A self-adaptive limiting regulation system and method for SBR decanter based on multi-parameter coupling

Abstract

This invention discloses an adaptive limit control system and method for SBR decanter based on multi-parameter coupling, belonging to the field of intelligent control technology for wastewater treatment. Addressing the non-contact sludge runoff phenomenon of "siphon following effect" caused by sludge expansion or deterioration of settling performance during the SBR decanting stage, this invention constructs a dual closed-loop control architecture of multi-parameter coupling, incorporating feedforward prediction and feedback self-learning. The system comprises a multi-parameter sensing module, a multi-parameter coupled modeling and control unit, an execution module, and a feedback correction module. By integrating multi-dimensional information such as online MLSS, temperature, real-time sludge level from the sludge interface meter, online effluent SS / TP monitoring, and laboratory SV30 / SVI data and their input timeliness, it establishes a "sedimentation risk-safety margin" mapping model expressed as a product of the sedimentation expansion coefficient k₁, activity inhibition coefficient k₂, density compensation coefficient k₃, and time-related decay coefficient k₄. This model dynamically generates a safety redundancy distance ΔS, and outputs the optimal lower limit Hopt = L + ΔS, pre-setting the limit before decanting begins, thus achieving a fundamental shift from reactive emergency shutdown to proactive prevention. The feedback correction module continuously corrects model parameters through automatic attribution judgment and trial-and-error optimization algorithms, maximizing the single-cycle treatment capacity while ensuring effluent compliance. This invention is the first to achieve proactive control of the siphon following effect, significantly improving the effluent compliance rate and operating efficiency of the SBR process under complex operating conditions.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for wastewater treatment, specifically relating to a limit control system and method for decanters in SBR processes. In particular, it is a multi-parameter coupled adaptive limit control system and method that integrates online water quality monitoring, sludge settling performance indicators and effluent feedback, and introduces data timeliness correction to prevent non-contact sludge runoff caused by the "siphon following effect". Background Technology

[0002] The decanting stage of the SBR process requires maximizing the discharge of supernatant without disturbing the bottom sludge layer. Existing decanter limit control mainly employs two methods: manual experience-based adjustment and fixed time / position control. Manual adjustment relies on operators visually or manually setting the lower limit based on laboratory data, resulting in significant lag and subjectivity, and failing to address dynamic changes in operating conditions. Fixed control executes according to preset times or depths; however, sudden changes in sludge settling properties can easily lead to substandard effluent. In recent years, emergency stop control schemes based on sludge level thresholds have emerged: using a sludge interface meter to monitor the sludge level, an emergency power cut-off stops the machine when the decanter approaches the sludge layer surface, preventing physical contact with the sludge layer. Additionally, there are variable-speed drainage schemes based on sludge concentration, which reduce mechanical disturbance to the sludge layer through stratified speed reduction. The aforementioned solutions share common limitations in terms of both the target and mechanism of sludge runoff—they all target only "mechanical contact-induced sludge runoff" or "high-speed water flow mechanical disturbance-induced sludge runoff," meaning their control logic is based on the physical scenario of "the decanter approaching or contacting the sludge layer, causing agitation." However, through long-term engineering observation and fluid dynamics analysis, the inventors of this invention have discovered an independent sludge runoff phenomenon that has long been overlooked by the industry—the "siphon following effect." This phenomenon is fundamentally different from the conventional "mechanical contact-induced sludge runoff": The formation mechanisms are different: conventional sludge runoff is triggered by mechanical contact or strong disturbance of the sludge layer at the decanter; the siphon following effect, on the other hand, is caused by the suction force generated by the high-speed confluence zone directly entraining loose flocs into the decanter, without mechanical contact. The triggering conditions are different: conventional sludge runoff is triggered when the sludge level reaches the limit; the siphon following effect occurs when the sludge level is far from reaching the decanter, triggered by deterioration of sludge settling performance (such as increased SVI, sludge expansion). The physical characteristics differ: conventional sludge run-off manifests as the sludge layer being agitated and lifted; the siphon following effect manifests as flocs "following" the water flow into the decanter under suction, without the macroscopic interface of the sludge layer moving upwards to the decanter position, making it undetectable by the sludge interface meter. Due to these differences, existing emergency stop schemes based on sludge level contacts are completely inadequate to handle the siphon following effect—when this phenomenon occurs, the sludge level has not reached the limit switch, the system does not trigger any protective action, and the effluent SS and TP have already risen sharply. Therefore, there is an urgent need for an intelligent control scheme that can predict sedimentation performance risks in advance and construct a safe decanting boundary for the siphon following effect. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned technical blind spots by providing an adaptive limit control system and method for SBR decanters based on multi-parameter coupling, specifically designed to prevent siphon following effects. By integrating multi-dimensional information such as sludge concentration, settling characteristics, temperature, sludge level, and effluent feedback, and considering the timeliness of laboratory data, a dynamic limit decision model combining feedforward prediction and feedback correction is constructed, fundamentally solving the non-contact siphon entrainment problem caused by fluctuations in settling performance.

[0004] (a) Terms and Definitions To more clearly define the technical content of this invention, the following terms are given operational definitions: Conventional mechanical contact sludge run: refers to a sludge run where, when the decanter branch pipe descends to contact or come very close to the surface of the sludge layer, the entire sludge layer is lifted up due to mechanical agitation or direct suction, resulting in a large amount of sludge entering the decanter. This can be reflected on the sludge interface meter as the sludge level reaching the limit.

[0005] Siphon-following effect: Unlike conventional mechanical contact-induced sludge runoff, this refers to the phenomenon where, during the SBR decanting stage, sludge expansion or deterioration of settling properties leads to a loose sludge layer and blurred density gradient. Even if the decanting branch outlet is maintained at a safe height above the sludge layer surface and no mechanical contact or strong disturbance occurs, the loose flocs on the upper layer of the sludge layer are selectively entrained and carried into the decanting outlet by the water flow under the low pressure gradient field formed by the high-speed suction force in the confluence area near the decanting branch outlet. This causes non-contact sludge runoff and excessive effluent levels. Its typical characteristics are: abnormally high SS and TP levels in the effluent, while the sludge interface meter shows that the sludge level has not reached the limit, and there is no significant jump in sludge level readings before and after decanting.

[0006] Settling expansion coefficient k1: A process scale-up factor determined based on the degree of sludge expansion and settling deterioration reflected by SVI and / or SV30. This coefficient is directly related to the expansion effect of sludge bulkiness on the siphon radius. k1≥1, and the higher the SVI / SV30 value, the larger k1 is.

[0007] Activity inhibition coefficient k2: The process enhancement weighting coefficient is determined by the degree of reduction in sludge flocculation activity reflected by temperature. This coefficient reflects the additional deterioration effect of low temperature on sludge settling performance. k2≥1, the lower the temperature, the larger k2 is; when the temperature is lower than the preset threshold, the conservative mode is automatically activated and a larger value is taken.

[0008] Density compensation coefficient k3: A process compensation factor for the increase in viscosity of supernatant and the increase in entrainment content per unit volume caused by the increase in MLSS (mixed liquid suspended solids concentration). This coefficient characterizes the amplification effect of high MLSS on the risk of siphon entrainment. k3≥1, the higher the MLSS, the larger k3.

[0009] The time-degradation coefficient k4 is a compensation factor for the uncertainty of process information determined by the time interval between the time of data entry of the laboratory SVI / SV30 and the current decanting cycle. This coefficient reflects the degree of risk underestimation that may be caused by the lag in laboratory data. k4≥1, and the longer the interval, the larger k4 is.

[0010] Safety redundancy distance ΔS: The minimum safe distance between the decanting branch outlet and the current sludge layer surface, set to effectively avoid the siphon following effect. It is dynamically calculated through the above multi-factor model, and its physical meaning is "the minimum space margin required for the siphon influence radius to not extend to the decanting outlet under the current operating conditions".

[0011] Optimal lower limit position Hopt: Hopt = L + ΔS, where L is the sludge level height measured in real time by the sludge interface instrument, ΔS is the safety redundancy distance, and Hopt is the lowest position that the decanter branch pipe inlet is allowed to descend in this decanting cycle.

[0012] (II) An adaptive limit control system It includes a multi-parameter perception module, a multi-parameter coupled modeling and control unit, an execution module, and a feedback correction module.

[0013] The multi-parameter sensing module integrates: an online MLSS meter for the biological tank, a thermometer, a sludge interface meter, an online SS meter for effluent, an online TP meter for effluent, and an auxiliary input terminal for entering SV30 and SVI data from the laboratory. The auxiliary input terminal records the timestamp of each data entry for data timeliness evaluation.

[0014] The multi-parameter coupled modeling and control unit is deployed in the central control system, with a built-in "sedimentation risk-safety margin" mapping model and limit calculation engine. This model uses the real-time sludge level L from the sludge interface meter as the absolute benchmark, and uses SVI / SV30, MLSS, temperature, and the timeliness of laboratory data as sedimentation risk factors. It dynamically generates the safe redundancy distance ΔS between the decanter branch pipe inlet and the sludge layer surface, and outputs the optimal lower limit Hopt = L + ΔS. The modeling logic delves into the influence mechanism of each factor on the "siphon following effect" as follows: SVI / SV30 factor—settling expansion coefficient k1: SVI ≥ 150 mL / g or SV30 ≥ 65% directly characterizes the deterioration of sludge bulkiness and settling properties, the blurring of the sludge density gradient, and the expansion of the effective radius of influence of siphon force from a few centimeters under normal conditions to tens of centimeters. Based on this, the model maps SVI / SV30 to the settling expansion coefficient k1, quantitatively characterizing the amplified safety distance requirement due to this expansion effect.

[0015] Temperature factor – activity inhibition coefficient k2: Low temperatures (e.g., ≤12°C) reduce microbial metabolic activity, decrease extracellular polymer secretion, and weaken the binding force between sludge flocs, making the siphon following effect, which should be overcome by gravity settling, more likely to occur. The model maps temperature to an activity inhibition coefficient k2, which serves as the synergistic weight of the SVI factor. Under low-temperature conditions, the effect of k1 is amplified, and the conservative mode is automatically activated when the temperature is below a set threshold.

[0016] MLSS factor—density compensation coefficient k3: High-concentration sludge (e.g., ≥4000 mg / L) increases the entrainment solids content per unit volume of mixed liquor, while also increasing the apparent viscosity of the supernatant, resulting in a steeper velocity gradient and stronger local suction in the catchment area. The model maps MLSS to the density compensation coefficient k3, quantitatively compensating for the amplified effect of high-solids-content environments on the risk of siphon entrainment.

[0017] The timeliness factor of laboratory data—timeliness decay coefficient k4: Considering that SVI / SV30 data is manually entered, there is an update lag, and settlement performance may deteriorate sharply between two tests due to factors such as rainstorm impact and toxic inflow. The model introduces the timeliness decay coefficient k4 to actively amplify the safety margin based on the time interval Δt between the laboratory data and the current period, in order to compensate for insufficient risk prediction caused by outdated information.

[0018] The product of the four coefficients (k1×k2×k3×k4) reflects the synergistic amplification relationship among the risk factors: low temperature not only worsens the settling performance itself (k2), but also exacerbates the structural looseness of high SVI sludge; high MLSS not only increases the entrained solids content (k3), but also interacts with the loose sludge layer to form more easily sucked flocs. The factors do not act independently, but rather interact and couple through the physical medium of the siphon flow field.

[0019] The execution module is a controlled electric slider. According to the Hopt command, the slider is locked in this position before the decanting starts, realizing prevention in advance, which is fundamentally different from the emergency stop logic of existing technologies.

[0020] The feedback correction module receives online monitoring signals of effluent SS and TP, and incorporates attribution judgment logic. When SS ≥ 8 mg / L or TP ≥ 0.4 mg / L is detected, and after automatic attribution analysis eliminates other factors such as influent impact and confirms that the risk of siphon entrainment has been missed, the module automatically generates a limit upward shift compensation amount ΔHcor, which is added to ΔS calculated by the model in the next cycle. If the standard is met for multiple consecutive cycles, the safety distance is tentatively reduced in small steps to approach the optimal balance point, maximizing the single-cycle water treatment volume while ensuring that the effluent meets the standard, thus forming a self-optimizing closed loop.

[0021] (III) An Adaptive Limit Control Method Includes the following steps: Step S1: Establish a multi-factor mapping model based on siphon risk Historical operational data was collected to construct a nonlinear mapping relationship with mud level L as the baseline, SVI, SV30, MLSS, temperature, and the timeliness of laboratory data as input variables, and the safety redundancy distance ΔS as the output. The model is specifically embodied as follows: ΔS = D_base × k1 × k2 × k3 × k4 Among them, D_base is the basic safety distance, initially set based on equipment parameters such as decanting flow rate and branch pipe diameter, representing the basic control range of the siphon influence radius under standard operating conditions; k1 is the settling expansion coefficient, quantifying the expansion effect of sludge looseness on the siphon influence radius; k2 is the activity inhibition coefficient, quantifying the additional deterioration effect of low temperature on settling performance; k3 is the density compensation coefficient, quantifying the amplification effect of high solids content environment on siphon entrainment risk; k4 is the time decay coefficient, quantifying the information uncertainty risk caused by the lag of test data. Each coefficient is directly related to the actual operating parameters of the SBR process, and its value is determined by statistical analysis of historical operating data of the wastewater treatment plant to establish the initial mapping relationship, and it has the ability to self-learn and update based on effluent feedback. The model is encapsulated in the central control system, with a reserved feedback correction interface.

[0022] Step S2: Real-time multi-source data acquisition At the end of the sedimentation stage or before the decanting stage of each SBR cycle, the MLSS, temperature, and sludge level signals of the current biological tank are automatically collected, and the SV30 / SVI value and its entry timestamp of the most recently manually entered data are retrieved. The lag time Δt of the test data from the current time is calculated to form a complete input vector.

[0023] Step S3: Feedforward Adaptive Limit Calculation and Execution Substitute the input vector into the mapping model to calculate the correlation coefficients of each process online: Calculate k1 using the table or formula based on SVI / SV30; Calculate k2 based on the current temperature using a table or formula. Calculate k3 using MLSS table lookup or formula; k4 is calculated based on the lag time Δt: when Δt is less than or equal to the preset freshness threshold, k4=1; when Δt exceeds the threshold, k4 increases according to the preset function. The optimal lower limit Hopt = L + ΔS is obtained by comprehensively calculating ΔS = D_base × k1 × k2 × k3 × k4. The controller drives the electric slide bar to precisely adjust the lower limit of the decanter to this height. This process is completed before water enters the decanter branch pipe, eliminating any lag.

[0024] Step S4: Feedback correction based on effluent water quality After this round of decanting, online monitoring data sequences of effluent SS and TP were obtained, and the following attribution judgment and correction were performed: Attribution judgment: If the effluent SS ≥ 8 mg / L or TP ≥ 0.4 mg / L, the following conditions will be automatically checked: (a) The periods of exceeding the standard overlap with the decanting operation periods; (b) The average SS or TP during the decanting stage shows a significant increase compared to the baseline value before decanting, and the increase exceeds the preset fluctuation range; (c) During the same period, the online COD, TP and other load indicators of the influent did not show impact peaks exceeding the historical normal amplitude, thus excluding external impact factors.

[0025] When conditions (a), (b), and (c) are met simultaneously, it is determined that there is a risk of siphon entrainment and a feedback compensation is triggered.

[0026] Compensation Correction: Based on the excess range, the compensation height ΔHcor is converted, and the reference value ΔS under the current working condition is corrected to ΔS' = ΔS + ΔHcor. The mapping parameters of the corresponding working condition area in the model are updated so that the safe distance will automatically increase when running under similar conditions next time.

[0027] Exploratory optimization: If the effluent SS and TP are consistently within the standard for N consecutive cycles, and the current safe redundancy distance is greater than the minimum value of historical successful operation, then the baseline value of ΔS is reduced by a small step size δ or the attenuation slope of k4 is reduced to gradually approach the equilibrium point of maximizing effluent flow, so as to avoid excessive conservatism leading to waste of treatment capacity. Beneficial effects

[0028] Compared with the prior art, the present invention has the following breakthroughs: This invention achieves proactive control of the "siphon following effect" for the first time, fundamentally differentiating itself from existing "mechanical contact-based sludge runoff" protection technologies. It identifies and defines the "siphon following effect," a non-contact sludge runoff phenomenon independent of mechanical contact, and constructs a safe decanting boundary from a hydrodynamic entrainment perspective using a product model of the SVI expansion coefficient, temperature inhibition coefficient, and MLSS density compensation coefficient. Even when the sludge level is far from reaching the decanting outlet, the system can still anticipate risks based on the deterioration of settling performance indicators and proactively raise the limit. In actual engineering verification, this solution improves the effluent SS compliance rate during the sludge expansion period (SVI≥150mL / g) from less than 60% in traditional sludge level emergency stop solutions to over 95%.

[0029] Achieving multi-factor collaborative decision-making involving concentration, settling, temperature, sludge level, and time-dependent effects significantly improves control precision: Deep integration of manual laboratory data (SVI) and its timeliness with online instruments forms a multi-parameter coupled model capable of sensing seasonal changes, sludge activity shifts, and shock loads. The correlation coefficients of each process directly relate to the actual physical mechanisms of the operating conditions, resulting in control precision and adaptability far exceeding any single-variable adjustment. Introducing the time-dependent decay coefficient k4 effectively avoids the risk of siphoning due to data lag, maintaining a reliable safety margin even when testing intervals are extended to 24 hours or more.

[0030] This system features a pioneering dual-closed-loop control architecture combining "feedforward prediction and feedback self-learning," enabling continuous self-optimization. It pre-sets safety limits before each decanting cycle and continuously corrects the model through automatic attribution based on effluent feedback. After multiple operating cycles, the safety redundancy distance automatically converges to the optimal range for the plant's sludge characteristics. Compared to a fixed safety margin scheme, this increases treatment capacity by 5% to 10% under the same effluent compliance rate. The longer the system operates, the more closely it conforms to the intrinsic characteristics of the plant's actual sludge, demonstrating a self-learning capability akin to "artificial intelligence."

[0031] Balancing effluent compliance rate and wastewater treatment efficiency while avoiding overly conservative approaches: Under the premise of stable control of SS and TP to meet standards, the system maximizes decanting depth through a trial-and-error optimization algorithm. When effluent meets standards for multiple consecutive cycles, the system automatically reduces the safety redundancy distance in small steps to find the optimal balance between effluent compliance and treated water volume. This avoids the single-cycle effective volume loss caused by traditional conservative control strategies, enabling wastewater treatment plants to meet both environmental protection and treatment capacity requirements in actual operation. Attached Figure Description

[0032] Figure 1 : Schematic diagram of the system structure of the present invention.

[0033] In the diagram: 1-Biological tank, 2-Online MLSS meter, 3-Thermometer, 4-Electric slide bar and slider, 5-Decanter body, 51-Decanter branch pipe, 6-Sludge layer, 7-Sludge interface meter, 8-Effluent online SS meter, 9-Effluent online TP meter, 10-Central control system (containing multi-parameter coupled modeling and control unit), 11-Laboratory data input terminal (including timestamp recording), H-Safety height (distance from branch pipe inlet to sludge layer surface, i.e., ΔS).

[0034] Figure 2 : Control flow diagram of the method of the present invention.

[0035] The diagram illustrates the closed-loop steps of S1→S2→S3→S4, highlighting the feedforward path of S3 (which includes the calculation of four process correlation coefficients) and the feedback correction path of S4 (which includes attribution judgment and trial reduction). Detailed Implementation

[0036] The embodiments of the present invention will be described in detail below, but the present invention is not limited to the described embodiments.

[0037] Example 1: System Configuration and Initial Parameter Calibration The SBR tank configuration of a municipal wastewater treatment plant includes: an online MLSS meter (range 0~10g / L), a thermometer, an ultrasonic sludge interface meter, and an online effluent SS / TP meter. The central control system incorporates a multi-parameter coupled modeling and control unit. The laboratory performs SV30 and SVI tests daily, and the data is entered via an auxiliary terminal after each test; the system automatically records the timestamp.

[0038] Initial parameter calibration: The basic safety distance D_base is initially set at 20cm, representing the basic control range of the siphon influence radius of the decanting equipment under standard operating conditions (which can be adjusted during actual commissioning based on the decanting flow rate of 1250m³ / h and the branch pipe diameter of DN300).

[0039] The mapping relationship between k1 and SVI (established based on historical data analysis of three winter operation cycles of the plant): k1=1.0 when SVI<80mL / g; k1=1.3 when 80≤SVI<150mL / g; k1=2.0 when SVI≥150mL / g (the same interval can also be triggered when SV30≥65%).

[0040] The mapping relationship between k2 and temperature: when temperature > 15℃, k2 = 1.0; when temperature 12℃ < temperature ≤ 15℃, k2 = 1.2; when temperature ≤ 12℃, it enters conservative mode, k2 = 1.8.

[0041] The mapping relationship between k3 and MLSS is as follows: k3 = 1.0 when MLSS < 3000 mg / L; k3 = 1.15 when 3000 ≤ MLSS < 4000 mg / L; k3 = 1.35 when MLSS ≥ 4000 mg / L.

[0042] k4 Freshness decay: The freshness threshold is set to 12 hours; when Δt≤12h, k4=1; for every hour Δt exceeds, k4 increases by 0.02, with an upper limit of 2.0.

[0043] Operating data for a certain period: sludge interface meter L=2.50m, online MLSS=4200mg / L, temperature 11.8℃, most recent SVI=165mL / g (entered 14 hours ago). Calculate the correlation coefficients for each process: k1=2.0, k2=1.8, k3=1.35, Δt=14h, k4=1+0.02×(14-12)=1.04. ΔS=20×2.0×1.8×1.35×1.04≈101cm. Hopt=2.50+1.01=3.51m. The actuator locks the slider at 3.51m, and the decanting branch pipe outlet is 1.01m from the sludge surface. This safety redundancy distance is significantly greater than the allowable depth of conventional emergency stop limit schemes, thus avoiding the risk of siphon following in advance.

[0044] Example 2: Attribution and Feedback Correction of Excessive Effluent Standards Following Example 1, after decanting, the online instrument displayed the highest instantaneous SS value of 12 mg / L and TP value of 0.6 mg / L, both exceeding the standard. Automatic attribution logic verification: (a) the period of exceeding the standard overlapped with the decanting period, establishing a temporal correlation; (b) the baseline SS value before decanting was approximately 5 mg / L and TP was approximately 0.25 mg / L, while the average values ​​during decanting suddenly increased to 11 mg / L and 0.55 mg / L respectively, exceeding the normal fluctuation range, indicating that sludge runoff occurred during decanting; (c) the influent COD load during the same period was within the historical normal fluctuation range, with no peak impact, excluding external load impact factors. With all three conditions met simultaneously, it was determined to be a siphon entrainment risk omission, triggering feedback compensation. The system calculated ΔHcor = 15 cm based on the severity of the exceedance, increasing the ΔS benchmark for the corresponding operating condition range by 15 cm. The updated model automatically increases ΔS under the same or similar conditions, preventing similar omissions from occurring in subsequent similar operating conditions.

[0045] Example 3: Trial Reduction Optimization For six consecutive cycles, the effluent SS was <8 mg / L and TP was <0.4 mg / L, indicating that the current safe redundancy distance is stable within a wide range, suggesting room for optimization. The feedback correction module gradually reduces D_base or decreases the decay slope of k4 in steps δ=1 cm, observing the effluent performance for the next two cycles after each reduction. If the effluent indicators remain stable and meet the standards after reduction, the reduction continues; if SS or TP shows a trend approaching the standard limit after reduction, the reduction is immediately stopped and the parameters are reverted to the previous step, locking in the current equilibrium point. After multiple rounds of trials, the system automatically converges to the minimum safe redundancy distance that meets the effluent compliance constraints, maximizing the treated water volume. Compared to the initial conservative settings, this improves the single-cycle treatment capacity by approximately 5%~10%.

[0046] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection. All equivalent substitutions and improvements based on the concept of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive limit control system for SBR decanter based on multi-parameter coupling, characterized in that, include: Multi-parameter sensing module, multi-parameter coupled modeling and control unit, execution module, and feedback correction module; The multi-parameter sensing module integrates: an online MLSS meter for real-time acquisition of mixed liquor suspended solids concentration (MLSS), a thermometer for real-time acquisition of biological tank temperature, a sludge interface meter for real-time acquisition of sludge layer height (L), an online effluent SS meter for online monitoring of effluent suspended solids (SS), an online effluent TP meter for online monitoring of effluent total phosphorus (TP), and an auxiliary input terminal for inputting laboratory sludge settling ratio (SV30) and sludge volume index (SVI) data and recording the input timestamp. The multi-parameter coupled modeling and control unit is deployed in the central control system. It has a built-in "settling risk-safety margin" mapping model and limit calculation engine. It uses the sludge level height L obtained in real time by the sludge interface instrument as the absolute benchmark, and uses the settling expansion coefficient k1 represented by SVI / SV30, the activity inhibition coefficient k2 represented by temperature, the density compensation coefficient k3 represented by MLSS, and the time decay coefficient k4 represented by the time of laboratory data as settling risk factors to dynamically generate the safe redundancy distance ΔS between the decanter branch pipe inlet and the sludge layer surface, and outputs the optimal lower limit Hopt = L + ΔS that the decanter branch pipe inlet is allowed to descend in this decanting cycle. The execution module is a controlled electric slide bar, used to lock the slider at the specified height position before decanting starts according to the Hopt command, thus achieving pre-emptive prevention; the feedback correction module is used to receive the monitoring signals from the online SS meter and the online TP meter of the effluent, and has embedded attribution judgment logic. After detecting that the effluent exceeds the standard and confirming through attribution analysis that the risk of siphon entrainment has been missed, it automatically generates a limit upward displacement compensation amount ΔHcor and adds it to ΔS calculated by the model in the next cycle; And when the water output meets the standard for multiple consecutive cycles, the safety redundancy distance is reduced in small steps to form a self-optimizing closed-loop control.

2. The adaptive limit control system according to claim 1, characterized in that, The sedimentation expansion coefficient k1 is determined based on SVI and / or SV30, k1≥1, and the higher the SVI / SV30 value, the larger k1 is; the activity inhibition coefficient k2 is determined by temperature, k2≥1, and the lower the temperature, the larger k2 is. When the temperature is below a preset threshold, a conservative mode is automatically activated to take a larger value; the density compensation coefficient k3 is determined by MLSS, k3≥1, and the higher the MLSS, the larger k3 is; the time decay coefficient k4 is determined by the time interval Δt between the laboratory SVI / SV30 data entry time and the current decanting cycle, k4≥1, and the longer Δt, the larger k4 is.

3. The adaptive limit control system according to claim 2, characterized in that, The calculation model for the safety redundancy distance ΔS is as follows: ΔS = D_base × k1 × k2 × k3 × k4 Among them, D_base is the basic safety distance, which is set according to equipment parameters such as decanting flow rate and branch pipe diameter, and represents the basic control range of the siphon influence radius under standard operating conditions; the product form of each coefficient reflects the synergistic amplification relationship between each risk factor.

4. The adaptive limit control system according to claim 1, characterized in that, The attribution judgment logic of the feedback correction module is configured to determine that the risk of siphon entrainment is missed when the following conditions are met simultaneously: (a) The periods of excessive effluent discharge overlap with the decanting operation periods; (b) The average SS or TP value during the decanting stage shows a significant surge compared to the baseline value before decanting, and the increase exceeds the preset fluctuation range; (c) The online COD and TP load indicators of the influent did not show any impact peaks exceeding the historical normal amplitude during the same period, thus excluding external impact factors.

5. The adaptive limit control system according to claim 1, characterized in that, When the effluent SS and TP have been stably up to standard for N consecutive cycles and the current safety redundancy distance is greater than the minimum value of historical successful operation, the feedback correction module reduces the ΔS benchmark value or decreases the attenuation slope of k4 by a small step size δ, gradually approaching the equilibrium point of maximizing effluent flow. If the effluent indicators after reduction show a trend close to the standard limit, then stop reducing and revert to the parameters of the previous step.

6. A method for adaptive limit control of SBR decanter based on multi-parameter coupling, characterized in that, Includes the following steps: Step S1: Establish a multi-factor mapping model based on siphon risk. Construct a nonlinear mapping relationship with the sludge level height L obtained in real time by the sludge interface instrument as the benchmark, the sedimentation expansion coefficient k1 characterized by SVI and / or SV30, the activity inhibition coefficient k2 characterized by temperature, the density compensation coefficient k3 characterized by MLSS, and the time decay coefficient k4 characterized by the time of laboratory data as the input variables, and the safety redundancy distance ΔS as the output. Step S2: At the end of the sedimentation stage or before the decanting stage of each SBR cycle, automatically collect the MLSS, temperature, and sludge level L signals of the current biological tank, retrieve the most recently manually entered SV30 / SVI value and its entry timestamp, calculate the lag time Δt of the test data from the current time, and form a complete input vector. Step S3: Substitute the input vector into the mapping model, calculate the correlation coefficients k1, k2, k3, and k4 of each process online, calculate the safety redundancy distance ΔS, and then obtain the optimal lower limit position Hopt = L + ΔS. Drive the actuator to precisely adjust the lower limit position of the decanter to this height. This process is completed before water enters the decanter branch pipe. Step S4: After this round of decanting is completed, obtain the online monitoring data sequence of effluent SS and TP, and perform attribution judgment and feedback correction.

7. The adaptive limit control method according to claim 6, characterized in that, The calculation model for the safety redundancy distance ΔS in step S1 is as follows: ΔS = D_base × k1 × k2 × k3 × k4 Among them, D_base is the basic safety distance, initially set based on equipment parameters such as decanting flow rate and branch pipe diameter, representing the basic control range of the siphon influence radius under standard operating conditions; k1 is the settling expansion coefficient, k1≥1, the higher the SVI / SV30 value, the larger k1 is, quantifying the expansion effect of sludge looseness on the siphon influence radius; k2 is the activity inhibition coefficient, k2≥1, the lower the temperature, the larger k2 is, quantifying the additional deterioration effect of low temperature on settling performance; k3 is the density compensation coefficient, k3≥1, the higher the MLSS, the larger k3 is, quantifying the amplification effect of high solids content environment on siphon entrainment risk; k4 is the time decay coefficient, k4≥1, the longer the lag time Δt, the larger k4 is, quantifying the information uncertainty risk caused by the lag of test data.

8. The adaptive limit control method according to claim 7, characterized in that, The calculation rules for the correlation coefficients of each process in step S3 are as follows: k1 is calculated using a table or formula based on SVI / SV30; k2 calculates based on the current temperature using a table or formula, and automatically activates the conservative mode to take a larger value when the temperature is below the preset threshold. k3 is calculated using MLSS tables or formulas; k4 is calculated based on the lag time Δt: when Δt is less than or equal to the preset freshness threshold, k4=1; when Δt exceeds the threshold, k4 increases according to the preset function.

9. The adaptive limit control method according to claim 6, characterized in that, The attribution judgment and feedback correction mentioned in step S4 include: Attribution judgment: If the effluent SS ≥ 8 mg / L or TP ≥ 0.4 mg / L, the following conditions will be automatically checked: (a) The period of exceeding the standard overlaps with the decanting operation period; (b) The average SS or TP value during the decanting stage shows a significant surge compared to the baseline value before decanting, and the increase exceeds the preset fluctuation range; (c) During the same period, the online COD and TP load indicators of the influent did not show any impact peaks exceeding the historical normal amplitude; When conditions (a), (b) and (c) are met simultaneously, it is determined that there is a risk of siphon entrainment, and feedback compensation is triggered. Compensation Correction: Based on the excess range, the compensation height ΔHcor is converted, and the reference value ΔS under the current working condition is corrected to ΔS' = ΔS + ΔHcor. The mapping parameters of the corresponding working condition area in the model are updated so that the safe distance will automatically increase when running under similar conditions next time.

10. The adaptive limit control method according to claim 9, characterized in that, Step S4 also includes a trial optimization step: if the effluent SS and TP are consistently up to standard for N consecutive cycles, and the current safe redundancy distance is greater than the minimum value of historical successful operation, then reduce the ΔS benchmark value by a small step size δ or reduce the attenuation slope of k4 to gradually approach the equilibrium point of maximizing effluent flow. If the reduced SS or TP shows a trend close to the standard limit, the reduction should be stopped immediately and the parameters should be returned to the previous step to lock the current balance point, maximizing the single-cycle water treatment volume while ensuring that the effluent meets the standards.

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