Pellet coating system based on double PID (Proportion Integration Differentiation) adjustment and control method

Abstract

The invention discloses a pellet coating system based on double PID adjustment, and relates to the field of slurry preparation, the pellet coating system comprises a stirring kettle which is communicated with a water inlet pipeline and a limestone feeding pipeline, and stirring blades in transmission connection with a stirring motor are arranged in the stirring kettle; the water inlet pipeline is provided with an electric control valve, the limestone feeding pipeline is provided with a mass flow meter, and a concentration sensor is arranged in the stirring kettle; the main control unit comprises a main PID controller and an auxiliary PID controller, the concentration sensor is connected with the input end of the main PID controller, the output end of the main PID controller is connected with the electric adjusting valve, the power signal output end of the stirring motor is connected with the input end of the auxiliary PID controller, and the output end of the auxiliary PID controller is connected with a parameter setting interface of the main PID controller. The invention further discloses a control method of the system, the proportionality coefficient or integral time parameter of the main PID is dynamically adjusted, and concentration adjustment lag caused by limestone feeding fluctuation or slurry characteristic change is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the field of slurry preparation, and more particularly to a pellet coating system and control method based on dual PID regulation. Background Art

[0002] In the pellet production process, the pellet coating step requires that the pellets be fully mixed with the slurry formed by mixing limestone and water so that the surface of the pellets is evenly coated with a layer of slurry to improve their high-temperature strength and metallurgical properties. In this process, the concentration stability and mixing uniformity of the slurry are key factors affecting the coating quality, which are directly related to the subsequent strength, reducibility and other indicators of the pellets after roasting. Therefore, the slurry concentration needs to be controlled in real time during production to ensure that the limestone and water in the slurry are fully mixed to avoid slurry defects caused by concentration fluctuations or uneven mixing.

[0003] In the existing pellet coating control system, a single PID control strategy is mostly used for slurry mixing. The concentration is maintained stable by detecting the slurry concentration and adjusting the water inlet or limestone feed rate through feedback. However, in actual production, the slurry concentration is affected by the coupling of multiple parameters such as limestone feed rate, water inlet rate, and stirring intensity. The single PID control only relies on a single concentration feedback signal for adjustment, which is difficult to quickly respond to concentration mutations under complex working conditions and has obvious adjustment lag. When the limestone feed rate fluctuates or the slurry characteristics change, the single PID controller needs to go through multiple links such as sensor sampling, error calculation, and actuator action, which often results in a long time delay. The concentration deviation is difficult to correct quickly and the fluctuation range is large. At the same time, traditional stirring devices mostly operate at fixed power or frequency. The stirring intensity cannot be dynamically adjusted according to changes in slurry viscosity and concentration, which can easily cause the slurry to settle due to gravity sedimentation. The slurry concentration in different areas of the mixing tank is significantly different, forming a large concentration gradient, which seriously affects the uniformity of pellet coating.

[0004] Therefore, there is an urgent need for a control system that can achieve high-precision and rapid adjustment of slurry concentration, dynamically optimize the stirring effect according to the slurry state, and effectively suppress sedimentation and stratification phenomena, so as to solve the problems of large concentration fluctuations and uneven mixing caused by control lag in the existing technology, and meet the strict requirements of industrial continuous production on pellet coating quality. Summary of the Invention

[0005] One object of the present invention is to provide a pellet coating system and control method based on dual PID regulation, which can achieve high-precision and rapid adjustment of slurry concentration, and at the same time dynamically optimize the stirring effect according to the slurry state, effectively suppressing slurry deposition and stratification.

[0006] An object of the present invention is to provide a pellet coating system based on dual PID regulation, comprising: A stirring kettle is connected to a water inlet pipe and a limestone feeding pipe. A stirring blade is provided inside the stirring kettle to fully mix the pellets with the slurry formed by mixing water and limestone. The stirring blade is connected to the stirring motor. a mass flow meter installed in the limestone feed pipeline; An electric regulating valve is installed in the water inlet pipe; A concentration sensor is installed in the stirred tank; The main control unit includes a main PID controller and a sub-PID controller, wherein the output end of the concentration sensor is connected to the input end of the main PID controller, the output end of the main PID controller is connected to the control end of the electric regulating valve, the power signal output end of the stirring motor is connected to the input end of the sub-PID controller, and the output end of the sub-PID controller is connected to the parameter setting interface of the main PID controller.

[0007] Another object of the present invention is to provide a control method for a pellet coating system based on dual PID regulation, which is applied to the above-mentioned pellet coating system based on dual PID regulation, and detects the limestone feeding amount in real time through a mass flow meter. M s , the unit is kg / h , the concentration sensor is used to detect the slurry concentration value N in real time, the unit is percentage, according to the formula Calculate water distribution M v , the unit is m³ / h ; The main PID controller sets the value according to the concentration N p Real-time detection of slurry concentration value N Calculate concentration deviation , calculate the output value ,in K p is the proportionality coefficient, T i is the integration time, T d is the differential time, a is the differential switching coefficient ，a∈ {0,1}, u(t) The preset range conversion relationship is mapped as an opening instruction to the electric control valve, and the electric control valve is adjusted according to the opening instruction to make the water inlet flow close to M v ; The auxiliary PID controller collects the power signal of the stirring motor in real time according to the monitoring cycle and performs power deviation calculation. ,in P a Set the concentration value N pThe corresponding motor reference power, P r is the measured power of the motor, when ΔP When the positive significant deviation condition is continuously met in several consecutive monitoring cycles, the main PID controller integral time is updated. ,when ΔP When the negative significant deviation condition is continuously met in several consecutive monitoring cycles, the proportional coefficient of the main PID controller is updated. , the main PID controller adopts the updated and Compute new output.

[0008] Preferably, the following conditions are met in at least three consecutive monitoring cycles: ΔP≥0.05 or ΔP≤-0.05 When , it is determined that the positive significant deviation condition or the negative significant deviation condition is met.

[0009] Preferably, a viscosity sensor is provided at the bottom of the stirring tank, and the signal output end of the viscosity sensor is connected to the input end of the auxiliary PID controller. m , use the stirring motor power to calculate the viscosity value ,in k is the conversion coefficient determined based on the stirring motor power-viscosity calibration test, P 0 The no-load power of the stirring motor is used to obtain the concentration setting value. N p The corresponding expected viscosity m a , when the viscosity difference When the viscosity tolerance threshold is exceeded: like P r ≥P a and m≥m a , it is determined that the actual viscosity has increased, and the main PID controller integral time is updated. ; like P r ≤P a and mm ≤ mm a , it is determined that the actual viscosity has decreased, and the proportional coefficient of the main PID controller is updated. ; like P r ＜P a and m>m a or P r＞P a and m<m a , freeze the main PID controller update, and the stirring motor enters the pulse stirring mode. In the pulse stirring mode, the power input frequency of the stirring motor periodically alternates between the pulse frequency and the normal frequency.

[0010] Preferably, the viscosity tolerance threshold d =min(0.05 m p , 0.05 m a ).

[0011] Preferably, several turbidity sensors and several pressure sensors are set in the stirred tank, wherein the turbidity sensors record the turbidity deviation ΔNTU, and the pressure sensors record the pressure gradient variance. After the freeze main PID controller is updated, the measured parameters collected by various sensors in real time are input into the pre-trained pulse strategy prediction model. The measured parameters include the relative deviation of power ΔP , viscosity difference , pressure gradient variance , turbidity deviation ΔNTU obtained by the turbidity sensor, pellet volume ratio parameter, temperature in the stirred tank and preset pulse frequency, the pulse strategy prediction model outputs the pulse mode duration t i , the stirring motor uses the shortest pulse mode duration t i The pulse stirring mode is executed at a corresponding preset pulse frequency, wherein the preset pulse frequency is a discrete candidate frequency set in the main control unit; In complete pulse mode duration t i Then, if both of the following are satisfied: (a) True viscosity increases or true viscosity decreases; (b) Pressure gradient variance ≤ d g ; (c) Turbidity deviation ΔNTU≤ d NTU ; Then the system resumes the main PID controller parameter update, where d g 、 d NTU are the pressure gradient variance threshold and the turbidity deviation threshold.

[0012] Preferably, n pressure sensors are arranged on the vertical axis of the side wall of the stirred tank, and all pressure sensors are equally spaced along the effective liquid level height direction. The measured pressure value of each pressure sensor at a selected time point is obtained by a synchronous data acquisition unit, and the measured pressure difference between adjacent pressure sensors is calculated to obtain a pressure difference sequence. , and the mean pressure gradient , pressure gradient variance ; Two turbidity sensors are arranged at the bottom of the stirred tank and at the effective liquid level height, and the turbidity deviation ΔNTU between the two turbidity sensors is recorded.

[0013] Preferably, the pulse strategy prediction model adopts a BP neural network model including an input layer, a hidden layer and an output layer. The input layer of the BP neural network model includes multiple input neuron nodes, and multiple hidden neuron nodes are arranged on the hidden layer. Each of the input neuron nodes is respectively connected to each of the hidden neuron nodes, and the hidden neuron nodes are all connected to the output layer. The power relative deviation, viscosity difference, pressure gradient variance, turbidity deviation, temperature in the stirred tank, and preset pulse frequency are used as input values ​​of the input neuron nodes, and the output result is the pulse mode duration.

[0014] Preferably, the training of the BP neural network model comprises the following steps: S1. Limestone-water slurries with different solid contents are filled into the mixing kettle in layers, and pellets are added according to the target ratio. The stirring motor is turned on and runs at a normal stirring frequency for a preset time, while the measured parameters of each sensor are recorded simultaneously. S2. Switch the stirring motor to the preset pulse frequency and continue stirring until the slurry stratification in the stirring tank disappears. Record the pulse mode duration corresponding to this process. S3. Repeat steps S1 and S2, conduct experiments on multiple layer states and different preset pulse frequency combinations, form a sample set, and train the BP neural network model.

[0015] The present invention has at least the following beneficial effects: First, the main PID controller adjusts the water flow rate based on the concentration deviation, while the auxiliary PID controller analyzes the stirring motor power signal in real time. When it detects that the stirring motor power continuously deviates from the reference value, it dynamically adjusts the proportional coefficient or integral time parameter of the main PID controller. This synergistic mechanism significantly reduces the concentration regulation lag caused by fluctuations in limestone feed or changes in slurry properties, narrowing the concentration fluctuation range to within the industrial allowable threshold. Second, by comparing and analyzing the actual viscosity sensor value and the viscosity value calculated from the stirring motor power, the actual viscosity change can be accurately distinguished. When the two logically conflict, the system freezes the main PID controller parameter update and starts the pulse stirring mode. By periodic frequency switching, the slurry sedimentation chain is broken, effectively suppressing stratification and agglomeration in the stirring tank and ensuring slurry uniformity. Third, in the pulse stirring mode, the system inputs multi-source sensor data such as power deviation, viscosity difference, pressure gradient variance, turbidity deviation and temperature into the pre-trained pulse strategy prediction model, and outputs the optimal pulse frequency and pulse mode duration. The pulse strategy prediction model is generated based on a large amount of stratified working condition experimental data training, and can adaptively match the current slurry state, eliminate stratification and restore the PID controller in the shortest time, thereby reducing the length of production interruptions.

[0016] Other advantages, objectives and features of the present invention will be reflected in part from the following description and will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the overall system in a technical solution of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can implement the invention with reference to the description.

[0019] It should be understood that terms such as “having”, “including” and “comprising” used herein do not preclude the existence or addition of one or more other elements or combinations thereof.

[0020] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials are commercially available unless otherwise specified. In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "set" should be understood in a broad sense, for example, they can be fixedly connected or set, or detachably connected or set, or integrally connected or set. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The orientations or positional relationships indicated by the terms "transverse", "longitudinal", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0021] like Figure 1 As shown, the present invention provides a pellet coating system based on dual PID regulation, comprising: A stirring kettle is provided with a water inlet pipeline and a limestone feeding pipeline, a stirring blade is provided inside the stirring kettle to fully mix the pellets with a slurry formed by mixing water and limestone, and the stirring blade is in transmission connection with a stirring motor; a mass flow meter is installed in the limestone feeding pipeline; an electric regulating valve is installed in the water inlet pipeline; a concentration sensor is installed in the stirring kettle; and a main control unit includes a main PID controller and a sub-PID controller, wherein the output end of the concentration sensor is connected to the input end of the main PID controller, the output end of the main PID controller is connected to the control end of the electric regulating valve, the power signal output end of the stirring motor is connected to the input end of the sub-PID controller, and the output end of the sub-PID controller is connected to the parameter setting interface of the main PID controller.

[0022] In this technical solution, when the system is running, limestone enters the mixing tank through the limestone feeding pipeline, the mass flow meter detects in real time and outputs the limestone discharge amount signal to the main control unit, and at the same time, water enters the mixing tank through the water inlet pipeline, and the electric regulating valve adjusts the opening according to the received control signal to control the water inlet flow. After the stirring motor is started, the stirring blade in the mixing tank is driven to rotate through the transmission connection, so that the water and limestone entering the tank are fully mixed to form a slurry. The concentration sensor detects the slurry concentration value in the mixing tank in real time and transmits the detected concentration signal to the input end of the main PID controller. The main PID controller compares the slurry concentration value with the preset concentration set value, calculates the deviation between the two, and then processes the deviation according to the proportional-integral-differential control algorithm. , and generate a corresponding control signal, which is transmitted to the control end of the electric control valve. The electric control valve adjusts the opening according to the control signal, so that the water inlet flow approaches the target value that meets the slurry concentration requirement, thereby realizing water inlet flow regulation based on concentration deviation. During the operation of the stirring motor, its power signal will be continuously output to the input end of the sub-PID controller. After receiving the power signal, the sub-PID controller will compare and analyze it with the preset reference power. If a continuous deviation is found in the power signal, a parameter adjustment signal will be generated and input through the parameter setting interface of the main PID controller to realize dynamic adjustment of key parameters such as the proportional coefficient and integral time of the main PID controller, so that the adjustment performance of the main PID controller can adapt to changes in the slurry state.

[0023] In another technical solution, for the control method of the pellet coating system based on dual PID regulation, the limestone feeding amount is detected in real time by a mass flow meter. M s , the unit is kg / h , the slurry concentration N is detected in real time by the concentration sensor, the unit is percentage, according to the formula Calculate water distributionM v , the unit is m³ / h, Specifically, according to the amount of limestone M s The water distribution amount is determined by the proportional relationship with the real-time slurry concentration N M v The electric regulating valve adjusts its opening accordingly, which corresponds to the water inlet mass flow required per unit time.

[0024] The main PID controller sets the value according to the concentration N p Real-time detection of slurry concentration value N Calculate concentration deviation , calculate the output value ,in K p is the proportionality coefficient, T i is the integration time, T d is the differential time, a is the differential switching coefficient ，a∈ {0,1}, u(t) The preset range conversion relationship is mapped as an opening instruction to the electric control valve, and the electric control valve is adjusted according to the opening instruction to make the water inlet flow close to M v , specifically, a It is the differential term switching coefficient. When the differential term is enabled a=1， The differential term is adjusted in advance according to the rate of change of the concentration deviation, which can suppress the possible overshoot phenomenon. For this system, since the slurry concentration signal is easily affected by stirring turbulence, uneven distribution of limestone particles, etc., high-frequency random fluctuations are generated, and there is a process delay from water inlet regulation to concentration feedback, the differential action may be too advanced and cause oscillation, so the differential term switching coefficient a The value can be 0; Proportional coefficient K p Used to amplify the regulation effect of the current deviation, integration time T i Determine the weight of the historical deviation accumulation, the main PID controller can dynamically output according to the real-time status of the concentration deviation u (t) And through the preset range, it is converted into the opening instruction of the electric regulating valve, and finally the water flow rate is adjusted to the water distribution volume. M v The precise approximation of the slurry concentration can be stably controlled.

[0025] The auxiliary PID controller collects the power signal of the stirring motor in real time according to the monitoring cycle and performs power deviation calculation. ,inP a Set the concentration value N p The corresponding motor reference power, P r is the measured power of the motor, when ΔP When the positive significant deviation condition is continuously met in several consecutive monitoring cycles, the main PID controller integral time is updated. ,when ΔP When the negative significant deviation condition is continuously met in several consecutive monitoring cycles, the proportional coefficient of the main PID controller is updated. , the main PID controller adopts the updated and Calculate new output; Specifically, the monitoring cycle of the sub-PID controller is a fixed time interval preset by the system for collecting the stirring motor power signal and various sensor data. P a During the system debugging phase, the slurry can be stably controlled at the concentration set value. N p , when the stirring motor is running in the ideal steady state, record the power value of the stirring motor as P a The positive significant deviation condition and the negative significant deviation condition respectively indicate that the stirring motor is in a continuous high power or low power state. At this time, the corresponding conditions of the slurry are respectively that the viscosity concentration increases or decreases. When in the positive significant deviation condition, the shortened integration time is used. It can enhance the main PID controller's cumulative response speed to historical deviations, accelerate water inlet compensation, and suppress the trend of concentration increase. When in a negative significant deviation condition, the increased proportional coefficient is used. It can improve the sensitivity of the main PID controller to the current concentration deviation, quickly reduce the water inlet flow, and prevent the slurry concentration from further decreasing; the deviation of the stirring motor power in a single monitoring cycle may be caused by the stirring motor operating noise, grid voltage fluctuation, instantaneous slurry turbulence, etc., and this technical solution uses multiple continuous monitoring cycles for judgment to filter out occasional interference.

[0026] In another technical solution, the following conditions are met in at least three consecutive monitoring cycles: ΔP≥0.05 or ΔP≤- 0.05 Specifically, the system can effectively filter the instantaneous fluctuations of the stirring motor power, ensuring that the main PID controller parameter adjustment is triggered only when the power deviation persists and reaches a significant level, reducing misadjustments caused by accidental factors and improving the accuracy of parameter updates.

[0027] In another technical solution, a viscosity sensor is also provided at the bottom of the stirring tank, and the signal output end of the viscosity sensor is connected to the input end of the auxiliary PID controller. m , use the stirring motor power to calculate the viscosity value ,in k is the conversion coefficient determined based on the stirring motor power-viscosity calibration test, P 0 The no-load power of the stirring motor is used to obtain the concentration setting value. N p The corresponding expected viscosity m a , when the viscosity difference When the viscosity tolerance threshold is exceeded: like P r ≥P a and m≥m a , it is determined that the actual viscosity has increased, and the main PID controller integral time is updated. ; like P r ≤P a and mm ≤ mm a , it is determined that the actual viscosity has decreased, and the proportional coefficient of the main PID controller is updated. ; like P r ＜P a and m>m a or P r ＞P a and m<m a , freeze the main PID controller update, and the stirring motor enters the pulse stirring mode. In the pulse stirring mode, the power input frequency of the stirring motor periodically alternates between the pulse frequency and the normal frequency.

[0028] In this technical solution, the conversion coefficient k It is determined based on the stirring motor power-viscosity calibration test. Specifically, during the system debugging phase, stirring experiments are carried out with a variety of fluids of known viscosity under fixed stirring equipment, speed and temperature conditions. The stirring shaft power of each fluid is accurately measured when it reaches a stable state. The measured stirring motor power data and the corresponding viscosity value are then plotted into a scatter plot. The relationship curve between the stirring motor power and viscosity is fitted by linear regression to obtain the conversion coefficient. kOptionally, in some working conditions where the slurry concentration is high and presents a non-Newtonian fluid, the calibration needs to be based on the concept of apparent viscosity and associated with a specific shear rate. The implementation method is to select a stirring blade speed during the system debugging phase, use a variety of standard non-Newtonian fluids with known rheological properties, measure their apparent viscosity at the shear rate corresponding to the stirring blade speed at a constant temperature, and accurately measure the stable stirring motor power at the same time. Draw a scatter plot of the stirring motor power and apparent viscosity and perform linear fitting to obtain the relationship curve between the stirring motor power and viscosity at this speed and obtain the conversion coefficient. k .

[0029] When the slurry concentration is close to the set value, but local concentration unevenness occurs due to sedimentation and agglomeration, the resistance of the stirring blade in different areas is different. The measured stirring motor power indirectly reflects the average resistance and cannot distinguish local abnormal conditions such as agglomeration and sedimentation inside the stirring tank. The measured stirring motor power will increase due to the increase in local resistance, but the actual overall slurry viscosity does not reach the expected value corresponding to the concentration. If the parameters of the main PID controller are adjusted only based on power, it may be misjudged as an increase in actual viscosity, and the integral effect will be wrongly enhanced, resulting in excessive concentration adjustment. Similarly, if the local slurry is too fluid and the motor power decreases, it may be misjudged as a decrease in viscosity, and the proportional effect will be enhanced, which will also disrupt the normal adjustment rhythm. Therefore, directly detecting the slurry viscosity through a viscosity sensor and combining it with the measured motor power can more accurately identify the actual working conditions. If the measured motor power is consistent with the viscosity sensor data trend, it is determined to be a real change in overall viscosity. If the trend is inconsistent, it indicates that there is a local abnormality and special treatment is required. This avoids the misjudgment problem of single power feedback, ensures the accuracy of the main PID controller parameter adjustment, and ensures precise control of slurry concentration.

[0030] In this technical solution, when the measured stirring motor power is inconsistent with the viscosity sensor measured value, it indicates that the slurry may have local agglomeration, stratification, sedimentation and other problems. At this time, if the PID parameter adjustment is continued, it may lead to improper adjustment due to misjudgment, exacerbating the slurry to deviate further from the concentration set value. In order to avoid the system from falling into an adjustment dead loop due to erroneous data, it is necessary to immediately freeze the main PID controller update and adopt corresponding measures to solve it. In the pulse stirring mode, by allowing the power input frequency of the stirring motor to periodically alternate between the pulse frequency and the conventional frequency, local agglomeration can be effectively broken and stratification can be eliminated. The strong convection generated by stirring at the pulse frequency can tear apart agglomerations, sedimentation, and mixed stratification areas. The lower conventional frequency maintains the basic mixing state to avoid excessive energy consumption and quickly restore the uniformity of the slurry. After the viscosity sensor measured value and the motor measured power have a consistent trend and the slurry state is stable, the pulse stirring mode is exited and the main PID controller parameter update is resumed to ensure the stability of the system under complex working conditions and reduce the pellet coating quality problems caused by slurry abnormalities.

[0031] In another technical solution, the viscosity tolerance threshold d =min(0.05 m p , 0.05 m a ), where the viscosity value is calculated by inverse calculation of the stirring motor power m p Expected viscosity m a , taking the smaller value of 5% of the two as the viscosity allowable error threshold, can take into account both the overall trend and the local state, avoid false triggering of adjustments due to small fluctuations, and ensure timely response in the event of significant deviations, thereby improving the accuracy and stability of system adjustments.

[0032] In another technical solution, several turbidity sensors and several pressure sensors are set in the stirred tank. The turbidity sensors record the turbidity deviation ΔNTU, and the pressure sensors record the pressure gradient variance. After the freeze main PID controller is updated, the measured parameters collected by various sensors in real time are input into the pre-trained pulse strategy prediction model. The measured parameters include the relative deviation of power ΔP , viscosity difference , pressure gradient variance , turbidity deviation ΔNTU obtained by the turbidity sensor, pellet volume ratio parameter, temperature in the stirred tank and preset pulse frequency, the pulse strategy prediction model outputs the pulse mode duration t i , the stirring motor uses the shortest pulse mode duration t i The corresponding preset pulse frequency executes the pulse stirring mode, and the preset pulse frequency is a discrete candidate frequency set in the main control unit; after completing the pulse mode duration t i After that, if the following conditions are met at the same time: the true viscosity increases or decreases, the pressure gradient variance ≤ d g , turbidity deviation ΔNTU≤ d NTU ; The system resumes the main PID controller parameter update, where d g 、 d NTU are the pressure gradient variance threshold and turbidity deviation threshold, d g 、 d NTU Determined artificially through experimentation.

[0033] Specifically, relying solely on interval pulses at conventional high frequencies is not suitable for all slurry situations. For example, for slight stratification of low-viscosity slurries, high-frequency stirring may cause particle breakage due to excessive shearing, which in turn aggravates local concentration fluctuations and increases unnecessary energy consumption. For hard lumps of high-viscosity slurries, relatively low-frequency stirring is difficult to provide sufficient shear force and cannot effectively break up the lumps. Therefore, for the special situation of slurries in the mixing tank, a pulse strategy prediction model is needed to determine the pulse frequency.

[0034] In the present technical solution, the preset pulse frequency is a discrete candidate frequency set in the main control unit, such as 30Hz, 35Hz, ... 50Hz. In the pulse stirring mode, the power input frequency of the stirring motor periodically alternates between the pulse frequency and the conventional frequency, and the duration of the pulse period and the conventional period are both artificially set with a fixed duration to reduce the variable dimension. When the main control unit simultaneously processes multiple types of sensor data such as turbidity, pressure, and temperature and runs the neural network model, the additional computing load caused by the dynamic adjustment of time parameters is avoided. At the same time, in the pulse stirring mode, the fixed design of the pulse period and the conventional period also improves the working stability of the stirring motor, and the mechanical stress and heat loss of the stirring motor during the cycle are controllable.

[0035] In this technical solution, considering that the content of pellets in the stirred tank will also affect the sedimentation and stratification of the slurry, a pellet volume ratio parameter is introduced to quantify this influence. The selection principle is based on the content of pellets in the effective reaction slurry. For example, the parameter calculation formula can be set as "volume of pellets delivered per unit time / total volume of slurry in the stirred tank", where the unit time is determined according to the actual rhythm of the production line. The pellet volume ratio parameter itself does not directly correspond to the specific proportion or concentration of pellets in the slurry. Its numerical value only participates in the neural network calculation as a characteristic quantity representing the intensity of pellet feeding. The measurement of the pellet delivery volume can be achieved by setting a mass sensor on the pellet conveyor belt to record the delivery mass of the pellets, and then combining it with the pellet density calculation.

[0036] In this technical solution, two turbidity sensors can be installed in the stirring tank, one at the upper part near the liquid surface and the other at the lower part near the stirring blade, to detect the turbidity of the slurry at different depths. The difference between the two, ΔNTU, can reflect the mixing uniformity of the slurry. Pressure sensors are distributed at different depths on the inner wall of the stirring tank. The pressure gradient variance is obtained by calculating the pressure values ​​at different positions to quantify the spatial distribution uniformity of the slurry density. The larger the gradient variance, the more local agglomeration or deposition exists in the slurry. The temperature is measured using an existing temperature sensor. These parameters are used together as input values ​​of the pulse strategy prediction model, which can fully reflect the dynamic state of the slurry and provide a multi-dimensional basis for the pulse strategy prediction model.

[0037] The pulse strategy prediction model can adopt the BP neural network model, which can efficiently handle the nonlinear coupling relationship between multiple parameters. The slurry state is affected by multiple factors such as power, viscosity, temperature, turbidity, and pressure. Traditional rule-based control is difficult to cover all working conditions, and the neural network can capture the implicit correlation between parameters through training. When various parameters and the preset pulse frequency are input into the pulse strategy prediction model, the pulse mode duration corresponding to the preset pulse frequency is output, and the pulse mode with the shortest duration is selected to execute the pulse stirring mode, ensuring the stirring effect while minimizing the slurry state recovery time and reducing interference with the normal production rhythm. Compared with traditional rule control, this method can avoid the inefficiency caused by empirical frequency selection, and avoid the risk of misjudgment of a single parameter through parameter coupling analysis, so that pulse stirring can always maintain high efficiency and pertinence under complex working conditions, and significantly improve the system's ability to respond quickly to abnormal slurry states.

[0038] In another technical solution, n pressure sensors are arranged on the vertical axis of the side wall of the stirred tank. All pressure sensors are evenly spaced along the effective liquid level height direction. The measured pressure value of each pressure sensor at a selected time point is obtained by a synchronous data acquisition unit, and the measured pressure difference between adjacent pressure sensors is calculated to obtain a pressure difference sequence. , and the mean pressure gradient , pressure gradient variance Two turbidity sensors are arranged at the bottom of the stirred tank and at the effective liquid level height, and the turbidity deviation ΔNTU between the two turbidity sensors is recorded. Specifically, through the pressure sensors evenly distributed in the vertical direction, the pressure changes of the slurry at different depths can be accurately captured, and the pressure gradient variance can intuitively reflect the spatial distribution uniformity of the slurry density.

[0039] In another technical solution, the pulse strategy prediction model adopts a BP neural network model including an input layer, a hidden layer and an output layer. The input layer of the BP neural network model includes a plurality of input neuron nodes, and a plurality of hidden neuron nodes are provided on the hidden layer. Each of the input neuron nodes is respectively connected to each of the hidden neuron nodes, and the hidden neuron nodes are connected to the output layer. The power relative deviation, pellet volume ratio parameter, viscosity difference, pressure gradient variance, turbidity deviation, temperature in the stirring tank, and preset pulse frequency are used as input values ​​of the input neuron node, and the output result is the pulse mode duration. Specifically, when establishing the training sample set of the BP neural network model, it is necessary to prepare the training sample set before the stirring tank is officially put into operation. Before production, targeted tests are carried out and multi-operating condition data are collected. For slurries of different concentrations and viscosities, normal mixing, slight stratification, severe agglomeration and other operating conditions are artificially created, and the relative power deviation, pellet volume ratio parameter, viscosity difference, pressure gradient variance, turbidity deviation, temperature and the corresponding preset pulse frequency are recorded. At the same time, the actual duration required from starting pulse stirring to the slurry state returning to normal when each preset pulse frequency is used under each operating condition is marked to form a "parameter-pulse time" mapping pair. These data are divided into training set and test set in a ratio of 7:3 or 8:2. During training, the BP neural network model can be optimized according to the PSO algorithm to continuously adjust the hidden layer weight parameters, so that the pulse time predicted by the model gradually approaches the actual value.

[0040] Exemplarily, the PSO algorithm is used to optimize the BP neural network model, including the following steps: A1. The relative power deviation, pellet volume ratio parameter, viscosity difference, pressure gradient variance, turbidity deviation, temperature and the corresponding preset pulse frequency are the influencing factors. The total number of influencing factors is used as the number of neuron nodes m, and the pulse mode duration is used as the only output value, that is, the number of nodes in the output layer. c =1, number of hidden layer nodes c 1 for , a is a random constant between 1 and 10; A2. Normalize the sample set data. The mathematical expression is: in, x i represents the sample data of the influencing factors, x min 、 x max are the minimum and maximum values ​​in the sample data, respectively. is the influencing factor data after dimensionless processing; A3. Initialize the mapping relationship between the swarm particles and the BP neural network weights and thresholds, including particle dimensions, initial velocity, swarm size, learning factor, and inertia weight; A4. Input the normalized input variables and output variables into the BP neural network model, calculate the fitness function value of the particle, and obtain the historical optimal fitness and global fitness of the particle. The fitness function value of the particle is the mean square error of the calculation result, and its function expression is: in, Expressed as the predicted value of the i-th sample, y i is the true value of the i-th sample, and n is the total number of calculation results of the neural network; A5. Iteratively calculate the particle fitness, and update the historical optimal fitness and the global fitness according to a preset update condition until a preset iteration end condition is met; A6. Update the weights and thresholds of the preset BP neural network model to obtain an optimized BP neural network model for calculating the current pulse strategy prediction model.

[0041] In another technical solution, the training of the BP neural network model includes the following steps: S1. Fill the mixing tank with limestone-water slurries of different solid contents in layers, and add pellets according to the target ratio. After the stirring motor is turned on and runs at a conventional stirring frequency for a preset time, the measured parameters of each sensor are recorded synchronously. For example, limestone-water slurries of different solid contents are first prepared outside the mixing tank. For example, two slurries with solid contents of 20% and 35% are configured. The concentration after the two slurries are completely mixed is set as the concentration setting value. The layered filling method is used to simulate the natural stratification state in actual production. At this time, the upper slurry has a lower density and the lower layer has a higher density, forming an obvious stratification, and then the stirring is started. The motor runs at a preset conventional stirring frequency for a preset time, and then adds pellets according to the target pellet volume ratio parameter to ensure that the pellets are evenly dispersed in the stratified slurry. The preset time needs to be controlled within a relatively short range to avoid excessive stirring to eliminate the stratification phenomenon, and retain the characteristics of the initial stratified state in the stirring tank. During this process, the measured parameters of each sensor are recorded in real time by the synchronous data acquisition unit: including the pressure values ​​of several pressure sensors on the vertical axis of the stirring tank, the turbidity deviation ΔNTU of the turbidity sensor at the bottom and the liquid level, the relative deviation of the stirring motor power, the slurry temperature, and the pellet volume ratio parameter.

[0042] S2. Switch the stirring motor to the preset pulse frequency and continue stirring until the slurry stratification in the stirring tank disappears, and record the pulse mode duration corresponding to this process. For example, after the conventional stirring in step S1 is completed, the stratified state of the slurry is kept unchanged. Since the stirring tank is in an obvious stratified state at this time, the overall characteristics of the slurry will show contradictory detection results. Switch the stirring motor to one of the preset discretized candidate pulse frequencies, run the pulse stirring mode, and continuously monitor the slurry state. Judge whether the stratification in the stirring tank has completely disappeared by the pressure gradient variance and turbidity deviation ΔNTU. For example, when the pressure gradient variance is 0, the liquid layers in the stirring tank are in a uniform state. Record the pulse mode duration at the corresponding moment.

[0043] S3, repeating steps S1-S2, conducting experiments on various stratification states, various pellet volume ratio parameters, and different preset pulse frequency combinations to form a sample set, and training the BP neural network model. For example, diversified samples are generated by adjusting key variables: for example, changing the stratification state, pellet volume ratio parameter, and preset pulse frequency. The experiment is repeated 3-5 times for each combination to reduce accidental errors, and finally a sample set containing thousands of sets of data is formed; The sample set is divided into a training set and a test set at an 8:2 ratio. The training set data is used to input the BP neural network model, and the hidden layer weights are iteratively adjusted to make the duration predicted by the model gradually approach the actual pulse pattern duration. After training is completed, the test set is used to verify the model's generalization ability, and finally a pulse strategy prediction model that can be directly deployed is formed.

[0044] It should be noted that although the steps are described above in a specific order, this does not necessarily mean that the steps must be performed in this specific order. In fact, some of these steps can be performed concurrently or even in a different order, as long as the required functions can be achieved. The number of devices and processing scales described here are intended to simplify the description of the present invention. Applications, modifications, and variations of the present invention will be apparent to those skilled in the art.

[0045] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. The pellet coating system based on dual PID regulation is characterized by: include: A stirring kettle is connected to a water inlet pipe and a limestone feeding pipe. A stirring blade is provided inside the stirring kettle to fully mix the pellets with the slurry formed by mixing water and limestone. The stirring blade is connected to the stirring motor. a mass flow meter installed in the limestone feed pipeline; An electric regulating valve is installed in the water inlet pipe; A concentration sensor is installed in the stirred tank; The main control unit includes a main PID controller and a sub-PID controller, wherein the output end of the concentration sensor is connected to the input end of the main PID controller, the output end of the main PID controller is connected to the control end of the electric regulating valve, the power signal output end of the stirring motor is connected to the input end of the sub-PID controller, and the output end of the sub-PID controller is connected to the parameter setting interface of the main PID controller.

2. A control method for a pellet coating system based on dual PID regulation, applied to the pellet coating system based on dual PID regulation according to claim 1, characterized in that: Real-time detection of limestone discharge volume through mass flow meter M s , the unit is kg / h , the concentration sensor is used to detect the slurry concentration value N in real time, the unit is percentage, according to the formula Calculate water distribution M v , the unit is m³ / h ; The main PID controller sets the value according to the concentration N p Real-time detection of slurry concentration value N Calculate concentration deviation , calculate the output value ,in K p is the proportionality coefficient, T i is the integration time, T d is the differential time, a is the differential switching coefficient ，a∈ {0,1}, u(t) The preset range conversion relationship is mapped as an opening instruction to the electric control valve, and the electric control valve is adjusted according to the opening instruction to make the water inlet flow close to M v ; The auxiliary PID controller collects the power signal of the stirring motor in real time according to the monitoring cycle and performs power deviation calculation. ,in P a Set the concentration value N p The corresponding motor reference power, P r is the measured power of the motor, when ΔP When the positive significant deviation condition is continuously met in several consecutive monitoring cycles, the main PID controller integral time is updated. ,when ΔP When the negative significant deviation condition is continuously met in several consecutive monitoring cycles, the proportional coefficient of the main PID controller is updated. , the main PID controller adopts the updated and Compute new output.

3. The control method of the pellet coating system based on dual PID regulation according to claim 2, characterized in that: Meet the following requirements during at least three consecutive monitoring periods: ΔP≥0.05 or ΔP≤-0.05 When , it is determined that the positive significant deviation condition or the negative significant deviation condition is met.

4. The control method for pellet coating system based on dual PID regulation according to claim 2, characterized in that: A viscosity sensor is also provided at the bottom of the stirring tank. The signal output end of the viscosity sensor is connected to the input end of the auxiliary PID controller. The measured value of the viscosity sensor is μ , use the stirring motor power to calculate the viscosity value ,in k is the conversion coefficient determined based on the stirring motor power-viscosity calibration test, P 0 The no-load power of the stirring motor is used to obtain the concentration setting value. N p The corresponding expected viscosity μ a , when the viscosity difference When the viscosity tolerance threshold is exceeded: like P r ≥P a and μ≥μ a , it is determined that the actual viscosity has increased, and the main PID controller integral time is updated. ; like P r ≤P a and μ≤μ a , it is determined that the actual viscosity has decreased, and the proportional coefficient of the main PID controller is updated. ; like P r ＜P a and μ>μ a or P r ＞P a and μ<μ a , freeze the main PID controller update, and the stirring motor enters the pulse stirring mode. In the pulse stirring mode, the power input frequency of the stirring motor periodically alternates between the pulse frequency and the normal frequency.

5. The control method for pellet coating system based on dual PID regulation according to claim 4, characterized in that: The viscosity allowable error threshold δ =min(0.05 μ p , 0.05 μ a ) 。 6. The control method of the pellet coating system based on dual PID regulation according to claim 4, characterized in that: Several turbidity sensors and several pressure sensors are set in the stirred tank. The turbidity sensors record the turbidity deviation ΔNTU, and the pressure sensors record the pressure gradient variance. After the freeze main PID controller is updated, the measured parameters collected by various sensors in real time are input into the pre-trained pulse strategy prediction model. The measured parameters include the relative deviation of power ΔP , viscosity difference , pressure gradient variance , turbidity deviation ΔNTU obtained by the turbidity sensor, pellet volume ratio parameter, temperature in the stirred tank and preset pulse frequency, the pulse strategy prediction model outputs the pulse mode duration t i , the stirring motor uses the shortest pulse mode duration t i The pulse stirring mode is executed at a corresponding preset pulse frequency, wherein the preset pulse frequency is a discrete candidate frequency set in the main control unit; In complete pulse mode duration t i Then, if both of the following are satisfied: True viscosity increases or true viscosity decreases; Pressure gradient variance ≤ δ g ; Turbidity deviation ΔNTU≤ δ NTU ; Then the system resumes the main PID controller parameter update, where δ g 、 δ NTU are the pressure gradient variance threshold and the turbidity deviation threshold.

7. The control method of the pellet coating system based on dual PID regulation according to claim 6, characterized in that: Arrange n pressure sensors on the vertical axis of the side wall of the stirred tank. All pressure sensors are equally spaced along the effective liquid level height direction. The measured pressure value of each pressure sensor at the selected time point is obtained by the synchronous data acquisition unit. The measured pressure difference between adjacent pressure sensors is calculated to obtain the pressure difference sequence. , and the mean pressure gradient , pressure gradient variance ; Two turbidity sensors are arranged at the bottom and effective liquid level of the stirred tank, and the turbidity deviation ΔNTU between the two turbidity sensors is recorded.

8. The control method for pellet coating system based on dual PID regulation according to claim 7, characterized in that: The pulse strategy prediction model adopts a BP neural network model including an input layer, a hidden layer and an output layer. The input layer of the BP neural network model includes multiple input neuron nodes, and the hidden layer is provided with multiple hidden neuron nodes. Each of the input neuron nodes is respectively connected to each of the hidden neuron nodes, and the hidden neuron nodes are all connected to the output layer. The power relative deviation, pellet volume ratio parameter, viscosity difference, pressure gradient variance, turbidity deviation, temperature in the stirring tank, and preset pulse frequency are used as input values ​​of the input neuron node, and the output result is the pulse mode duration.

9. The control method for pellet coating system based on dual PID regulation according to claim 8, characterized in that: The training of the BP neural network model includes the following steps: S1. Limestone-water slurries with different solid contents are filled into the mixing kettle in layers, and pellets are added according to the target ratio. The stirring motor is turned on and runs at a normal stirring frequency for a preset time, while the measured parameters of each sensor are recorded simultaneously. S2. Switch the stirring motor to the preset pulse frequency and continue stirring until the slurry stratification in the stirring tank disappears. Record the pulse mode duration corresponding to this process. S3. Repeat steps S1 and S2, conduct experiments on various stratification states, various pellet volume ratio parameters and different preset pulse frequency combinations, form a sample set, and train the BP neural network model.

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