Water purifying material automatic adding system for sewage treatment

Abstract

The application relates to a water purification material automatic adding system for sewage treatment, belonging to the technical field of sewage treatment, which comprises a multidimensional state sensing module for real-time acquisition of multi-physical field key parameters affecting flocculation process stability; a risk assessment module for receiving the multi-physical field key parameters output by the multidimensional state sensing module and calculating a risk index representing a collapse critical risk of the flocculation process based on a preset physical model; a nonlinear decoupling control module for receiving the risk index calculated by the risk assessment module and generating control instructions for flocculant adding speed and stirring speed according to the comparison result of the risk index, a preset safety threshold and a danger threshold; and an execution module for adjusting the flocculant adding speed and the stirring speed in response to the control instructions generated by the nonlinear decoupling control module. The application can accurately predict the collapse possibility before macroscopic deterioration of the flocculation effect.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment, specifically to an automatic dosing system for wastewater treatment purification materials. Background Technology

[0002] With the development of modern wastewater treatment technologies, the complexity of wastewater quality and quantity fluctuations has increased significantly. This complexity presents many challenges, especially in controlling the flocculant dosing and mixing processes.

[0003] Currently, traditional wastewater treatment systems typically rely on operator experience or hysteresis-based feedback control based on effluent turbidity. Technicians periodically adjust flocculant dosage and agitation speed by observing effluent quality or using turbidimeters to maintain flocculation effectiveness. However, this manual monitoring and maintenance method depends on physical inspection and manual testing, requiring technicians to personally check and record data. While these methods provide detailed information on flocculation status, they are generally inefficient and cannot detect problems in real time.

[0004] Therefore, how to avoid and resolve the problem of sudden drop in efficiency or even collapse during the flocculation process in a timely manner has become an urgent problem to be solved in this field.

[0005] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an automatic dosing system for wastewater treatment materials to solve the problems mentioned in the background art.

[0007] The technical solution of the present invention includes:

[0008] The multi-dimensional state perception module is used to acquire key multi-physics parameters that affect the stability of the flocculation process in real time.

[0009] The risk assessment module is used to receive key multi-physics parameters output by the multi-dimensional state perception module and calculate the risk index characterizing the critical risk of collapse in the flocculation process based on a preset physical model.

[0010] The nonlinear decoupling control module receives the risk index calculated by the risk assessment module and generates control commands for the flocculant dosing rate and stirring speed based on the comparison results between the risk index and the preset safety threshold and danger threshold.

[0011] The execution module is used to adjust the flocculant dosing rate and stirring speed in response to control commands generated by the nonlinear decoupling control module.

[0012] Preferably, the key parameters of the multiphysics field include:

[0013] Microcurrent density gradient intensity factor output by a high-frequency ultrasonic scattering sensor array deployed in the high-shear region of the stirring system;

[0014] Chemical interference coefficients output by an online three-dimensional fluorescence spectrometer;

[0015] The energy dissipation rate of the microvortex was calculated by measuring the turbulent energy dissipation rate spectrum within the reactor using an acoustic Doppler velocity profiler.

[0016] Acoustic coupling factor output by piezoelectric accelerometers installed on the reactor wall and pump body.

[0017] Preferably, the calculation of the risk assessment module includes the following steps:

[0018] Based on the energy dissipation rate of microvortices, the Kolmogorov time microscale is calculated.

[0019] Based on the chemical interference coefficient and the preset flocculant baseline stretching time, the functional stretching time of the polymer chain was calculated.

[0020] Based on the calculated Kolmogorov time microscale and the functional stretching time of the polymer chain, a core resonance factor characterizing the time-scale resonance relationship is generated;

[0021] By constructing a risk amplification coefficient and correcting the core resonance factor, the risk index is finally calculated.

[0022] Preferably, the step of calculating the risk index includes:

[0023] A risk amplification factor was constructed, which included the microcurrent density gradient intensity factor, chemical interference coefficient, and acoustic coupling factor.

[0024] The core resonance factor is modified using a risk amplification factor to generate the final risk index.

[0025] Preferably, the nonlinear decoupling control module generates control commands in the following manner:

[0026] When the risk index is below the safety threshold, the standard PID loop, which targets the effluent turbidity, is activated to adjust the flocculant dosing rate.

[0027] When the risk index is between the safe threshold and the dangerous threshold, an avoidance strategy is implemented, such as reducing the stirring speed.

[0028] When the risk index is higher than the danger threshold, the interruption-recovery emergency procedure is executed, reducing the flocculant dosing rate to a preset low level and significantly reducing the stirring speed to a weak turbulence mode.

[0029] Preferably, the avoidance strategy involves reducing the stirring speed to decrease the energy dissipation rate of the microvortex, thereby increasing the Kolmogorov time microscale and causing the ratio of the polymer chain functional stretching time to the Kolmogorov time microscale to deviate from the resonance point.

[0030] Preferably, the interruption-recovery emergency procedure reduces the flocculant dosing rate to prevent new agent molecules from entering the high-shear zone and being ineffectively decomposed; and significantly reduces the stirring speed to provide a safe recovery environment for the system.

[0031] Preferably, the model parameters of the preset physical model in the risk assessment module are determined by running a learning and calibration program in the early stage of system deployment and using an optimization algorithm to fit the collected experimental data.

[0032] Preferably, the preset safety threshold and danger threshold in the nonlinear decoupling control module are determined by statistical analysis of the risk index before a flocculation collapse event occurs in historical data.

[0033] This invention provides an improved automatic dosing system for wastewater treatment materials, which has the following improvements and advantages compared with the prior art:

[0034] 1. The technical solution enables precise quantification and early warning of flocculation collapse risk. The system uses a multi-dimensional state sensing module to capture key parameters of multiple physics fields in real time, including micro-current density gradient intensity factor, chemical interference coefficient, micro-vortex energy dissipation rate, and acoustic coupling factor. These parameters are no longer isolated, superficial measurements, but directly point to the core physics of flocculation process stability. The risk assessment module receives these multi-dimensional parameters and performs deep coupling calculations based on a pre-set physical model. This model reveals the intrinsic relationship between the Kolmogorov time microscale of flow field shearing and the functional expansion time of the polymer chains of the flocculant molecules themselves. By calculating the core resonance factor characterizing the time-scale resonance relationship between the two and correcting it with other perturbation parameters, the system can generate a forward-looking risk index that quantifies the critical collapse trend. This physics-based risk assessment allows the system to accurately predict the possibility of collapse before the flocculation effect macroscopically deteriorates.

[0035] 2. This technical solution achieves nonlinear decoupling control and proactive risk avoidance based on risk warning. The nonlinear decoupling control module executes a logically clear and physically meaningful hierarchical intervention strategy according to the different ranges of the risk index. When the risk index is below the safety threshold, the system executes conventional optimization control. When the risk index is between the safety threshold and the danger threshold, the system activates an avoidance strategy and actively reduces the stirring speed. This is not a blind adjustment, but a precise increase in the Kolmogorov time microscale by reducing the energy dissipation rate of the micro-vortex, thereby actively causing the system to deviate from the danger point of time scale resonance and mitigating the risk of collapse from a physical root. When the risk index is above the danger threshold, the system executes an interruption-recovery emergency procedure, decisively reducing the flocculant dosage and significantly reducing the stirring speed. This combined operation, on the one hand, prevents new reagent molecules from entering the high-shear environment and being ineffectively decomposed, and on the other hand, provides a safe recovery environment for the system. This targeted control logic based on a deep understanding of the physical process completely breaks the vicious cycle in traditional technologies where adding reagents worsens water quality.

[0036] 3. This technical solution possesses high adaptability and operational reliability. The physical model parameters in the risk assessment module are not based on general experience settings, but are determined by running a learning and calibration program during the initial system deployment phase and using optimization algorithms to fit experimental data collected on-site. Similarly, the safety and danger thresholds in the nonlinear decoupling control module are scientifically determined through statistical analysis of the risk index before flocculation collapse events in historical data. This data-driven personalized calibration method ensures that the entire early warning and control system can accurately match the physicochemical characteristics and equipment operating conditions of specific wastewater treatment scenarios, thereby exhibiting excellent robustness and reliability in complex and ever-changing actual operating environments, ultimately ensuring long-term stability of effluent quality and achieving economic efficiency in chemical dosing. Attached Figure Description

[0037] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0038] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0040] Example 1

[0041] Please see Figure 1 This invention provides an automatic dosing system for wastewater treatment purification materials, comprising:

[0042] The multi-dimensional state perception module is used to acquire key multi-physics parameters that affect the stability of the flocculation process in real time.

[0043] The risk assessment module is used to receive key multi-physics parameters output by the multi-dimensional state perception module and calculate the risk index characterizing the critical risk of collapse in the flocculation process based on a preset physical model.

[0044] The nonlinear decoupling control module receives the risk index calculated by the risk assessment module and generates control commands for the flocculant dosing rate and stirring speed based on the comparison results between the risk index and the preset safety threshold and danger threshold.

[0045] The execution module is used to adjust the flocculant dosing rate and stirring speed in response to the control commands generated by the nonlinear decoupling control module.

[0046] An automatic dosing system for wastewater treatment materials aims to address the problems of lagging flocculant dosing control strategies and inability to cope with critical collapse of the flocculation process in traditional wastewater treatment by constructing a risk warning and control closed loop based on physical mechanisms. In this embodiment, the system is constructed as a collaborative whole, including a multi-dimensional state perception module, a risk assessment module, a nonlinear decoupling control module, and an execution module. These four modules together constitute a complete technical link from real-time perception, risk quantification, intelligent decision-making to precise execution, thereby achieving proactive avoidance of the risk of flocculation process collapse.

[0047] The multi-dimensional state perception module aims to capture key parameters of multi-scale and multi-physical fields that reflect the stability of the flocculation process in real time and in situ, so as to provide accurate data input for subsequent risk assessment.

[0048] The risk assessment module is designed to process the real-time data stream input from the multi-dimensional state perception module and, through a built-in mathematical model based on the shear resonance physical mechanism, calculate in real time a risk index that can quantify the critical risk of system collapse.

[0049] The nonlinear decoupling control module receives the risk index output by the risk assessment module and, based on the comparison between the index and a preset threshold, executes a nonlinear control strategy centered on risk avoidance, independently generating control commands for the flocculant dosing rate and stirring speed.

[0050] The execution module is designed to precisely respond to and execute commands issued by the nonlinear decoupled control module. Through frequency conversion control of the precision metering pump and the agitator, it can adjust the flocculant dosing rate and the stirring speed.

[0051] Through the coordinated operation of the above modules, this system can assess in real time the risk of the flocculation process approaching the specific physical collapse point of time-scale resonance-shear failure, and execute a nonlinear, decoupled control strategy based on risk warning. The system actively avoids the working conditions that lead to a catastrophic decline in flocculation efficiency, blocks the fatal positive feedback that increases in reagents will worsen the water quality, and ensures the stability of the effluent water quality and the economy of reagent addition.

[0052] Example 2

[0053] Key parameters of multiphysics include:

[0054] Microcurrent density gradient intensity factor output by a high-frequency ultrasonic scattering sensor array deployed in the high-shear region of the stirring system;

[0055] Chemical interference coefficients output by an online three-dimensional fluorescence spectrometer;

[0056] The energy dissipation rate of the microvortex was calculated by measuring the turbulent energy dissipation rate spectrum within the reactor using an acoustic Doppler velocity profiler.

[0057] The acoustic coupling factor is output by piezoelectric accelerometers installed on the reactor wall and pump body;

[0058] Based on the system of Example 1, the multi-dimensional state perception module is further specified. The purpose of this module is to deconstruct the abstract flocculation process stability into a set of key multi-physics parameters that can be accurately measured. In this example, these key multi-physics parameters are acquired through a set of specific sensors and measurement units, and their composition is described below.

[0059] The microcurrent density gradient intensity factor, in this embodiment, is denoted as... , refers to the normalized signal output by a set of high-frequency ultrasonic scattering sensor arrays deployed in the high-shear region of the mixing system; the function of this parameter is to characterize the local density non-uniformity of the fluid caused by sudden changes in the influent water quality. This non-uniformity can induce additional local shear, which is an important disturbance source affecting the structural stability of flocs; the source is a non-negative dimensionless value obtained by real-time in-situ measurement by the sensor array and normalization processing.

[0060] The chemical interference coefficient, in this embodiment, is denoted as This refers to a dimensionless coefficient output by an online three-dimensional fluorescence spectrometer after analyzing the molecular morphology of dissolved organic matter in water. This parameter quantifies the dual interference effects of specific dissolved organic matter on the flocculation process: firstly, it alters the expansion dynamics of flocculant polymer chains in water; secondly, it affects the binding efficiency between the flocculant and the particle surface through steric hindrance competition. The source is a non-negative dimensionless value analyzed and output by the spectrometer in real time.

[0061] The energy dissipation rate of the microvortex, in this embodiment, is denoted as This refers to the result calculated by an acoustic Doppler velocity profiler by measuring the turbulent energy dissipation rate spectrum within a reactor and integrating it in the high wavenumber range. This parameter precisely quantifies the energy intensity of the tiny vortices within the reactor that can effectively shear and disrupt the flocculant polymer chains. It is derived from real-time measurements and calculations by the velocity profiler, and its physical dimensions are... ;

[0062] The acoustic coupling factor, in this embodiment, is denoted as . , refers to the equipment vibration signal monitored by multiple piezoelectric accelerometers installed on the reactor wall and pump body, and output as a factor after normalization; the function of this parameter is to characterize the external energy disturbance generated by equipment vibration on the flocculation process through the acoustic extensibility effect; its source is a non-negative dimensionless value obtained by real-time monitoring and normalization of the accelerometers.

[0063] By introducing the aforementioned key parameters of the multiphysics field, this system can capture the core physical factors affecting the stability of the flocculation process more comprehensively and deeply. This multi-dimensional data input provides a solid foundation for the accuracy and reliability of the subsequent risk assessment model, thereby improving the adaptability and early warning capability of the entire system to complex working conditions.

[0064] Its purpose is to deconstruct the abstract, potentially critically destructive stability of the flocculation process into a set of multiphysics key parameters that can be precisely measured.

[0065] The risk assessment module calculation includes the following steps:

[0066] Based on the energy dissipation rate of microvortices, the Kolmogorov time microscale is calculated.

[0067] Based on the chemical interference coefficient and the preset flocculant baseline stretching time, the functional stretching time of the polymer chain was calculated.

[0068] Based on the calculated Kolmogorov time microscale and the functional stretching time of the polymer chain, a core resonance factor characterizing the time-scale resonance relationship is generated;

[0069] By constructing a risk amplification coefficient and correcting the core resonance factor, the risk index is finally calculated.

[0070] The steps to calculate the risk index include:

[0071] A risk amplification factor was constructed, which included the microcurrent density gradient intensity factor, chemical interference coefficient, and acoustic coupling factor.

[0072] The core resonance factor is corrected using a risk amplification factor to generate the final risk index;

[0073] Based on the system in Example 1, the internal calculation logic of the risk assessment module is specified. The purpose of this module is to integrate the seemingly isolated physical parameters input by the multidimensional state perception module into a unified mathematical model that can reveal the inherent physical correlation and predict future risks. To further clarify, the calculation of the risk index includes the following steps.

[0074] Based on microvortex energy dissipation rate Calculate the Kolmogorov time microscale; the Kolmogorov time microscale is denoted as in this embodiment. This refers to the average lifetime of high-energy microvortices within the reactor that can effectively break down polymer chains; its function is to quantify the temporal characteristics of the physical destructive effect of the flow field on flocculant molecules; to calculate this parameter, the following formula is introduced:

[0075]

[0076] In this formula, The Kolmogorov timescale, with dimensions in s, is calculated in this step; Let be the kinematic viscosity of the fluid, with dimensions . ; The energy dissipation rate of the microvortex, with dimensions of Provided in real time by the multi-dimensional state perception module;

[0077] Based on chemical interference coefficient The polymer chain functional stretching time is calculated by comparing it with a preset flocculant baseline stretching time; the polymer chain functional stretching time is denoted as [missing information] in this embodiment. , refers to the characteristic time required for flocculant molecules to evolve from their coiled state upon injection to fully unfold and form a linear structure with bridging function; its purpose is to quantify the chemical kinetic time characteristics of the flocculant molecules themselves in performing their function; to calculate this parameter, the following linear approximation model is introduced:

[0078]

[0079] In this formula, The functional stretching time of the polymer chain, in seconds, is calculated in this step. The reference relaxation time of the flocculant in pure water is measured in seconds and is derived from the inherent material parameters pre-calibrated through offline experiments. It is a dimensionless sensitivity coefficient used to quantify the effect of dissolved organic matter on the stretching rate of polymer chains; The chemical interference coefficient is a dimensionless value, provided in real time by the multidimensional state perception module.

[0080] Based on the calculation and This generates a core resonance factor characterizing the resonance relationship over a time scale; its underlying logic lies in quantifying a specific physical phenomenon: the time it takes for the flocculant molecules to fully relax. Lifecycle of high-energy destructive microvortices When resonance matching occurs, i.e., the ratio approaches 1, the flocculation efficiency will catastrophically collapse; in this embodiment, the core resonance factor is constructed as a Gaussian function, which in... The maximum value is obtained at that time;

[0081] The core resonance factor can be expressed as: ,in It is a dimensionless sharpness coefficient used to control the width of the curve; when When the factor reaches its maximum value. ;

[0082] By combining the core resonance factor and other perturbation parameters, the final risk index is calculated. To ensure the model reflects the amplification effect of external perturbations on the total risk, this embodiment constructs a risk amplification coefficient; this coefficient includes the microcurrent density gradient intensity factor. Chemical interference coefficient and acoustic coupling factor This risk amplification factor is used to correct the core resonance factor to generate the final risk index R; the calculation formula is defined as follows:

[0083]

[0084] In this formula, R is the final risk index, which is a dimensionless value; These are their respective dimensionless weighting coefficients; It is a dimensionless parameter used to adjust the sharpness of the peak; These are the microcurrent density gradient intensity factor, chemical interference coefficient, and acoustic coupling factor, respectively. They are all normalized non-negative dimensionless values, and their sources are all provided in real time by the multi-dimensional state sensing module. : Polymer chain functional stretching time; Kolmogorov timescale; Exponential function ,in It is a natural constant;

[0085] The polymer chain functional stretching time model employs a linear approximation to rapidly estimate the impact of chemical interference under normal operating conditions. For extreme water quality conditions, such as extremely high concentrations of dissolved organic matter, a more complex nonlinear model can be used to more accurately describe its nonlinear influence on the flocculant molecule stretching dynamics.

[0086] To more accurately reflect the nonlinear synergistic effects among the factors, in some more complex implementation schemes, the risk amplification factor can be constructed as a nonlinear function, for example... ,in The cross-coupling coefficient is used to quantify the synergistic effect of fluid density inhomogeneity and chemical interference.

[0087] This model uses linear superposition to approximate the amplifying effect of external disturbances on risk. In practical applications, if significant nonlinear coupling effects are found among the various disturbance factors, a more complex nonlinear model can be considered to improve the model's accuracy.

[0088] Through the above steps, the risk assessment module transforms multi-source, heterogeneous sensor data into a single, clear risk index R using a semi-empirical model based on core physical mechanisms. This model not only quantifies the core intrinsic risk caused by the mismatch between flow field shear and reagent release time, but also logically and consistently integrates the amplification effects of multiple external risks such as water quality chemical interference, local density gradient disturbances, and equipment vibration. This comprehensive assessment method greatly improves the accuracy and comprehensiveness of risk warning.

[0089] Example 3

[0090] The nonlinear decoupling control module generates control commands in the following way:

[0091] When the risk index is below the safety threshold, the standard PID loop, which targets the effluent turbidity, is activated to adjust the flocculant dosing rate.

[0092] This PID loop uses the real-time reading of the effluent turbidity meter as the process variable PV and the preset target value of effluent turbidity as the set value SP. The control output CO, i.e. the adjustment amount of the flocculant dosing rate, is calculated through the PID algorithm. This loop is independent of risk assessment and avoidance strategies and only provides fine-tuning when the system is operating stably and there is no risk of collapse, so as to ensure the stability of effluent water quality and the economy of chemical dosing.

[0093] When the risk index is between the safe threshold and the dangerous threshold, an avoidance strategy is implemented, such as reducing the stirring speed.

[0094] When the risk index is higher than the danger threshold, execute the interruption-recovery emergency procedure, reduce the flocculant dosing rate to a preset low level and significantly reduce the stirring speed to a weak turbulence mode;

[0095] When the risk index drops below the safety threshold, or within the preset recovery time (e.g., 15 minutes), the system will gradually restore the flocculant dosage and stirring speed according to the preset smoothing curve until it returns to normal operation dominated by the PID control loop. During the recovery process, the system continuously monitors the effluent turbidity. If the turbidity increases significantly, the flocculant dosage will be slightly increased to maintain the effluent quality without triggering a higher risk.

[0096] The avoidance strategy involves reducing the stirring speed to decrease the energy dissipation rate of the microvortex, thereby increasing the Kolmogorov time microscale and causing the ratio of the polymer chain functional stretching time to the Kolmogorov time microscale to deviate from the resonance point.

[0097] The interruption-recovery emergency procedure reduces the flocculant dosing rate to prevent new agent molecules from entering the high-shear zone and being ineffectively decomposed; and significantly reduces the stirring speed to provide a safe recovery environment for the system.

[0098] Based on the system in Example 1, the method for generating control commands by the nonlinear decoupling control module is specified. The purpose of this module is to abandon the lag logic of traditional control systems that track setpoints, and instead adopt a proactive, risk-avoiding control logic. In this example, the generation of control commands is determined based on a comparison between the risk index R and two preset thresholds. The safety threshold is denoted as [missing information] in this example. The danger threshold, in this embodiment, is denoted as... ;

[0099] When the risk index R is below the safety threshold When the system is in a safe operating range, the module activates a standard PID control loop targeting the effluent turbidity to make routine fine adjustments to the flocculant dosing rate. This control method ensures that the best effluent quality can be maintained in the most economical way when the system is stable.

[0100] When the risk index R is at a safe threshold With danger threshold When the system is in a certain state, it is determined to have entered a warning zone; in this state, the module executes an avoidance strategy, the core action of which is to reduce the stirring speed. ;

[0101] When the risk index R is at a safe threshold With danger threshold During this period, the decrease in stirring speed is linearly related to the increase in the risk index, and the control command is:

[0102]

[0103] in, To reduce the stirring speed beforehand, k is an adjustable proportional coefficient, with a value ranging from 0.1 to 0.3; R: Reduced stirring speed, which is the target value of the control command; Risk index; Safety threshold; Danger threshold;

[0104] When the risk index R is higher than the danger threshold At the same time, reduce the flocculant dosage rate. Reduce the stirring speed to below 5% of the original setting and decrease the stirring speed. Reduce to a weakly turbulent mode, i.e., maintain the Reynolds number. Less than 2000 to minimize shear force;

[0105] The physical significance of this is that the stirring speed... The reduction will directly lead to the energy dissipation rate of micro vortices The decrease; according to the aforementioned formula , The reduction will cause the Kolmogorov time microscale The increase in [the value of] ... With Kolmogorov's time microscale The ratio deviates from the most dangerous resonance point (i.e., when the ratio is 1), thereby reducing the core resonance factor of the risk index R and effectively reducing the risk of system collapse; this strategy actively adjusts the flow field environment to match the kinetic characteristics of the agent, which is a highly efficient and low-cost risk avoidance method.

[0106] When the risk index R is higher than the danger threshold When the system is deemed to have entered a danger zone, the module executes the interrupt-recovery emergency procedure, which includes two parallel core actions:

[0107] Reduce flocculant dosage rate The direct purpose of this measure is to immediately prevent new drug molecules from entering the high-shear, highly destructive flow field environment and being ineffectively decomposed, thereby avoiding waste of the drug and further deterioration of the water quality.

[0108] Significantly reduce stirring speed Weakest turbulence mode; this approach aims to maximize the Kolmogorov time microscale. This provides a physically safe, low-shear recovery environment for existing but not yet completely ineffective flocs and agent molecules in the system, giving them the opportunity to reform an effective floc structure.

[0109] During the model calibration process, special attention should be paid to the collection of data under extreme conditions, such as high or low shear, high chemical interference, etc., to ensure that the model’s behavior under these boundary conditions conforms to physical laws. After calibration, an independent robustness test will be conducted to evaluate the model’s performance when the input parameters are close to 0 or at their maximum values, in order to verify its reliability over the entire operating range.

[0110] Through this three-stage nonlinear control logic, the system achieves a shift from passive response to active intervention. This control strategy can not only accurately respond to different levels of risk, but more importantly, each step of the operation is supported by a clear physical mechanism, ensuring the rationality and effectiveness of the control behavior. Thus, while ensuring the quality of the effluent, it achieves robust control of the stability of the flocculation process.

[0111] The model parameters of the physical model preset in the risk assessment module are determined by running a learning and calibration program in the early stage of system deployment and using optimization algorithms to fit the collected experimental data.

[0112] Based on the system in Example 1, the source of the model parameters for the preset physical model in the risk assessment module is specified; these model parameters include weighting coefficients. resonance peak sharpness coefficient and sensitivity coefficient The determination of these values ​​directly affects the accuracy of the risk assessment model. In this embodiment, these parameters are not based on empirical settings, but are determined by running a learning and calibration program during the initial deployment of the system. The program works as follows: during a calibration period, the system synchronously collects a dataset for calibration. This dataset contains two types of variables: one is the vector of operating condition parameters used as input. It is measured by the multi-dimensional state perception module under different operating conditions. The other category consists of corresponding performance measurements that characterize the flocculation effect. For example, effluent turbidity; using optimization algorithms in the field of system identification, by minimizing the vector based on operating condition parameters. The calculated risk index R and the actual performance observation The prediction error between the two is fitted to a large amount of experimental data to solve for a set of optimal model parameters;

[0113] These parameters were determined using a genetic algorithm-based optimization method to minimize the mean square error between the predicted risk index and the actual performance observations. The objective function for optimization is:

[0114]

[0115] Where N is the number of samples in the calibration dataset. Let be the calculated risk index for the i-th sample. For the performance observation of the effluent turbidity or flocculation efficiency corresponding to the i-th sample, this process needs to cover more than 500 sets of experimental data collected under various working conditions such as sudden changes in influent water quality, changes in stirring speed and fluctuations in flocculant dosage. : Optimize the objective function, whose value represents the prediction error; Model parameters, which need to be determined through optimization; i: sample index, from 1 to N; The operating parameter vector of the i-th sample corresponds to the micro-current density gradient intensity factor, chemical interference coefficient, acoustic coupling factor, and micro-vortex energy dissipation rate, respectively.

[0116] Through actual calibration, the typical parameter value range was found to be: Between 0.5 and 0.8 Between 0.8 and 1.2 Between 0.1 and 0.3, Between 2 and 5, Between 0.2 and 0.6;

[0117] This method of determining model parameters enables the model to have adaptive and self-learning capabilities; it ensures that the model can accurately reflect the unique physicochemical properties of a specific wastewater treatment scenario; and the data-driven approach to personalized model calibration greatly improves the accuracy and reliability of risk assessment, which is the technical prerequisite for the successful implementation of risk warning and control by this system.

[0118] The preset safety threshold and danger threshold in the nonlinear decoupling control module are determined by statistical analysis of the risk index before flocculation collapse events in historical data;

[0119] Based on the system in Example 1, the preset safety threshold in the nonlinear decoupling control module is adjusted. With danger threshold The source of these two thresholds is specified; the setting of these two thresholds is crucial for switching control strategies, and their rationality directly affects the sensitivity and reliability of the system's early warning. In this embodiment, the determination of these two thresholds is not based on human experience, but rather on statistical analysis of historical data accumulated by the system during the learning and calibration phase. By retrospectively analyzing the time points in historical data where flocculation collapse events have been clearly recorded, the time series data of the risk index R calculated by the risk assessment module within a specific time window before these events occur are extracted; hazard thresholds The statistical high quantile of these pre-failure risk index sequence datasets is set, for example, the 95th percentile.

[0120] Statistical analysis of historical data revealed a typical danger threshold. Set at Around, and the safety threshold Then set accordingly in 40% of about;

[0121] The technical principle behind this setting is to select a sufficiently high threshold to ensure that the most urgent control procedure is triggered only when the system state deviates significantly from normal and is highly likely to collapse in a short period of time, thereby avoiding overreaction of the control system.

[0122] It is worth noting that this model primarily focuses on the risk arising from the mismatch between flow field shear and reagent release time, considering it as the core intrinsic risk of flocculation collapse. Although this system also integrates other external perturbation factors, it still cannot exhaust all the complex factors that may lead to the collapse of the flocculation process.

[0123] Danger threshold The selection is based on the probability density distribution curve of the pre-failure risk index sequence; the 95th quantile is chosen to ensure that the probability of system crashing is at least 95% when emergency procedures are triggered; safety threshold. Set as The 40% figure is based on considerations of system response time. Through offline simulation and historical data backtesting analysis, it was determined that this 40% proportion can influence the risk index from... Climb to Within a given timeframe, allow the system a 15-30 minute reaction window to effectively execute avoidance strategies and thus prevent it from entering the danger zone;

[0124] When the risk index drops below the safety threshold, or within the preset recovery time, the system will gradually restore the flocculant dosage and stirring speed according to the preset smoothing curve, until it returns to the normal operation state dominated by the PID control loop.

[0125] Based on the established danger threshold Safety threshold It is then set to A predetermined proportion, such as 40%; the technical principle behind this setting is to maintain a sufficiently wide warning range below the danger threshold, thereby providing the system with sufficient lead time and reaction time to implement avoidance strategies and achieve early intervention in risks;

[0126] During this process, the system continuously monitors the turbidity of the effluent. If the turbidity increases significantly, the flocculant dosage is slightly increased to maintain the effluent quality without triggering a higher risk.

[0127] Statistical analysis of historical data revealed a typical danger threshold. Set at Around, and the safety threshold Then set accordingly in 40% of about;

[0128] This threshold determination method based on historical data statistical analysis ensures the objectivity and scientific nature of threshold setting; it closely links the timing of control strategy switching with the actual probability of system collapse, thereby achieving an effective balance between the sensitivity of early warning and the stability of control, and improving the intelligence level and operational reliability of the entire automatic dosing system.

[0129] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An automatic water purification material dosing system for wastewater treatment, characterized in that, include: The multi-dimensional state perception module is used to acquire key multi-physics parameters that affect the stability of the flocculation process in real time. The risk assessment module is used to receive key multi-physics parameters output by the multi-dimensional state perception module and calculate the risk index characterizing the critical risk of collapse in the flocculation process based on a preset physical model. The nonlinear decoupling control module receives the risk index calculated by the risk assessment module and generates control commands for the flocculant dosing rate and stirring speed based on the comparison results between the risk index and the preset safety threshold and danger threshold. The execution module is used to adjust the flocculant dosing rate and stirring speed in response to the control commands generated by the nonlinear decoupling control module. The key parameters of the multiphysics field include: Microcurrent density gradient intensity factor output by a high-frequency ultrasonic scattering sensor array deployed in the high-shear region of the stirring system; Chemical interference coefficients output by an online three-dimensional fluorescence spectrometer; The energy dissipation rate of the microvortex was calculated by measuring the turbulent energy dissipation rate spectrum within the reactor using an acoustic Doppler velocity profiler. Acoustic coupling factor output by piezoelectric accelerometers installed on the reactor wall and pump body; The risk assessment module's calculations include the following steps: Calculate the Kolmogorov time microscale based on the energy dissipation rate of the microvortex. ; To calculate this parameter, the following formula is introduced: ； In this formula, The Kolmogorov timescale, with dimensions in s, is calculated in this step; Let be the kinematic viscosity of the fluid, with dimensions . ; The microvortex energy dissipation rate refers to the result calculated by an acoustic Doppler velocity profiler by measuring the turbulent energy dissipation rate spectrum within a reactor and integrating it in the high wavenumber region. Its dimensions are... Provided in real time by the multi-dimensional state perception module; Based on the chemical interference coefficient and the preset flocculant baseline stretching time, the functional stretching time of the polymer chain was calculated. Based on the calculated Kolmogorov time microscale and the functional stretching time of the polymer chain, a core resonance factor characterizing the time-scale resonance relationship is generated; By constructing a risk amplification coefficient and correcting the core resonance factor, the risk index is finally calculated. The steps for calculating the risk index include: Construct a risk amplification factor that includes the microcurrent density gradient intensity factor, chemical interference coefficient, and acoustic coupling factor; microcurrent density gradient intensity factor This refers to the normalized signal output by an array of high-frequency ultrasonic scattering sensors deployed in the high-shear region of the stirring system; chemical interference coefficient. This refers to the dimensionless coefficient output by an online three-dimensional fluorescence spectrometer after analyzing the molecular morphology of dissolved organic matter in water; acoustic coupling factor. This refers to the equipment vibration signals monitored by multiple piezoelectric accelerometers installed on the reactor wall and pump body, and the output factor after normalization. The core resonance factor is corrected using a risk amplification factor to generate the final risk index; The calculation formula is defined as follows: ； In this formula, The final risk index is a dimensionless value. These are their respective dimensionless weighting coefficients; It is a dimensionless parameter used to adjust the sharpness of the peak; These are the microcurrent density gradient intensity factor, chemical interference coefficient, and acoustic coupling factor, respectively. They are all normalized non-negative dimensionless values, and their sources are all provided in real time by the multi-dimensional state sensing module. : Polymer chain functional stretching time; Kolmogorov timescale; Exponential function ,in It is a natural constant.

2. The automatic dosing system for wastewater treatment materials according to claim 1, characterized in that, The nonlinear decoupling control module generates control commands in the following way: When the risk index is below the safety threshold, the standard PID loop, which targets the effluent turbidity, is activated to adjust the flocculant dosing rate. When the risk index is between the safe threshold and the dangerous threshold, an avoidance strategy is implemented, such as reducing the stirring speed. When the risk index is higher than the danger threshold, the interruption-recovery emergency procedure is executed, reducing the flocculant dosing rate to a preset low level and significantly reducing the stirring speed to a weak turbulence mode.

3. The automatic dosing system for wastewater treatment materials according to claim 2, characterized in that, The avoidance strategy involves reducing the stirring speed to decrease the energy dissipation rate of the microvortex, thereby increasing the Kolmogorov time microscale and causing the ratio of the polymer chain functional stretching time to the Kolmogorov time microscale to deviate from the resonance point.

4. The automatic dosing system for wastewater treatment materials according to claim 3, characterized in that, The interruption-recovery emergency procedure reduces the flocculant dosing rate to prevent new agent molecules from entering the high-shear zone and being ineffectively decomposed; and significantly reduces the stirring speed to provide a safe recovery environment for the system.

5. The automatic dosing system for wastewater treatment materials according to claim 1, characterized in that, The model parameters of the physical model preset in the risk assessment module are determined by running a learning and calibration program in the early stage of system deployment and using an optimization algorithm to fit the collected experimental data.

6. The automatic dosing system for wastewater treatment materials according to claim 1, characterized in that, The preset safety threshold and danger threshold in the nonlinear decoupling control module are determined by statistical analysis of the risk index before flocculation collapse events in historical data.

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