Model simulation processing method for intelligent sewage treatment system

Abstract

The invention relates to a model simulation processing method for an intelligent sewage treatment system, and belongs to the technical field of intelligent sewage treatment and process control, and the method specifically comprises the following steps: S1, collecting system state data in real time; the system state data comprises an instant toxicity load index, a granular sludge physical property index and a transmembrane pressure difference; s2, performing normalization processing and linear weighting operation on the basis of the physical characteristic indexes of the granular sludge to construct an integrity index of the granular sludge; s3, inputting the granular sludge integrity index and the instant toxicity load index into a preset dynamic evolution prediction model to generate an integrity index prediction value of the next control period; and inputting the integrity index prediction value into the preset nonlinear correlation prediction model to generate the transmembrane pressure difference increment of the next control period. According to the method, a solid data basis is provided for subsequent accurate prediction, and the accuracy and depth of system state sensing are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent sewage treatment and process control, in particular to a model simulation processing method for an intelligent sewage treatment system. BACKGROUND

[0002] In the intelligent sewage treatment, especially in the system using granular sludge and membrane separation technology, the traditional control method mainly relies on the monitoring of the transmembrane pressure difference and the effluent water quality. These data can only reflect the problems that have already occurred, and the health status of the core biological unit, granular sludge, is not sensitive enough to provide early warning. This situation makes it difficult for managers to find and identify the root cause before the system is unstable, such as water toxicity shock or excessive internal operating stress, thereby affecting the timeliness and effectiveness of intervention measures; in addition, the traditional method lacks the ability to predict the future state of the system, and cannot provide a forward-looking scientific basis for operation decisions.

[0003] The above situation and deficiencies are mainly due to the lag of monitoring indicators and the limitations of data processing technology. The traditional control strategy usually takes response measures after significant membrane fouling is monitored, i.e., the transmembrane pressure difference rises sharply, at which time the structure of the granular sludge may have been destroyed and the system stability has been severely deteriorated; when the system encounters toxicity shock or internal stress accumulation, managers cannot predict the risk in advance and take preventive measures, often leading to delayed response, and even causing serious consequences such as decreased treatment efficiency, permanent damage to membrane components, and even system collapse.

[0004] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide a model simulation processing method for an intelligent sewage treatment system to solve the problems raised in the background art.

[0006] The technical solution of the present application is as follows: S1, real-time acquisition of system state data; the system state data includes instant toxicity load indicators, granular sludge physical property indicators and transmembrane pressure difference; S2, normalization processing and linear weighting operation based on the granular sludge physical property indicators to construct a granular sludge integrity index; S3, inputting the granular sludge integrity index and the instant toxicity load indicators into a preset dynamic evolution prediction model to generate a predicted value of the integrity index in the next control cycle; and inputting the predicted value of the integrity index into a preset nonlinear correlation prediction model to generate a transmembrane pressure difference increment in the next control cycle; S4, based on the integrity index prediction value and the transmembrane pressure difference increment, regulating the aeration shear force and the acoustic standing wave field cleaning intensity by a preset control decision rule.

[0007] Preferably, the granular sludge physical property indicators include average particle size, sludge settling performance and free extracellular polymer concentration.

[0008] Preferably, S2 includes: The average particle size, sludge settling performance and free extracellular polymer concentration are normalized according to a preset reference value to obtain dimensionless indicators; Based on the dimensionless indicators, the granular sludge integrity index is calculated by a linear weighted model determined by historical data multivariate regression analysis.

[0009] Preferably, in S3, the granular sludge integrity index, the real-time toxicity load indicator and the current aeration shear force as a control variable are collectively input as external inputs into a dynamic evolution prediction model for operation to generate the integrity index prediction value of the next control period.

[0010] Preferably, S3 further includes: the generated integrity index prediction value and the current acoustic standing wave field cleaning intensity as a control variable are collectively input as external inputs into a nonlinear correlation prediction model for operation to generate the transmembrane pressure difference increment of the next control period.

[0011] Preferably, the execution of the control decision rule depends on a preset toxicity warning threshold, a granular stability warning threshold and a maximum operating pressure difference.

[0012] Preferably, S4 includes: determining whether the real-time collected real-time toxicity load indicator exceeds the toxicity warning threshold or the generated integrity index prediction value is lower than the granular stability warning threshold; If yes, the aeration shear force is regulated to a preset minimum protection value, and the acoustic standing wave field cleaning intensity is set to a preventive cleaning intensity.

[0013] Preferably, S4 further includes: If the real-time collected real-time toxicity load indicator does not exceed the toxicity warning threshold and the generated integrity index prediction value is not lower than the granular stability warning threshold, the aeration shear force is maintained at a normal operating value, and the acoustic standing wave field cleaning intensity is set to a zero-power standby state.

[0014] Preferably, S4 further includes: The real-time collected transmembrane pressure difference and the generated transmembrane pressure difference increment are summed to obtain the pressure difference prediction value at the next time; If it is determined that the differential pressure prediction value of the next moment exceeds the highest operating differential pressure, the aeration shear force is forcibly locked at the lowest protection value, and the cleaning intensity of the acoustic standing wave field is increased to the highest corrective cleaning intensity.

[0015] The present application provides a model simulation processing method for a smart sewage treatment system by improvement, which has the following improvements and advantages compared with the prior art: 1. By collecting system state data in real time, a comprehensive granular sludge integrity index is constructed; the index scientifically integrates multiple key physical characteristic indicators such as average particle size, sludge settling performance, and free extracellular polymer concentration; compared with traditional methods that rely on a single, isolated parameter for judgment, this composite index constructed through normalization processing and linear weighting operation can more comprehensively and sensitively represent the macrostructure stability and true health status of granular sludge, providing a solid data foundation for subsequent accurate prediction, and significantly improving the accuracy and depth of system state perception; 2. A logically progressive double-model series prediction system is established, realizing a technical leap from state perception to risk prediction; the dynamic evolution prediction model not only considers immediate toxic load, an external chemical shock, but also innovatively includes aeration shear force, an internal physical operating variable, into the input, so as to more accurately predict the future trend of the granular sludge integrity index under the combined stress of chemistry and physics; the nonlinear correlation prediction model further calculates the transmembrane pressure difference increment caused by sludge state changes based on the predicted value of the integrity index; this series prediction mechanism establishes a complete causal prediction chain from disturbance and control to the state of the core biological unit and the efficiency of the physical separation unit, enabling the system to foresee future risks and changing the passive situation of traditional technology lagging response; 3. Based on the forward-looking prediction results, the present technical solution realizes closed-loop regulation and control of aeration shear force and the cleaning intensity of the acoustic standing wave field through pre-set control decision rules, achieving a proactive intervention effect; the control decision logic is not a simple threshold switch, but a differentiated control strategy executed in layers based on the toxic early warning threshold, the granular stability warning threshold, and the highest operating differential pressure; when toxic shock or granular instability risk is predicted, the system actively enters the protection mode, reduces physical damage, and applies preventive cleaning, effectively avoiding vicious cycles of system state; when the system is in good health, it switches to the regular operation mode for high efficiency and energy saving; more importantly, when the transmembrane pressure difference is predicted to break through the physical safety upper limit, the system will forcibly execute the emergency cleaning mode to prioritize hardware protection; this multi-modal, forward-looking control strategy based on risk prediction not only effectively resolves system instability risks, but also considers operation economy and hardware safety, significantly improving the operation stability, reliability, and intelligence level of the entire smart sewage treatment system. BRIEF DESCRIPTION OF DRAWINGS

[0016] The application will be further explained in connection with the accompanying drawings and embodiments: Figure 1 is a flow chart of a model simulation processing method for a smart sewage treatment system. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions and advantages of the application clearer, the application will be further described in detail below with reference to specific embodiments.

[0018] Embodiment 1 Please refer to Figure 1 The application provides a model simulation processing method for a smart sewage treatment system, and the specific steps include: S1, collecting system state data in real time; the system state data includes an instantaneous toxicity load index, a granular sludge physical property index and a transmembrane pressure difference; S2, performing normalization processing and linear weighting operation based on the granular sludge physical property index to construct a granular sludge integrity index; S3, inputting the granular sludge integrity index and the instantaneous toxicity load index into a preset dynamic evolution prediction model to generate an integrity index prediction value of the next control period; and inputting the integrity index prediction value into a preset nonlinear correlation prediction model to generate a transmembrane pressure difference increment of the next control period; S4, based on the integrity index prediction value and the transmembrane pressure difference increment, regulating and controlling aeration shear force and acoustic standing wave field cleaning intensity through a preset control decision rule; The embodiment discloses a model simulation processing method for a smart sewage treatment system, and the technical solution is that the steps are sequentially executed. The method starts from step S1 to collect system state data in real time; the system state data refers to a key parameter set that can comprehensively represent the current operating condition of the sewage treatment system, and the role is to provide real-time and accurate data input for subsequent model prediction and control decision; in the embodiment, the system state data specifically includes: an instantaneous toxicity load index, which is an index representing the comprehensive concentration of substances in the influent water quality that have inhibitory or toxic effects on microbial activity; a granular sludge physical property index, which is a group of physical quantities used to macroscopically evaluate the structural stability and health condition of the granular sludge; and a transmembrane pressure difference, which is a direct physical quantity representing the degree of pollution of the membrane assembly; the sources of these data are obtained by continuously or high-frequency measurement through online sensing devices deployed at key nodes of the sewage treatment process, such as online total organic carbon analyzers, fluorescence spectrum analyzers, particle size analyzers and pressure sensors, etc. In the embodiment, the instantaneous toxicity load index The quantification calculation is performed by normalizing and linearly weighting the measured values of the online sensors, with the formula as follows: ; wherein, is the instantaneous toxicity load index at time t; and are the real-time measurement values of the online total organic carbon analyzer and the fluorescence spectrum analyzer at time t, respectively; and are the statistical average values of the corresponding measurement values obtained by long-term operation of the system under normal and low load conditions, serving as reference benchmark values; and are the respective weight coefficients. These weight coefficients are determined offline by analyzing historical data, combining different and correlation analysis with subsequent system processing efficiency decline or granular sludge integrity index deterioration events, aiming to make the calculated value most sensitive to the comprehensive toxicity impact intensity on microbial activity; On the basis of collecting system state data, step S2 is performed to perform normalization processing and linear weighting operation based on the collected granular sludge physical property indexes to construct a granular sludge integrity index. The granular sludge integrity index refers to a dimensionless processed composite index for comprehensively evaluating the macrostructure integrity and stability of the granular sludge, and its purpose is to convert multiple independent indexes of different physical dimensions into a unified evaluation benchmark that can be used for dynamic model prediction; Step S3 is performed to input the granular sludge integrity index constructed in the previous step and the real-time collected instantaneous toxicity load index into a preset dynamic evolution prediction model. The preset dynamic evolution prediction model refers to a mathematical model that can describe the change of the granular sludge integrity index over time under the combined action of external disturbance and internal stress, and its function is to predict the system health status in the next control period based on the current state. The model generates a predicted value of the integrity index in the next control period through calculation, and inputs this predicted value of the integrity index into a preset nonlinear correlation prediction model. The preset nonlinear correlation prediction model refers to a mathematical model that can quantify the nonlinear relationship between the granular sludge health status and the membrane pollution rate, and its function is to predict the change in the degree of membrane pollution caused by the change in the granular sludge state. The model generates an increase in transmembrane pressure difference in the next control period through calculation. The preset properties of the above model refer to the model structure and key parameters that are determined in advance by performing system identification and offline calibration on the historical operation data of a specific wastewater treatment system under multiple conditions, so as to ensure the applicability and accuracy of the model; Performing step S4, based on the integrity index predicted value generated in the last step and the transmembrane pressure difference increment, through a set of preset control decision rules, the aeration shear force and the acoustic standing wave field cleaning intensity in the system are dynamically regulated; The preset control decision rule refers to a set of logical judgment and execution instruction set formulated according to the system safety operation boundary and the predictive risk assessment result, the purpose is to convert the prediction result of the model into specific preventive or corrective control action that can avoid risks; The technical scheme establishes a complete technical link from state perception to risk prediction to active intervention through real-time data acquisition, construction of comprehensive health index, double model series prediction and closed-loop control based on the prediction result; The technical effect is that the risk of granular sludge instability caused by water toxicity shock or internal operation stress can be identified in advance, and preventive measures can be taken before membrane pollution significantly worsens, thereby significantly improving the operation stability, reliability and treatment efficiency of the whole intelligent sewage treatment system, and avoiding the system collapse caused by lag response in traditional methods.

[0019] The physical property indexes of granular sludge include average particle size, sludge settling performance and free extracellular polymer concentration; In this embodiment, the composition of the physical property indexes of granular sludge is limited; The physical property indexes of granular sludge are defined to include average particle size, sludge settling performance and free extracellular polymer concentration; The average particle size is the core index for evaluating the maturity and compactness of granular sludge; The sludge settling performance reflects the solid-liquid separation efficiency of the sludge, which is obtained by measuring the sludge settling rate in this embodiment; The free extracellular polymer concentration reveals the dissolution degree of the surface biopolymer of granular sludge, which is a direct precursor of the disintegration of granular structure; The gain technical effect brought by the technical scheme is that by selecting the three macroscopic physical indexes which are recognized as the most representative in the theory of microbial polymer stability, the constructed granular sludge integrity index can more accurately and comprehensively reflect the real health status of the granular sludge; Compared with a single index, this multi-dimensional index system is more sensitive and reliable for early warning of system instability, providing a solid foundation for the accuracy of the subsequent prediction model.

[0020] S2 includes: The average particle size, sludge settling performance and free extracellular polymer concentration are normalized according to the preset reference value to obtain dimensionless indexes; Based on the dimensionless indexes, the granular sludge integrity index is calculated through a linear weighting model determined by historical data multiple regression analysis; In this embodiment, the way of constructing the granular sludge integrity index in step S2 is described in detail; In the specific implementation of this step, the three physical characteristic indexes of average particle size, sludge settling performance and free extracellular polymer concentration are normalized according to their respective preset reference values; the reference value refers to the statistical average value of each physical characteristic index when the system is stably running under long-term excellent working conditions, and is determined by statistical analysis of a large number of historical excellent working condition data; the purpose of normalization processing is to eliminate the dimensional differences between different indexes to obtain dimensionless indexes, and the calculation method is as follows: and ; wherein, are the normalized average particle size, sludge settling performance and free extracellular polymer concentration indexes at time t respectively; is the real-time measurement value at the corresponding time obtained by the online sensing device; is the respective corresponding reference value; After normalization is completed, based on the above dimensionless indexes, the granular sludge integrity index is calculated through a linear weighted model determined by historical data multivariate regression analysis; the linear weighted model aims to maximize the correlation between the comprehensive index and the system instability probability, and the preset refers to that the weight coefficients in the model are determined offline; the form of the model is as follows: ; wherein, is the granular sludge integrity index at time t; are the weight coefficients corresponding to the three dimensionless indexes respectively, and the physical meaning is the contribution of each index to the sludge integrity; the source of these weight coefficients is the multivariate regression analysis of the complete process data from stable to unstable in the system historical database, so as to ensure that the calculated value can most sensitively reflect the change trend of system stability; The gain technical effect brought by the technical solution is that a standardized and quantifiable granular sludge integrity index construction method is provided; through normalization processing and a weighted model based on data driving, this method not only scientifically integrates multi-source information, but also ensures that the finally generated integrity index has clear physical meaning and high instability indication, greatly improving the accuracy and reliability of subsequent dynamic prediction.

[0021] In S3, the granular sludge integrity index, the instantaneous toxicity load index and the current aeration shear force as a control variable are jointly used as external inputs, and are substituted into the dynamic evolution prediction model to generate the integrity index prediction value of the next control period; To improve the prediction accuracy, in the embodiment, the dynamic evolution prediction model is optimized, and in addition to the particle sludge integrity index and the instantaneous toxicity load index, the current aeration shear force as a control variable is further included; the current aeration shear force refers to the physical shear stress generated by the aeration equipment on the particle sludge, which is normalized as a dimensionless control variable representing the ratio of the actual output power to the maximum power, and is derived from the aeration equipment operation parameters recorded by the control system in real time; the purpose of introducing it into the model is to quantify the physical damage effect of the operation itself on the particle sludge structure; The improved dynamic evolution prediction model is used for predicting the integrity index at the next control period t+1 under the combined action of the current toxicity load and the aeration shear force The mathematical form is constructed as: In this model, is the predicted value of the integrity index at the next time, is the current time integrity index calculated by step S2, is the instantaneous toxicity load collected by the online sensing device in real time, is the aeration shear force in the current control period, is the toxicity shock sensitivity coefficient, is the shear force damage coefficient, is the natural recovery coefficient of the system, is the theoretical optimal integrity index; the first term on the right side of the formula describes the integrity index decay process caused by the combined action of the toxicity load and the aeration shear force in the control period; the second term describes the biological repair process of the system itself; the model parameters are obtained by collecting the operation data of a specific system under multiple working conditions and using system identification algorithm for offline calibration; : exponential function; The technical effect brought by the technical scheme is that the dynamic evolution prediction model can consider both chemical shock, toxicity and physical damage, and shear force, two main stress sources; this makes the prediction result of the integrity index more close to the physical reality, and can more accurately simulate the comprehensive influence of different operation strategies on the health status of the particle sludge, thereby providing a model basis for realizing fine and low-damage aeration control.

[0022] S3 further includes: taking the generated integrity index prediction value and the current acoustic standing wave field cleaning intensity as control variables as external inputs, and substituting them into the nonlinear correlation prediction model to generate the transmembrane pressure difference increment of the next control period; ​​The input variables of the nonlinear correlation prediction model are supplemented, and the synergy with the dynamic evolution prediction model is described. The integrity index prediction value generated by the dynamic evolution prediction model and the current acoustic standing wave field cleaning intensity as a control variable are jointly used as external inputs and substituted into the nonlinear correlation prediction model for operation. The current acoustic standing wave field cleaning intensity refers to the energy intensity applied by the acoustic field generator for cleaning membrane pollution, which is also normalized as a dimensionless control variable representing the ratio of actual output power to maximum power, and the source is the real-time recorded operating parameters of the acoustic field generator by the control system. The improved nonlinear correlation prediction model is used to calculate the increment of transmembrane pressure difference in the next control period , which is mathematically constructed as: ; In this model, is the transmembrane pressure difference increment prediction value at the next time, is the integrity index prediction value output by the dynamic evolution prediction model, is the acoustic field cleaning intensity in the current control period, is the pollution conversion coefficient, is the acoustic field cleaning efficiency coefficient, is the theoretical optimal integrity index; the first term on the right side of the formula represents the membrane pollution effect caused by the disintegration of granular sludge, and the fractional structure reflects the nonlinear characteristic that the membrane pollution rate increases sharply when the sludge state deteriorates; the second term represents the pressure difference reduction effect brought by the active application of acoustic field cleaning; to ensure dimensional consistency, the units of model parameters are all in pressure units, and the values are obtained by experimental calibration; among them, the pollution conversion coefficient represents the intensity of membrane pollution effect caused by the deterioration of granular sludge integrity, and the acoustic field cleaning efficiency coefficient represents the maximum pressure difference reduction amount that can be achieved by the maximum acoustic field intensity; A logically progressive series prediction system is constructed; the dynamic evolution model first predicts the control actions, the effects of aeration and external disturbance, and toxicity on the sludge health state, and then the nonlinear correlation model further calculates the net effect of acoustic field cleaning on membrane pollution based on the prediction results of the state. The technical effect of this synergy is that the system obtains the ability to simulate the control-state-target complete causal chain, thereby constituting the technical core of model predictive control, greatly improving the accuracy and foresight of decision-making.

[0023] Example 2 The execution of the control decision rule depends on the preset toxicity warning threshold, granular stability warning threshold, and maximum operating pressure difference. In the present embodiment, key threshold parameters relied on by the execution of the control decision rule are defined; the setting of these thresholds provides explicit, quantifiable boundaries for the triggering of the control logic; including: Toxicity warning threshold: a normalized toxicity load reference value; its role is to define whether the incoming water toxicity has reached a level sufficient to have a significant impact on the system; the value is based on statistical analysis of historical operation data and the safety operating boundary set by process experts' experience, once the real-time collected instantaneous toxicity load index exceeds this threshold, it indicates that the system has encountered a toxicity impact event that needs to be responded immediately; Particle stability warning threshold: a lower limit value of the particle sludge integrity index; its role is to define the critical point at which the particle sludge is about to enter an unstable state; the determination method is based on historical data analysis, and the inflection point at which the probability of system instability increases sharply when the integrity index is below this value is calibrated; when the model predicts that the next cycle integrity index is below this threshold, it indicates that the health of the particle sludge is about to deteriorate to an unacceptable level; Maximum operating pressure difference: a constant safety upper limit set according to the physical bearing capacity of the membrane module and the process safety regulations; its role is to provide an absolute, non-crossable physical protection boundary for the operating range of the transmembrane pressure difference; the source is the product specification provided by the membrane module supplier and the related safety operation regulations, exceeding this pressure difference will likely cause irreversible physical damage to the membrane; The technical effect brought by the present technical solution is that by pre-setting these key thresholds, the complex system operation state judgment is converted into clear logical condition comparison; this makes the execution of the control decision rule standardized and automated, avoiding the subjectivity and uncertainty of human judgment, ensuring that the control system can make timely, reliable and safety-first response under any operating condition, significantly enhancing the robustness of the system.

[0024] S4 includes: determining whether the real-time collected instantaneous toxicity load index exceeds the toxicity warning threshold, or the generated integrity index prediction value is lower than the particle stability warning threshold; If so, adjust the aeration shear force to the pre-set minimum protection value, and set the sound-controlled standing wave field cleaning intensity to the preventive cleaning intensity; In the present embodiment, one execution logic of the control decision rule in step S4, i.e. the pre-emptive protection mode, is described; The triggering judgment condition of this mode is: whether the real-time collected instantaneous toxicity load index exceeds the pre-set toxicity warning threshold, or the integrity index prediction value generated by the dynamic evolution prediction model is lower than the pre-set particle stability warning threshold; one of the two conditions is met, which triggers the protection mechanism; If the triggering condition is met, the system immediately performs the following cooperative control actions: the global aeration shear force is regulated to a preset minimum protection value; the minimum protection value refers to the aeration intensity that can only meet the dissolved oxygen required for microbial basic metabolism, but minimizes the physical shear damage to the granular sludge; The specific determination method of the minimum protection value is: through offline batch experiments, the specific oxygen uptake rate of granular sludge under different aeration intensities is monitored, and the SOUR can be maintained at a level required to meet the basic microbial metabolic activity, for example, the minimum aeration intensity is not less than 30% of the normal value, which is calibrated as the minimum protection value; At the same time, regardless of the value of the current transmembrane pressure difference, the acoustic standing wave field cleaning intensity is set to a preset preventive cleaning intensity; the preventive cleaning intensity is a low-power cleaning mode, which aims to gently remove the early and unstable attachments that may form on the membrane surface in a non-contact manner, to prevent them from evolving into stubborn pollution layers when the granular sludge is fragile; The value of the preventive cleaning intensity is usually set to 10%-25% of the maximum output power of the acoustic field generator, and the specific value is calibrated according to the principle of being the lowest energy intensity that can effectively prevent the initial attachment of pollutants on the membrane surface, while ensuring that no measurable physical damage is caused to the granular sludge structure, such as being observed by a microscope or particle size analysis; The technical effect brought by the technical scheme is that an active protection mechanism based on risk prediction is established; the core idea of the mechanism is that when the system core biological unit, granular sludge, encounters impact or is about to become fragile, the physical stress that may exacerbate its damage, aeration, is reduced first, and gentle preventive measures are taken to maintain the cleaning of the physical separation unit, the membrane; this strategy can effectively prevent the spiral deterioration of the system state, help the system smoothly pass through the impact period at the lowest cost, and embodies the control strategy of proactive intervention based on risk prediction.

[0025] S4 further comprises: If the real-time collected instantaneous toxicity load index does not exceed the toxicity warning threshold, and the generated integrity index prediction value is not lower than the granular stability warning threshold, the aeration shear force is maintained at the normal operating value, and the acoustic standing wave field cleaning intensity is set to zero power standby state; In this embodiment, another execution logic of the control decision rule in step S4, i.e., a conventional efficient operation mode, is described; The execution condition of this mode is: if the real-time collected instantaneous toxicity load index does not exceed the toxicity warning threshold, and the integrity index prediction value generated by the dynamic evolution prediction model is also not lower than the granular stability warning threshold, i.e., the system is determined to be in a safe and stable operating state; Under this condition, the system performs the following control actions: maintaining the aeration shear force at a preset normal operation value; the normal operation value refers to an optimized aeration intensity that balances between ensuring the pollutant treatment efficiency and providing the conventional membrane surface scouring effect; The normal operation value is determined by establishing a relationship model between the pollutant removal rate, the aeration energy consumption and the aeration intensity according to the historical data of the design treatment load and the influent water quality of the sewage treatment plant, and seeking the optimal solution, i.e. the working point with the lowest energy consumption per unit pollutant removal; Meanwhile, the cleaning intensity of the acoustic standing wave field is set to zero power standby state, i.e. the acoustic field generator is turned off; The technical effect brought by the technical scheme is that the system realizes efficient and energy-saving operation under safe working conditions; when the system is in good health, maintaining a high aeration intensity can ensure the best biochemical treatment effect, and turning off the high-energy acoustic field cleaning device can significantly save energy; this differentiated control strategy that distinguishes between different working conditions optimizes the economic efficiency of the system under the premise of ensuring system stability and treatment effect.

[0026] Embodiment 3 S4 further comprises: The real-time collected transmembrane pressure difference and the generated transmembrane pressure difference increment are summed to obtain the pressure difference prediction value at the next moment; If it is determined that the pressure difference prediction value at the next moment exceeds the highest operating pressure difference, the aeration shear force is forcibly locked at the lowest protection value, and the cleaning intensity of the acoustic standing wave field is increased to the highest corrective cleaning intensity; In this embodiment, the final safety guarantee logic of the control decision rule in step S4, i.e. the emergency acoustic intensification cleaning mode, is described; The triggering judgment logic of this mode is that the real-time collected current transmembrane pressure difference and the transmembrane pressure difference increment generated by the nonlinear correlation prediction model are summed to obtain the pressure difference prediction value at the next moment; it is determined whether the pressure difference prediction value exceeds the preset highest operating pressure difference; If the determination result is yes, regardless of other conditions, the system will forcibly trigger the emergency cleaning mode and perform the following control actions: the aeration shear force is forcibly locked at the lowest protection value, which aims to avoid high-intensity aeration from intensifying the disintegration of granular sludge, thereby reducing the generation of polluting substances; at the same time, the cleaning intensity of the acoustic standing wave field is increased to a preset highest corrective cleaning intensity; the corrective cleaning intensity is usually set to the maximum output power of the acoustic field generator, which aims to strip the pollution layer that has been formed with the strongest ability, rapidly reduce the transmembrane pressure difference, restore the membrane flux and avoid physical damage; In implementation, this value is directly set to the rated maximum power marked on the nameplate of the acoustic standing wave field generator device to ensure that the maximum physical cleaning efficiency can be achieved in an emergency without additional experimental calibration. The technical effect brought by the technical solution is that a safety redundancy and hardware protection barrier is provided for the entire control system; even in the extreme case that the previous prediction and protection measures fail to completely prevent the membrane pollution from being aggravated, the mode can take the most decisive corrective measures based on the direct prediction of the physical limit; this mandatory control logic with the protection hardware as the highest priority is a key redundant measure to ensure the hardware safety and long-term stable operation of the intelligent sewage treatment system.

[0027] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A model simulation method for intelligent wastewater treatment systems, characterized in that, The specific steps include: S1. Real-time acquisition of system status data; system status data includes immediate toxicity load indicators, granular sludge physical property indicators, and transmembrane pressure difference; S2. Normalization and linear weighting calculations are performed based on the physical property indicators of granular sludge to construct the granular sludge integrity index. S3. Input the granular sludge integrity index and the instantaneous toxicity load index into the preset dynamic evolution prediction model to generate the integrity index prediction value for the next control cycle; and input the integrity index prediction value into the preset nonlinear correlation prediction model to generate the transmembrane pressure difference increment for the next control cycle. S4. Based on the predicted value of the integrity index and the incremental transmembrane pressure difference, the aeration shear force and the cleaning intensity of the acoustic standing wave field are adjusted through preset control decision rules.

2. The model simulation method for a smart wastewater treatment system according to claim 1, characterized in that, The physical properties of granular sludge include average particle size, sludge settling performance, and free extracellular polymeric substances (FIS) concentration.

3. The model simulation processing method for a smart wastewater treatment system according to claim 1, characterized in that, S2 include: The average particle size, sludge settling performance, and free extracellular polymer concentration were normalized according to preset reference values ​​to obtain dimensionless indices. Based on dimensionless indicators, the granular sludge integrity index is calculated using a linear weighted model determined by multiple regression analysis of historical data.

4. The model simulation method for a smart wastewater treatment system according to claim 1, characterized in that, In S3, the granular sludge integrity index, the immediate toxicity load index, and the current aeration shear force as control variables are used as external inputs and substituted into the dynamic evolution prediction model for calculation to generate the predicted value of the integrity index for the next control cycle.

5. The model simulation method for a smart wastewater treatment system according to claim 4, characterized in that, S3 also includes: taking the generated integrity index prediction value and the current acoustic-controlled standing wave field cleaning intensity as control variables as external inputs, and substituting them into the nonlinear correlation prediction model for calculation to generate the transmembrane pressure difference increment for the next control cycle.

6. The model simulation method for a smart wastewater treatment system according to claim 1, characterized in that, The execution of the control decision rules depends on preset toxicity warning thresholds, particle stability warning thresholds, and maximum operating pressure differentials.

7. The model simulation processing method for a smart wastewater treatment system according to claim 6, characterized in that, S4 include: Determine whether the real-time toxicity load index collected in real time exceeds the toxicity warning threshold, or whether the generated integrity index prediction value is lower than the particle stability warning threshold. If so, adjust the aeration shear force to the preset minimum protection value and set the sound-controlled standing wave field cleaning intensity to the preventive cleaning intensity.

8. The model simulation method for a smart wastewater treatment system according to claim 7, characterized in that, S4 also includes: If the real-time toxicity load index collected does not exceed the toxicity warning threshold, and the generated integrity index prediction value is not lower than the particle stability warning threshold, then the aeration shear force will be maintained at the normal operating value, and the sound-controlled standing wave field cleaning intensity will be set to zero power standby mode.

9. The model simulation method for a smart wastewater treatment system according to claim 7, characterized in that, S4 also includes: The pressure difference prediction value for the next moment is obtained by summing the real-time collected transmembrane pressure difference with the generated transmembrane pressure difference increment. If the predicted differential pressure value for the next moment is determined to exceed the maximum operating differential pressure, the aeration shear force will be forcibly locked at the minimum protection value, and the cleaning intensity of the acoustically controlled standing wave field will be increased to the highest corrective cleaning intensity.

Images