A model simulation processing method for a smart sewage treatment system

Abstract

The model simulation processing method of the intelligent sewage treatment system belongs to the technical field of intelligent sewage treatment and process control, and the specific steps comprise: S1, real-time acquisition of system state data; the system state data comprises instant toxicity load indexes, particle sludge physical property indexes and transmembrane pressure differences; S2, normalization processing and linear weighting operation are carried out based on the particle sludge physical property indexes to construct a particle sludge integrity index; S3, the particle sludge integrity index and the instant toxicity load indexes are input into a preset dynamic evolution prediction model to generate an integrity index prediction value of the next control period; and the integrity index prediction value is input into a preset nonlinear correlation prediction model to generate a transmembrane pressure difference increment of the next control period. The present application provides a solid data basis for subsequent accurate prediction, and significantly improves the accuracy and depth of system state perception.

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 intelligent sewage treatment, especially in systems using granular sludge and membrane separation technology, traditional control methods mainly rely on monitoring of lagging indicators such as transmembrane pressure difference and effluent water quality. These data can only reflect problems that have already occurred, and are not sensitive enough to changes in the health of the core biological unit, granular sludge, and cannot provide early warning. This situation makes it difficult for managers to identify the root cause, such as toxic shock or excessive internal operating stress, before the system becomes unstable, thereby affecting the timeliness and effectiveness of intervention measures; in addition, traditional methods lack the ability to predict future system states and cannot provide forward-looking scientific basis for operational decisions.

[0003] The above situation and deficiencies are mainly due to the lagging nature of monitoring indicators and the limitations of data processing technology. Traditional control strategies usually take response measures only after significant membrane fouling is detected, i.e., a sharp rise in transmembrane pressure, at which point the structure of granular sludge may have been damaged and the system stability has deteriorated seriously; when the system encounters toxic shock or internal stress accumulation, managers cannot predict the risk in advance and take preventive measures, often resulting in a delayed response, and even causing serious consequences such as a decrease in treatment efficiency, permanent damage to membrane components, and even system collapse.

[0004] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the 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, the specific steps include:

[0007] S1, real-time acquisition of system state data; the system state data includes immediate toxicity load indicators, granular sludge physical property indicators and transmembrane pressure difference;

[0008] S2, normalization processing and linear weighting operation based on the granular sludge physical property indicators to construct a granular sludge integrity index;

[0009] S3, inputting the granular sludge integrity index and the real-time toxicity load index into a preset dynamic evolution prediction model to generate an integrity index prediction value for 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 for the next control period;

[0010] S4, based on the integrity index prediction value and the transmembrane pressure difference increment, regulating and controlling the aeration shear force and the acoustic standing wave field cleaning intensity through a preset control decision rule.

[0011] Preferably, the granular sludge physical property indexes include average particle size, sludge settling performance, and free extracellular polymer concentration.

[0012] Preferably, S2 comprises:

[0013] The average particle size, the sludge settling performance, and the free extracellular polymer concentration are normalized according to a preset reference benchmark value to obtain dimensionless indexes.

[0014] Based on the dimensionless indexes, a linear weighting model determined by historical data multivariate regression analysis is used to calculate the granular sludge integrity index.

[0015] Preferably, in S3, the granular sludge integrity index, the real-time toxicity load index, and the current aeration shear force as a control variable are collectively used as external inputs and substituted into the dynamic evolution prediction model for operation to generate the integrity index prediction value for the next control period.

[0016] Preferably, S3 further comprises: the generated integrity index prediction value and the current acoustic standing wave field cleaning intensity as a control variable are collectively used as external inputs and substituted into the nonlinear correlation prediction model for operation to generate the transmembrane pressure difference increment for the next control period.

[0017] 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.

[0018] Preferably, S4 comprises:

[0019] determining whether the real-time toxicity load index collected in real time exceeds the toxicity warning threshold or the generated integrity index prediction value is lower than the granular stability warning threshold;

[0020] 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.

[0021] Preferably, S4 further comprises:

[0022] If the real-time toxicity load index does not exceed the toxicity warning threshold, and the generated integrity index prediction value is not lower than the particle 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.

[0023] Preferably, S4 further comprises:

[0024] The real-time transmembrane pressure difference is summed with the generated transmembrane pressure difference increment to obtain a pressure difference prediction value at the next moment;

[0025] 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.

[0026] The present application provides a model simulation processing method for a smart sewage treatment system, which has the following improvements and advantages compared with the prior art:

[0027] 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;

[0028] 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 toxicity load, an external chemical shock, but also innovatively incorporates 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 prediction 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;

[0029] 3. The present technical solution realizes closed-loop regulation and control of aeration shear force and sound-controlled standing wave field cleaning intensity based on prospective prediction results through preset control decision rules, achieving a preemptive and proactive intervention effect. The control decision logic is not a simple threshold switch, but a differentiated control strategy executed in layers according to a toxicity early warning threshold, a particle stability warning threshold and a maximum operating pressure difference. When a toxicity impact or particle instability risk is predicted, the system actively enters a 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 a regular operation mode that is efficient and energy-saving. More importantly, when the transmembrane pressure difference is about to break through the physical safety upper limit, the system will forcibly execute an emergency cleaning mode to give the highest priority to hardware protection. This multi-modal and prospective control strategy based on risk prediction not only effectively resolves system instability risks, but also takes into account operation economy and hardware safety, significantly improving the operation stability, reliability and intelligence level of the entire intelligent sewage treatment system. BRIEF DESCRIPTION OF DRAWINGS

[0030] The present application will be further explained in conjunction with the accompanying drawings and embodiments:

[0031] Figure 1 is a flow chart of a model simulation processing method for an intelligent sewage treatment system. DETAILED DESCRIPTION

[0032] To make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with specific embodiments.

[0033] Embodiment 1

[0034] Please refer to Figure 1 The present application provides a model simulation processing method for an intelligent sewage treatment system, which includes the following specific steps:

[0035] S1, real-time acquisition of system state data; the system state data includes an instantaneous toxicity load index, a particle sludge physical property index and a transmembrane pressure difference;

[0036] S2, normalization processing and linear weighting operation based on the particle sludge physical property index to construct a particle sludge integrity index;

[0037] S3, inputting the particle sludge integrity index and the instantaneous toxicity load index into a preset dynamic evolution prediction model to generate an integrity index prediction value for 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 for the next control period;

[0038] S4, based on the integrity index prediction value and the transmembrane pressure difference increment, regulating and controlling the aeration shear force and the acoustic standing wave field cleaning intensity through a preset control decision rule;

[0039] The embodiment discloses a model simulation processing method for a smart sewage treatment system, and the technical scheme is that steps are sequentially executed.

[0040] The method starts from step S1, and real-time collection of system state data is performed; the system state data refers to a set of key parameters capable of comprehensively representing the current operating condition of the sewage treatment system, and the system state data serves 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 that inhibit or poison microbial activity in the influent water quality; a granular sludge physical property index, which is a set of physical quantities for macroscopically evaluating 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 data is obtained by continuously or high-frequency measurement through online sensing devices arranged at key nodes of the sewage treatment process, such as an online total organic carbon analyzer, a fluorescence spectrum analyzer, a particle size analyzer and a pressure sensor and the like;

[0041] In the embodiment, the instantaneous toxicity load index The measurement value of the online sensor is quantitatively calculated by normalization and linear weighting, and the formula is as follows:

[0042] ;

[0043] 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 baseline values; and are the respective weight coefficients. The weight coefficients are determined offline by analyzing historical data, combining different and and performing correlation analysis on subsequent system treatment efficiency decline or granular sludge integrity index deterioration events, so that the calculated value can most sensitively reflect the comprehensive toxicity impact intensity on microbial activity;

[0044] On the basis of collecting system state data, step S2 is performed, and normalization processing and linear weighting operation are performed on the collected granular sludge physical property indexes to construct a granular sludge integrity index; the granular sludge integrity index refers to a composite index that is dimensionless and is used for comprehensively evaluating the macrostructure integrity and stability of the granular sludge, and the purpose is to convert multiple independent indexes with different physical dimensions into a unified evaluation criterion that can be used for dynamic model prediction;

[0045] Step S3 is performed, and the granular sludge integrity index constructed in the previous step and the real-time collected instantaneous toxicity load index are jointly input 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 with time under the joint action of external disturbance and internal stress, and the function is to predict the system health state 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 operation; at the same time, the predicted value of the integrity index is input 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 state and the membrane pollution rate, and the function is to predict the change of the membrane pollution degree caused by the change of the granular sludge state; the model generates a transmembrane pressure difference increment in the next control period through operation; the preset properties of the above model refer to the model structure and key parameters that are determined in advance through system identification and offline calibration of historical operation data of a specific wastewater treatment system under multiple working conditions, so as to ensure the applicability and accuracy of the model;

[0046] Step S4 is performed, and based on the predicted value of the integrity index and the transmembrane pressure difference increment generated in the previous step, a set of preset control decision rules are used to dynamically regulate the aeration shear force and the cleaning intensity of the acoustic standing wave field in the system; the preset control decision rules refer to a set of logical judgment and execution instructions formulated according to the system safety operating boundary and the predictive risk assessment results, and the purpose is to convert the prediction results of the model into specific preventive or corrective control actions that can avoid risks;

[0047] The technical scheme establishes a complete technical link from state perception to risk prediction to active intervention through real-time data collection, construction of a comprehensive health index, double-model serial prediction, and closed-loop control based on the prediction results; the technical effect is that the granular sludge instability risk caused by the toxicity impact of the influent or the internal operation stress can be identified in advance, and preventive measures can be taken before the membrane pollution significantly deteriorates, thereby significantly improving the operation stability, reliability and treatment efficiency of the entire intelligent wastewater treatment system, and avoiding the system collapse caused by the lagging response of the traditional method.

[0048] The particle sludge physical property indexes include average particle size, sludge settling performance and free extracellular polymer concentration.

[0049] In the embodiment, the composition of the particle sludge physical property indexes is limited; the particle sludge physical property indexes are defined to include the average particle size, the sludge settling performance and the free extracellular polymer concentration; wherein the average particle size is a core index for evaluating the maturity and compactness of the particle sludge; the sludge settling performance reflects the solid-liquid separation efficiency of the sludge, which is obtained by measuring the sludge settling rate in the embodiment; and the free extracellular polymer concentration reveals the dissolution degree of the surface biological polymers of the particle sludge, which is a direct precursor of the disintegration of the particle structure.

[0050] The gain technical effect brought by the technical solution is that, by selecting the three macroscopic physical indexes which are generally recognized as the most representative in the microbial polymer stability theory, the particle sludge integrity index constructed can more accurately and comprehensively reflect the real health condition of the particle sludge; compared with a single index, the multi-dimensional index system is more sensitive and reliable for early warning of system instability, thereby providing a solid foundation for the accuracy of the subsequent prediction model.

[0051] S2 includes:

[0052] The average particle size, the sludge settling performance and the free extracellular polymer concentration are normalized according to preset reference benchmark values to obtain dimensionless indexes;

[0053] Based on the dimensionless indexes, the particle sludge integrity index is calculated through a linear weighting model determined by historical data multivariate regression analysis;

[0054] In the embodiment, the way of constructing the particle sludge integrity index in step S2 is described in detail.

[0055] In the specific implementation of the step, the average particle size, the sludge settling performance and the free extracellular polymer concentration are normalized according to the respective preset reference benchmark values; the reference benchmark value refers to the statistical average value of each physical property index when the system is stably operated under long-term excellent working conditions, which is determined by statistical analysis on a large number of historical excellent working condition data; the purpose of the normalization processing is to eliminate the dimensional differences between different indexes to obtain dimensionless indexes, and the calculation method is as follows:

[0056] And ;

[0057] Wherein, t, t and t are the normalized average particle size, sludge settling performance and free extracellular polymer concentration indexes at time t, respectively. are real-time measurement values at corresponding time points acquired by online sensing devices; are respective corresponding reference values;

[0058] After normalization, based on the above dimensionless indexes, a linear weighted model determined by historical data multiple regression analysis is used to calculate the granular sludge integrity index; the linear weighted model aims to maximize the correlation between the comprehensive index and the system instability probability, and the preset weight coefficients in the model are determined offline;

[0059] ;

[0060] wherein, is the granular sludge integrity index at time t; are weight coefficients corresponding to the three dimensionless indexes respectively, and the physical meaning is the contribution of each index to the sludge integrity; the sources of these weight coefficients are multiple regression analysis on the complete process data from stability to instability in the system historical database, to ensure that the calculated value can most sensitively reflect the change trend of system stability;

[0061] The technical effect brought by the technical scheme 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, the 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.

[0062] In S3, the granular sludge integrity index, the instantaneous toxicity load index, and the current aeration shear force as a control variable are collectively used as external inputs, and are substituted into the dynamic evolution prediction model to generate a predicted value of the integrity index in the next control period;

[0063] To improve the prediction accuracy, in this embodiment, the dynamic evolution prediction model is optimized, and in addition to the granular 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 on the granular sludge by the aeration equipment, which is normalized as a dimensionless control variable representing the ratio of actual output power to maximum power, and the source is the aeration equipment operation parameter 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 granular sludge structure;

[0064] The improved dynamic evolution prediction model is used to predict the granular sludge integrity index in the next control period under the current toxicity load and the aeration shear force under the joint action, the integrity index at the next control period t+1 , the mathematical form is constructed as:

[0065] ;

[0066] 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 impact sensitive 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 toxicity load and aeration shear force during 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 various working conditions, and using system identification algorithm for offline calibration; : exponential function;

[0067] The gain technical effect brought by the technical solution is that the dynamic evolution prediction model can consider chemical impact, toxicity and physical damage, and shear force, two main stress sources, at the same time; 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 granular sludge, providing a model basis for realizing fine and low-damage aeration control.

[0068] S3 further comprises: taking the generated integrity index prediction value and the current acoustic standing wave field cleaning intensity as control variables as external inputs, substituting them into the nonlinear correlation prediction model for operation, to generate the transmembrane pressure difference increment of the next control period;

[0069] The embodiment supplements the input variables of the nonlinear correlation prediction model, and describes the synergistic effect of the dynamic evolution prediction model; the integrity index prediction value generated by the dynamic evolution prediction model, and the current acoustic standing wave field cleaning intensity as control variables, are taken as external inputs, 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 removing membrane pollution, which is also normalized into a dimensionless control variable representing the ratio of actual output power to maximum power, and the source is the operation parameter of the acoustic field generator recorded by the control system in real time;

[0070] The improved nonlinear correlation prediction model is used to calculate the increment of the transmembrane pressure difference in the next control period , the mathematical form is constructed as:

[0071] ;

[0072] In this model, is the predicted value of the transmembrane pressure difference increment at the next moment, is the predicted value of the integrity index output by the dynamic evolution prediction model, is the sound field cleaning intensity in the current control period, is the pollution conversion coefficient, is the sound 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 characteristics that the membrane pollution rate increases sharply when the sludge state deteriorates; the second term represents the pressure difference reduction effect brought by the actively applied sound field cleaning; to ensure dimensional consistency, the units of the model parameters are all pressure units, and the values are obtained by experiment calibration; among them, the pollution conversion coefficient characterizes the strength of the membrane pollution effect caused by the deterioration of the integrity of the granular sludge, and the sound field cleaning efficiency coefficient represents the maximum pressure difference reduction amount that can be achieved by the maximum sound field intensity;

[0073] 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 sound field cleaning on membrane pollution based on the prediction results of the state; the technical effect of the gain brought by this synergistic effect 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 precision and foresight of decision-making.

[0074] Example 2

[0075] The execution of the control decision rule depends on the preset toxicity warning threshold, the granular stability warning threshold, and the highest operating pressure difference;

[0076] In this embodiment, the key threshold parameters relied on by the execution of the control decision rule are defined; the setting of these thresholds provides clear and quantifiable boundaries for the triggering of the control logic; including:

[0077] Toxicity warning threshold: a normalized reference value of toxicity load; 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 immediate response;

[0078] Particle stability warning threshold: a lower limit value of the granular sludge integrity index; its role is to define the critical point when the granular sludge is about to become unstable; the determination method is based on historical data analysis, and the inflection point when the integrity index is lower than the value, the probability of system instability increases sharply; when the model predicts the next cycle integrity index is lower than this threshold, it indicates that the health of the granular sludge is about to deteriorate to an unacceptable level;

[0079] 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 cause irreversible physical damage to the membrane;

[0080] The technical effect brought by the technical scheme 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 control decision rules standardized and automated, avoids the subjectivity and uncertainty of human judgment, ensures that the control system can make timely, reliable and safety-first response under any working condition, and significantly enhances the robustness of the system.

[0081] S4 comprises:

[0082] Determine whether the real-time collected instantaneous toxicity load index exceeds the toxicity warning threshold, or the predicted value of the integrity index generated by the dynamic evolution prediction model is lower than the particle stability warning threshold;

[0083] If yes, adjust the aeration shear force to the pre-set minimum protection value, and set the acoustic standing wave field cleaning intensity to the preventive cleaning intensity;

[0084] In this embodiment, an execution logic of the control decision rule in step S4, i.e. the pre-emptive protection mode, is described;

[0085] 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 predicted value of the integrity index generated by the dynamic evolution prediction model is lower than the pre-set particle stability warning threshold; one of the two conditions is met, and the protection mechanism is triggered;

[0086] 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;

[0087] 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, the SOUR can be maintained at a level required for 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;

[0088] At the same time, regardless 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;

[0089] 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 on the premise of ensuring that no measurable physical damage is caused to the granular sludge structure, such as determined by microscope observation or particle size analysis, and the minimum energy intensity that can effectively prevent the initial attachment of pollutants on the membrane surface;

[0090] 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 to reduce the physical stress that may exacerbate the damage to the granular sludge, aeration, when the system core biological unit, the granular sludge, encounters impact or is about to become fragile, and to take gentle preventive measures 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 minimum cost, and embodies the control strategy of proactive intervention based on risk prediction.

[0091] S4 further comprises:

[0092] If the real-time toxicity load index collected in real time 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;

[0093] In this embodiment, another execution logic of the control decision rule in step S4, i.e., the conventional efficient operation mode, is described;

[0094] The execution condition of the mode is that if the real-time collected instant toxicity load index does not exceed the toxicity early warning threshold value, and the integrity index prediction value generated by the dynamic evolution prediction model is also not lower than the particle stability warning threshold value, the system is determined to be in a safe and stable running state;

[0095] Under this condition, the system performs the following control actions: maintaining the aeration shear force at a preset normal running value; the normal running value refers to an optimized aeration intensity that balances between ensuring the pollutant treatment efficiency and providing a conventional membrane surface scouring effect;

[0096] The normal running value is determined by establishing a relationship model among the pollutant removal rate, aeration energy consumption and 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;

[0097] At the same time, the sound-controlled standing wave field cleaning intensity is set to zero power standby state, i.e., the sound field generator is turned off;

[0098] The technical effect brought by the technical scheme is that the system realizes efficient and energy-saving operation in a safe working condition; when the system is in good health, maintaining a high aeration intensity can ensure the best biochemical treatment effect, and turning off the sound field cleaning device with high energy consumption 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.

[0099] Embodiment 3

[0100] S4 further comprises:

[0101] 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;

[0102] 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 sound-controlled standing wave field is increased to the highest corrective cleaning intensity;

[0103] 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;

[0104] The triggering judgment logic of the 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;

[0105] If the result is yes, the system will force the emergency cleaning mode and perform the following control actions regardless of other conditions: the aeration shear force is forced to lock at the lowest protection value, which aims to avoid high-intensity aeration aggravating the disintegration of granular sludge, thereby reducing the generation of pollutants; 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, quickly reduce the transmembrane pressure difference, restore the membrane flux, and avoid physical damage.

[0106] In implementation, the 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.

[0107] 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 where the previous prediction and protection measures fail to completely prevent membrane pollution from aggravating, this mode can take the most decisive corrective measures based on direct prediction of the physical limit; this mandatory control logic with 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.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. 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 by equivalents 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. The physical properties of granular sludge include average particle size, sludge settling performance, and free extracellular polymeric substances (FIS) concentration. S2 includes: 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 was calculated using a linear weighted model determined by multiple regression analysis of historical data. 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 integrity index prediction value for the next control cycle. 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.

2. 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.

3. The model simulation processing method for a smart wastewater treatment system according to claim 2, 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.

4. The model simulation method for a smart wastewater treatment system according to claim 3, 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.

5. The model simulation method for a smart wastewater treatment system according to claim 4, 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 maximum corrective cleaning intensity.

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