Automatic sludge discharge coordination control method and system for sedimentation tank sludge

Abstract

This invention relates to the field of sludge discharge control technology in sedimentation tanks, and discloses a method and system for automatic sludge discharge control in sedimentation tanks. The method includes: acquiring multi-source data from the sedimentation tank and extracting features to construct a multi-dimensional feature set; analyzing the multi-dimensional feature set to determine the current operating condition category of the sedimentation tank and, based on this, determining the lag time constant of the impact of sludge discharge actions on effluent water quality; constructing an initial digital twin model of the sedimentation tank based on solid flux settling theory, and obtaining the optimal digital twin model through inversion calibration based on multi-source data; simulating the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the lag time constant, and determining the optimal sludge discharge strategy from the results to control the automatic sludge discharge from the sedimentation tank. This invention solves the technical problems of poor adaptability to changes in operating conditions and the disconnect between sludge discharge actions and water quality response in existing technologies, achieving precise, intelligent, and long-term stable operation of sedimentation tank sludge discharge control.

Description

Technical Field

[0001] This invention relates to the field of sludge discharge control technology in sedimentation tanks, and in particular to a method and system for automatic sludge discharge control in sedimentation tanks. Background Technology

[0002] As a core component of solid-liquid separation in wastewater treatment systems, sedimentation tanks directly impact the stability of effluent quality and the operational load of subsequent sludge treatment units through their sludge discharge control. Currently, wastewater treatment plants are expanding in scale, experiencing frequent fluctuations in influent water quality, and facing increasingly stringent discharge standards, placing higher demands on the precision, adaptability, and intelligence of sludge discharge control in sedimentation tanks.

[0003] Existing sludge discharge control methods for sedimentation tanks mainly include: timed sludge discharge method, which relies on manual experience to set fixed time intervals or durations, cannot respond to dynamic changes in influent load, and is prone to insufficient sludge discharge causing sludge to float or excessive sludge discharge causing sludge loss; sludge level feedback control method, which controls the sludge layer height in real time by measuring the sludge level with a sludge level gauge, but this method is a post-event adjustment and ignores the lag effect of sludge discharge on effluent water quality. The sludge discharge effect may not be reflected in the effluent indicators for tens of minutes or even several hours, resulting in control lag, serious overshoot, and long-term operation may cause system oscillation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and system for automatic sludge discharge and coordinated control of sedimentation tanks, comprising: Acquire multi-source data from sedimentation tanks and extract features characterizing sludge settling state from the multi-source data to construct a multi-dimensional feature set. The multidimensional feature set is analyzed to determine the current operating condition category of the sedimentation tank, and the lag time constant of the impact of sludge discharge on the effluent quality is determined based on the operating condition category. An initial digital twin model of the sedimentation tank was constructed based on the solid flux sedimentation theory, and the digital twin model was inverted and calibrated based on multi-source data to obtain the optimal digital twin model. The sludge discharge control sequence of the sedimentation tank was simulated based on the optimal digital twin model and the lag time constant, and the optimal sludge discharge strategy was determined from the simulation results to control the automatic sludge discharge of the sedimentation tank.

[0005] Furthermore, the acquisition of multi-source data from the sedimentation tank and the extraction of features characterizing the sludge settling state from the multi-source data to construct a multi-dimensional feature set include: Acquire multi-source data from sedimentation tanks and extract features characterizing sludge settling state from the multi-source data. Features characterizing sludge settling state include sludge level change rate, sludge concentration gradient entropy value, and solids flux load. The sludge level change rate characteristics, sludge concentration gradient entropy characteristics, and solid flux load characteristics are normalized and spliced ​​to obtain a multidimensional feature set.

[0006] Furthermore, the analysis of the multidimensional feature set to determine the current operating condition category of the sedimentation tank includes: Historical multi-source data and historical multi-dimensional feature sets are obtained, and clustering training is performed on the historical multi-source data and historical multi-dimensional feature sets based on clustering algorithms to obtain a clustering model; Historical multi-source data were divided into three operating condition clusters: high-load settling period, low-load stable period, and sludge bulking warning period. The centroid coordinates and cluster radius thresholds of each operating condition cluster were recorded. The multidimensional feature set is input into the clustering model, the Mahalanobis distance between the multidimensional feature set and the centroid of each working condition cluster is calculated, and the working condition in which the Mahalanobis distance is less than the cluster radius threshold of the corresponding cluster is determined as the working condition category of the current sedimentation tank.

[0007] Furthermore, the determination of the lag time constant for the impact of sludge discharge on effluent quality based on operating condition category includes: Determine the time series analysis window that is pre-matched with the operating condition category, and extract the time series sequence of sludge discharge valve opening and the time series sequence of total phosphorus / ammonia nitrogen concentration in the effluent from the multi-source data within the time series analysis window; The stationarity of the sludge discharge valve opening time series and the effluent total phosphorus / ammonia nitrogen concentration time series was tested, and the non-stationary series were processed by first-order difference to obtain the stationary sludge discharge valve opening time series and the effluent total phosphorus / ammonia nitrogen concentration time series. Granger causality tests were performed on the time series of sludge discharge valve opening and the time series of total phosphorus / ammonia nitrogen concentration in the effluent after stabilization. The lag order that makes the statistic most significant among the preset lag orders was selected as the lag time constant.

[0008] Furthermore, the initial digital twin model of the sedimentation tank is constructed based on the solid flux sedimentation theory, and the optimal digital twin model is obtained by inversion calibration based on multi-source data, including: An initial digital twin model was constructed based on the solid flux settling theory. The digital twin model includes settling velocity parameters and compressibility coefficient parameters. The settling velocity equation is used to describe the relationship between sludge settling velocity and sludge concentration. The limiting settling velocity and the settling resistance coefficient in the settling velocity equation were determined, and the limiting settling velocity, the settling resistance coefficient, and the compressibility coefficient of the sludge were sequentially assimilated based on the ensemble Kalman filter algorithm. Identify the members of multiple parameter perturbation sets that run in parallel during the assimilation process, and calculate the root mean square error between the predicted and measured values ​​of each parameter perturbation set member; The optimal digital twin model is obtained by selecting the parameter perturbation set with the smallest root mean square error from multiple root mean square errors.

[0009] Furthermore, the settlement velocity equation is: , in, For sludge settling velocity, For the ultimate settlement velocity, The coefficient of resistance to settlement is . Sludge concentration, It is the natural logarithm function.

[0010] Furthermore, the simulation of the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the hysteresis time constant includes: Determine a preset multiple, starting from the current time, and use the preset multiple length of the lag time constant as the rolling time domain window; Within the rolling time window, the start-stop sequence of the sludge pump and the opening degree of the sludge valve are used as decision variables to generate a candidate set of sludge control sequences. The candidate set includes at least continuous high-frequency sludge discharge sequences, intermittent low-frequency sludge discharge sequences, and sludge discharge sequences with a denser start and a sparser finish. Each candidate sludge discharge control sequence is input into the optimal digital twin model for forward simulation prediction, and the sludge level height and return sludge concentration at the end of the rolling time domain under each candidate sludge discharge control sequence are obtained.

[0011] Furthermore, determining the optimal sludge removal strategy from the simulation results to control the automatic sludge removal from the sedimentation tank includes: Determine the pre-set upper limit of sludge level and the stable range of return sludge concentration, and eliminate candidate sludge discharge control sequences in the simulation results that exceed the upper limit of sludge level or the return sludge concentration value exceeds the stable range of return sludge concentration. For the remaining candidate sludge discharge control sequences in the simulation results, the candidate sludge discharge control sequence that minimizes the comprehensive cost function value is selected; The candidate sludge discharge control sequence is taken as the optimal sludge discharge strategy, and the sludge in the sedimentation tank is automatically discharged based on the optimal sludge discharge strategy.

[0012] Furthermore, the comprehensive cost function is: , in, To determine the overall cost-effectiveness, α is the first pre-set weighting coefficient. The simulated predicted value is the mud level. The target setting value for the mud level. This is the second preset weighting coefficient. The simulated predicted value for the concentration of returned sludge. The target set value for the concentration of returned sludge. The third preset weighting coefficient, Estimate the energy consumption of the sludge pump.

[0013] The present invention also provides an automatic sludge discharge and coordinated control system for sedimentation tanks, comprising: The acquisition module is used to acquire multi-source data from the sedimentation tank and extract features that characterize the sludge settling state from the multi-source data to construct a multi-dimensional feature set. The analysis module is used to analyze the multidimensional feature set, determine the current operating condition category of the sedimentation tank, and determine the lag time constant of the impact of sludge discharge on the effluent quality based on the operating condition category. The modeling module is used to construct an initial digital twin model of the sedimentation tank based on the solid flux sedimentation theory, and to perform inversion calibration on the digital twin model based on multi-source data to obtain the optimal digital twin model. The control module is used to simulate the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the lag time constant, and to determine the optimal sludge discharge strategy from the simulation results to control the automatic sludge discharge of the sedimentation tank.

[0014] Compared with the prior art, the automatic sludge discharge and coordinated control method and system for sedimentation tanks according to embodiments of the present invention have the following advantages: This invention extracts multidimensional features from multi-source data and clusters them to identify hidden operating conditions such as high load and expansion warning. It can dynamically match differentiated lag time constants, effectively solving the problem of sludge discharge being delayed or advanced when water quality changes suddenly in traditional fixed delay control. It significantly improves the time synchronization accuracy between sludge discharge action and changes in influent load. This invention combines the solid flux settling mechanism with ensemble Kalman filter data assimilation to perform sequential inversion calibration of model parameters, enabling the model to not only accurately reproduce the current operating state, but also to physically and consistently predict the future evolution trend of mud level and recirculation concentration, overcoming the defect of pure black box models being prone to instability under extreme operating conditions. This invention relies on a precise prediction model with dynamic hysteresis compensation. Rolling time-domain simulation can select the optimal strategy with the minimum comprehensive cost function from multiple candidate control sequences, thereby automatically reducing unnecessary sludge discharge energy consumption and equipment wear while ensuring that the effluent water quality meets the standards. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the process structure of the automatic sludge discharge and coordinated control method for sedimentation tank sludge in an embodiment of the present invention; Figure 2 This is a schematic diagram of the composition of the automatic sludge discharge and coordinated control system for sedimentation tanks in an embodiment of the present invention. Detailed Implementation

[0016] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0017] like Figure 1 As shown in the embodiments of this application, an automatic sludge discharge and coordinated control method for sedimentation tanks is provided, comprising: S100: Acquire multi-source data from the sedimentation tank and extract features characterizing the sludge settling state from the multi-source data to construct a multi-dimensional feature set.

[0018] In a specific implementation process, the acquisition of multi-source data from the sedimentation tank and the extraction of features characterizing the sludge settling state from the multi-source data to construct a multi-dimensional feature set include: Acquire multi-source data from sedimentation tanks and extract features characterizing sludge settling state from the multi-source data. Features characterizing sludge settling state include sludge level change rate, sludge concentration gradient entropy value, and solids flux load. The sludge level change rate characteristics, sludge concentration gradient entropy characteristics, and solid flux load characteristics are normalized and spliced ​​to obtain a multidimensional feature set.

[0019] Specifically, this invention acquires multi-source data from sedimentation tanks and extracts three key features characterizing sludge settling status: sludge level change rate, reflecting the dynamic trend of sludge level rise and fall, obtained by fitting the slope through a sliding window, which can predict the acceleration of sludge level rise or fall in advance; sludge concentration gradient entropy, calculated using concentration sensor data at different elevations of the tank wall, characterizing the density and vertical distribution uniformity of the sludge compression layer, effectively indicating the risk of sludge expansion or caking; and solids flux load, quantifying the current solids impact intensity experienced by the sedimentation tank through convolution of influent flow rate and concentration. This step achieves a three-dimensional representation of settlement status from three dimensions: dynamic trend, spatial distribution, and load intensity. It normalizes and concatenates these three types of features to construct a multi-dimensional feature set, solving the technical challenge of inconsistent feature dimensions and the inability to perform joint analysis. This enables subsequent cluster analysis to measure similarity based on a unified scale, significantly improving the sensitivity and matching accuracy of the working condition identification algorithm in distinguishing historical data clusters. It provides structured and standardized high-information input for accurate subsequent working condition category determination, laying a data foundation for causal lag identification and twin model calibration. This effectively avoids the one-sidedness and false alarm / false negative problems of single-parameter threshold judgment, greatly improving the system's ability to perceive and respond to complex working condition changes. S200: Analyze the multidimensional feature set to determine the current operating condition category of the sedimentation tank, and determine the lag time constant of the impact of sludge discharge on the effluent quality based on the operating condition category.

[0020] In a specific implementation process, the analysis of the multidimensional feature set to determine the current operating condition category of the sedimentation tank includes: Historical multi-source data and historical multi-dimensional feature sets are obtained, and clustering training is performed on the historical multi-source data and historical multi-dimensional feature sets based on clustering algorithms to obtain a clustering model; Historical multi-source data were divided into three operating condition clusters: high-load settling period, low-load stable period, and sludge bulking warning period. The centroid coordinates and cluster radius thresholds of each operating condition cluster were recorded. The multidimensional feature set is input into the clustering model, the Mahalanobis distance between the multidimensional feature set and the centroid of each working condition cluster is calculated, and the working condition in which the Mahalanobis distance is less than the cluster radius threshold of the corresponding cluster is determined as the working condition category of the current sedimentation tank.

[0021] Specifically, this invention acquires historical multi-source data and corresponding historical multi-dimensional feature sets, and uses a clustering algorithm to train them unsupervised, constructing a clustering model for operating condition identification. This clustering model automatically divides historical operating data into three typical operating condition clusters: high-load settling period, low-load stable period, and sludge bulking warning period, and accurately records the centroid coordinates and cluster radius thresholds of each operating condition cluster, forming a benchmark spatial domain representing different operating states. In real-time applications, the current multi-dimensional feature set is input into the trained clustering model, and the Mahalanobis distance between the feature vector and the centroid of each operating condition cluster is calculated. The Mahalanobis distance can effectively eliminate the influence of differences in the dimensions of each feature and the correlation between features, making the similarity measurement more objective and accurate. By comparing the Mahalanobis distance with the radius threshold of the corresponding cluster, the category to which the current operating condition belongs is determined. When the real-time feature vector falls within the radius threshold range of a certain operating condition cluster, it is confirmed that the current sedimentation tank belongs to that operating condition category. This step enables objective, quantitative, and automated identification of the operating conditions of sedimentation tanks, effectively avoiding the subjectivity and lag of human experience-based judgment. After cluster training, when the real-time feature vector falls within a certain cluster range, it can directly and accurately make a judgment, significantly improving the efficiency and robustness of operating condition identification. This provides a reliable basis for subsequent differentiated matching of sludge discharge lag parameters and targeted optimization of sludge discharge strategies.

[0022] In a specific implementation process, the determination of the lag time constant of the impact of sludge discharge action on effluent quality based on the operating condition category includes: Determine the time series analysis window that is pre-matched with the operating condition category, and extract the time series sequence of sludge discharge valve opening and the time series sequence of total phosphorus / ammonia nitrogen concentration in the effluent from the multi-source data within the time series analysis window; The stationarity of the sludge discharge valve opening time series and the effluent total phosphorus / ammonia nitrogen concentration time series was tested, and the non-stationary series were processed by first-order difference to obtain the stationary sludge discharge valve opening time series and the effluent total phosphorus / ammonia nitrogen concentration time series. Granger causality tests were performed on the time series of sludge discharge valve opening and the time series of total phosphorus / ammonia nitrogen concentration in the effluent after stabilization. The lag order that makes the statistic most significant among the preset lag orders was selected as the lag time constant.

[0023] Specifically, this invention pre-matches differentiated time-series analysis windows for different operating conditions. A short window is matched during the high-load settling period to capture rapid response characteristics; a long window is matched during the low-load stable period to obtain sufficient statistical information; and an ultra-short window is matched during the sludge bulking warning period to achieve early risk identification. The sludge discharge valve opening time series and the effluent total phosphorus / ammonia nitrogen concentration time series within these windows are extracted from multi-source data as the basic data pairs for causal analysis. A stationarity test is performed on these two series. If they are determined to be non-stationary, first-order differencing is performed to stationarize them, effectively avoiding interference from trend-related spurious correlations in the time-series data. Subsequently, a Granger causality test is performed on the stationary two series. The F-statistic is calculated sequentially among multiple preset lag orders, and the lag order with the most significant statistic is selected as the lag time constant for the impact of sludge discharge on effluent quality. This step achieves objective quantification and dynamic identification of the time delay relationship between sludge discharge disturbance and water quality response, overcoming the limitations of traditional methods that rely on fixed lag times based on experience. Through window matching based on different operating conditions and stationarity preprocessing, the statistical reliability and identification accuracy of Granger causality tests in practical wastewater treatment applications are significantly improved. The output lag time constant is directly used as the prediction step size benchmark for subsequent digital twin model rolling optimization, effectively eliminating the timing misalignment between control actions and system responses, and greatly enhancing the predictive ability and response timeliness of sludge discharge control to water quality impacts.

[0024] S300: An initial digital twin model of the sedimentation tank is constructed based on the solid flux sedimentation theory, and the digital twin model is inverted and calibrated based on multi-source data to obtain the optimal digital twin model.

[0025] In a specific implementation process, the initial digital twin model of the sedimentation tank is constructed based on the solid flux sedimentation theory, and the optimal digital twin model is obtained by inversion calibration based on multi-source data, including: An initial digital twin model was constructed based on the solid flux settling theory. The digital twin model includes settling velocity parameters and compressibility coefficient parameters. The settling velocity equation is used to describe the relationship between sludge settling velocity and sludge concentration. The limiting settling velocity and the settling resistance coefficient in the settling velocity equation were determined, and the limiting settling velocity, the settling resistance coefficient, and the compressibility coefficient of the sludge were sequentially assimilated based on the ensemble Kalman filter algorithm. Identify the members of multiple parameter perturbation sets that run in parallel during the assimilation process, and calculate the root mean square error between the predicted and measured values ​​of each parameter perturbation set member; The optimal digital twin model is obtained by selecting the parameter perturbation set with the smallest root mean square error from multiple root mean square errors.

[0026] Specifically, this invention constructs an initial digital twin model based on solid flux settling theory. This model includes settling velocity parameters and sludge compressibility parameters, wherein a settling velocity equation is used to describe the exponential decay relationship between sludge settling velocity and concentration. The limiting settling velocity and settling resistance coefficient in the settling velocity equation are determined, and a sequential data assimilation of the limiting settling velocity, settling resistance coefficient, and sludge compressibility coefficient is performed based on an ensemble Kalman filter algorithm. Specifically, multiple parameter perturbation sets are initialized, and each set parameter is sampled with Gaussian perturbation near its initial value according to a preset variance. Within each sampling period, each set... The parameters are substituted into the model to solve for the predicted values. Combined with real-time collected observation data such as influent and effluent sludge concentration, vertical concentration distribution, and sludge level height, the prediction covariance calculation, Kalman gain solution, and analysis update steps are executed sequentially to complete the recursive and progressive optimization of the ultimate settling velocity, settling resistance coefficient, and compressibility coefficient. During the assimilation process, all parameter perturbation set members are run in parallel, and the normalized root mean square error between the predicted and measured values ​​of each member is calculated. The parameter perturbation set member corresponding to the smallest value among multiple root mean square errors is selected as the current optimal parameter set. Substituting it into the model framework yields the optimal digital twin model. This step deeply integrates the structural prior of the mechanistic model with the dynamic correction capability of data assimilation, enabling the model parameters to be updated online adaptively with the seasonal evolution of sludge characteristics or sudden shocks. This effectively overcomes the technical defects of traditional offline calibration parameters that cause prediction distortion due to long-term mismatch. The parallel optimization mechanism of multiple ensemble members quantifies the uncertainty of parameter inversion, ensures the statistical reliability of the optimal parameters, and significantly improves the prediction accuracy and generalization ability of the twin model for sludge level and recirculation concentration. It provides a high-fidelity virtual simulation environment for the rolling optimization of subsequent sludge discharge strategies.

[0027] In a specific implementation process, the settlement velocity equation is: , in, For sludge settling velocity, For the ultimate settlement velocity, The coefficient of resistance to settlement is . Sludge concentration, It is the natural logarithm function.

[0028] S400: Based on the optimal digital twin model and lag time constant, the sludge discharge control sequence of the sedimentation tank is simulated, and the optimal sludge discharge strategy is determined from the simulation results to control the automatic sludge discharge of the sedimentation tank.

[0029] In a specific implementation process, the simulation of the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the lag time constant includes: Determine a preset multiple, starting from the current time, and use the preset multiple length of the lag time constant as the rolling time domain window; Within the rolling time window, the start-stop sequence of the sludge pump and the opening degree of the sludge valve are used as decision variables to generate a candidate set of sludge control sequences. The candidate set includes at least continuous high-frequency sludge discharge sequences, intermittent low-frequency sludge discharge sequences, and sludge discharge sequences with a denser start and a sparser finish. Each candidate sludge discharge control sequence is input into the optimal digital twin model for forward simulation prediction, and the sludge level height and return sludge concentration at the end of the rolling time domain under each candidate sludge discharge control sequence are obtained.

[0030] Specifically, this invention takes the current moment as the starting point and uses a preset multiple of the lag time constant as the length of the rolling time domain window. It embeds the actual response rhythm of the sludge discharge action to the effluent quality into the prediction framework, ensuring that the simulation time domain matches the dynamic characteristics of the controlled object, and avoiding the introduction of invalid noise due to an excessively long time domain or the loss of key response information due to an excessively short time domain. Within the rolling time domain window, the start-stop sequence of the sludge discharge pump and the opening degree of the sludge discharge valve are used as decision variables to generate a diverse candidate set, including continuous high-frequency sludge discharge sequences, intermittent low-frequency sludge discharge sequences, and sludge discharge sequences with a denser initial phase and a sparser later phase. The above three types of sequences correspond to different sludge discharge logics such as rapid peak shaving under high load, stable quality maintenance under low load, and smooth adjustment during the transition period, respectively, realizing full coverage of complex operating conditions by the candidate strategies. Each candidate sludge discharge control sequence is input into the optimal digital twin model for forward simulation prediction, and the sludge level height and return sludge concentration value corresponding to each candidate strategy at the end of the rolling time domain are deduced. This step uses a high-fidelity twin model based on data assimilation and calibration as a virtual test field, which can evaluate the differentiated control effects of multiple sludge removal strategies in parallel without trial and error in the actual system. This significantly reduces the trial and error cost and risk of strategy optimization while ensuring process safety. The architecture of multi-strategy parallel simulation provides sufficient decision-making information support for the subsequent screening of the optimal sludge removal control sequence based on the comprehensive cost function, so that the sludge removal strategy selected at the end has already completed the selection of the best and worst based on the predicted effect before actual execution.

[0031] In a specific implementation process, determining the optimal sludge discharge strategy from simulation results to control the automatic sludge discharge from the sedimentation tank includes: Determine the pre-set upper limit of sludge level and the stable range of return sludge concentration, and eliminate candidate sludge discharge control sequences in the simulation results that exceed the upper limit of sludge level or the return sludge concentration value exceeds the stable range of return sludge concentration. For the remaining candidate sludge discharge control sequences in the simulation results, the candidate sludge discharge control sequence that minimizes the comprehensive cost function value is selected; The candidate sludge discharge control sequence is taken as the optimal sludge discharge strategy, and the sludge in the sedimentation tank is automatically discharged based on the optimal sludge discharge strategy.

[0032] Specifically, this invention pre-sets a safe upper limit for sludge level and a stable range for return sludge concentration as hard safety constraints. Simulation results of each candidate sludge discharge control sequence are substituted into these constraints for feasibility screening. All candidate sequences whose sludge level exceeds the safe upper limit or whose return sludge concentration exceeds the stable range are eliminated. This ensures that the sludge discharge strategy entering the subsequent optimization stage is absolutely feasible within the physical safety boundary, preventing operational risks such as sludge loss due to excessive sludge discharge or sludge floating due to insufficient sludge discharge from the source. For the remaining candidate sequences that pass the safety constraint screening, a comprehensive cost function is used as the evaluation criterion for quantitative optimization. This function uses multiple indicators, including weighted sludge level deviation, return concentration deviation, and estimated energy consumption of the sludge discharge pump, to prioritize sludge level stability while minimizing return sludge concentration fluctuations and saving sludge discharge energy. The candidate sequence that minimizes the comprehensive cost function value is selected as the optimal sludge discharge strategy. The optimal strategy is then issued and executed as a control command, guiding the sedimentation tank to automatically discharge sludge according to the optimal timing. This step constructs a progressive decision-making framework that prioritizes safety and seeks multi-objective optimization, ensuring that control decisions are both safe and feasible, as well as economically viable. Potentially dangerous strategies are thoroughly filtered out during the virtual simulation stage, achieving proactive safety assurance rather than reactive remediation, and significantly improving the safety margin of the sludge discharge process. The comprehensive cost function quantifies and unifies the multi-objective trade-offs, effectively solving the collaborative optimization problem of mutual constraints between sludge level stability, reflux concentration fluctuations, and sludge discharge energy consumption, enabling the automatic sludge discharge from the sedimentation tank to achieve comprehensive optimization in terms of safety, stability, and economy.

[0033] In a specific implementation process, the comprehensive cost function is: , in, To determine the overall cost-effectiveness, α is the first pre-set weighting coefficient. The simulated predicted value is the mud level. The target setting value for the mud level. This is the second preset weighting coefficient. The simulated predicted value for the concentration of returned sludge. The target set value for the concentration of returned sludge. The third preset weighting coefficient, Estimate the energy consumption of the sludge pump.

[0034] like Figure 2 As shown in the embodiments of this application, an automatic sludge discharge and coordinated control system for sedimentation tanks is provided, comprising: The acquisition module 100 is used to acquire multi-source data from the sedimentation tank and extract features that characterize the sludge settling state from the multi-source data to construct a multi-dimensional feature set. Analysis module 200 is used to analyze the multidimensional feature set, determine the current operating condition category of the sedimentation tank, and determine the lag time constant of the impact of sludge discharge action on the effluent quality based on the operating condition category. Modeling module 300 is used to construct an initial digital twin model of the sedimentation tank based on the solid flux sedimentation theory, and to perform inversion calibration on the digital twin model based on multi-source data to obtain the optimal digital twin model; The control module 400 is used to simulate the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the lag time constant, and to determine the optimal sludge discharge strategy from the simulation results to control the automatic sludge discharge of the sedimentation tank.

[0035] In summary, this invention provides a method and system for automatic sludge discharge coordination control in sedimentation tanks. The method includes: acquiring multi-source data from the sedimentation tank and extracting features to construct a multi-dimensional feature set; analyzing the multi-dimensional feature set to determine the current operating condition category of the sedimentation tank and, based on this, determining the lag time constant of the impact of sludge discharge actions on effluent water quality; constructing an initial digital twin model of the sedimentation tank based on solid flux settling theory and performing inversion calibration based on multi-source data to obtain the optimal digital twin model; simulating the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the lag time constant, and determining the optimal sludge discharge strategy from the results to control the automatic sludge discharge from the sedimentation tank. This invention solves the technical problems of poor adaptability to changes in operating conditions and the disconnect between sludge discharge actions and water quality response in existing technologies, achieving precise, intelligent, and long-term stable operation of sedimentation tank sludge discharge control.

[0036] Finally, it should be noted that those skilled in the art can obviously make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for automatically controlling sludge discharge of a sedimentation tank, characterized by, include: Acquire multi-source data from sedimentation tanks and extract features characterizing sludge settling state from the multi-source data to construct a multi-dimensional feature set. The multidimensional feature set is analyzed to determine the current operating condition category of the sedimentation tank, and the lag time constant of the impact of sludge discharge on the effluent quality is determined based on the operating condition category. An initial digital twin model of the sedimentation tank was constructed based on the solid flux sedimentation theory, and the digital twin model was inverted and calibrated based on multi-source data to obtain the optimal digital twin model. The sludge discharge control sequence of the sedimentation tank was simulated based on the optimal digital twin model and the lag time constant, and the optimal sludge discharge strategy was determined from the simulation results to control the automatic sludge discharge of the sedimentation tank.

2. The automatic sludge discharge coordination control method for a sedimentation tank according to claim 1, characterized in that, The process of acquiring multi-source data from the sedimentation tank and extracting features characterizing the sludge settling state from the multi-source data to construct a multi-dimensional feature set includes: Acquire multi-source data from sedimentation tanks and extract features characterizing sludge settling state from the multi-source data. Features characterizing sludge settling state include sludge level change rate, sludge concentration gradient entropy value, and solids flux load. The sludge level change rate characteristics, sludge concentration gradient entropy characteristics, and solid flux load characteristics are normalized and spliced ​​to obtain a multidimensional feature set.

3. The method according to claim 1, characterized in that, The analysis of the multidimensional feature set to determine the current operating condition category of the sedimentation tank includes: Historical multi-source data and historical multi-dimensional feature sets are obtained, and clustering training is performed on the historical multi-source data and historical multi-dimensional feature sets based on clustering algorithms to obtain a clustering model; Historical multi-source data were divided into three operating condition clusters: high-load settling period, low-load stable period, and sludge bulking warning period. The centroid coordinates and cluster radius thresholds of each operating condition cluster were recorded. The multidimensional feature set is input into the clustering model, the Mahalanobis distance between the multidimensional feature set and the centroid of each working condition cluster is calculated, and the working condition in which the Mahalanobis distance is less than the cluster radius threshold of the corresponding cluster is determined as the working condition category of the current sedimentation tank.

4. The automatic sludge discharge coordination control method for a sedimentation tank according to claim 1, characterized in that, The time lag constant for determining the impact of sludge discharge on effluent quality based on operating condition category includes: Determine the time series analysis window that is pre-matched with the operating condition category, and extract the time series sequence of sludge discharge valve opening and the time series sequence of total phosphorus / ammonia nitrogen concentration in the effluent from the multi-source data within the time series analysis window; The stationarity of the sludge discharge valve opening time series and the effluent total phosphorus / ammonia nitrogen concentration time series was tested, and the non-stationary series were processed by first-order difference to obtain the stationary sludge discharge valve opening time series and the effluent total phosphorus / ammonia nitrogen concentration time series. Granger causality tests were performed on the time series of sludge discharge valve opening and the time series of total phosphorus / ammonia nitrogen concentration in the effluent after stabilization. The lag order that makes the statistic most significant among the preset lag orders was selected as the lag time constant.

5. The method according to claim 1, wherein the method is characterized by, The initial digital twin model of the sedimentation tank is constructed based on the solid flux sedimentation theory, and the optimal digital twin model is obtained by inversion calibration based on multi-source data, including: An initial digital twin model was constructed based on the solid flux settling theory. The digital twin model includes settling velocity parameters and compressibility coefficient parameters. The settling velocity equation is used to describe the relationship between sludge settling velocity and sludge concentration. The limiting settling velocity and the settling resistance coefficient in the settling velocity equation were determined, and the limiting settling velocity, the settling resistance coefficient, and the compressibility coefficient of the sludge were sequentially assimilated based on the ensemble Kalman filter algorithm. Identify the members of multiple parameter perturbation sets that run in parallel during the assimilation process, and calculate the root mean square error between the predicted and measured values ​​of each parameter perturbation set member; The optimal digital twin model is obtained by selecting the parameter perturbation set with the smallest root mean square error from multiple root mean square errors.

6. The automatic sludge discharge coordination control method for a sedimentation tank according to claim 5, characterized in that, The settlement velocity equation is: ， wherein, is the sludge settling velocity, is the limiting settling velocity, is the hindered settling coefficient, is the sludge concentration, is the natural logarithm function.

7. The method according to claim 1, wherein the method is characterized by, The simulation of the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the lag time constant includes: Determine a preset multiple, starting from the current time, and use the preset multiple length of the lag time constant as the rolling time domain window; Within the rolling time window, the start-stop sequence of the sludge pump and the opening degree of the sludge valve are used as decision variables to generate a candidate set of sludge control sequences. The candidate set includes at least continuous high-frequency sludge discharge sequences, intermittent low-frequency sludge discharge sequences, and sludge discharge sequences with a denser start and a sparser finish. Each candidate sludge discharge control sequence is input into the optimal digital twin model for forward simulation prediction, and the sludge level height and return sludge concentration at the end of the rolling time domain under each candidate sludge discharge control sequence are obtained.

8. The method according to claim 7, wherein the method is characterized by, The process of determining the optimal sludge removal strategy from simulation results to control the automatic sludge removal from the sedimentation tank includes: Determine the pre-set upper limit of sludge level and the stable range of return sludge concentration, and eliminate candidate sludge discharge control sequences in the simulation results that exceed the upper limit of sludge level or the return sludge concentration value exceeds the stable range of return sludge concentration. For the remaining candidate sludge discharge control sequences in the simulation results, the candidate sludge discharge control sequence that minimizes the comprehensive cost function value is selected; The candidate sludge discharge control sequence is taken as the optimal sludge discharge strategy, and the sludge in the sedimentation tank is automatically discharged based on the optimal sludge discharge strategy.

9. The method according to claim 8, wherein the method is characterized by, The comprehensive cost function is: ， wherein, is a comprehensive generation value, and a is a first preset weight coefficient, is a simulation prediction value of the sludge level, is a target setting value of the sludge level, is a second preset weight coefficient, is a simulation prediction value of the return sludge concentration, is a target setting value of the return sludge concentration, is a third preset weight coefficient, is an estimated energy consumption value of the sludge pump.

10. A coordinated control system for automatic sludge discharge from sedimentation tanks, characterized in that, include: The acquisition module is used to acquire multi-source data from the sedimentation tank and extract features that characterize the sludge settling state from the multi-source data to construct a multi-dimensional feature set. The analysis module is used to analyze the multidimensional feature set, determine the current operating condition category of the sedimentation tank, and determine the lag time constant of the impact of sludge discharge on the effluent quality based on the operating condition category. The modeling module is used to construct an initial digital twin model of the sedimentation tank based on the solid flux sedimentation theory, and to perform inversion calibration on the digital twin model based on multi-source data to obtain the optimal digital twin model. The control module is used to simulate the sludge discharge control sequence of the sedimentation tank based on the optimal digital twin model and the lag time constant, and to determine the optimal sludge discharge strategy from the simulation results to control the automatic sludge discharge of the sedimentation tank.

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