Intelligent diluent concentration dispensing method and system

Abstract

The present application relates to the field of intelligent manufacturing, and particularly relates to a diluent concentration intelligent matching method and system.A diluent concentration intelligent matching system comprises a parameter setting and self-learning module, a controller initialization module, an intelligent control module and a closed-loop control module.The present application realizes full-automatic intelligent setting and optimization of control parameters by introducing deep fusion of a material characteristic self-learning mechanism and fuzzy adaptive PID control;the system can automatically trigger a self-learning process when a mother liquor variety is replaced, identify key dynamic characteristic parameters such as lag time, response time constant and steady-state gain coefficient of the system in real time by analyzing concentration response data, and automatically correct a fuzzy control rule base based on these parameters, so that the controller has the ability to adapt to different material characteristics.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, specifically to a method and system for intelligent adjustment of diluent concentration. Background Technology

[0002] In many industrial sectors such as chemicals, coatings, pesticides, and electronic chemicals, precise diluent concentration preparation is a key process for ensuring product quality. Traditional concentration preparation methods mainly rely on manual experience or simple automated control, which has the following significant drawbacks:

[0003] First, traditional methods generally adopt a fixed-parameter PID control strategy. This control method is rigid when facing different material characteristics. Since different batches of mother liquor have differences in viscosity, density, mixing characteristics, etc., fixed control parameters are difficult to adapt to all working conditions, resulting in problems such as overshoot, oscillation or excessively long adjustment time in the blending process, which seriously affects production efficiency and product consistency.

[0004] Secondly, existing technologies lack the ability to adaptively learn the dynamic characteristics of materials. When changing the type of material or encountering changes in environmental conditions, traditional systems require operators to manually readjust the control parameters. This not only depends on the experience level of the operators, but also the debugging process is time-consuming and labor-intensive, which cannot meet the requirements of modern production for flexibility and intelligence.

[0005] Furthermore, traditional concentration control strategies are often based on static models or simple feedback control, failing to fully consider the dynamic characteristics of the mixing process, such as system hysteresis and inertia. This results in the control system struggling to meet process requirements in terms of control accuracy and stability when facing mixing processes with large hysteresis and nonlinear characteristics. At the same time, the correlation between historical allocation data, material characteristic parameters, and control effects has not been effectively explored and utilized, failing to form an optimization closed loop that becomes increasingly intelligent with use, thus hindering the continuous optimization of the production process.

[0006] Therefore, this invention proposes a method and system for intelligent adjustment of diluent concentration to meet the demands of modern industrial production for high quality, high efficiency, and high flexibility. Summary of the Invention

[0007] This invention achieves fully automatic intelligent tuning and optimization of control parameters by introducing a material characteristic self-learning mechanism and a deep integration of fuzzy adaptive PID control. The system can automatically trigger the self-learning process when the mother liquor is changed. By analyzing the concentration response data, it identifies key dynamic characteristic parameters such as the system's lag time, response time constant, and steady-state gain coefficient in real time, and automatically corrects the fuzzy control rule base based on these parameters, enabling the controller to adapt to different material characteristics.

[0008] A method for intelligently adjusting the concentration of a diluent includes:

[0009] Step S1: Receive the target concentration value through the human-machine interface and call the corresponding basic process parameters from the formula database;

[0010] When a change in the type of mother liquor is detected or a forced learning command is received, the material characteristic self-learning process is executed, the diluent flow rate is controlled to change in a preset mode, and the concentration response data of the mixed liquid is collected through an online concentration sensor.

[0011] The collected concentration response data and corresponding diluent flow rate change data are identified and analyzed to calculate a set of parameters characterizing the current dynamic characteristics of the material, including system lag time, response time constant and steady-state gain coefficient.

[0012] Step S2: Use the set of dynamic characteristic parameters as the initialization configuration parameters for the fuzzy adaptive PID controller;

[0013] Step S3: Start the mother liquor delivery and diluent adjustment, begin the mixing process, and continuously acquire real-time concentration detection values;

[0014] The current concentration deviation is calculated based on the difference between the target concentration value and the real-time detection value, and the rate of change of the deviation relative to the previous sampling time is also calculated.

[0015] The concentration deviation and the rate of change of deviation are input into the fuzzy inference engine, and the inference calculation is performed based on the fuzzy rule base corrected by the dynamic characteristic parameters. The output is the real-time adjustment of the proportional coefficient, integral coefficient and derivative coefficient of the PID controller.

[0016] The operating parameters of the PID controller are updated based on the adjustment amount output by fuzzy inference, and the adjustment value of the diluent flow rate is calculated through the updated PID controller.

[0017] The flow rate regulation value is converted into a control signal and sent to the diluent regulating device;

[0018] Step S4: Repeat step S3 to stabilize the concentration value within the preset error range of the target concentration through continuous concentration detection and flow rate adjustment; when the concentration value reaches a stable state, stop the mixing operation and store the dynamic characteristic parameters, real-time concentration curve, and control command sequence into the historical database.

[0019] Preferably, in step S1, the diluent flow rate is controlled to vary in a preset mode, specifically including any of the following modes:

[0020] A step signal mode is used to abruptly increase the diluent flow rate from its initial value to a set value and maintain it.

[0021] The ramp signal mode is used to increase the diluent flow rate from the initial value to the set value at a constant rate;

[0022] A pseudo-random binary sequence signal mode is used to randomly switch the diluent flow rate between multiple preset level values.

[0023] Preferably, step S1 involves identifying and analyzing the collected concentration response data and the corresponding diluent flow rate change data, specifically including:

[0024] A dynamic relationship model between diluent flow rate and concentration change is established based on system identification theory. This model uses standard difference equations to describe the mathematical relationship between the concentration value and the diluent flow rate value at the current and historical moments. It includes model order parameters and lag time parameters that characterize the dynamic characteristics of the system.

[0025] The recursive least squares estimation algorithm, which is well-known in the field of control, is used to identify model parameters. This algorithm initializes the parameter vector and covariance matrix, and recursively updates the parameter estimates based on new measurement data in each sampling period. The gain matrix is ​​used to weigh the weight relationship between historical estimates and new measurement data, while the covariance matrix reflects the uncertainty of parameter estimates.

[0026] Based on the identified model parameters, the system lag time, response time constant, and steady-state gain coefficient are calculated.

[0027] Preferably, based on the identified model parameters, the system lag time, response time constant, and steady-state gain coefficient are calculated, as follows:

[0028] According to the standard method in control theory, the system characteristic roots are obtained by solving the characteristic equation corresponding to the difference equation model. The modal time constant corresponding to the dominant characteristic root with the largest modulus is taken as the response time constant, which characterizes the inertial characteristics of the system response.

[0029] Based on the final value theorem, the steady-state gain coefficient is obtained by calculating the input-output relationship of the difference equation model under steady-state conditions. This coefficient characterizes the steady-state sensitivity of the system.

[0030] By analyzing the system's response characteristics under a step input signal, the system's lag time is determined based on the starting moment of the significant change in concentration response. This parameter characterizes the system's propagation delay characteristics.

[0031] Preferably, the specific process of correcting the fuzzy rule base based on dynamic characteristic parameters in step S2 includes:

[0032] The strength of the integral action in the fuzzy rule is adjusted according to the length of the system lag time; the longer the lag time, the weaker the weight of the integral action.

[0033] The strength of the proportional effect in the fuzzy rule is adjusted according to the magnitude of the response time constant; the larger the time constant, the greater the weight of the proportional effect.

[0034] The scaling factor of all output variables in the fuzzy rule is adjusted according to the magnitude of the steady-state gain coefficient. The larger the gain coefficient, the smaller the domain of the output variables.

[0035] Preferably, the construction process of the fuzzy inference engine includes:

[0036] The basic domain of concentration bias is divided into seven fuzzy levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The membership distribution of each fuzzy level is determined by combining triangular membership functions and trapezoidal membership functions.

[0037] The basic domain of the deviation change rate is also divided into seven fuzzy levels, in the same way as the fuzzy level division of the concentration deviation, and the domain range of each fuzzy level is determined through actual debugging.

[0038] The output domains of the proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment are divided into seven fuzzy levels, using the same fuzzy partitioning strategy as the input variables.

[0039] A complete fuzzy control rule base is established, which consists of 49 conditional statements. The antecedent of each rule is a fuzzy level combination of concentration deviation and deviation change rate, and the consequent is a fuzzy level combination of three parameter adjustment amounts.

[0040] The Mamdani-type fuzzy inference method is used for inference calculation, and the fuzzy output obtained by inference is defuzzified by the centroid method, so as to obtain the precise adjustment of the proportional coefficient, integral coefficient and differential coefficient.

[0041] Preferably, in step S3, the acquisition of real-time concentration detection values, the calculation of concentration deviation and deviation change rate, fuzzy inference, PID parameter update, calculation of diluent flow rate adjustment values, and the issuance of control signals are completed within one control cycle.

[0042] A smart diluent concentration dispensing system, comprising:

[0043] The parameter setting and self-learning module includes:

[0044] The parameter setting unit is used to receive the target concentration value through the human-machine interface and call the corresponding basic process parameters from the formula database.

[0045] The self-learning execution unit is used to execute the material characteristic self-learning process when a change in the type of mother liquor is detected or a forced learning command is received. It controls the diluent flow rate to change in a preset mode, and at the same time collects the concentration response data of the mixed liquid through an online concentration sensor.

[0046] The characteristic identification unit is used to identify and analyze the collected concentration response data and the corresponding diluent flow rate change data, and calculate the set of parameters characterizing the current dynamic characteristics of the material, including system lag time, response time constant and steady-state gain coefficient.

[0047] The controller initialization module is used to use the set of dynamic characteristic parameters as the initialization configuration parameters of the fuzzy adaptive PID controller.

[0048] The intelligent control module includes:

[0049] The data acquisition unit is used to initiate the mother liquor delivery and diluent adjustment, start the mixing process, and continuously acquire real-time concentration detection values.

[0050] The deviation calculation unit is used to calculate the current concentration deviation based on the difference between the target concentration value and the real-time detection value, and to calculate the rate of change of the deviation relative to the previous sampling time.

[0051] The fuzzy inference unit is used to input the concentration deviation and the rate of change of deviation into the fuzzy inference engine, perform inference calculations based on the fuzzy rule base modified by dynamic characteristic parameters, and output the real-time adjustment of the proportional coefficient, integral coefficient and derivative coefficient of the PID controller.

[0052] The parameter update unit is used to update the operating parameters of the PID controller based on the adjustment amount output by fuzzy inference, and to calculate the adjustment value of the diluent flow rate through the updated PID controller.

[0053] The control execution unit is used to convert the flow rate regulation value into a control signal and send it to the diluent regulating device;

[0054] The closed-loop control module is used to repeatedly call the real-time intelligent control module to stabilize the concentration value within the preset error range of the target concentration through continuous concentration detection and flow regulation. When the concentration value reaches a stable state, the mixing operation stops and the dynamic characteristic parameters, real-time concentration curve, and control command sequence are stored in the historical database.

[0055] The present invention has the following advantages:

[0056] 1. This invention achieves fully automatic intelligent tuning and optimization of control parameters by introducing a material characteristic self-learning mechanism and a deep integration of fuzzy adaptive PID control. The system can automatically trigger the self-learning process when the mother liquor type is changed. By analyzing the concentration response data, it can identify key dynamic characteristic parameters such as the system's lag time, response time constant, and steady-state gain coefficient in real time. Based on these parameters, it can automatically correct the fuzzy control rule base, enabling the controller to adapt to different material characteristics.

[0057] 2. This invention achieves precision and optimization of the formulation process by constructing a complete intelligent control closed loop. The system integrates real-time concentration detection, fuzzy inference decision-making, online adjustment of PID parameters and actuator control into a fast-response control cycle, forming a complete closed loop of perception-decision-execution. At the same time, the system also establishes a database of association between formulation parameters and characteristic parameters. Through the accumulation and reuse of historical data, the system has the ability to continuously optimize. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the intelligent diluent concentration adjustment system used in an embodiment of the present invention. Detailed Implementation

[0059] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention.

[0060] Example 1: A method for intelligently adjusting the concentration of a diluent, comprising:

[0061] Step S1: Receive the target concentration value through the human-machine interface and call the corresponding basic process parameters from the formula database; the human-machine interface is implemented using an industrial touch screen, and the formula database adopts a relational database management system to store data including mother liquor type, target concentration range, and safe operating parameters;

[0062] When a change in mother liquor type is detected or a forced learning command is received, a material characteristic self-learning process is executed. The detection of the change in mother liquor type is achieved through RFID tag identification technology. Each mother liquor container is equipped with a unique RFID tag, and the self-learning process is automatically triggered when new tag information is read. During the self-learning process, the diluent flow rate is controlled to change in a preset mode, while the concentration response data of the mixed liquid is collected by an online concentration sensor. The online concentration sensor is a refractive index concentration meter, whose measurement principle is based on the difference in the refractive index of light for liquids of different concentrations, and the measurement accuracy reaches ±0.1%.

[0063] The collected concentration response data and corresponding diluent flow rate change data are identified and analyzed to calculate a set of parameters characterizing the current dynamic characteristics of the material, including system lag time, response time constant, and steady-state gain coefficient. The identification and analysis process is based on dynamic system modeling theory. By analyzing the correspondence between system input (diluent flow rate) and output (concentration value), a dynamic mathematical model of the system is established.

[0064] Step S2: Use the set of dynamic characteristic parameters as the initialization configuration parameters of the fuzzy adaptive PID controller; the fuzzy adaptive PID controller is an intelligent controller that introduces a fuzzy logic reasoning mechanism on the basis of the traditional PID controller and can automatically adjust the control parameters according to the system operating status.

[0065] Step S3: Start the mother liquor delivery and diluent adjustment, begin the mixing process, and continuously acquire real-time concentration detection values;

[0066] The current concentration deviation is calculated based on the difference between the target concentration value and the real-time detection value, and the rate of change of the deviation relative to the previous sampling time is also calculated.

[0067] The concentration deviation and the rate of change of deviation are input into the fuzzy inference engine, and the inference calculation is performed based on the fuzzy rule base corrected by the dynamic characteristic parameters. The output is the real-time adjustment of the proportional coefficient, integral coefficient and derivative coefficient of the PID controller.

[0068] The operating parameters of the PID controller are updated based on the adjustment amount output by fuzzy inference, and the adjustment value of the diluent flow rate is calculated through the updated PID controller.

[0069] The flow rate regulation value is converted into a control signal and sent to the diluent regulating device;

[0070] Step S4: Repeat step S3, continuously monitor the concentration and adjust the flow rate to stabilize the concentration value within the preset error range of the target concentration. The condition for determining that the concentration value has reached a stable state is that the absolute deviation between the real-time concentration detection value and the target concentration value is less than half of the preset error range within 5 consecutive control cycles. When the concentration value reaches a stable state, stop the mixing operation and store the dynamic characteristic parameters, real-time concentration curve, and control command sequence in the historical database. The dynamic characteristic parameters stored in the historical database are associated with the basic process parameters. When the same basic process parameters are called again, the system will preferentially recommend using the stored set of dynamic characteristic parameters.

[0071] In step S1, controlling the diluent flow rate varies in a preset mode, specifically including any of the following modes:

[0072] A step signal mode is employed, where the diluent flow rate is abruptly increased from its initial value to a set value and held. This mode observes the system's step response characteristics by instantaneously adjusting the diluent flow rate from its initial value to the set value and maintaining it. In practice, the initial value is typically set to 20%-30% of the normal operating flow rate, and the set value is set to 70%-80% of the normal operating flow rate. For example, when preparing a certain type of coating, the initial flow rate is set to 2 L / min, and at the adjustment moment, it is instantaneously switched to 8 L / min and held, while the response data of the concentration sensor is recorded at a sampling frequency of not less than 10 Hz. The advantage of this mode is that it can quickly excite the main dynamic modes of the system, making it easy to directly observe the system's lag time, steady-state gain, and main time constant. Its mathematical principle is that the initial stage of the step response reflects the system's lag characteristics, the rising stage reflects the system's inertial characteristics, and the steady stage directly reflects the system's steady-state gain.

[0073] The ramp signal mode increases the diluent flow rate from its initial value to the set value at a constant rate. This mode gradually increases the diluent flow rate from its initial value to the set value at a constant rate of change. In practice, the choice of the rate of change is crucial and is usually adjusted based on the estimated system inertia. For materials with high viscosity or slow-changing mixing processes, a lower rate of change of 0.5-1 L / min / s is used; for low-viscosity, easily mixed materials, a higher rate of change of 2-5 L / min / s can be used. For example, in the dilution of electronic chemicals, a ramp rate of 1.5 L / min / s is used to linearly increase the flow rate from 3 L / min to 33 L / min within 20 seconds. The advantage of this mode is that the excitation signal is smooth, avoiding severe impact on sensitive materials, while providing dynamic characteristic information of the system at different operating points, making it particularly suitable for mixing processes with strong nonlinearity.

[0074] A pseudo-random binary sequence signal mode is employed to randomly switch the diluent flow rate between multiple preset levels. This mode switches the diluent flow rate between multiple preset levels according to a pseudo-random sequence. In specific implementation, the upper and lower limits of the flow rate are first determined, typically set between 20% and 80% of the normal operating range, and then this range is divided into several levels. For example, four flow rates are set: 4 L / min, 8 L / min, 12 L / min, and 16 L / min, generating a switching sequence. The holding time of each level is set to 1.5-2 times the estimated system lag time. The advantage of this mode is that it can excite the system within a wide frequency band and obtain rich dynamic information, making it particularly suitable for occasions requiring precise establishment of a system mathematical model. Its theoretical basis lies in the continuous excitation condition in system identification theory, ensuring that all major dynamic characteristics of the system can be identified.

[0075] In practical applications, the appropriate mode can be selected according to the material characteristics and identification requirements. For conventional liquid mixing, it is recommended to use the step signal mode for rapid identification first. For materials that are prone to precipitation or are sensitive to shear, it is recommended to use the ramp signal mode. When it is necessary to establish an accurate mathematical model for advanced control strategies, the pseudo-random binary sequence signal mode is preferable.

[0076] Step S1 involves identifying and analyzing the collected concentration response data and the corresponding diluent flow rate change data, specifically including:

[0077] Establish a system difference equation model for the changes in diluent flow rate and concentration:

[0078]

[0079] in for Concentration value at time, for The diluent flow rate at any given time. For system lag time, , The model order;

[0080] In practical applications, model order and The choice of model needs to balance model accuracy and computational complexity; for most liquid mixing processes, it is recommended to use... =2, A second-order model with a time lag of 2 can accurately describe the inertial and oscillatory characteristics of the system; the system lag time... The initial value can be estimated based on the pipeline length and flow velocity, and then accurately corrected in the subsequent identification process; this difference equation model describes the dynamic characteristics of the mixing process, where the left-hand side reflects the inertial characteristics of the system and the right-hand side reflects the excitation response characteristics of the system.

[0081] Parameter identification is performed using the recursive least squares method, specifically including:

[0082] Parameter initialization: Set the initial parameter vector Initial covariance matrix ,in For sufficiently large positive numbers, It is the identity matrix;

[0083] During parameter initialization, The value is typically taken to be between 1000 and 10000 to ensure that the algorithm has a sufficient search range in the initial stage; identity matrix The dimension is (n+m+1)×(n+m+1), corresponding to the total number of parameters to be identified. Initial parameter vector. It is usually set to a zero vector, indicating that there are no prior assumptions about the system characteristics before the identification begins;

[0084] Recursive calculation: For each sampling time... Calculate the gain matrix Update parameter estimates Update the covariance matrix ;

[0085] in It is a data vector;

[0086] The recursive calculation process is executed in real time during each sampling period, ensuring that the parameter estimation can track changes in system characteristics; gain matrix Essentially, these are weighting coefficients, which determine the degree to which new measurement data influences parameter correction; as identification progresses, the covariance matrix... As the data vector gradually decreases, the corresponding gain matrix also decreases, reflecting the adaptive process of the algorithm from "coarse tuning" to "fine tuning"; It contains historical state information of the system, providing sufficient excitation information for parameter estimation;

[0087] Based on the identified parameter vector Calculate the system lag time, response time constant, and steady-state gain coefficient.

[0088] The calculation of system dynamic characteristic parameters based on the identified parameter vectors specifically includes:

[0089] According to the characteristic equation corresponding to the difference equation model Solve for the eigenvalues, and use the modal time constant corresponding to the dominant eigenvalue (i.e. the eigenvalue with the largest modulus) as the response time constant;

[0090] The characteristic equation is the core mathematical tool for analyzing the dynamic characteristics of a system, where λ represents the characteristic roots of the system, and each characteristic root corresponds to a specific dynamic mode in the system response;

[0091] Taking a real mixing process as an example, when the diluent flow rate changes, the concentration does not immediately reach a new steady state, but rather undergoes a dynamic change process; this change process can be decomposed into the superposition of multiple dynamic modes, each with its own rate of change (determined by characteristic roots); the characteristic equation... The solution is to find the patterns of change in all these dynamic modes;

[0092] The dominant eigenvalue is the eigenvalue with the largest modulus. It corresponds to the slowest decay mode in the system response and is the key factor determining the overall system response speed; the modal time constant is equal to... ,in The sampling period is The dominant characteristic root is the time constant, which quantifies the inertial characteristics of the mixing process. The larger the time constant, the slower the system responds to changes in flow rate, requiring the controller to adopt a more gradual adjustment strategy. Conversely, a smaller time constant means that the system responds quickly and can use more aggressive control parameters.

[0093] The steady-state gain coefficient is calculated using the following formula:

[0094] ;

[0095] steady-state gain coefficient It has a clear physical meaning, representing the steady-state concentration change caused by a unit change in flow rate; the derivation of this formula is based on the final value theorem, which states that when the system reaches steady state, all difference terms tend to be constant, thus yielding this concise expression; in practical applications, the gain coefficient... The higher the gain coefficient, the more sensitive the system is to changes in flow rate, and the lower the output gain of the controller needs to be to avoid overshoot.

[0096] The system lag time It can be directly determined by analyzing the initial lag phase of the system's step response.

[0097] The specific process of correcting the fuzzy rule base based on dynamic characteristic parameters in step S2 includes:

[0098] The strength of the integral action in the fuzzy rules is adjusted according to the length of the system lag time; the longer the lag time, the weaker the weight of the integral action. The integral action is used to eliminate steady-state error in control, but in the presence of significant lag, an excessively strong integral action can cause severe integral saturation in the early stages of the response, resulting in a large overshoot. When a long lag time is identified, the system automatically reduces the weight of all outputs related to the integral action in the fuzzy rule base. For example, when preparing high-viscosity materials transported through long pipelines, the lag time may reach several seconds. In this case, the system will significantly weaken the strength of the integral action and rely more on proportional and derivative actions to maintain stability. Once the concentration begins to change significantly, the integral action will be gradually restored, thereby effectively suppressing the overshoot phenomenon.

[0099] The strength of the proportional action in the fuzzy rules is adjusted according to the magnitude of the response time constant. The larger the time constant, the stronger the weight of the proportional action. The response time constant reflects the inertia of the mixing process. The larger the time constant, the slower the concentration response, and the stronger the initial driving force required for the system to begin effective regulation. Therefore, when a large response time constant is identified, the system will correspondingly increase the weight of the proportional action in the fuzzy rules. Taking the preparation of viscous emulsions as an example, the mixing process has large inertia and slow response. By increasing the proportional action, the controller can issue a stronger regulation command at the initial stage of concentration deviation, effectively overcoming system inertia, speeding up the response speed, and avoiding excessively long regulation time due to insufficient regulation force.

[0100] The scaling factor of all output variables in the fuzzy rule is adjusted according to the magnitude of the steady-state gain coefficient. The larger the gain coefficient, the smaller the universe of discourse of the output variables. The steady-state gain coefficient characterizes the sensitivity of the process. The larger the gain coefficient, the more significant the concentration fluctuation caused by a small change in the diluent flow rate, indicating that the process is very sensitive. For such sensitive processes, the system will proportionally reduce the universe of discourse of all output variables (including the adjustment of proportional, integral, and derivative coefficients). For example, when diluting certain highly sensitive chemical reagents, the gain coefficient may be very high. In this case, the system will automatically limit the maximum adjustment range of the controller output, transforming the originally large adjustment into a series of fine adjustments, thereby avoiding oscillation and instability caused by excessive control action and ensuring smooth convergence of the control process.

[0101] The construction process of the fuzzy inference engine includes:

[0102] The basic domain of concentration bias is divided into seven fuzzy levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The membership distribution of each fuzzy level is determined by combining triangular membership functions and trapezoidal membership functions.

[0103] The basic domain of the deviation change rate is also divided into seven fuzzy levels, in the same way as the fuzzy level division of the concentration deviation, and the domain range of each fuzzy level is determined through actual debugging.

[0104] The output domains of the proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment are divided into seven fuzzy levels, using the same fuzzy partitioning strategy as the input variables.

[0105] A complete fuzzy control rule base is established, which consists of 49 conditional statements. The antecedent of each rule is a fuzzy level combination of concentration deviation and deviation change rate, and the consequent is a fuzzy level combination of three parameter adjustment amounts.

[0106] The Mamdani-type fuzzy inference method is used for inference calculation, and the fuzzy output obtained by inference is defuzzified by the centroid method, so as to obtain the precise adjustment of the proportional coefficient, integral coefficient and differential coefficient.

[0107] During the construction process, the basic domain of concentration deviation is set according to the target concentration value. For example, when the target concentration is 15%, the domain range can be set to [-6%, +6%]; the domain of deviation change rate is determined by debugging the actual response data of the system, with a typical range of [-1.5% / s, +1.5% / s]; the domain of output variable is set according to the initial parameters of the PID controller to ensure that the adjustment can effectively change the control characteristics without causing system oscillation.

[0108] The triangular membership function is used for the intermediate fuzzy levels to provide precise sensitivity; the trapezoidal membership function is used for the negative and positive levels at both ends to better express extreme cases; the 49 fuzzy rules completely cover all possible input combinations, and each rule embodies a specific control strategy. For example, when the concentration deviation is positive and the deviation change rate is negative, the output proportional coefficient adjustment is negative, the integral coefficient adjustment is negative, and the derivative coefficient adjustment is positive, so as to achieve fast and stable adjustment when the deviation is high but the change is slow.

[0109] Mamdani-type inference methods map inputs to fuzzy outputs through fuzzy implication and aggregation operations; the centroid method for defuzzification calculates the centroid of the output fuzzy set to obtain precise parameter adjustment values, ensuring smooth changes in control parameters.

[0110] In step S3, the acquisition of real-time concentration detection values, the calculation of concentration deviation and deviation change rate, fuzzy inference, PID parameter update, calculation of diluent flow rate adjustment value, and the issuance of control signals are all completed within one control cycle; the control cycle ranges from 50 milliseconds to 500 milliseconds.

[0111] Example 2, a smart diluent concentration dispensing system, such as Figure 1 As shown, it includes:

[0112] The parameter setting and self-learning module includes:

[0113] The parameter setting unit is used to receive the target concentration value through the human-machine interface and call the corresponding basic process parameters from the formula database.

[0114] The self-learning execution unit is used to execute the material characteristic self-learning process when a change in the type of mother liquor is detected or a forced learning command is received. It controls the diluent flow rate to change in a preset mode, and at the same time collects the concentration response data of the mixed liquid through an online concentration sensor.

[0115] The characteristic identification unit is used to identify and analyze the collected concentration response data and the corresponding diluent flow rate change data, and calculate the set of parameters characterizing the current dynamic characteristics of the material, including system lag time, response time constant and steady-state gain coefficient.

[0116] The controller initialization module is used to use the set of dynamic characteristic parameters as the initialization configuration parameters of the fuzzy adaptive PID controller.

[0117] The intelligent control module includes:

[0118] The data acquisition unit is used to initiate the mother liquor delivery and diluent adjustment, start the mixing process, and continuously acquire real-time concentration detection values.

[0119] The deviation calculation unit is used to calculate the current concentration deviation based on the difference between the target concentration value and the real-time detection value, and to calculate the rate of change of the deviation relative to the previous sampling time.

[0120] The fuzzy inference unit is used to input the concentration deviation and the rate of change of deviation into the fuzzy inference engine, perform inference calculations based on the fuzzy rule base modified by dynamic characteristic parameters, and output the real-time adjustment of the proportional coefficient, integral coefficient and derivative coefficient of the PID controller.

[0121] The parameter update unit is used to update the operating parameters of the PID controller based on the adjustment amount output by fuzzy inference, and to calculate the adjustment value of the diluent flow rate through the updated PID controller.

[0122] The control execution unit is used to convert the flow rate regulation value into a control signal and send it to the diluent regulating device;

[0123] The closed-loop control module is used to repeatedly call the real-time intelligent control module to stabilize the concentration value within the preset error range of the target concentration through continuous concentration detection and flow regulation. When the concentration value reaches a stable state, the mixing operation stops and the dynamic characteristic parameters, real-time concentration curve, and control command sequence are stored in the historical database.

[0124] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for intelligently adjusting the concentration of a diluent, characterized in that, include: Step S1: Receive the target concentration value through the human-machine interface and call the corresponding basic process parameters from the formula database; When a change in the type of mother liquor is detected or a forced learning command is received, the material characteristic self-learning process is executed, the diluent flow rate is controlled to change in a preset mode, and the concentration response data of the mixed liquid is collected through an online concentration sensor. The collected concentration response data and corresponding diluent flow rate change data are identified and analyzed to calculate a set of parameters characterizing the current dynamic characteristics of the material, including system lag time, response time constant and steady-state gain coefficient. Step S2: Use the set of dynamic characteristic parameters as the initialization configuration parameters for the fuzzy adaptive PID controller; Step S3: Start the mother liquor delivery and diluent adjustment, begin the mixing process, and continuously acquire real-time concentration detection values; The current concentration deviation is calculated based on the difference between the target concentration value and the real-time concentration detection value, and the rate of change of the deviation relative to the previous sampling time is also calculated. The concentration deviation and the rate of change of deviation are input into the fuzzy inference engine, and the inference calculation is performed based on the fuzzy rule base corrected by the dynamic characteristic parameters. The output is the real-time adjustment of the proportional coefficient, integral coefficient and derivative coefficient of the PID controller. The operating parameters of the PID controller are updated based on the real-time adjustment output of the fuzzy inference, and the diluent flow regulation value is calculated through the updated PID controller. The diluent flow rate adjustment value is converted into a control signal and sent to the diluent adjustment device; Step S4: Repeat step S3 to stabilize the concentration value within the preset error range of the target concentration value through continuous concentration detection and flow rate adjustment; when the concentration value reaches a stable state, stop the mixing operation and store the dynamic characteristic parameters, real-time concentration curve, and control command sequence into the historical database.

2. The intelligent diluent concentration adjustment method according to claim 1, characterized in that, In step S1, controlling the diluent flow rate varies in a preset mode, specifically including any of the following modes: A step signal mode is used to abruptly increase the diluent flow rate from its initial value to a set value and maintain it. The ramp signal mode is used to increase the diluent flow rate from the initial value to the set value at a constant rate; A pseudo-random binary sequence signal mode is used to randomly switch the diluent flow rate between multiple preset level values.

3. The intelligent diluent concentration adjustment method according to claim 2, characterized in that, Step S1 involves identifying and analyzing the collected concentration response data and the corresponding diluent flow rate change data, specifically including: A dynamic relationship model between diluent flow rate and concentration change is established based on system identification theory. This model uses a difference equation model to describe the mathematical relationship between the concentration value and the diluent flow rate value at the current and historical moments. It includes model order parameters and system lag time that characterize the dynamic characteristics of the system. The recursive least squares estimation algorithm is used to identify model parameters. This algorithm initializes the parameter vector and covariance matrix, and recursively updates the parameter estimates based on new measurement data in each sampling period. The gain matrix is ​​used to weigh the weight relationship between historical estimates and new measurement data, while the covariance matrix reflects the uncertainty of parameter estimates. Based on the identified model parameters, the system lag time, response time constant, and steady-state gain coefficient are calculated.

4. The intelligent diluent concentration adjustment method according to claim 3, characterized in that, Based on the identified model parameters, the system lag time, response time constant, and steady-state gain coefficient are calculated, as follows: The system eigenvalues ​​are obtained by solving the characteristic equations corresponding to the difference equation model. The modal time constant corresponding to the dominant eigenvalue with the largest modulus is taken as the response time constant, which characterizes the inertial characteristics of the system response. Based on the final value theorem, the steady-state gain coefficient is obtained by calculating the input-output relationship of the difference equation model under steady-state conditions. This coefficient characterizes the steady-state sensitivity of the system. By analyzing the system's response characteristics under a step input signal, the system's lag time is determined based on the starting moment of the significant change in concentration response. This parameter characterizes the system's transmission delay characteristics.

5. The intelligent diluent concentration adjustment method according to claim 4, characterized in that, The specific process of correcting the fuzzy rule base based on dynamic characteristic parameters in step S2 includes: The strength of the integral action in the fuzzy rule is adjusted according to the length of the system lag time; the longer the system lag time, the weaker the weight of the integral action. The strength of the proportional effect in the fuzzy rule is adjusted according to the magnitude of the response time constant; the larger the response time constant, the greater the weight of the proportional effect. The scaling factor of all output variables in the fuzzy rule is adjusted according to the magnitude of the steady-state gain coefficient. The larger the steady-state gain coefficient, the smaller the domain range of the proportional coefficient adjustment, integral coefficient adjustment and differential coefficient adjustment.

6. The intelligent diluent concentration adjustment method according to claim 5, characterized in that, The construction process of the fuzzy inference engine includes: The basic domain of concentration bias is divided into seven fuzzy levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The membership distribution of each fuzzy level is determined by combining triangular membership functions and trapezoidal membership functions. The basic domain of the deviation change rate is also divided into seven fuzzy levels, in the same way as the fuzzy level division of the concentration deviation. The output domains of the proportional coefficient adjustment, integral coefficient adjustment, and differential coefficient adjustment are divided into seven fuzzy levels, using the same fuzzy partitioning strategy as concentration deviation and deviation change rate. A complete fuzzy control rule base is established, which consists of 49 conditional statements. The antecedent of each rule is a fuzzy level combination of concentration deviation and deviation change rate, and the consequent is a fuzzy level combination of three parameter adjustment amounts. The Mamdani-type fuzzy inference method is used for inference calculation, and the fuzzy output obtained by inference is defuzzified by the centroid method, so as to obtain the precise adjustment of the proportional coefficient, integral coefficient and differential coefficient.

7. The intelligent diluent concentration adjustment method according to claim 6, characterized in that, In step S3, the acquisition of real-time concentration detection values, the calculation of concentration deviation and deviation change rate, fuzzy inference, PID parameter update, calculation of diluent flow rate adjustment value, and the issuance of control signals are all completed within one control cycle.

8. A smart diluent concentration dispensing system, characterized in that, The system is applied to the intelligent diluent concentration adjustment method according to any one of claims 1-7, comprising: The parameter setting and self-learning module includes: The parameter setting unit is used to receive the target concentration value through the human-machine interface and call the corresponding basic process parameters from the formula database. The self-learning execution unit is used to execute the material characteristic self-learning process when a change in the type of mother liquor is detected or a forced learning command is received. It controls the diluent flow rate to change in a preset mode, and at the same time collects the concentration response data of the mixed liquid through an online concentration sensor. The characteristic identification unit is used to identify and analyze the collected concentration response data and the corresponding diluent flow rate change data, and calculate the set of parameters characterizing the current dynamic characteristics of the material, including system lag time, response time constant and steady-state gain coefficient. The controller initialization module is used to use the set of dynamic characteristic parameters as the initialization configuration parameters of the fuzzy adaptive PID controller. The intelligent control module includes: The data acquisition unit is used to initiate the mother liquor delivery and diluent adjustment, start the mixing process, and continuously acquire real-time concentration detection values. The deviation calculation unit is used to calculate the current concentration deviation based on the difference between the target concentration value and the real-time concentration detection value, and to calculate the rate of change of the deviation relative to the previous sampling time. The fuzzy inference unit is used to input the concentration deviation and the rate of change of deviation into the fuzzy inference engine, perform inference calculations based on the fuzzy rule base modified by dynamic characteristic parameters, and output the real-time adjustment of the proportional coefficient, integral coefficient and derivative coefficient of the PID controller. The parameter update unit is used to update the operating parameters of the PID controller based on the real-time adjustment amount output by fuzzy inference, and to calculate the diluent flow rate adjustment value through the updated PID controller. The control execution unit is used to convert the diluent flow rate adjustment value into a control signal and send it to the diluent adjustment device; The closed-loop control module is used to repeatedly call the real-time intelligent control module to stabilize the concentration value within the preset error range of the target concentration value through continuous concentration detection and flow regulation. When the concentration value reaches a stable state, the mixing operation stops and the dynamic characteristic parameters, real-time concentration curve, and control command sequence are stored in the historical database.

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