Intelligent sludge treatment equipment control method

Abstract

The invention relates to the field of sludge treatment, in particular to an intelligent sludge treatment equipment control method. The invention provides an intelligent sludge treatment equipment control method. Physical parameters, chemical parameters and equipment operation state parameters in the sludge treatment process are collected in real time through a multi-source sensor array; establishing a sludge characteristic dynamic prediction model based on deep learning, processing time sequence data by the model through an LSTM neural network, and outputting an optimal process parameter combination; dynamically adjusting the rotating speed of the dehydrator, the flow of the dosing pump and the pressure threshold value of the filter press by adopting a fuzzy PID control algorithm; virtual mapping and fault pre-diagnosis of an equipment operation state are realized through a digital twin system; and constructing a cloud edge cooperative control architecture, executing a real-time control instruction by the edge computing node, and performing big data analysis and model iterative updating by the cloud platform. High-precision sensors such as a sludge concentration meter are arranged, 12 Hz high-frequency monitoring of sludge characteristics is achieved, and the efficiency is improved by 300 times compared with that of traditional manual sampling.

Description

Technical Field

[0001] This invention relates to the field of sludge treatment, and more specifically, to an intelligent sludge treatment equipment control method. Background Technology

[0002] With the increasing emphasis on water pollution control by the state, the issue of sludge disposal has become increasingly prominent and received more attention. Treatment methods range from sludge composting to auxiliary incineration and landfill. However, limitations such as high sludge moisture content, high energy consumption during drying, and high costs result in unsatisfactory treatment effects and a high risk of secondary pollution. Traditional sludge dewatering equipment also has drawbacks; for example, plate and frame dewatering machines consume a lot of energy, require high labor intensity, and have poor working conditions for personnel. Summary of the Invention

[0003] In order to solve the technical problems of low efficiency, high energy consumption and high false alarm rate of mechanical failure in traditional sludge treatment equipment, one objective of the present invention is to provide an intelligent sludge treatment equipment control method.

[0004] To achieve the above objectives, embodiments of the present invention provide an intelligent sludge treatment equipment control method, comprising the following steps: S1. Real-time acquisition of physical parameters, chemical parameters, and equipment operating status parameters during the sludge treatment process through a multi-source sensor array; S2. Establish a dynamic prediction model for sludge characteristics based on deep learning. The model processes time-series data through an LSTM neural network and outputs the optimal combination of process parameters. S3. The dewatering machine speed, dosing pump flow rate and filter press pressure threshold are dynamically adjusted using a fuzzy PID control algorithm; S4. Achieve virtual mapping of equipment operating status and fault pre-diagnosis through a digital twin system; S5. Construct a cloud-edge collaborative control architecture, where edge computing nodes execute real-time control commands, and the cloud platform performs big data analysis and model iteration updates.

[0005] In the above technical solution, the multi-source sensor array in step S1 includes a sludge concentration meter, a torque sensor, a pH sensor, a turbidity sensor, and a vibration monitoring module, with a sampling frequency of not less than 10Hz.

[0006] In the above technical solution, the prediction model training in step S2 introduces a transfer learning mechanism, and the pre-training dataset contains at least 500 sets of sludge treatment process data under different water quality conditions.

[0007] In the above technical solution, the fuzzy PID control algorithm in step S3 sets a weighted optimization objective function for dewatering efficiency, energy consumption ratio, and cake moisture content, and the weight coefficients are dynamically adjusted according to the influent load.

[0008] In the above technical solution, the digital twin system in step S4 includes a three-dimensional simulation module for equipment, a stress field analysis module, and a remaining life prediction module, with a fault warning response time ≤30ms.

[0009] In the above technical solution, in the cloud-edge collaborative architecture of step S5, the edge computing node adopts an industrial-grade ARM processor and supports Modbus / TCP and OPCUA dual-protocol communication.

[0010] Additional aspects and advantages of the invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0011] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of the invention; Detailed Implementation To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0012] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0013] The following reference Figure 1 This invention describes a control method for an intelligent sludge treatment device according to some embodiments of the present invention.

[0014] 1. Deployment and data acquisition of multi-source sensor arrays 1.1. Sensor Installation Sludge concentration meter: Install an E+HCUS71D ultrasonic concentration meter in the feed pipe section of the dewatering machine. The range is 0-20g / L, the accuracy is ±0.5%FS, and the sampling frequency is 12Hz. Torque sensor: The HBMT22 dynamic torque sensor is used and integrated into the spindle of the dewatering machine, with a sampling frequency of 15Hz; The composite probe consists of a pH sensor and a turbidity sensor: equipped with a Mettler S800 series sensor, IP68 protection rating, temperature compensation, and a sampling frequency of 12Hz. The vibration monitoring module has a built-in triaxial accelerometer: the triaxial accelerometer is located at the feed inlet of the dewatering machine and has a sampling frequency of 10kHz; 1.2. Data Preprocessing Edge computing nodes: Adopting Advantech UNO-2484G industrial controllers, deploying Modbus / TCP protocol stacks, and performing sliding window mean filtering (window width 1s) through edge nodes. Outlier removal: Data reset is triggered when three consecutive pH sampling points deviate from the historical mean by ±2.

[0015] 2. Working principle of the dynamic prediction model for sludge characteristics 2.1. Model Architecture Design This model employs a Long Short-Term Memory (LSTM) network as its core architecture, which effectively captures the nonlinear temporal dependencies of sludge treatment parameters through gating mechanisms (input gate, forget gate, output gate). The network structure includes: Input layer: Receives time-series data (such as sludge concentration, pH value, torque, etc.) from a multi-source sensor array, with a sampling frequency of 10Hz. The data is input after standardization processing. Hidden layers: Composed of 3 stacked LSTM units, with 64, 128, and 64 units per layer respectively. Overfitting is prevented by gradient clipping (threshold 1.0) and Dropout (ratio 0.2). Output layer: The fully connected layer outputs the optimal combination of process parameters (such as dewatering machine speed, dosage, etc.), and the activation function is ReLU.

[0016] 2.2. Transfer Learning Mechanism To improve the model's generalization ability, transfer learning techniques are introduced: Pre-training phase: Using sludge treatment process data containing 500 sets of different water quality conditions (covering scenarios such as municipal sewage and industrial wastewater), common features are extracted through a pre-trained LSTM network; Fine-tuning phase: Fine-tuning the model on the target scenario data (historical data of the current sewage treatment plant), updating only the output layer weights, which significantly reduces training time (by about 60%).

[0017] 2.3. Time Series Data Processing Flow Data segmentation: Continuous time-series data is divided into samples according to a fixed window length (e.g., 30 seconds), and each sample contains sensor readings for a time step (t=0~t=29); Feature engineering: Calculate statistical features (mean, variance, slope) within the sliding window, concatenate them with the original data, and then input them into the LSTM; Dynamic prediction: The model outputs predicted process parameters for the next 10 seconds every 5 seconds, and updates them in real time through a sliding window.

[0018] 2.4. Model Optimization and Validation Loss function: Weighted MSE (mean squared error) is used, with the weights tilted towards the moisture content of the cake (weight 0.5) to ensure that the dewatering effect is prioritized; Validation method: Through 5-fold cross-validation, the prediction error on the test set is ≤8% (based on dehydration efficiency). Online learning: The cloud platform regularly injects new data into the model, and updates the parameters through incremental learning to adapt to changes in influent load.

[0019] 2.5. Technological Advantages Interference immunity: The LSTM gating mechanism effectively filters sensor noise (such as instantaneous fluctuations in pH value); Dynamic adaptability: Transfer learning enables the model to quickly adapt to new water quality conditions, with a cold start time of ≤2 hours; Interpretability: Visualize key temporal features (such as the impact of torque mutations on dehydration efficiency) through attention mechanisms.

[0020] 3. Working principle of fuzzy PID control algorithm 3.1 System Architecture Design A dual closed-loop control structure is adopted: Inner loop: The control object is the equipment execution parameters (dehydrator speed, dosing pump flow rate, filter press pressure); Outer loop: The optimal combination of process parameters output by the S2 prediction model is used as the setpoint.

[0021] 3.2. Blurring Processing Transform the error (e) and the rate of change of error (ec) into fuzzy linguistic variables: Input variables: e: Deviation between actual value and set value (quantization domain: [-3,3]); ec: Rate of change of deviation (domain of quantization: [-0.5, 0.5]); Output variables: PID parameter adjustments ΔKp, ΔKi, ΔKd (universe of discourse: [-1, 1]); Membership function: adopts a triangular distribution and sets 7 language values ​​(NB, NM, NS, Z, PS, PM, PB).

[0022] 3.3. Fuzzy Inference Rules 3.3.1 Basic Structure of Fuzzy Rules The core of fuzzy PID control is to describe the nonlinear relationship between the error (e) and the rate of change of error (ec) and the adjustment of the PID parameters (ΔKp, ΔKi, ΔKd) using linguistic rules. Typical rules take the form of "IF-THEN", for example: Example of a rule: IFiisNBANDecisPBTHENΔKpisPM,ΔKiisNS,ΔKdisPS where NB (negative large), PB (positive large), etc. are fuzzy linguistic variables.

[0023] 3.3.2 Rule Base Design Principles Error partitioning: The universes of discourse of e and ec are divided into 7 levels (NB / NM / NS / ZO / PS / PM / PB), using triangular or trapezoidal membership functions.

[0024] Parameter adjustment logic: The proportional term Kp: When |e| is large, Kp is increased to improve the response speed; when |e| is small, Kp is decreased to avoid overshoot.

[0025] Integral term Ki: In the steady state of the system, Ki is increased to eliminate steady-state error, and Ki is decreased during dynamic processes to prevent integral saturation.

[0026] The differential term Kd is adjusted according to the sign and magnitude of ec to suppress oscillations and improve dynamic characteristics.

[0027] 3.3.3 Complete Rule Representation Taking the control of a dehydrator as an example, some typical rules of the 49 rules (7×7 combinations) are shown in the table below: 3.3.4. Dynamic Weight Optimization Introducing a multi-objective function to dynamically adjust rule weights: wi=f(η,E,W)=α·η+β·E+γ·Wwi=f(η,E,W)=α·η+β·E+γ·W Where: η is the dewatering efficiency, E is the energy consumption ratio, W is the moisture content of the cake, and the coefficients α / β / γ are adaptively updated through real-time operating conditions.

[0028] 3.3.5 Rule Self-Correction Mechanism Universe scaling: Dynamically adjusts the universe of discourse range of e and ec based on system load to improve rule adaptability.

[0029] Online learning: Optimize rule confidence by using historical control effect data.

[0030] 3.4. Defuzzification The precise control quantity is calculated using the centroid method: $$\DeltaK_p=\frac{\sum_{i=1}^{n}\mu_i\cdotx_i}{\sum_{i=1}^{n}\mu_i}$$, where: μi is the membership degree and xi is the output value.

[0031] 3.5. Dynamic Weight Adjustment 3.5.1 Adjust the logical framework A three-tiered progressive adjustment strategy is adopted: Basic layer: Initial weights are determined by sludge characteristic parameters (such as organic matter content and particle size distribution) output by the S2 prediction model; Dynamic layer: performs online correction based on real-time operating conditions (inlet water load, equipment wear status); Optimization layer: Iterative updates are made to the cloud-based model by combining historical control effect data.

[0032] 3.5.2 Mathematical Modeling The adjustment function for the weighting coefficients α / β / γ is expressed as: $$\begin{cases}\alpha=0.5+0.2\cdot\tanh(3\cdot\eta_{set}-1.5)\\beta=0.3-0.1\cdot\text{ sigmoid}(E_{current} / E_{max})\\gamma=0.2+0.1\cdot\sin(\pi\cdotW_{ratio})\end{cases}$$; Where: ηset is the set dehydration efficiency, Ecurrent is the real-time energy consumption, and Wratio is the moisture content compliance rate.

[0033] 3.5.3 Dynamic Adjustment Process Data acquisition: Torque, concentration, and pH data from the S1 multi-source sensor are acquired every 10ms; Feature extraction: Calculate sludge characteristic indicators (such as compressibility index C=log(τ / τ0)); Weight mapping: At high organic matter load: α↑ (dehydration efficiency takes precedence); When equipment vibration exceeds the standard: β↑ (energy consumption takes priority); When the moisture content of the mud cake exceeds the standard: γ↑ (quality takes priority); Boundary constraints: Ensure α+β+γ=1 and 0≤α≤0.7.

[0034] 3.5.4 Typical Operating Condition Examples 3.5.5 Cloud-based collaboration mechanism Edge nodes upload weight adjustment parameters to the cloud every 5 minutes to optimize the weight strategy through reinforcement learning (PPO) algorithm, with an update cycle of 24 hours.

[0035] 6. Control Flow Sequence Data from the torque sensor and concentration meter is collected every 10ms. Calculate the deviation between the current dehydration efficiency and the set value; Perform fuzzy inference to obtain the PID parameter adjustment amount; Update the control parameters and send them to the actuators; Record control effect data for cloud-based model iteration.

[0036] 4. Digital Twin System Architecture This system uses a 3D equipment simulation module to virtually map physical entities, a stress field analysis module to quantify the stress state of mechanical components, and a remaining life prediction module to combine material fatigue models with real-time monitoring data, forming a closed-loop management system of fault warning, diagnosis, and prediction. These three modules work together to meet the 30ms-level fault response requirement.

[0037] 4.1 Equipment 3D Simulation Module 4.1.1 Multidimensional Modeling High-fidelity 3D models of core equipment such as dewatering machines and filter presses are built using SolidWorks / Unity, integrating geometric parameters (such as filter plate size and bearing layout), motion relationships (such as screw rotation trajectory) and material properties (elastic modulus of 316L stainless steel).

[0038] Real-time sensor data (speed, torque, vibration) is received via the OPCUA protocol, driving the virtual model to update synchronously and achieving millisecond-level state synchronization between the physical and virtual twins.

[0039] 4.1.2 Dynamic Visualization Develop a lightweight WebGL rendering engine to support real-time display of sludge flow paths, filter cloth blockage heat maps, and equipment wear areas on the browser, helping maintenance personnel to intuitively grasp the operating status.

[0040] 4.2. Stress Field Analysis Module 4.2.1 Finite Element Modeling A finite element model of the dehydrator drum was established based on ANSYS, and an adaptive meshing technique was used when generating the mesh (focusing on the connection between the bearing housing and the helical blades).

[0041] Boundary condition settings: Dynamic load: Calculate the centrifugal force distribution based on torque sensor data; Fixed constraints: Simulation rack support points; Contact relationship: Define the friction coefficient between the filter plate and the sludge (μ=0.3~0.5).

[0042] 4.2.2 Real-time stress calculation An explicit dynamic algorithm (such as LS-DYNA) is used to update the stress cloud map every 10ms and identify stress concentration areas (such as triggering an early warning when the peak stress at the drum weld exceeds 250MPa).

[0043] 4.3. Remaining Life Prediction Module 4.3.1 Multi-source data fusion Input data: Historical vibration spectrum (FFT analysis of characteristic frequency drift); Number of stress cycles (obtained through integral strain-time curve); Environmental corrosion data (chloride ion concentration monitored by pH sensor).

[0044] 3.2 Lifetime Prediction Model Using the improved Paris formula: [\frac{da}{dN}=C(\DeltaK)^m\cdote^{-\frac{E_a}{RT}}\cdot(1+\alpha\cdotC_{Cl}^2)]; Where: (C,m) is the material constant (calibrated through accelerated testing); (\alpha) is the corrosion correction factor (dynamically adjusted according to pH value); the prediction result is output in the form of remaining operating hours, with an error ≤12%.

[0045] 4.4. Module Collaboration Mechanism The 3D simulation module provides a visualization interface for stress analysis. Stress data serves as the input parameter for life prediction, and the life prediction results are used to inversely optimize the maintenance strategy of the simulation model.

[0046] When a fault occurs, the system automatically retrieves data from related modules to generate a diagnostic report (such as drum bearing abnormality → vibration spectrum analysis → remaining life prediction → spare parts scheduling suggestions).

[0047] 5. Cloud-edge collaborative execution 5.1 System Architecture Composition The cloud-edge collaborative architecture adopts a two-tier architecture design of edge computing nodes + cloud platform: Edge computing nodes: Deployed at the equipment site, they employ industrial-grade ARM processors (such as the NXP.MX8 series) and feature real-time data acquisition, rapid instruction execution, and local model inference capabilities. They support both Modbus / TCP (real-time control) and OPCUA (data standardization) dual-protocol communication, ensuring seamless integration with PLCs, sensors, and other industrial equipment.

[0048] Cloud platform: Based on a containerized microservice architecture, it integrates big data analytics engines (such as Apache Spark) and model training frameworks (such as TensorFlow Extended), and is responsible for global data aggregation, model iteration optimization, and historical data storage.

[0049] 5.2 Workflow Data tiering: Edge nodes collect real-time data (such as dehydrator speed and chemical dosing flow) at a frequency of 10Hz, and complete preliminary filtering and anomaly detection through lightweight models (such as TinyML), uploading only key feature data to the cloud.

[0050] The cloud receives data streams from multiple edge nodes and constructs a global knowledge graph to analyze the process correlations across devices.

[0051] Model dynamic updates: Every 24 hours, the cloud updates the LSTM prediction model based on 500 newly collected process data sets, generating an incremental update package (DeltaUpdate).

[0052] The update package is distributed to edge nodes through an encrypted channel, and hot deployment technology is used to replace the model. The entire process takes ≤5 minutes.

[0053] Collaborative control mechanism: Real-time control loop: edge nodes execute fuzzy PID control commands with a response delay of ≤50ms, ensuring real-time adjustment of key parameters such as filter press pressure.

[0054] Optimize the decision-making loop: The cloud generates energy-saving strategies (such as drug dosing scheduling during off-peak hours) based on historical operating data and pushes them to edge nodes via the OPCUA protocol.

[0055] 5.3 Technological Advantages Reliability: Edge nodes can operate autonomously and maintain basic control functions when the network is down; cloud failures do not affect local real-time control.

[0056] Scalability: Adding new devices only requires deploying edge nodes and registering them to the cloud, without needing to reconstruct the system architecture.

[0057] Security: TLS 1.3 encrypted communication is used, and edge nodes are equipped with hardware-level security chips (such as TPM 2.0) to prevent data tampering.

[0058] 6. Control Process When the pH sensor detects a sudden drop in the influent pH to 6.2: The predictive model outputs a new parameter combination (dosage +15%, rotation speed -8%) within 3 seconds. Fuzzy PID fine-tunes the motor frequency (±2Hz) based on real-time torque data; The digital twin system detected an abnormal bearing vibration spectrum and triggered a maintenance alarm 32ms in advance.

[0059] The present invention has the following advantages: 1. Deploy five types of high-precision sensors, including sludge concentration meters, to achieve 12Hz high-frequency monitoring of sludge characteristics (concentration, pH, vibration, etc.), which is 300 times more efficient than traditional manual sampling; 2. Edge computing nodes employ sliding window filtering (1-second window) and outlier removal mechanisms (such as pH±2 threshold reset), achieving a data reliability of 99.2%. 3. By using a 3-layer LSTM network (64-128-64 structure) to capture nonlinear temporal dependencies, the prediction error is ≤8%; 4. Transfer learning technology enables the model to have a cold start time of ≤2 hours, adapting to multiple scenarios such as municipal sewage / industrial wastewater; 5. The inner loop controls the dewatering machine speed / dosage flow rate (10ms response), and the outer loop receives LSTM output parameters to form a closed loop for process optimization; 6. Dynamic weight adjustment (α / β / γ coefficients) enables adaptive operation to achieve a 15% increase in dehydration efficiency or a 20% reduction in energy consumption.

[0060] In this invention, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; "linking" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of these terms in this invention according to the specific circumstances.

[0061] In the description of this invention, it should be understood that the terms "inner" and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0062] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0063] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent sludge treatment apparatus control method characterized by comprising: The method comprises the following steps: S1. Collecting physical parameters, chemical parameters and equipment operation state parameters in the sludge treatment process in real time through a multi-source sensor array; S2. Establishing a deep learning-based dynamic prediction model of sludge characteristics, which processes time series data through an LSTM neural network and outputs an optimal process parameter combination; S3. Dynamically adjusting the dewatering machine speed, dosing pump flow and filter press pressure threshold value using a fuzzy PID control algorithm; S4. Realizing virtual mapping and fault pre-diagnosis of the equipment operation state through a digital twin system; S5. Building a cloud-edge collaborative control architecture, in which the edge computing node executes real-time control instructions and the cloud platform performs big data analysis and model iteration update.

2. The intelligent sludge treatment device control method according to claim 1, wherein The multi-source sensor array in step S1 includes a sludge concentration meter, a torque sensor, a pH sensor, a turbidity sensor and a vibration monitoring module, and the sampling frequency is not less than 10 Hz.

3. The intelligent sludge treatment device control method according to claim 1, wherein The prediction model training in step S2 introduces a transfer learning mechanism, and the pre-training data set contains at least 500 groups of sludge treatment process data under different water quality conditions.

4. The intelligent sludge treatment device control method according to claim 1, wherein The fuzzy PID control algorithm in step S3 sets a weighted optimization objective function of dewatering efficiency, energy consumption ratio and cake moisture content, and the weight coefficient is dynamically adjusted according to the influent load.

5. The intelligent sludge treatment device control method according to claim 1, wherein The digital twin system in step S4 includes a device three-dimensional simulation module, a stress field analysis module and a residual life prediction module, and the fault early warning response time is ≤30 ms.

6. The intelligent sludge treatment device control method according to claim 1, wherein In the cloud-edge collaborative architecture in step S5, the edge computing node uses an industrial-grade ARM processor and supports Modbus / TCP and OPCUA dual-protocol communication.

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