A mud conditioning automatic dosing control method and system based on fuzzy PID

Abstract

The present application belongs to the field of automatic control technology, and particularly relates to a mud conditioning automatic dosing control method and system based on fuzzy PID, which comprises the following steps: extracting mud concentration and mud flow to construct a sequence, and calculating dosing concentration error and dosing concentration error change rate; determining mud mutation energy in combination with equipment physical range; constructing a wolf pack model to map the position of a scout wolf to a transformation coefficient of a fuzzy controller, and calculating fitness error; fusing mud mutation energy and fitness error to construct a scout wolf walking step, updating the position of the scout wolf to obtain an optimal transformation coefficient; and combining dosing concentration error and its change rate with the optimal transformation coefficient to perform fuzzy reasoning, and adjusting the real-time rotating speed of a dosing pump. The present application can dynamically adjust the optimization step according to the physical impact of fluid, and adaptively reconstruct the control parameters, thereby overcoming the saturation of fuzzy domain and serious response lag caused by the solidification of traditional coefficients, and realizing efficient and low-delay dosing closed-loop control of reagents under harsh working conditions.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology. More specifically, this invention relates to an automatic chemical dosing control method and system for mud conditioning based on fuzzy PID control. Background Technology

[0002] Sludge dewatering is a core component of environmental water treatment. The sludge conditioning process determines the efficiency and quality of solid-liquid separation. Currently, in the field of automatic dosing for sludge conditioning, the main approach is to dynamically adjust the dosing pump frequency based on a fuzzy proportional-integral-derivative control strategy.

[0003] Existing traditional control methods typically use sensors to collect in real time the deviation between the target concentration of the drug and the actual detected concentration, as well as the rate of change of the deviation. These deviations are then multiplied by pre-set input transformation coefficients and mapped to the fuzzy domain. The system then performs fuzzy inference based on a fixed table of expert experience rules and multiplies the final output control increment by a preset output transformation coefficient to convert it into the control signal of the frequency converter, thus achieving closed-loop control.

[0004] In complex engineering environments, mud exhibits highly nonlinear, dynamic, and time-varying characteristics. Its physical flow rate and concentration are frequently subjected to drastic shocks from upstream processes, resulting in irregular surges or drops. Traditional fuzzy control strategies, with their completely fixed input and output transformation coefficients and reliance on manual experience for adjustment, are prone to saturation of the fuzzy domain beyond its set boundaries when faced with drastic mud state changes. Alternatively, weak input signals may fail to effectively trigger fuzzy rules. This severe lag caused by parameter fixation prevents the dosing system from responding promptly to changes in mud load. In the initial stages of sudden changes, this can lead to significant waste of chemicals and filter cloth clogging. When the load decreases, underdosing can occur, severely deteriorating water quality and significantly increasing operation and maintenance costs. Summary of the Invention

[0005] To address the technical problem of inaccurate dosing due to fixed traditional control coefficients causing sudden changes in operating conditions, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides an automatic dosing control method for mud conditioning based on fuzzy PID, comprising: Extract mud concentration and mud flow rate according to a set time window to construct the current mud concentration sequence and the current mud flow rate sequence; calculate the dosing concentration error and the rate of change of the dosing concentration error at the current time node based on the actual dosing concentration and the target dosing concentration; determine the mud mutation energy characterizing the instability of the fluid system based on the current mud concentration sequence, the current mud flow rate sequence, the maximum physical range of mud concentration, and the maximum design physical flow rate of mud; construct a wolf pack model containing multiple scouts, map the positions of the scouts to the transformation coefficients of the fuzzy controller, perform historical backtracking simulation control operations based on the current mud concentration sequence and the current mud flow rate sequence, and calculate the fitness error of each scout; fuse the mud mutation energy and the fitness error to construct the scout walking step size, update the position of each scout using the scout walking step size, and obtain the optimal transformation coefficient after iterative convergence; map the dosing concentration error and the rate of change of the dosing concentration error together with the optimal transformation coefficient to the fuzzy universe of discourse, perform fuzzy inference to obtain incremental signals to adjust the real-time speed of the dosing pump.

[0007] This invention extracts the current mud concentration sequence and current mud flow sequence within a set time window and combines them with the physical range limit of the equipment to determine the mud mutation energy, transforming the nonlinear physical impact brought by the upstream process into an explicit dynamic index characterizing the instability of the fluid system. This invention constructs a wolf pack model to map the wolf's position to the transformation coefficient of the fuzzy controller, and calculates the fitness error through historical backtracking simulation, thereby deeply integrating the mud mutation energy with the fitness error to adaptively construct the wolf's walking step size and iteratively optimize the transformation coefficient. This allows the control system to dynamically adjust the parameter search step size according to the severity of the underlying fluid state. When encountering a surge or drop in mud properties, it can quickly cross the search space to reconstruct the control parameters, overcoming the defects of fuzzy domain boundary saturation and severe hysteresis caused by the complete solidification of transformation coefficients in traditional fuzzy controllers. This invention combines the dosage concentration error and the rate of change to perform fuzzy inference to adjust the real-time speed of the dosing pump, achieving efficient, low-latency, and highly robust adaptive dosing of chemicals under complex and harsh dynamic time-varying conditions.

[0008] Preferably, constructing the current mud concentration sequence and the current mud flow rate sequence includes: collecting the initial mud flow rate and the initial mud concentration; performing noise reduction processing using a moving average filtering algorithm and time alignment according to a uniform sampling period to obtain the pre-processed mud concentration and mud flow rate; extracting the mud concentration and mud flow rate corresponding to the current time node and several historical time nodes before the current time node according to a pre-set time window length to construct the current mud concentration sequence and the current mud flow rate sequence.

[0009] Preferably, the mud mutation satisfies the expression:

[0010] In the formula, Indicates the mud mutation energy; Indicates the length of the time window; Indicates the discrete time node number; This indicates the number of times the mud concentration is in the current sequence. Mud concentration at each time point; Indicates the current mud flow rate sequence of the th Mud flow rate at each time point; This indicates the number of times the mud concentration is in the current sequence. Mud concentration at each time point; Indicates the current mud flow rate sequence of the th Mud flow rate at each time point; This indicates the maximum physical range of mud concentration; This indicates the maximum design physical flow rate of the mud. Represents the maximum value function; Represents an exponential function with the natural constant as its base; Represents the absolute value symbol.

[0011] This invention can intuitively capture sudden solid-phase mass physical shocks brought about by upstream processes by calculating the absolute value of the difference between the product of mud concentration and mud flow rate at adjacent time points. By using the product of the maximum physical range of mud concentration and the maximum design physical flow rate of mud as the denominator to process the absolute value of this difference, the physical dimensions of fluid characteristics can be eliminated, thereby accurately assessing the relative severity of the current transient impact in the overall load-bearing limit of the equipment. By using an exponential function for nonlinear amplification mapping, the underlying implicit fluid composite impact is made explicit and amplified many times, so that the generated mud mutation can keenly perceive slight but fatal changes in physical load under harsh working conditions.

[0012] Preferably, mapping the position of the wolf detector to the transformation coefficients of the fuzzy controller includes: the position vector of each wolf detector contains coordinate values ​​in three independent dimensions, which correspond to the input transformation coefficient of the dosing concentration error, the input transformation coefficient of the dosing concentration error change rate, and the output transformation coefficient of the dosing pump frequency, respectively.

[0013] Preferably, the step of performing historical backtracking simulation control operations based on the current mud concentration sequence and the current mud flow rate sequence to calculate the fitness error of each probe wolf includes: performing historical backtracking simulation control operations based on the current mud concentration sequence and the current mud flow rate sequence; at each discrete time node of the simulation operation cycle, calculating the concentration deviation between the preset target dosing concentration and the actual dosing concentration simulated under the current transformation coefficient configuration, forming a discrete error sequence; calculating the discrete error sequence using the time multiplication absolute error integral rule, and using the result as the fitness error corresponding to the probe wolf; marking the probe wolf with the smallest fitness error among all probe wolves as the alpha wolf.

[0014] This invention constructs a discrete error sequence by calculating the concentration deviation at each discrete time point during the simulation operation cycle. When calculating the fitness error of each probe, it introduces the time-multiplied absolute error integral rule, which can weight and amplify the persistent steady-state deviation accumulated over time. This prompts the optimization algorithm to pay more attention to the convergence speed and stability of the later stage of the control process, effectively filtering out pseudo-optimal transformation coefficient configurations that have a fast response in the early stage but violent oscillations in the later stage. By extracting the probe with the smallest fitness error as the alpha, it ensures that the selected controller configuration can not only quickly eliminate the initial dosing concentration error, but also suppress the physical adjustment overshoot phenomenon in the later stage to the greatest extent.

[0015] Preferably, the wolf-probe walking step length satisfies the expression:

[0016] In the formula, Indicates the first The roaming stride of a wolf scout; Represents the physical upper limit vector; Represents the physical lower limit vector; Represents the L2 norm operation of vectors; This indicates the fitness error of the alpha wolf; Indicates the first The fitness error of the individual wolf scouts; Represents the maximum value function; Indicates the mud mutation energy; This represents an exponential function with the natural constant as its base.

[0017] This invention determines the search benchmark by calculating the difference between the physical upper limit vector and the physical lower limit vector, and constructs an exponential decay term using the fitness error between the alpha and alpha wolves. This accurately assesses the quality of the current alpha wolf parameter configuration, allowing wolves with large fitness errors to have a larger wandering space to prevent them from getting stuck in local deadlock. At the same time, it uses mud mutation energy to construct a dynamic compensation fractional term. When the external fluid environment experiences a severe physical shock, the overall alpha wolf wandering step size is forcibly increased, driving all wolves to quickly find new combinations of transformation coefficients over a large range. When the fluid conditions are stable, the alpha wolf wandering step size automatically shrinks, ensuring that the parameters are finely adjusted near the optimal value. This effectively solves the contradiction between high-frequency oscillation of the inverter control signal and parameter lag failure during sudden changes.

[0018] Preferably, updating the position of each scout wolf using its walking stride includes:

[0019] In the formula, Indicates the first The updated position vectors of the scout wolves; Indicates the first The position vector of the scout wolf before the update; This represents the position vector of the alpha wolf; Indicates the first The roaming stride of a wolf scout; This represents a random variable that is uniformly distributed between the values ​​0 and 1. Pi is a constant. Represents the sine function; Represents the L2 norm operation of vectors; This represents the maximum value function; when the updated position vector of the Detecting Wolf exceeds the physical upper limit vector or falls below the physical lower limit vector, it is forcibly pulled back to the corresponding physical upper limit vector or physical lower limit vector.

[0020] This invention introduces a sine function and random variables when updating the position using the wolf roaming step length and the relative distance ratio between the roaming wolf and the alpha wolf. By applying reasonable periodic random perturbations, it effectively avoids the misjudgment phenomenon of falling into local optima due to electrical noise interference from the underlying sensors during the optimization process. At the same time, by establishing a forced pullback mechanism when the position exceeds the limit, a strict engineering safety defense line is constructed to ensure that all generated optimal transformation coefficients are always precisely limited within the physical safety response threshold of the dosing pump frequency converter. This solves the risk of overload wear or damage to the underlying mechanical equipment caused by extreme control parameter outputs, and greatly ensures the long-term continuous operation safety of the automatic dosing system.

[0021] Preferably, obtaining the optimal transformation coefficients after iterative convergence includes: updating the position of each wolf using the wolf's walking step size, recalculating the fitness error of all wolves and performing multiple iterations; when the preset number of iterations is reached, extracting the final alpha wolf's position vector as the optimal input transformation coefficient for the drug concentration error, the optimal input transformation coefficient for the drug concentration error change rate, and the optimal output transformation coefficient for the drug pump frequency.

[0022] Preferably, the step of mapping the dosing concentration error and the rate of change of the dosing concentration error to a fuzzy universe of discourse using the optimal transform coefficient, and performing fuzzy inference to obtain an incremental signal to adjust the real-time speed of the dosing pump, includes: multiplying the dosing concentration error and the rate of change of the dosing concentration error at the current time node by the corresponding optimal input transform coefficients, and mapping the multiplication result to a preset fuzzy universe of discourse; performing fuzzy inference rules to obtain an incremental signal of proportional-integral-derivative, and multiplying the incremental signal by the optimal output transform coefficient of the dosing pump frequency after defuzzification processing to obtain the driving frequency control quantity of the dosing pump; and sending the driving frequency control quantity of the dosing pump to the frequency converter driver of the dosing pump to adjust the real-time speed of the dosing pump.

[0023] Secondly, the present invention provides an automatic dosing control system for mud conditioning based on fuzzy PID, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned automatic dosing control method for mud conditioning based on fuzzy PID is implemented.

[0024] By adopting the above technical solution, a computer program for the above-mentioned automatic dosing control method for mud conditioning based on fuzzy PID is generated and stored in the memory so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0025] The beneficial effects of this invention are as follows: This invention constructs mud mutation energy by extracting the variation characteristics of mud concentration and mud flow rate, transforming the dual physical impact of mechanical conveying and medium concentration brought by upstream processes into a quantified dynamic fluctuation index, and deeply integrating it with the fitness evaluation mechanism of the wolf pack model to construct a wolf-hunting walking step size that can adaptively expand and contract with the intensity of fluid physical disturbance. This allows for online optimization to obtain the optimal transformation coefficient of the fuzzy controller. When the mud operation is stable, the optimization step size can be autonomously contracted for fine-tuning to maintain the steady-state accuracy of dosing and prevent frequency converter oscillation. When encountering complex and severe sudden fluid shocks, the search radius can be instantly and significantly expanded, and the driving parameters can be rapidly reconstructed in a multi-dimensional space. This overcomes the problems of fuzzy domain boundary saturation and off-target failure caused by the solidification of transformation coefficients in traditional fuzzy control, effectively suppressing the serious response lag, large overshoot, and excessive waste of reagents in traditional dosing systems when dealing with sudden changes in operating conditions. This achieves efficient and low-delay closed-loop control of mud conditioning reagents. Attached Figure Description

[0026] Figure 1 This is a schematic flowchart illustrating an automatic dosing control method for mud conditioning based on fuzzy PID in this invention; Figure 2 This is a schematic diagram showing the changes in current mud concentration and current mud flow rate; Figure 3 This is a graph showing the change in mud mutation energy. Figure 4 A diagram illustrating the changes in the stride length of a wolf while roaming; Figure 5 This is a comparison chart of the steady-state performance of chemical dosing under extreme sudden changes in mud conditions. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] This invention discloses an automatic chemical dosing control method for mud conditioning based on fuzzy PID control, referring to... Figure 1 This includes steps S1-S5: S1: Extract mud concentration and mud flow rate according to the set time window, construct the current mud concentration sequence and the current mud flow rate sequence, and calculate the dosing concentration error and the dosing concentration error change rate at the current time node based on the actual dosing concentration and the target dosing concentration.

[0030] Specifically, the initial mud operating status of the bottom fluid is collected in real time by using an ultrasonic flow meter and a suspended solids concentration meter installed at the inlet of the mixing pipeline. The initial mud operating status includes the initial mud flow rate and the initial mud concentration, which characterizes the mud content in the mud.

[0031] The initial mud operating state was denoised using a moving average filtering algorithm, and then time alignment was performed according to a uniform sampling period to obtain the pre-processed mud concentration and mud flow rate.

[0032] The actual concentration of the pesticide is obtained by monitoring sensors on the dosing pipeline side. The preset target concentration is obtained. The target concentration is subtracted from the actual concentration to obtain the dosing concentration error at the current time point. The difference between the dosing concentration error at the current time point and the dosing concentration error at the previous time point is calculated to obtain the dosing concentration error change rate at the current time point.

[0033] A time window length is set, and the mud concentration and mud flow rate corresponding to the current time node and several historical time nodes before the current time node are extracted according to the time window length to construct the current mud concentration sequence and the current mud flow rate sequence. In this embodiment, to ensure that the current mud concentration sequence and the current mud flow rate sequence data exactly cover a complete single physical mixing cycle while avoiding the introduction of irrelevant historical disturbances, the time window length is set to 30 sampling cycles. In other embodiments, the implementer can set the time window length according to the actual pipeline size and pumping capacity.

[0034] Figure 2 The diagram shows the changes in current mud concentration and current mud flow rate. It can be seen that within the time node from 150 to 250, the mud concentration and mud flow rate experienced a dramatic surge, reflecting the input state of nonlinear physical impact generated by upstream processes under extremely harsh operating conditions.

[0035] S2: Determine the mud mutation energy characterizing the instability of the fluid system based on the current mud concentration sequence, the current mud flow rate sequence, the maximum physical range of mud concentration, and the maximum design physical flow rate of mud.

[0036] It should be noted that in actual sludge dewatering operations, the real physical impact load that determines the actual consumption of flocculant is jointly determined by the sudden changes in the mechanical transport flow rate of the sludge and the sudden changes in the concentration of the medium itself. Since traditional single monitoring variables are insufficient to fully reflect this complex and drastic fluctuation problem, the system often experiences a lag in dosing response when encountering sudden changes in upstream operating conditions. Therefore, this invention constructs a sludge mutation energy, deeply nonlinearly coupling mechanical disturbance and medium disturbance, transforming it into a dynamic benchmark index characterizing the instability of the fluid system.

[0037] Specifically, obtain the maximum physical range of mud concentration and the maximum design physical flow rate of mud recorded on the equipment nameplate.

[0038] Based on the current mud concentration sequence, current mud flow rate sequence, maximum physical range of mud concentration, and maximum design physical flow rate of mud, determine the mud mutation energy:

[0039] In the formula, Indicates the mud mutation energy; Indicates the length of the time window; Indicates the discrete time node number; This indicates the number of times the mud concentration is in the current sequence. Mud concentration at each time point; Indicates the current mud flow rate sequence of the th Mud flow rate at each time point; This indicates the number of times the mud concentration is in the current sequence. Mud concentration at each time point; Indicates the current mud flow rate sequence of the th Mud flow rate at each time point; This indicates the maximum physical range of mud concentration; This indicates the maximum design physical flow rate of the mud. Represents the maximum value function; Represents an exponential function with the natural constant as its base; Represents the absolute value symbol.

[0040] The product of mud concentration and mud flow rate characterizes the instantaneous solid mass load of the mud in the pipeline. This invention, by calculating the absolute value of the difference in solid mass load at adjacent time points, can intuitively capture the sudden media impact brought by upstream processes; and utilizes the maximum physical range of mud concentration. With the maximum design physical flow rate of mud The product of these factors, used as the denominator, is applied to the absolute value of the difference, eliminating the physical dimensions of the fluid characteristics and assessing the relative severity of the current transient impact within the overall load-bearing limit of the equipment. Since the instability risk of the chemical dosing system under severe sudden changes exhibits a nonlinear amplification characteristic, this invention calculates the average relative severity within a time window and then uses an exponential function for nonlinear amplification mapping. When the mud concentration at adjacent time nodes... With mud flow When the product of the two values ​​fluctuates drastically, it indicates a sharp change in the solid-phase physical load of the input mud source. The absolute value of the difference increases significantly, and after amplification by the exponential function, the mud mutation energy becomes more pronounced. The rapid increase transforms the underlying, latent fluid shocks into explicit physical signals that drive the algorithm's adaptive reconstruction.

[0041] Figure 3 The graph shows the variation of mud mutation energy. It can be seen that when the mud is running smoothly, the mud mutation energy remains at an extremely low level. When encountering a sudden shock of a double surge in mud concentration and mud flow rate, the mud mutation energy surges instantaneously and sensitively, accurately quantifying the physical impact load of the system.

[0042] S3: Construct a wolf pack model containing multiple scout wolves, map the positions of the scout wolves to the transformation coefficients of the fuzzy controller, perform historical backtracking simulation control operations based on the current mud concentration sequence and the current mud flow sequence, and calculate the fitness error of each scout wolf.

[0043] It should be noted that, since the transformation coefficients of traditional fuzzy controllers are highly dependent on human experience and cannot be adaptively adjusted, the control effect is poor when the operating conditions change. Therefore, this embodiment constructs a wolf pack model containing multiple wolves, maps the position of each wolf to the transformation coefficient of the fuzzy controller, and calculates the fitness error of each wolf in combination with the drug concentration error.

[0044] Specifically, the number of scouts is set to 20 in this embodiment to ensure global search capability in multidimensional space while avoiding severe control delays caused by overloading the computing power of the edge industrial control computer. In other embodiments, the implementer can set the number of scouts according to the processor performance of the edge industrial control computer.

[0045] Furthermore, a wolf pack model containing multiple scout wolves is constructed. The position vector of each scout wolf contains coordinate values ​​in three independent dimensions, which correspond to the input transformation coefficient of the drug concentration error, the input transformation coefficient of the drug concentration error change rate, and the output transformation coefficient of the drug pump frequency, respectively.

[0046] The position vector of each scavenger is substituted into the fuzzy controller as the configuration parameter of the transformation coefficient. Based on the current mud concentration sequence and the current mud flow sequence, historical backtracking simulation control operation is performed. At each discrete time node of the simulation operation cycle, the concentration deviation between the preset target concentration of chemical dosing and the actual concentration of chemical dosing output under the current transformation coefficient configuration is calculated. The concentration deviations of all discrete time nodes in the simulation operation cycle are arranged in chronological order to form a discrete error sequence.

[0047] The discrete error sequence is calculated using the time-multiplied absolute error integral rule. This involves multiplying the absolute value of each concentration deviation in the discrete error sequence by its corresponding time weight, integrating the results, and summing the sum. The final integral result is taken as the fitness error of that scout wolf. The scout wolf with the smallest fitness error among all scout wolves is officially designated as the alpha wolf.

[0048] S4: The mud mutation energy and fitness error are fused to construct the wolf roaming step size. The position of each wolf is updated using the wolf roaming step size, and the optimal transformation coefficients are obtained after iterative convergence.

[0049] It should be noted that in actual mud dosing control, if the step size of the intelligent algorithm for searching and transforming parameters remains fixed, when the mud properties are stable, an excessively large search step size will cause the generated control parameters to frequently exceed the limits, leading to high-frequency oscillations and mechanical wear of the dosing pump inverter. Conversely, when the mud concentration or flow rate changes abruptly, an excessively small search step size will prevent the controller parameters from quickly traversing the search space to respond to fluid changes, resulting in severe physical lag in chemical dosing. Therefore, this embodiment integrates the mud abrupt change energy reflecting the physical impact of the underlying fluid with the fitness error of the probe itself to construct a probe walking step size that can adaptively expand and contract with the working conditions, and uses the probe walking step size to reconstruct and update the position of each probe.

[0050] Specifically, the physical upper limit vector and physical lower limit vector of the fuzzy controller transform coefficients are obtained based on the equipment safety response threshold.

[0051] The walk step size of the rovers is determined based on the fitness error of all rovers, the mud mutation energy, the physical upper bound vector and the physical lower bound vector of the fuzzy controller transformation coefficients:

[0052] In the formula, Indicates the first The roaming stride of a wolf scout; Represents the physical upper limit vector; Represents the physical lower limit vector; Represents the L2 norm operation of vectors; This indicates the fitness error of the alpha wolf; Indicates the first The fitness error of the individual wolf scouts; Represents the maximum value function; Indicates the mud mutation energy; This represents an exponential function with the natural constant as its base.

[0053] In this invention, negative exponent terms are utilized. The merits of the current parameter configuration are evaluated to constrain the wolf probe to converge toward the alpha wolf, and dynamic compensation is performed by introducing the physical impact state of the external fluid environment using fractional terms. When the... fitness error of individual scouts The larger the value, the further the transformation coefficient configuration represented by the probe deviates from the optimal parameters required by the actual fluid; in this case, the negative exponent term... Approaching a value of 1, the scout is given a larger baseline step size to enable rapid global exploration within the multidimensional parameter space; conversely, when the fitness error approaches 1, it is less than 1. The smaller and closer the fitness error is to that of the alpha wolf When this occurs, it indicates that the current transformation coefficients are close to optimal, and the negative exponent term... The smaller step size drives the convergence of the baseline step size. When a strong physical disturbance occurs within the pipe, causing a sudden change in the mud's energy... During a surge, the original optimal dosing coefficient becomes instantly invalid, at which point the fractional term... The value rapidly approaches 1, forcibly increasing the overall wolf-finding roaming stride. This drives all the probes to quickly search for new combinations of transformation coefficients over a wide area to cope with the current fluid shock, overcoming the parameter lag defect of traditional algorithms when dealing with sudden changes in working conditions; conversely, when the fluid working conditions are stable, allowing for sudden changes in mud flow... When the value approaches 0, the fractional term Approaching 0, the wolf's roaming stride length The significant reduction ensures that the detector is finely tuned near its optimal parameters, avoiding high-frequency oscillations in the inverter control signal.

[0054] Figure 4 The diagram illustrates the change in the wolf pack's walking step length, showing the trajectory of the wolf pack algorithm's adaptive adjustment of the wolf pack's walking step length according to the working conditions. During the stable operation phase of the system, the wolf pack's walking step length autonomously contracts for fine-tuning to prevent oscillations. When encountering a sudden shock, the wolf pack's walking step length expands dramatically in an instant, driving all the wolves to perform leapfrog global optimization in a multi-dimensional space, which matches the fluctuation trend of the mud mutation energy.

[0055] Furthermore, the position of each scout wolf is updated using its walking step length, and the position update of the scout wolf satisfies the expression:

[0056] In the formula, Indicates the first The updated position vectors of the scout wolves; Indicates the first The position vector of the scout wolf before the update; This represents the position vector of the alpha wolf; Indicates the first The roaming stride of a wolf scout; This represents a random variable that is uniformly distributed between the values ​​0 and 1. Pi is a constant. Represents the sine function; Represents the L2 norm operation of vectors; This represents the maximum value function.

[0057] In this invention, the walking stride length of a wolf is utilized. The relative distance ratio between the alpha wolf and the stalker wolf determines the update span of the transformation coefficients, using a sine function. This is used to introduce reasonable random perturbations to avoid local misjudgments caused by electrical noise from the underlying sensors during the optimization process. (When the wolf-detecting step size...) When the increase is driven by the physical impact of mud, the first The updated position vector of the scout The span increases accordingly, enabling large-scale directional optimization of parameters to quickly suppress fluid disturbances; conversely, the update span decreases, enabling parameters to be optimized towards the alpha position vector. Rapid convergence to lock in steady-state control parameters.

[0058] It should be noted that when the first The updated position vector of the scout Exceeding the physical upper limit vector or below the physical lower limit vector When necessary, it is forcibly pulled back to the corresponding physical upper limit vector or physical lower limit vector. This mechanism serves as a strict engineering safety barrier, ensuring that all generated transformation coefficients are always within the safety response threshold of the dosing pump frequency converter, preventing damage to underlying mechanical equipment caused by extreme control parameter outputs.

[0059] To ensure that the wolf pack algorithm fully converges to the optimal parameter set and that the iterative calculation is completed within one sampling period of the edge industrial control computer, a preset number of iterations is set. In this embodiment, the preset number of iterations is set to 50. In other embodiments, the implementer can set the preset number of iterations according to the real-time requirements of the control system and the actual computing power of the edge industrial control computer.

[0060] After updating the position of the scout wolves, the fitness error of all scout wolves is recalculated and multiple iterations are performed. When the preset number of iterations is reached, the position vector of the final alpha wolf is extracted as the optimal input transformation coefficient of the drug concentration error, the optimal input transformation coefficient of the drug concentration error change rate, and the optimal output transformation coefficient of the drug pump frequency.

[0061] S5: Map the dosing concentration error and the rate of change of the dosing concentration error to the fuzzy domain by combining the optimal transformation coefficients, and perform fuzzy inference to obtain incremental signals to adjust the real-time speed of the dosing pump.

[0062] Specifically, the dosage concentration error at the current time point is multiplied by the corresponding optimal input transformation coefficient, and the rate of change of the dosage concentration error at the current time point is multiplied by the corresponding optimal input transformation coefficient. The multiplication result is mapped to a preset fuzzy domain. The incremental signal of proportional-integral-derivative is obtained by executing fuzzy inference rules. After defuzzification processing, it is multiplied by the optimal output transformation coefficient of the dosing pump frequency to obtain the driving frequency control quantity of the dosing pump.

[0063] The drive frequency control of the dosing pump is converted into an analog electrical signal and sent to the frequency converter of the dosing pump to adjust the real-time speed of the dosing pump, thereby realizing the automatic closed-loop regulation of mud conditioning agent injection.

[0064] Figure 5 The diagram shows a comparison of the steady-state performance of the dosing system under extreme mud shock conditions. It illustrates the difference in actual detected concentrations between the traditional fixed-coefficient control method and the adaptive wolf pack coefficient control method of this invention under the same upstream physical disturbance. It can be seen that the actual detected concentration corresponding to the traditional fixed coefficient exhibits severe hysteresis drop and huge recovery overshoot when encountering extreme shocks. In contrast, the actual detected concentration corresponding to the adaptive wolf pack coefficient of this invention can closely follow the target dosing concentration under severe shocks, with only a tiny smooth fluctuation that is instantly pulled back to steady state, thus improving the anti-interference capability and robustness of the dosing system.

[0065] This invention also discloses an automatic dosing control system for mud conditioning based on fuzzy PID, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an automatic dosing control method for mud conditioning based on fuzzy PID according to the present invention.

[0066] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. An automatic dosing control method for mud conditioning based on fuzzy PID, characterized in that, include: Extract mud concentration and mud flow rate according to the set time window, and construct the current mud concentration sequence and the current mud flow rate sequence; Based on the actual dosage concentration and the target dosage concentration, calculate the dosage concentration error and the rate of change of dosage concentration error at the current time point; Based on the current mud concentration sequence, the current mud flow rate sequence, the maximum physical range of mud concentration, and the maximum design physical flow rate of mud, determine the mud mutation energy that characterizes the instability of the fluid system. A wolf pack model containing multiple scout wolves is constructed, and the positions of the scout wolves are mapped to the transformation coefficients of the fuzzy controller. Based on the current mud concentration sequence and the current mud flow sequence, historical backtracking simulation control operation is performed, and the fitness error of each scout wolf is calculated. The mud mutation energy and fitness error are fused to construct the wolf roaming step size. The position of each wolf is updated using the wolf roaming step size, and the optimal transformation coefficients are obtained after iterative convergence. The dosage concentration error and the rate of change of dosage concentration error are combined with the optimal transformation coefficient and mapped to the fuzzy domain. Fuzzy inference is then performed to obtain incremental signals to adjust the real-time speed of the dosing pump.

2. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 1, characterized in that, The construction of the current mud concentration sequence and the current mud flow rate sequence includes: Initial mud flow rate and initial mud concentration are collected, noise reduction is performed using a moving average filtering algorithm, and time alignment is performed according to a uniform sampling period to obtain pre-processed mud concentration and mud flow rate. Mud concentration and mud flow rate corresponding to the current time node and several historical time nodes before the current time node are extracted according to a pre-set time window length to construct the current mud concentration sequence and the current mud flow rate sequence.

3. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 1, characterized in that, The mud mutation satisfies the expression: In the formula, Indicates the mud mutation energy; Indicates the length of the time window; Indicates the discrete time node number; This indicates the number of times the mud concentration is in the current sequence. Mud concentration at each time point; Indicates the current mud flow rate sequence of the th Mud flow rate at each time point; This indicates the number of times the mud concentration is in the current sequence. Mud concentration at each time point; Indicates the current mud flow rate sequence of the th Mud flow rate at each time point; This indicates the maximum physical range of mud concentration; This indicates the maximum design physical flow rate of the mud. Represents the maximum value function; Represents an exponential function with the natural constant as its base; Represents the absolute value symbol.

4. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 1, characterized in that, The process of mapping the wolf's position to the transformation coefficients of the fuzzy controller includes: Each wolf's position vector contains coordinate values ​​in three independent dimensions, corresponding to the input transformation coefficient of the dosing concentration error, the input transformation coefficient of the dosing concentration error change rate, and the output transformation coefficient of the dosing pump frequency, respectively.

5. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 1, characterized in that, The historical backtracking simulation control operation based on the current mud concentration sequence and the current mud flow sequence is performed to calculate the fitness error of each scout, including: Based on the current mud concentration sequence and the current mud flow sequence, historical backtracking simulation control calculations are performed. At each discrete time node of the simulation operation cycle, the concentration deviation between the preset target dosing concentration and the actual dosing concentration output under the current transformation coefficient configuration is calculated, forming a discrete error sequence. The discrete error sequence is calculated using the time multiplication absolute error integral rule, and the result is used as the fitness error corresponding to the alpha wolf. The alpha wolf with the smallest fitness error among all alpha wolves is marked as the alpha wolf.

6. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 1, characterized in that, The wolf's walking stride length satisfies the expression: In the formula, Indicates the first The roaming stride of a wolf scout; Represents the physical upper limit vector; Represents the physical lower limit vector; Represents the L2 norm operation of vectors; This indicates the fitness error of the alpha wolf; Indicates the first The fitness error of the individual wolf scouts; Represents the maximum value function; Indicates the mud mutation energy; This represents an exponential function with the natural constant as its base.

7. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 1, characterized in that, The method of updating the position of each scout wolf using its walking stride includes: In the formula, Indicates the first The updated position vectors of the scout wolves; Indicates the first The position vector of the scout wolf before the update; This represents the position vector of the alpha wolf; Indicates the first The roaming stride of a scout wolf; This represents a random variable that is uniformly distributed between the values ​​0 and 1. Pi is a constant. Represents the sine function; Represents the L2 norm operation of vectors; Represents the maximum value function; When the updated position vector of the Detective Wolf exceeds the physical upper limit vector or falls below the physical lower limit vector, it is forcibly pulled back to the corresponding physical upper limit vector or physical lower limit vector.

8. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 4, characterized in that, The process of obtaining the optimal transformation coefficients after iterative convergence includes: After updating the position of each scout wolf using the scout wolf's walking step length, the fitness error of all scout wolves is recalculated and multiple iterations are performed. When the preset number of iterations is reached, the position vector of the final alpha wolf is extracted as the optimal input transformation coefficient of the drug concentration error, the optimal input transformation coefficient of the drug concentration error change rate, and the optimal output transformation coefficient of the drug dosing pump frequency.

9. The automatic dosing control method for mud conditioning based on fuzzy PID according to claim 8, characterized in that, The step of mapping the dosing concentration error and the rate of change of the dosing concentration error to the fuzzy domain using the optimal transformation coefficients, and performing fuzzy inference to obtain incremental signals to adjust the real-time speed of the dosing pump includes: The dosage concentration error and the rate of change of dosage concentration error at the current time point are multiplied by the corresponding optimal input transformation coefficients, and the multiplication result is mapped to the preset fuzzy domain. The fuzzy inference rules are executed to obtain the incremental signal of proportional-integral-derivative. After the incremental signal is defuzzified, it is multiplied by the optimal output transformation coefficient of the dosing pump frequency to obtain the driving frequency control quantity of the dosing pump. The driving frequency control quantity of the dosing pump is sent to the frequency converter of the dosing pump to adjust the real-time speed of the dosing pump.

10. An automatic dosing control system for mud conditioning based on fuzzy PID, characterized in that, include: The processor and memory, wherein the memory stores computer program instructions that, when executed by the processor, implement an automatic dosing control method for mud conditioning based on fuzzy PID according to any one of claims 1-9.

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