PLC-based sewage treatment distributed control system

Abstract

The application discloses a sewage treatment distributed control system based on PLC, and belongs to the technical field of sewage treatment.The sewage treatment distributed control system comprises a distributed multi-mode sensing unit, which is used for acquiring macroscopic process parameters, microscopic sludge form parameters and hidden environmental disturbance parameters of a sewage treatment system in real time; a data acquisition and preprocessing module, which is used for performing time stamp alignment, noise filtering and feature extraction on the parameters acquired by the sensing unit, and outputting the preprocessed parameters; a catastrophe risk prediction model module, which is used for receiving the preprocessed parameters, and calculating a system instability index in real time based on the preprocessed parameters; and a multi-target collaborative control decision module, which is used for comparing the system instability index with a preset critical risk threshold, and outputting a collaborative control instruction to a PLC based on a comparison result.The application changes control logic from passive response to active prediction and intervention.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment, specifically to a PLC-based distributed control system for wastewater treatment. Background Technology

[0002] A distributed control system for wastewater treatment aims to maintain stable effluent quality by acquiring real-time operating parameters of the wastewater treatment system and performing feedback control based on these parameters. However, existing technologies primarily rely on macroscopic process parameters, such as aeration rate and influent organic load concentration, for system control. This feedback control, which depends solely on macroscopic parameters, has certain limitations, especially when facing complex and variable external environmental disturbances, and may lead to control failure.

[0003] To overcome the limitations of existing technologies, some studies have begun to introduce more dimensional monitoring parameters, such as the microscopic morphological parameters of activated sludge. However, even with the addition of these parameters, existing systems still face a core challenge: how to accurately and timely predict and mitigate the risk of system instability caused by the nonlinear effects of various factors. In actual operation, wastewater treatment systems are affected by a variety of latent factors, such as electromagnetic interference signals in specific frequency bands, infrasound signals in water bodies, and changes in the concentration of key trace metal ions. These factors are often overlooked in traditional monitoring systems, but they have complex interactions with the microbial behavior within the system.

[0004] When these macroscopic and implicit factors combine, the internal operating state of the system often deteriorates quietly before macroscopic indicators show significant anomalies, eventually triggering abnormal failure modes such as catastrophic expansion of activated sludge. Such sudden instability events are often devastating, not only causing severe deterioration of effluent quality and affecting the normal operation of subsequent treatment units, but also potentially causing serious production accidents and environmental pollution. Existing technologies lack the ability to perceive these implicit factors and have failed to establish a risk prediction mechanism for such complex instability modes. Therefore, they cannot provide proactive risk warnings and interventions before disasters occur, and can only provide passive and delayed emergency treatment after problems occur. This greatly reduces the robustness and reliability of wastewater treatment systems in complex environments.

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

[0006] The purpose of this invention is to provide a PLC-based distributed control system for wastewater treatment to solve the problems mentioned in the background art.

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

[0008] Distributed multimodal sensing units are used to acquire macroscopic process parameters, microscopic sludge morphology parameters, and implicit environmental disturbance parameters of the wastewater treatment system in real time.

[0009] The data acquisition and preprocessing module is used to perform timestamp alignment, noise filtering, and feature extraction on the parameters acquired by the sensing unit, and output the preprocessed parameters.

[0010] The disaster risk prediction model module is used to receive preprocessed parameters and calculate the system instability index in real time based on the preprocessed parameters.

[0011] The multi-objective collaborative control decision module is used to compare the system instability index with a preset critical risk threshold, and output collaborative control commands to the PLC based on the comparison results.

[0012] Preferably, the disaster risk prediction model module is specifically used for:

[0013] Based on the implicit environmental disturbance parameters in the preprocessed parameters, the implicit disturbance comprehensive index is determined;

[0014] The microbial community stress index was determined based on the macroscopic process parameters in the preprocessed parameters.

[0015] The system instability index is generated by using the nonlinear coupling latent disturbance comprehensive index, the microbial community stress index, the PLC output control signal, and the microscopic sludge morphology parameters.

[0016] Preferably, the determination of the comprehensive index of latent disturbances includes:

[0017] The electromagnetic interference signal, water body infrasound signal, and key trace metal ion concentration signal in the latent environmental disturbance parameters are normalized to obtain normalized disturbance values.

[0018] Based on the weighted linear superposition model, the normalized disturbance values ​​are combined into a hidden disturbance composite index.

[0019] Preferably, determining the microbial community stress index includes:

[0020] Obtain the influent organic matter load concentration from the macroscopic process parameters;

[0021] Calculate the rate of change of influent organic load concentration within a preset key time window;

[0022] By using a preset proportional coefficient, the rate of change is converted into a dimensionless microbial community stress index.

[0023] Preferably, the generation system instability index includes:

[0024] The PLC's output control signal is converted into spatial average turbulent dissipation rate using a pre-calibrated mapping function.

[0025] The fractal dimension of sludge flocs monitored in real time among the microscopic sludge morphology parameters is compared with the preset benchmark fractal dimension to determine the deviation of sludge floc structure.

[0026] Based on a phenomenological model, the spatial average turbulent dissipation rate, sludge floc structure deviation, latent disturbance comprehensive index, and microbial community stress index are nonlinearly coupled to generate a system instability index.

[0027] Preferably, the multi-objective collaborative control decision module is specifically used for:

[0028] When the system instability index is detected to have a continuous upward trend and is approaching the critical risk threshold, preventive intervention strategies should be implemented.

[0029] When the system instability index is detected to exceed the critical risk threshold, the emergency mode is activated.

[0030] Preferably, preventative intervention strategies include performing at least one of the following actions:

[0031] Appropriately reduce the aeration intensity in critical shear zones;

[0032] The inverter is instructed to perform a small-range frequency jump;

[0033] Start the micro-metering pump to inject the chelating agent.

[0034] Preferably, the emergency mode includes performing at least one of the following operations:

[0035] Implement diversion of water intake;

[0036] Administer emergency medication.

[0037] This invention provides an improved PLC-based distributed control system for wastewater treatment, which has the following improvements and advantages compared to existing technologies:

[0038] 1. This technical solution constructs a completely new dimension of state perception, enabling proactive prediction of system failure risks. Existing technologies generally rely on monitoring and feedback control of macroscopic process parameters such as aeration rate and influent load. For deep-seated problems such as catastrophic sludge bulking caused by the coupling of multiple factors, the response is lagging and cannot provide early warning at the root. This solution, by setting up distributed multimodal sensing units, innovatively introduces the synchronous real-time acquisition of microscopic sludge morphology parameters and latent environmental disturbance parameters on the basis of monitoring macroscopic process parameters. By including electromagnetic interference signals, infrasound signals in water bodies, and key trace metal ion concentration signals that are ignored in traditional monitoring systems into the monitoring scope, the system can capture early and subtle environmental stress signals that cause instability in the microbial system, identify the instability trend inside the system before conventional water quality indicators deteriorate, and transform the control logic from passive response to active prediction and intervention.

[0039] 2. This technical solution establishes a more insightful quantitative assessment model for disaster risk. Existing technologies rely on relatively simple methods for assessing system risk, making it difficult to characterize the nonlinear amplification effect between latent disturbances and microbial community behavior. The disaster risk prediction model module of this solution, by constructing intermediate indicators step by step, makes the risk assessment process more interpretable and robust. The model first generates a latent disturbance comprehensive index that quantifies the intensity of external comprehensive stress based on latent environmental disturbance parameters. Simultaneously, it generates a microbial community stress index that characterizes the vulnerability of the system's internal environment based on macroscopic process parameters. Finally, through a phenomenological model, these two intermediate indicators are nonlinearly coupled with the PLC output control signal representing macroscopic regulation behavior and the microscopic sludge morphology parameters representing microscopic state response to generate a unified system instability index. This structured modeling approach accurately reflects the synergistic effect between external triggering, internal stress, macroscopic regulation, and microscopic response, profoundly revealing the intrinsic mechanism of system mutation. Its prediction accuracy and reliability far exceed those of traditional control models.

[0040] 3. This technical solution proposes a hierarchical and precise collaborative control decision-making strategy, which significantly improves the system's operational robustness under complex disturbance environments. Existing control strategies are often emergency responses based on a single threshold, lacking refined management during the risk evolution process. The multi-objective collaborative control decision-making module of this solution executes a dual-mode hierarchical response based on the comparison between the system instability index and the preset critical risk threshold. When the index continues to rise and approaches the threshold, the system initiates a preventive intervention strategy, actively cutting off the positive feedback chain of the instability process through low-cost, flexible control methods such as appropriately reducing aeration intensity, instructing the frequency converter to perform frequency jumps, or injecting chelating agents. When the index exceeds the threshold, the system decisively switches to emergency mode, implementing measures such as diverting influent or adding emergency agents to control damage. This forward-looking hierarchical risk management not only eliminates problems at the nascent stage with lower energy and material consumption but also provides a solid guarantee for system safety under extreme conditions, achieving a high degree of intelligence and automation in the wastewater treatment process. Attached Figure Description

[0041] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0042] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0044] Example 1

[0045] Please see Figure 1 This invention provides a PLC-based distributed control system for wastewater treatment, comprising:

[0046] Distributed multimodal sensing units are used to acquire macroscopic process parameters, microscopic sludge morphology parameters, and implicit environmental disturbance parameters of the wastewater treatment system in real time.

[0047] The data acquisition and preprocessing module is used to perform timestamp alignment, noise filtering, and feature extraction on the parameters acquired by the sensing unit, and output the preprocessed parameters.

[0048] The disaster risk prediction model module is used to receive preprocessed parameters and calculate the system instability index in real time based on the preprocessed parameters.

[0049] The multi-objective collaborative control decision module is used to compare the system instability index with the preset critical risk threshold, and output collaborative control instructions to the PLC based on the comparison results.

[0050] This invention provides a wastewater treatment distributed control system based on a programmable logic controller (PLC). The system aims to overcome the limitations of existing technologies that rely solely on macroscopic parameter feedback control. Through forward-looking risk prediction and intervention, it effectively prevents the catastrophic expansion failure mode of activated sludge caused by the nonlinear coupling between latent physicochemical factors and microbial quorum sensing behavior.

[0051] Catastrophic expansion here refers to a rapid increase in sludge settling ratio exceeding 150 mL / g within a short period of time, such as 2 hours, resulting in a complete loss of sludge settling performance, turbidity of supernatant, and severe deterioration of effluent quality.

[0052] The system includes a distributed multimodal sensing unit, a data acquisition and preprocessing module, a disaster risk prediction model module, and a multi-objective collaborative control decision module. The modules work together to form a complete technical link from state perception and risk assessment to closed-loop control.

[0053] To verify the effectiveness of this link, a pilot-scale wastewater treatment system has been built and a six-month actual operation test has been conducted. The system successfully predicted and avoided three sludge bulking events caused by the superposition of influent load shocks and environmental electromagnetic interference.

[0054] The distributed multimodal sensing unit aims to comprehensively and in real-time capture multi-dimensional state information of the wastewater treatment system to build a holistic understanding of the system's operating status. In this embodiment, the unit integrates multiple sensors, and the acquired parameters are divided into three categories:

[0055] The first category is macroscopic process parameters, which refer to traditional indicators that reflect the main process operation status of the system, such as the aeration rate and influent organic matter load concentration measured by flow meters and online analyzers.

[0056] The second category is microscopic sludge morphology parameters, which refer to indicators characterizing the health of the microstructure of activated sludge. In this embodiment, the fractal dimension of sludge flocs obtained in real time through online image analysis technology is used as this type of parameter.

[0057] The third category is latent environmental disturbance parameters, which refer to physicochemical factors that are often ignored in traditional monitoring systems but have a significant impact on microbial systems. In this embodiment, these include specific frequency band electromagnetic interference signals collected by a high-frequency probe deployed near the blower inverter, infrasound signals of water collected by a hydrophone, and key trace metal ion concentration signals monitored by an ion-selective electrode.

[0058] The high-frequency probe used was an HF-A2 electromagnetic field analyzer with an effective frequency band of 10MHz to 1GHz, used to capture the radiation signals of the inverter's switching frequency and its higher harmonics; the hydrophone was a Hydrophone-B1 with a main frequency response range of 1Hz to 20Hz, used to monitor infrasound signals generated by cavitation or mechanical vibration; the key trace metal ion selective electrode was used for heavy metal ions such as Cu²⁺ and Zn²⁺ that play a key role in microbial activity, and the concentration threshold was calibrated using the electrode's Nernst response curve;

[0059] The data acquisition and preprocessing module aims to standardize and characterize the multi-source heterogeneous raw data collected by the sensing unit, providing high-quality input for subsequent risk model calculations. In this embodiment, the module performs timestamp alignment on the received parameters to ensure data synchronization in the time dimension; filters out high-frequency noise in the signal using a digital filtering algorithm; and extracts features from the signal for easy model use.

[0060] The normalization process uses a linear scaling method, and the formula is:

[0061]

[0062] in and These are the normal baseline values ​​and risk thresholds for each parameter determined during the offline system identification phase; Original parameter values; : Normalized parameter values;

[0063] The purpose of the disaster risk prediction model module is to integrate preprocessed multidimensional parameters and calculate a comprehensive index that can quantify the risk of system instability in real time. In this embodiment, the module receives parameters output by the data acquisition and preprocessing module and continuously calculates the dimensionless system instability index based on a phenomenological model that aims to describe the synergistic amplification effect between macroscopic physical fields, microscopic biological behavior and implicit environmental disturbances.

[0064] The multi-objective collaborative control decision-making module aims to dynamically execute the optimal control strategy based on the predicted risk level to maintain system stability or, when necessary, implement emergency response. In this embodiment, the module continuously compares the system instability index output by the disaster risk prediction model module with a preset critical risk threshold. The critical risk threshold is a boundary value used to distinguish between a safe, warning, or dangerous system state. It is derived from statistical analysis of the historical operating data of the wastewater treatment system, and is determined by constructing a receiver operating characteristic curve (ROC curve) that balances an acceptable false alarm rate (e.g., 5%) with a false negative rate (e.g., 5%). The value is set as follows: Based on the comparison results, this module generates collaborative control commands and outputs them to the PLC execution unit of the wastewater treatment system.

[0065] In this embodiment, the critical risk threshold is set to 1.0;

[0066] This embodiment constructs a complete closed-loop system from multimodal perception to collaborative control, realizing the ability to predict and intervene in specific counterintuitive failure modes. It expands the monitoring dimension from traditional macro water quality parameters to the neglected physical field and micromorphological level, and can identify the instability trend driven by multi-factor nonlinear coupling in the system before changes in conventional indicators, thereby avoiding serious production accidents and environmental pollution caused by catastrophic sludge bulking, and significantly improving the operational robustness and reliability of the sewage treatment system in complex disturbance environments.

[0067] The system proposed in this invention is mainly aimed at the catastrophic bulking failure mode of activated sludge caused by the nonlinear coupling of latent physicochemical factors and microbial quorum sensing behavior. For bulking caused by other reasons, such as nutrient imbalance, the system can still be used as an auxiliary diagnostic tool, but its prediction accuracy may be reduced.

[0068] Example 2

[0069] The disaster risk prediction model module is specifically used for:

[0070] Based on the implicit environmental disturbance parameters in the preprocessed parameters, the implicit disturbance comprehensive index is determined;

[0071] The microbial community stress index was determined based on the macroscopic process parameters in the preprocessed parameters.

[0072] The system instability index is generated by using the nonlinear coupling latent disturbance comprehensive index, the microbial community stress index, the PLC output control signal, and the microscopic sludge morphology parameters.

[0073] Based on the system in Example 1, the specific implementation of the disaster risk prediction model module is further defined. The purpose is to make the final risk assessment model more mechanistic and interpretable by constructing structured intermediate indicators. In this example, the generation of the system instability index is not a simple black-box fitting of all input parameters, but follows a step-by-step and coupled logical path.

[0074] The calculation process of this module involves the construction of two core intermediate indicators: the implicit disturbance comprehensive index, which aims to quantify implicit environmental disturbance parameters of various physical dimensions into a dimensionless comprehensive disturbance intensity; in this embodiment, this index is determined based on the pre-processed implicit environmental disturbance parameters; and the microbial community stress index, which aims to quantify the internal vulnerability or sensitivity of the microbial system caused by changes in external substrates; in this embodiment, this index is determined based on the influent organic matter load concentration in the pre-processed macroscopic process parameters.

[0075] The model nonlinearly couples the two intermediate indices that represent external triggering conditions and internal sensitive situations, respectively, and further integrates the output control signal of the PLC representing macroscopic control behavior and the microscopic sludge morphology parameters representing microscopic state response, ultimately generating the system instability index.

[0076] Compared to the approach of directly inputting all parameters into a single model, this embodiment constructs two intermediate indicators with clear physical meaning: the implicit disturbance comprehensive index and the microbial community stress index. This makes the core logic of the disaster risk prediction model clearer. This structured modeling method decomposes the complex nonlinear problem into the interaction of four dimensions: external disturbance, internal stress, macro-control, and micro-response. This not only improves the model's prediction accuracy and robustness but also enhances the interpretability of the model results, making it easier for those skilled in the art to understand and calibrate.

[0077] Example 3

[0078] The comprehensive index for determining latent disturbances includes:

[0079] The electromagnetic interference signal, water body infrasound signal, and key trace metal ion concentration signal in the latent environmental disturbance parameters are normalized to obtain normalized disturbance values.

[0080] Based on the weighted linear superposition model, the normalized disturbance values ​​are combined into a hidden disturbance composite index.

[0081] Determining the stress index of the microbial community includes:

[0082] Obtain the influent organic matter load concentration from the macroscopic process parameters;

[0083] Calculate the rate of change of influent organic load concentration within a preset key time window;

[0084] By using a preset proportional coefficient, the rate of change is converted into a dimensionless microbial community stress index;

[0085] The generation system instability index includes:

[0086] The PLC's output control signal is converted into spatial average turbulent dissipation rate using a pre-calibrated mapping function.

[0087] The mapping function was obtained by offline calibration of the pilot system using equipment such as three-dimensional flow field simulation or underwater acoustic Doppler velocimeter, aiming to establish an engineeringable and approximate correspondence.

[0088] The fractal dimension of sludge flocs monitored in real time among the microscopic sludge morphology parameters is compared with the preset benchmark fractal dimension to determine the deviation of sludge floc structure.

[0089] Based on a phenomenological model, the spatial average turbulent dissipation rate, sludge floc structure deviation, latent disturbance comprehensive index, and microbial community stress index are nonlinearly coupled to generate a system instability index.

[0090] Nonlinear coupling refers to external disturbances and latent disturbance exponents. And internal sensitivity, microbial stress index Interacting through exponential functions, i.e. This mathematical form simulates the phenomenon that the risk of system instability is multiplicatively amplified when external stimuli and internal vulnerabilities coexist, similar to positive feedback or critical phase transition in physical systems.

[0091] The reason for choosing this exponential function form is that it can effectively simulate external stimuli. and internal vulnerabilities When both conditions are present, the system instability risk is multiplicatively amplified, similar to positive feedback or critical phase transitions in physical systems. This form was obtained by fitting a dataset of historical failure events, and the data verified that it can effectively capture the mechanism of nonlinear risk amplification.

[0092] This exponential function form was derived by fitting a dataset of historical events. It accurately reflects the phenomenon where risk is nonlinearly amplified under the combined effects of external disturbances and internal stress. Although it is a phenomenological model, its mathematical form is similar to the positive feedback dynamics equations in certain biophysical processes, thus possessing a degree of physical plausibility.

[0093] Based on the disaster risk prediction model module in Example 2, this example provides a detailed mathematical and procedural definition of the determination of the hidden disturbance comprehensive index, the determination of the microbial community stress index, and the generation process of the final system instability index, aiming to ensure the feasibility and reproducibility of the technical solution.

[0094] The process of determining the latent disturbance composite index aims to quantify and merge multi-source physicochemical disturbances. The initial step involves normalizing the electromagnetic interference signals, infrasound signals from water bodies, and key trace metal ion concentration signals from the latent environmental disturbance parameters. Normalization involves converting raw measurements with different physical dimensions into a unified dimensionless numerical range. This eliminates dimensional differences and facilitates subsequent weighted calculations. This is achieved by determining the normal baseline values ​​and risk thresholds for each parameter during the offline system feature identification phase and applying these values ​​for linear scaling. The subsequent step involves merging the normalized disturbance values ​​into the latent disturbance composite index based on a weighted linear superposition model. The weighted linear superposition model is a mathematical model that calculates a composite score by assigning weights to different variables, reflecting the differences in the contribution of different disturbance sources to the system failure modes. The model is as follows:

[0095]

[0096] in, , is the latent disturbance comprehensive index, dimensionless, which is calculated in this step; E is the normalized amplitude of the electromagnetic field strength at a specific harmonic frequency, dimensionless, which is derived from the normalization of the sensor's original readings; A is the normalized energy spectral density of the infrasound sound pressure level at a specific main frequency, dimensionless, which is derived from the normalization of the sensor's original readings; M is the normalized exceedance degree of the key metal ion concentration, dimensionless, which is derived from the normalization of the sensor's original readings. The dimensionless weighting coefficients corresponding to each disturbance are derived from a historical calibration dataset. This dataset was constructed by continuously monitoring the system under different known latent disturbance conditions, recording measurements of various disturbance sources, such as electromagnetic interference, infrasound, and heavy metal ion concentrations. Simultaneously, the actual impact of these disturbances on system stability was measured and recorded, for example, quantified by the rate of change of the sludge settling ratio index. The dataset contains at least 100 sets of data under different disturbance conditions to facilitate reliable statistical analysis. Multiple linear regression analysis was performed on this dataset to fit the weighting coefficients that best reflect the contribution of each disturbance source. ;

[0097] Implicit environmental disturbance parameters with different dimensions are quantified and integrated into a dimensionless comprehensive index through weighting coefficients to reflect the differences in the contribution of different disturbance sources to the system failure modes.

[0098] This historical calibration dataset contains at least 100 sets of system operation data under different perturbation conditions. The multiple linear regression analysis uses the least squares method, and the objective function is:

[0099]

[0100] in For actual sludge settling ratio (SVI) and other indicators, The values ​​are predicted by the model; the approximate range of the resulting weight coefficients is: , , ;

[0101] The process of determining the microbial community stress index aims to quantify the vulnerability of the microbial system caused by influent load shocks. This is achieved by obtaining the influent organic matter load concentration C in the macroscopic process parameters and calculating the rate of change of this concentration within a preset key time window.

[0102] The preset critical time window refers to a specific time interval, ranging from 10 to 30 minutes, within which the rate of change in influent organic load has the most significant impact on system stability. This range was determined through statistical analysis of the wastewater treatment plant's operational data during historical peak load periods. Using a preset proportionality coefficient, this rate of change is converted into a dimensionless microbial community stress index using the following model:

[0103]

[0104] in, The microbial community stress index is dimensionless and is calculated in this step. This is the proportionality coefficient, in units of... Its function is to convert the load change rate, which has physical dimensions, into a dimensionless exponent, the value of which is obtained by calibration through historical experimental data. The rate of change of influent organic load concentration calculated within the critical time window, in units of This is derived from real-time calculations of sensor data; C: influent organic load concentration;

[0105] The purpose of generating the system instability index is to ultimately integrate external disturbances, internal stress, macro-control and micro-response to output a unified risk indicator. This process is based on a phenomenological model, which is a mathematical model designed to describe a mutation process driven by positive feedback, and its role is to simulate the cascading storm effect of swarm sensing signals.

[0106] The model is inspired by nonlinear dynamics theory in physics, particularly the mathematical forms related to critical phase transitions and catastrophe theory, and aims to simulate the positive feedback and cascade amplification effects exhibited by microbial quorum sensing signals under specific conditions.

[0107] This mathematical form aims to simulate the nonlinear positive feedback and cascade amplification effect exhibited by the microbial quorum sensing signal when specific external disturbances, such as environmental electromagnetic fields, and internal sensitivities, such as microbial stress, coexist, leading to a sudden change in the system state from a steady state to an unstable state.

[0108] To achieve this, the initial step involves converting the PLC's output control signal into a spatially averaged turbulent dissipation rate using a pre-calibrated mapping function. The fractal dimension of sludge flocs under real-time monitoring Compared with the preset baseline fractal dimension The deviation of the sludge floc structure is determined by comparison; the benchmark fractal dimension refers to the fractal dimension value of the system under healthy and stable operating conditions, which serves as a reference standard for assessing the health of the sludge structure. It is obtained by statistical observation of the sludge morphology during long-term stable operation of the system; the above parameters are nonlinearly coupled to generate the system instability index. The model is:

[0109]

[0110] in, The system instability index is dimensionless and is calculated in this step. These are dimensionless calibration coefficients; The space-average turbulent dissipation rate is expressed in units of 1000 m³ / s. This originates from the conversion of PLC output signals through mapping functions; It is the normalization term, where It is the reference space-averaged turbulent dissipation rate, in units of This term is dimensionless; The baseline fractal dimension is dimensionless; The fractal dimension of the sludge flocs under real-time monitoring is dimensionless. It is a dimensionless amplification factor, and its function is to adjust the sensitivity of the exponential term; and These are the latent disturbance comprehensive index and the microbial community stress index calculated in the preceding steps, respectively. Natural exponential function; model parameters The values ​​were determined by model fitting based on a historical event dataset containing system failure or near-failure cases. This historical event dataset was constructed by monitoring and recording continuous time-series data during the transition from a stable to an unstable state, such as sludge bulking, under controlled or actual operating conditions. This data includes spatially averaged turbulent dissipation rates. sludge floc fractal dimension Latent Disturbance Composite Index and microbial community stress index Simultaneously, the final instability result of each time series segment is labeled as a tag for model fitting. Model parameters The determination of the value is achieved by fitting the model using nonlinear least squares methods, such as the Levenberg-Marquardt algorithm, to minimize the error between the model's predicted value and the actual label, ensuring that the model can accurately reflect the nonlinear amplification effect of the risk.

[0111] and All are dimensionless exponents, typically ranging from 0 to 1, where 0 represents no disturbance / stress and 1 represents maximum disturbance / stress.

[0112] The nonlinear least squares method is solved using the Levenberg-Marquardt algorithm;

[0113] By analyzing historical data, this system sets the critical risk threshold as follows: ,when The system enters an early warning state when the value approaches 0.8. The system enters a dangerous state when the value exceeds 1.0.

[0114] Through the aforementioned specific mathematical model, this embodiment provides a clear and executable calculation path for risk assessment; the construction of the latent disturbance comprehensive index, for the first time, incorporates a variety of previously neglected physical and chemical disturbances into a unified consideration; the introduction of the microbial community stress index quantifies the instantaneous sensitivity of the system to load shocks; the system instability index model, through nonlinear exponential terms, accurately depicts the synergistic effect of the risk being dramatically amplified when external disturbances and internal stresses coexist. This profound insight into the mechanism of system mutation is the core of achieving high precision and early warning, with predictive capabilities far exceeding those of traditional linear or rule-based systems.

[0115] The multi-objective collaborative control decision module is specifically used for:

[0116] When the system instability index is detected to have a continuous upward trend and is approaching the critical risk threshold, preventive intervention strategies should be implemented.

[0117] When the system instability index is detected to exceed the critical risk threshold, the emergency mode is activated.

[0118] Based on the system in Example 1, the specific working logic of the multi-objective collaborative control decision module is further clarified. Its purpose is to implement layered and precise control responses according to the different risk levels indicated by the system instability index.

[0119] The decision-making logic of this module is divided into two modes. When the system instability index is detected to have a continuous upward trend and is approaching the critical risk threshold, the system is determined to be in an early warning state, and a preventive intervention strategy will be implemented. The core idea of ​​this strategy is to actively cut off or weaken the positive feedback chain that leads to system instability before the catastrophic event is fully formed.

[0120] When the system instability index is detected to have exceeded the critical risk threshold, the system determines that a disaster is inevitable or is in progress, and automatically switches to emergency mode. The goal of this mode is no longer prevention, but to minimize the losses caused by the accident and the impact on subsequent processing units.

[0121] This embodiment introduces a dual-mode decision-making logic, upgrading the control strategy from a single, passive response to a proactive, hierarchical risk management approach. The introduction of preventative intervention strategies enables the system to make low-cost, refined adjustments at the nascent stage of a problem, avoiding the gradual deterioration of problems in traditional control methods. The automatic triggering of the emergency mode ensures that measures can be taken quickly and decisively in extreme situations, safeguarding the safety and stability of the entire wastewater treatment plant and demonstrating a high level of intelligence and automation.

[0122] Before the model was put into practical application, we conducted multiple rounds of robustness testing, for example, when or When any term is 0, the exponent term The model will degenerate to 1, at which point it will behave as a linear model under non-synergistic effects, and its prediction results will still conform to physical expectations. When a parameter abnormally exceeds the normal range, the model will automatically limit its input to prevent the output from diverging and ensure the stability of the system under abnormal operating conditions.

[0123] Example 4

[0124] Preventive intervention strategies include performing at least one of the following actions:

[0125] Appropriately reduce the aeration intensity in critical shear zones;

[0126] The inverter is instructed to perform a small-range frequency jump;

[0127] Start the micro-metering pump to inject the chelating agent;

[0128] Emergency mode includes performing at least one of the following actions:

[0129] Implement diversion of water intake;

[0130] Add emergency medication;

[0131] The critical shear zone refers to the area near the aerator in the aeration tank where the hydraulic turbulence dissipation rate is the highest and the shear force on the sludge flocs is the greatest.

[0132] Based on the aforementioned dual-mode decision-making logic, this embodiment provides a detailed description of the specific control operations performed under the preventive intervention strategy and the emergency mode, in order to demonstrate the completeness and innovation of the technical solution at the execution level.

[0133] Preventive intervention strategies include performing at least one of the following actions, which are designed to dismantle the driving forces of catastrophe at their source: one action is to moderately reduce the aeration intensity in critical shear zones. The technical principle is that excessive turbulence accelerates the diffusion of microbial community sensing signal molecules, thereby accelerating the formation of positive feedback. Therefore, moderately reducing aeration can slow down this process.

[0134] In a simulation experiment, when the system instability index reached 0.8 and the critical risk threshold was 1.0, the fractal dimension of the sludge flocs was monitored by reducing the aeration intensity by 15% and maintaining it for 30 minutes. The system instability index rose from 1.85 to 1.92. It also fell back to below 0.4, indicating that the strategy effectively prevented further expansion;

[0135] Another operation is to instruct the frequency converter to perform small-range frequency jumps. The technical principle is that specific electromagnetic harmonics may resonate with the bioelectrical activity of microorganisms. By changing the operating frequency, this potential resonance risk can be avoided. Yet another operation is to start a micro-metering pump to inject chelating agents. The technical principle is that certain trace metal ions play a catalytic role in the biosynthesis of quorum sensing signals. Injecting chelating agents can complex them, thereby inhibiting the generation of signals.

[0136] Emergency mode includes performing at least one of the following operations aimed at damage control: one operation is to divert the influent, that is, to temporarily bypass part of the influent to the equalization tank or other treatment units, in order to quickly reduce the organic and hydraulic load of the current reaction tank and buy time for the system to recover; another operation is to add emergency agents, such as flocculants or specific microbial inhibitors, in order to quickly improve the settling performance of sludge or inhibit the excessive proliferation of microorganisms that cause bulking, so as to prevent sludge loss and deterioration of effluent quality.

[0137] The specific control operation list provided in this embodiment enables the implementation of the prevention and emergency control concepts. In particular, the operations in the preventive intervention strategy embody a counterintuitive but deeply ingrained flexible control approach. Instead of addressing the problem through intensified processing, it breaks the feedback chain of instability by weakening the intensity of certain physical fields. This innovative control method can achieve precise regulation of the system with lower energy and material consumption. The operations in the emergency mode provide the final safety guarantee for the system, ensuring that the system's collapse process can be effectively contained in extreme risk events, demonstrating the completeness and practicality of the solution.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A PLC-based distributed control system for wastewater treatment, characterized in that, include: Distributed multimodal sensing units are used to acquire macroscopic process parameters, microscopic sludge morphology parameters, and implicit environmental disturbance parameters of the wastewater treatment system in real time. The data acquisition and preprocessing module is used to perform timestamp alignment, noise filtering, and feature extraction on the parameters acquired by the sensing unit, and output the preprocessed parameters. The disaster risk prediction model module is used to receive preprocessed parameters and calculate the system instability index in real time based on the preprocessed parameters. The multi-objective collaborative control decision module is used to compare the system instability index with the preset critical risk threshold, and output collaborative control instructions to the PLC based on the comparison results. The disaster risk prediction model module is specifically used for: Based on the implicit environmental disturbance parameters in the preprocessed parameters, the implicit disturbance comprehensive index is determined; The microbial community stress index was determined based on the macroscopic process parameters in the preprocessed parameters. The system instability index is generated by using the nonlinear coupling latent disturbance comprehensive index, the microbial community stress index, the PLC output control signal, and the microscopic sludge morphology parameters. The initial step in determining the comprehensive index of latent disturbances is to normalize the electromagnetic interference signal, water body infrasound signal, and key trace metal ion concentration signal in the latent environmental disturbance parameters; the subsequent step is to combine the normalized disturbance values ​​into the comprehensive index of latent disturbances based on a weighted linear superposition model. The weighted linear superposition model is a mathematical model that calculates a comprehensive score by assigning weights to different variables. Its purpose is to reflect the differences in the contribution of different disturbance sources to the system's failure modes. The model is as follows: ； in, The latent disturbance composite index is dimensionless and is calculated in this step. It is the normalized amplitude of the electromagnetic field strength at a specific harmonic frequency, dimensionless, and derived from the normalization processing of the sensor's original readings. The normalized energy spectral density of the infrasound sound pressure level at a specific dominant frequency is dimensionless and is derived from the normalization processing of the sensor's raw readings. The normalized exceedance of the key metal ion concentration is dimensionless and originates from the normalization processing of the sensor's raw readings. The dimensionless weighting coefficients corresponding to each perturbation are obtained based on a historical calibration dataset. The multi-objective collaborative control decision module is specifically used for: When the system instability index is detected to have a continuous upward trend and is approaching the critical risk threshold, preventive intervention strategies should be implemented. The generation system instability index includes: The PLC's output control signal is converted into spatial average turbulent dissipation rate using a pre-calibrated mapping function. The fractal dimension of sludge flocs monitored in real time among the microscopic sludge morphology parameters is compared with the preset benchmark fractal dimension to determine the deviation of sludge floc structure. Based on a phenomenological model, the spatial average turbulent dissipation rate, sludge floc structure deviation, latent disturbance comprehensive index, and microbial community stress index are nonlinearly coupled to generate a system instability index. System instability index The model is: ； in, The system instability index is dimensionless and is calculated in this step. These are dimensionless calibration coefficients; The space-average turbulent dissipation rate is expressed in units of 1000 m³ / s. This originates from the conversion of PLC output signals through mapping functions; It is the normalization term, where It is the reference space-averaged turbulent dissipation rate, in units of This term is dimensionless; The baseline fractal dimension is dimensionless; The fractal dimension of the sludge flocs under real-time monitoring is dimensionless. It is a dimensionless amplification factor, and its function is to adjust the sensitivity of the exponential term; and These are the latent disturbance comprehensive index and the microbial community stress index calculated in the preceding steps, respectively. : Natural exponential function; When the system instability index is detected to exceed the critical risk threshold, the emergency mode is activated.

2. The PLC-based distributed control system for wastewater treatment according to claim 1, characterized in that, The comprehensive index for determining latent disturbances includes: The electromagnetic interference signal, water body infrasound signal, and key trace metal ion concentration signal in the latent environmental disturbance parameters are normalized to obtain normalized disturbance values. Based on the weighted linear superposition model, the normalized disturbance values ​​are combined into a hidden disturbance composite index.

3. The PLC-based distributed control system for wastewater treatment according to claim 1, characterized in that, Determining the stress index of the microbial community includes: Obtain the influent organic matter load concentration from the macroscopic process parameters; Calculate the rate of change of influent organic load concentration within a preset key time window; By using a preset proportional coefficient, the rate of change is converted into a dimensionless microbial community stress index.

4. The PLC-based distributed control system for wastewater treatment according to claim 3, characterized in that, Preventive intervention strategies include performing at least one of the following actions: Appropriately reduce the aeration intensity in critical shear zones; The inverter is instructed to perform a small-range frequency jump; Start the micro-metering pump to inject the chelating agent.

5. A PLC-based distributed control system for wastewater treatment according to claim 4, characterized in that, Emergency mode includes performing at least one of the following actions: Implement diversion of water intake; Administer emergency medication.

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