjustments.

[0058] A linear relationship was established between pH (y) and sulfuric acid flow rate (x1) and initial pH (x2): y = β0 + β1x1 + β2x2 + ε (y is the target pH value, x1 is the volume of sulfuric acid added, x2 is the initial pH value, β0, β1, and β2 are the parameters to be determined, and ε is the error term.) The least squares method is used to approximate the fit, and the parameters to be determined are calculated in order to achieve the best fit to the data: For n sets of historical data, we want to minimize the sum of squared errors: Take the partial derivatives of L with respect to β0, β1, and β2 respectively, and set them to 0: Rearranging the above equations, we get: nβ0+β1Σx 1i +β2Σx 2i =Σy i β0Σx 1i +β1Σx 1i 2 +β2Σx 1i x 2i =Σx 1i y i β0Σx 2i +β1Σx 1i x 2i +β2Σx 2i 2 =Σx 2i y i It can be written in matrix form as Xβ=Y: Solving for coefficients β=(X'X) -1 X'Y Where X' is the transpose of X, (X'X) -1 X'X is the inverse matrix, and Y is the actual pH value vector.

[0059] pH value prediction After obtaining β0, β1, and β2, calculate the possible future pH value based on the current flow rate x1_new and the initial pH value x2_new: y_pred=β0+β1x1_new+β2x2_new Where y_pred is the predicted pH value, x1_new is the current flow rate, and x2_new is the current pH value of the water.

[0060] PID algorithm: Calculates PID output based on predicted pH value (or actual pH value, depending on settings), which helps the system respond quickly, eliminate steady-state error and reduce oscillation; Standard PID equation: u(t)=Kp×e(t)+Ki×∫e(t)dt+Kd×de(t) / dt Where u(t) is the control output, e(t) is the error signal, and Kp, Ki, and Kd are the proportional, integral, and derivative coefficients, respectively.

[0061] Convert to discretized PID equations: u(k)=Kp×e(k)+Ki×Ts×Σe(i)+Kd×(e(k)-e(k-1)) / Ts Where u(k) is the control output, Ts is the sampling period, k is the current sampling point, e(k) is the current error, e(k-1) is the previous error, and Σe(i) is the sum of errors within the sampling time.

[0062] Nonlinear Compensation: The relationship between pH value and acid addition is not linear, and the algorithm makes nonlinear adjustments based on the PID output. pH titration curves are typically S-shaped, with the steepest change near neutral pH values, while the change is relatively gentle in the extremely acidic and extremely alkaline regions. We use a function similar to an S-shaped curve to simulate this characteristic; currently, the hyperbolic tangent function (tanh) is used to fit the S-shaped characteristics of the pH titration curve.

[0063] A nonlinear relationship between pH change and acid addition was established using a modified hyperbolic tangent function: pH(v) = pH0 + A × tanh(k(v - v0)) Where pH(v) is the pH value after adding v ml of acid, pH0 is the midpoint pH value of the curve, A is the amplitude coefficient of the curve, k is the steepness coefficient of the curve, v0 is the midpoint position of the curve, and v is the amount of acid added.

[0064] Define the nonlinear compensation coefficient η: η(pH)=β×(1+tanh 2 (α(pH-7))) Where β is the compensation intensity coefficient and α is the steepness coefficient of the compensation curve.

[0065] The nonlinear parameters α (steepness coefficient of the compensation curve) and β (compensation intensity coefficient) are determined as follows: α represents the change in the steepness of the S-curve amplitude, and β represents the compensation intensity coefficient of the nominal pH value. The actual effect of wastewater treatment can be used to fit the curve, and then the magnitudes of the α and β constants are determined to compensate for the PID data results.

[0066] Calculate the PID output with non - linear compensation: u_nl(k) = η(pH)×u(k) Substitute the compensation formula into the discretized PID equation to obtain the complete control equation: u_nl(k) = β×(1 + tanh 2 (α(pH - 7)))×[Kp×e(k)+Ki×Ts×Σe(i)+Kd×(e(k)-e(k - 1)) / Ts] Adjust according to the actual process, and reasonably select PID parameters and non - linear compensation coefficients: 1) Near the neutral region, reduce the compensation coefficient to avoid overshoot.

[0067] 2) In the two - end regions, increase the compensation coefficient to improve the regulation speed.

[0068] 3) When pH > 7: Use a stronger compensation.

[0069] 4) When 6 < pH < 8: Use a smaller compensation.

[0070] 5) When pH < 6: Use a medium - strength compensation.

[0071] 6) When the deviation is large, limit the integral action.

[0072] 7) When the deviation is very small, keep the output stable.

[0073] 8) Calibrate the pH meter regularly and adjust the control parameters according to the actual operation effect.

[0074] Add feed - forward control based on the sudden change of influent parameters: When the influent flow rate or the influent pH value suddenly changes Feed - forward control based on the calculation of influent pH and flow rate: u_pH(k) = K gain *(pH_inlet(k + 1)-pH_inlet(k)) where, u_pH(k) is the current influent pH feed - forward regulation value, pH_inlet(k + 1) is the next influent pH value, pH_inlet(k) is the current influent pH, and K gain is the feed - forward gain.

[0075] Feed - forward control based on influent flow rate: u flow(k) = K rate ×(Q(k + 1)-Q(k)) where, u_flow(k) is the current influent flow rate feed - forward regulation value, Q(k) is the current flow rate, Q(k + 1) is the next influent flow rate, and K rateBased on the base injection rate.

[0076] u_forward=u_pH(k)×u_flow(k)×u_nominal Where u_nominal is the nominal dosage of medicine required to add 1,000 cubic meters of water according to the specified ratio.

[0077] K can be continuously adjusted based on actual operating data. gain and K rate The two parameters, along with feedforward control based on influent pH and influent flow rate, help to respond more quickly to changes in influent pH and flow rate to achieve optimal results.

[0078] Adaptive operating conditions: For different wastewater treatment conditions, such as situations where even if some ions in the wastewater are neutral, adjusting the pH to suit specific chemical reactions or biological treatment processes still requires significant acid or alkali neutralization (e.g., different influent temperatures leading to varying treatment effects), load-based operating condition identification is introduced. Different PID parameters are set for high-load conditions, with each condition having a unique parameter set to handle faster pH changes. Output Calculation: By comprehensively calculating the PID output, feedforward control, and nonlinear compensation, the final acid dosage is calculated to ensure that the final output is within a safe range. The result is then fed back to the next round of control. u_nl(k)=β(1-tanh 2 (α(pH-pH_target))×[Kp×e(k)+Ki×Ts×Σe(i)+Kd×(e(k)-e(k-1)) / Ts]+u_pH(k)+u_flow(k) Add anomaly detection logic, such as pH value mutation detection, and trigger emergency handling when anomalies occur.

[0079] Example 1 The agent is raised to a high-level collector and allowed to flow by gravity to the end. A linear regulating valve is added at the end to adjust the dosing flow rate.

[0080] Taking a three-phase catalytic oxidation process in a wastewater treatment plant as an example: Based on design calculations, the maximum required sulfuric acid dosage is 2 m³ / h. 3 / h, the height of the three-phase catalytic oxidation reactor is 15m. The sulfuric acid collector is set above the three-phase catalytic oxidation reactor. The normal liquid level of sulfuric acid in the sulfuric acid collector is 0.5-1.5m. The sulfuric acid is lifted into the sulfuric acid collector using a lift pump. The height of the acid addition point is -2m. A DN50 diameter pipe is used for addition from the collector to the addition point.

[0081] Ignoring pipeline head loss, the calculation is based on the lowest liquid level in the collector: At the injection point, a ZDLP electronic single-seat control valve is selected, consisting of a PS series and 3610 series linear electric actuator and a low-flow-resistance straight-through single-seat valve. The electric actuator is an integrated electronic structure with a built-in servo amplifier. The valve opening can be controlled by inputting a control signal (4-20mA DC) and power supply, thus achieving the regulation of flow parameters. It features sensitive operation and high regulation accuracy.

[0082] The following data was obtained from testing and verifying the system under different operating conditions: Table 1 Test and Verification Traffic Data Table The flow characteristic curve of the ZDLP electronic electric single-seat regulating valve was plotted based on the measured data. By using a linear valve, the linear change of the dosing flow can be precisely controlled. The adjustment range is wide, and it can be perfectly controlled even at small flow rates.

[0083] Example 2 Taking the sulfuric acid dosing control of a three-phase catalytic oxidation process in a wastewater treatment plant as an example, the pH prediction method described above is based on a Siemens 410smart-PLC. The method collects signals such as influent flow rate, influent temperature, influent pH, current pH after mixing, and dosing flow rate of the three-phase catalytic oxidation Fenton system through the IO acquisition module.

[0084] Based on actual measurements, with constant influent flow rate and pH value, changes in sulfuric acid dosage are expected to cause the target pH value to change within two minutes. The PLC controller is triggered at 10-second intervals to automatically record specific data values, which are then transmitted to a historical data array. Thirty sets of data are retrieved, and the pH value and chemical flow rate are updated in real-time. Based on the invention, an SCL language assembly algorithm is used to predict the historical data.

[0085] 1) Establish a historical data collection module: 2) Establish a historical data collection module: 3) The function Calculate_Regression_Parameters for calculating regression parameters: Least squares method The following calculations are made based on a portion of the original historical data from the example project: Table 2 Forecast Data Table Calculate the target pH prediction for the 31st data group according to the above SCL algorithm formula: PH = 12.978 + (-4.482) * 0.989 + (-0.493) * 8.867 = 4.174 During this process, a current PH value of 3.95 (the 30th group of data) and a predicted PH value of 4.174 (predicted from 30 groups of data) will be obtained. Through the analysis of the data from the long-term continuous operation of the system, during the large-range adjustment of PH, the deviation between the predicted value and the actual value is large. When PH fluctuates within a small range near the target value, the predicted value is accurate and close to the actual value. Set the adjustment variable parameters, and perform timed sampling and analysis based on the current PH value. If the change trend of the current actual PH value is relatively stable and the change range is small, select the predicted PH value as the input actual value for PID calculation. Otherwise, select the current actual PH value.

[0086] Calculate PID using the predicted pH value (or the actual pH value, depending on the setting), laying a foundation for the fast and accurate dosing output.

[0087] Example 3 PID calculation output, S-shaped curve linear compensation, Based on Example 2, this example performs S-shaped curve linear compensation and feedforward adjustment on PID calculation through the scl language assembly algorithm.

[0088] 1) Use the chemical dosing amount required for PID calculation For the current sewage treatment conditions, select a suitable pid, Kp = 0.5; Ki = 0.05; Kd = 0.02; t = 10s; Set the current parameters in the PLC's PID adjustment system. The initial PH value is 10, and the target ph value is 4. During the PID adjustment process, intercept the change curves of chemical dosing and PH value as Figure 4 : Conventional PID adjustment curve (relationship between PH and sulfuric acid dosing amount): Analyze the curve: 3) When pH is between 8 - 10: The acid liquid dosing amount is continuously increasing, but the change of the PH value is slowly decreasing. The adjustment time process of ph change is long, and at this time, the acid liquid dosing amount is still continuously increasing; 4) When 6 < pH < 8: Due to the previous pid adjustment, the acid liquid dosing amount is very large, and ph decreases rapidly with a fast change rate. Under the integral inhibition effect of pid, the acid liquid dosing amount starts to decrease, but the ph value still decreases rapidly, and the PH value jitters within a large range during the adjustment process; 5) When pH < 6: After the jitter of the PH value ends, the PH value approaches the target value, but the adjustment amplitude responds slowly and the continuous oscillation time is very long.

[0089] 2) Use non-linear compensation to correct the pid calculation result Adding S-curve compensation calculation to conventional PID control Based on the current data analysis of wastewater acid pH adjustment, alpha = 0.45 and beta = 1 were selected, and a compensation curve for pH values ​​1-13 was plotted, as shown below. Figure 5 As shown. The PID output is compensated according to the current compensation curve, and readjusted and debugged. The initial pH value is 10, and the target pH value is 4. The curves showing the changes in reagent dosage and pH value during the PID adjustment process are shown below. Figure 6 Nonlinear compensated PID control curve (relationship between pH and sulfuric acid dosage): Analysis of the compensation effect on the curve: Near the target value (pH=7), the compensation coefficient is close to 1, maintaining the original characteristics of the PID controller. The further the deviation from the target value, the stronger the compensation effect, automatically adjusting the control gain to form an adaptive control characteristic. It responds quickly to the difference between the target and actual values, reducing system overshoot, shortening the settling time, and improving control accuracy. By continuously compensating and adjusting the output signal, it eliminates the system's steady-state error and reduces oscillations.

[0090] Traditional Fenton oxidation: H₂O₂ decomposes under the catalysis of Fe²⁺ to produce ·OH, which oxidizes and decomposes organic matter into smaller molecules through electron transfer and other pathways. Simultaneously, Fe²⁺ is oxidized to Fe³⁺, producing coagulation and precipitation, removing a large amount of organic matter. Fenton oxidation has both oxidation and coagulation effects in water treatment. While Fenton oxidation can degrade organic matter, its drawback is the low utilization rate of H₂O₂, which cannot fully mineralize the organic matter.

[0091] Three-phase catalytic oxidation: Three-phase catalytic oxidation technology (three phases, namely, innovative composite catalytic materials in the solid phase, hydrogen peroxide and ferrous sulfate solution in the liquid phase, and air aeration in the gas phase) is developed based on traditional Fenton (homogeneous catalytic oxidation) and electrochemical methods. It takes innovative composite catalytic materials and reactor four as the core and couples them with magnetization processes and other device systems. It has both homogeneous catalytic oxidation and heterogeneous catalytic oxidation systems. It belongs to the combination and coupling of multiple technologies such as catalytic reduction, Fenton oxidation, high-efficiency coagulation, and magnetization. It is mainly suitable for the deep treatment of recalcitrant industrial wastewater from coking, chemical, and printing and dyeing industries. It has stable effects and strong resistance to load shocks.

[0092] pH Adjustment: pH value is the negative logarithm of the hydrogen ion concentration in a solution and is an important indicator of the acidity or alkalinity of a solution. In daily life and industrial production, we often need to adjust the pH value of solutions to meet different needs. Acid-base neutralization is one of the most common methods of pH adjustment. When a solution is too acidic or too alkaline, an appropriate amount of acid or base can be added to neutralize the solution and bring its pH value to the desired range.

[0093] Automatic Control System: The automatic control system takes a PLC controller as its core, and uses IO modules to collect data from field instruments and equipment operating status, and control equipment start and stop. Through the development of logic running programs, it achieves precise control of the entire process. The process data is transmitted to the SCADA system via the industrial Ethernet protocol. A production monitoring interface is developed in the SCADA system to realize remote monitoring and control of the production process.

[0094] The specific embodiments described herein are merely illustrative examples illustrating the spirit of the invention. The above embodiments only express several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art to which this application pertains can make various modifications or additions to the described specific embodiments or use similar methods to substitute them, but without departing from the spirit of this application or exceeding the scope defined by the appended claims. For those skilled in the art, multiple variations and improvements can be made without departing from the concept of this application. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for adjusting the pH value in a three-phase catalytic oxidation Fenton process, characterized in that, Includes the following steps: S1. Collect influent parameters and mixed parameters and set system operating conditions; S2. Real-time rolling updates of pH values ​​and acid / alkali reagent dosing flow rates; S3. Perform pH prediction calculations to reduce the error caused by the lag in pH changes; S4. Calculate the PID output based on the predicted pH value to control the dosing flow rate of acid and alkali reagents; S5. Based on the PID output, perform nonlinear compensation and feedforward control to adjust the acid and alkali reagent dosing flow rate.

2. The method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 1, characterized in that, The parameters collected in step S1 include influent flow rate, influent temperature, and influent pH. The parameters after mixing include mixed temperature and pH.

3. The method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 1 or 2, characterized in that, Step S2 specifically includes automatically recording specific data values ​​according to the time interval trigger conditions set for the system operating conditions, transmitting the data to the historical data array, and updating the recorded pH value and dosing flow rate in real time.

4. A method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 1 or 2, characterized in that, Step S3 specifically includes establishing a linear relationship between the target pH value y and the acid / base agent dosing flow rate x1 and the initial pH value x2, and solving for the parameter β0, the parameter β1 of the acid / base agent dosing flow rate x1, and the parameter β2 of the initial pH value x2.

5. The method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 4, characterized in that, The least squares method is used to approximate the fit, and the parameters to be found are calculated in order to achieve the best fit to the data: for n sets of historical data, the sum of squared errors is minimized, and then the partial derivatives of β0, β1 and β2 are calculated and set to 0. The partial derivatives of β0, β1 and β2 are arranged into matrix form and solved.

6. The method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 5, characterized in that, After obtaining the parameters β0, β1 of the acid / base reagent dosing flow rate x1, and β2 of the initial pH value x2, the future pH value is predicted under the current acid / base reagent dosing flow rate x1_new and the current initial pH value x2_new.

7. A method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 1 or 2, characterized in that, The nonlinear compensation step S5 specifically includes establishing a nonlinear relationship between pH value change and acid / alkali agent dosage using a modified hyperbolic tangent function tanh, inputting the midpoint position v0 of the curve, the difference between the acid / alkali agent dosage v, and the steepness coefficient k of the curve into the hyperbolic tangent function tanh, increasing the amplitude coefficient A of the curve, and then solving for the pH value after adding v ml of acid / alkali agent based on the midpoint pH value of the curve.

8. The method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 7, characterized in that, Define the nonlinear compensation coefficient η, determine the steepness coefficient α and the compensation intensity coefficient β of the compensation curve, calculate the PID output of the nonlinear compensation, and substitute the compensation formula into the discretized PID equation to obtain the complete control equation.

9. A method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 1 or 2, characterized in that, The feedforward control step S5 specifically includes calculation-based feedforward control based on influent pH and flow rate, inputting the difference between the next influent pH value pH_inlet(k+1) and the current influent pH value pH_inlet(k), and the feedforward gain K. gain Multiply by these to obtain the current feedforward pH adjustment value u_pH(k) for the influent.

10. A method for adjusting the pH value of a three-phase catalytic oxidation Fenton process according to claim 1 or 2, characterized in that, The feedforward control in step S5 specifically includes feedforward control based on the influent flow rate. The input is the difference between the next influent flow rate Q(k+1) and the current flow rate Q(k), and the base dosing rate K. rate Multiply by each product to obtain the current feedforward adjustment value of the influent flow rate, u_flow(k).

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