PHG incongruous bridging reaction system for front-end pretreatment of wastewater

Abstract

The invention relates to the technical field of water treatment, in particular to a PHG different-direction bridging reaction system for front-end pretreatment of wastewater, which comprises a reaction monitoring module, a data analysis module, a strategy execution module and an early warning treatment module. According to the system, parallel detection light is projected to a reaction area, scattered light and transmission light are received, and image acquisition is synchronously combined, so that multi-dimensional monitoring of floc micromorphology and macroscopic spatial distribution is realized. The data analysis module is used for carrying out primary abnormity judgment according to the visual characteristics of the floc, diagnosing an abnormity source in combination with scattered light, and distinguishing abnormal hydraulic stirring conditions or abnormal flocculant response. And the strategy execution module generates a differential control instruction according to the diagnosis result, adjusts stirring or dosing parameters, and evaluates the effect after adjustment. The system provided by the invention has the functions of automatic early warning and advanced self-treatment, realizes closed-loop intelligent regulation and control of the flocculation process, and improves the quality stability of effluent and the self-adaptive capability of the system.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, and in particular to a PHG reverse bridging reaction system for wastewater pretreatment. Background Technology

[0002] In the pretreatment processes of water treatment, the effectiveness of coagulation and flocculation is crucial, directly affecting the operating load of subsequent units and the quality of effluent. However, precise control of this process has long faced significant challenges. Traditional methods rely heavily on operator experience, adjusting reagent dosage by visually observing floc morphology. This approach is not only highly subjective but also exhibits significant time lag, making it difficult to address real-time fluctuations in water quality. While online instruments such as turbidimeters provide some data support, they still reflect the macroscopic results after mixing and cannot reveal the essential state of the dynamic microscopic process of flocculation.

[0003] In recent years, machine vision-based floc monitoring systems have seen some application. However, these technologies primarily focus on extracting static microscopic features such as floc size and quantity from a single image, resulting in fundamental limitations in their sensing capabilities. On one hand, they struggle to capture the spatial uniformity of floc distribution across the entire cross-section of the reactor, thus failing to diagnose macroscopic process defects caused by uneven mixing or hydraulic short circuits. On the other hand, simple image analysis cannot provide continuous trend information on floc settling or migration, and is insensitive to early-stage process anomalies. Furthermore, their effectiveness is easily affected by changes in water color, suspended bubbles, and lighting conditions, leading to insufficient reliability in complex industrial environments. This results in most existing automatic control systems remaining at the level of simple feedback control based on a single parameter threshold, unable to distinguish the root cause of anomalies, and therefore unable to execute precise, differentiated control strategies. Summary of the Invention

[0004] To address this issue, the present invention provides a PHG anisotropic bridging reaction system for wastewater pretreatment, which overcomes the problem in the prior art that relies solely on single-point visual imaging and cannot simultaneously acquire the macroscopic spatial distribution and dynamic migration trend of flocs, resulting in the system's inability to accurately diagnose the root cause of abnormalities and perform precise closed-loop control.

[0005] To achieve the above objectives, the present invention provides a PHG countercurrent bridging reaction system for wastewater pretreatment, comprising: The reaction monitoring module is used to project several beams of detection light parallel in the vertical direction onto the reaction area, receive scattered light to generate a scattered light signal reflecting the overall distribution state of the flocs, receive transmitted light signals to generate transmitted light signals, and take pictures of the reaction area to obtain image sequence signals of the flocs. The data analysis module is used to extract visual feature parameters characterizing the microscopic morphology of the flocs from the image sequence signal to determine whether the initial state is abnormal; to extract the changing trend features reflecting the macroscopic distribution of the flocs from the scattered light signal to generate the final state diagnosis result; and to generate a light intensity distribution sequence map of the reaction area based on the brightness distribution of the transmitted light signal. The strategy execution module is used to generate control strategy instructions based on the final state diagnosis results, drive the corresponding dosing device or stirring device to perform adjustment operations, and determine the flocculation state characterization quantity based on the light intensity distribution sequence diagram after the adjustment operation is completed, and evaluate the control effect based on the new data after a preset period. The early warning processing module issues an early warning based on the diagnostic results of the data analysis module and autonomously initiates at least one advanced processing procedure, wherein the advanced processing procedure includes fine-tuning the stirring parameters and then re-monitoring and evaluating or triggering a system self-check and parameter reset process.

[0006] As a preferred technical solution for a PHG reverse bridging reaction system used in wastewater pretreatment, the reaction monitoring module includes: The optical emission unit consists of several monochromatic point light sources arranged at equal intervals along the vertical direction, used to project several beams of detection light parallel in the vertical direction onto the reaction area; The light receiving unit includes a transmitted light receiver disposed opposite to the optical emitting unit, and an array of side-scattered light receivers disposed at a predetermined angle to the axis of the detection light beam, which are used to receive transmitted light and side-scattered light respectively to generate the transmitted light signal and the scattered light signal. The image acquisition unit includes an industrial camera and a coaxial illumination source integrated therewith. The optical axis of the lens of the industrial camera is orthogonally arranged to the detection light plane, so as to capture the reaction area to obtain the image sequence signal. The field of view of the image acquisition unit covers the reaction area through which the detection light passes.

[0007] As a preferred technical solution for the PHG anisotropic bridging reaction system for wastewater pretreatment, the data analysis module extracts the temporal trend features reflecting the floc settling state and initial concentration changes from the scattered light signal, segments and identifies the floc images in the image sequence signal, calculates and obtains visual feature parameters characterizing the microscopic morphology of individual flocs, and receives the transmitted light signal. Based on the spatial coordinates of each sensing unit in the transmitted light receiver and its output light intensity value, it generates a light intensity distribution sequence map reflecting the spatial distribution of light intensity in the reaction area at that moment. The visual feature parameters include the equivalent diameter of the floc and the fractal dimension of the contour.

[0008] As a preferred technical solution for the PHG anisotropic bridging reaction system for wastewater pretreatment, the data analysis module compares the equivalent diameter of the flocs and the fractal dimension of the profile with the corresponding normal range thresholds to determine the primary state. If the initial determination result is that at least one visual feature parameter exceeds its corresponding normal range threshold, it is determined to be an abnormal state, and a final state diagnosis based on the scattered light signal is triggered.

[0009] As a preferred technical solution for a PHG anisotropic bridging reaction system used for wastewater pretreatment, the data analysis module is configured to analyze the scattered light signal in response to the initial state determination result being an abnormal state, and extract characteristic parameters reflecting the spatial distribution uniformity and overall migration trend of the flocs for final state diagnosis: If the spatial distribution uniformity is lower than the preset distribution threshold, the diagnosis result is abnormal hydraulic stirring conditions; If the spatial distribution uniformity is not lower than a preset distribution threshold and the overall migration trend characteristic parameters are abnormal, the diagnosis result is that the flocculant response is abnormal.

[0010] As a preferred technical solution for a PHG reverse bridging reaction system used for wastewater pretreatment, the strategy execution module is configured to generate control strategy instructions corresponding to the diagnosis results after completing the final state diagnosis, wherein: If the diagnosis result indicates abnormal hydraulic mixing conditions, the generated control strategy command will be to adjust the parameters of the mixing device speed and / or operating mode. If the diagnosis result indicates an abnormal flocculant response, the generated control strategy instruction will be a parameter to adjust the dosage ratio of coagulant or coagulant aid.

[0011] As a preferred technical solution for the PHG anisotropic bridging reaction system for wastewater front-end pretreatment, the strategy execution module is configured to determine the flocculation state characterization quantity based on the acquired light intensity distribution sequence map at a preset period after the adjustment operation is completed. The flocculation state characterization quantity is calculated and determined based on the light intensity values ​​of each spatial coordinate point in the light intensity distribution sequence diagram, in order to characterize the uniformity of floc distribution within the adjusted reaction area.

[0012] As a preferred technical solution for a PHG anisotropic bridging reaction system used for wastewater pretreatment, the strategy execution module determines the control effect of the adjustment operation based on the flocculation state characterization parameters: If the flocculation state characterization quantity does not reach the uniformity target threshold, the control effect is determined to be substandard, and a control effect substandard signal is sent to the early warning processing module.

[0013] As a preferred technical solution for a PHG reverse bridging reaction system used for wastewater pretreatment, the early warning processing module is configured to issue an early warning and autonomously start at least one of the advanced processing procedures upon receiving a signal that the control effect has not met the standard.

[0014] As a preferred technical solution for a PHG reverse bridging reaction system used for wastewater pretreatment, the advanced processing procedure initiated by the early warning processing module includes: After fine-tuning the stirring parameters, the system re-monitors and evaluates the data, generates a readjustment command containing the fine-tuning parameters, sends it to the strategy execution module for execution, and triggers the reaction monitoring module and data analysis module to re-collect and evaluate the state after execution. The system self-test and parameter reset process sequentially performs self-tests on the operating status of the optical emission unit, light receiving unit, and image acquisition unit of the reaction monitoring module, and resets the system's key control parameters to preset initial values.

[0015] Compared with existing technologies, the advantages of this invention lie in its integration of parallel light curtain detection, side-scattered light reception, and machine vision imaging to construct a multi-dimensional, high-precision floc state perception system, achieving comprehensive synchronous monitoring from microscopic morphology to macroscopic distribution. The system performs initial anomaly screening based on visual features and accurately diagnoses the root cause of anomalies using scattered light signals, effectively distinguishing between abnormal hydraulic stirring conditions and flocculant response anomalies. This generates differentiated control commands, enabling intelligent adjustment of stirring or dosing parameters. Furthermore, after adjustment, the system evaluates the control effect based on light intensity distribution sequence diagrams, forming a closed-loop control mechanism. Equipped with early warning and advanced self-processing functions, it significantly improves the system's adaptability and fault response capabilities. Ultimately, this system ensures effluent water quality stability while reducing manual reliance, improving the control precision and operational reliability of the flocculation process, and is suitable for complex and variable wastewater pretreatment scenarios. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the PHG countercurrent bridging reaction system for wastewater pretreatment according to an embodiment of the present invention; Figure 2 This is a diagram showing the turbidity test results in an embodiment of the present invention. Figure 3 This is a diagram showing the iron removal operation test results of an embodiment of the present invention; Figure 4 This is a graph showing the results of a hardness test in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] In this invention, PHG stands for Polymer Heterogeneous Guided, which refers to the formation of a bridging structure along a non-unidirectional path by flocs under hydraulic action, thereby improving settling efficiency.

[0022] The basic process components include: a micro-flocculation dosing device comprising a coagulant dosing unit and an alkali dosing unit, wherein the coagulant is a 5% concentration of compound polyaluminum chloride (PAC), which, through precise dosing, causes colloidal particles and tiny suspended solids in the water to collide and aggregate rapidly under Brownian motion, forming large-volume flocs that are easily trapped; the alkali dosing unit is used to adjust the pH of the influent to the optimal coagulation range of 8-10 to ensure flocculation reaction efficiency. The micro-flocculation filter uses a honeycomb water distribution device to ensure that the incoming water is evenly sprayed onto the cross-section of the filter media, avoiding local water flow concentration and improving the uniformity of water distribution and backwashing effect. The filter media is filled in layers from top to bottom according to different particle sizes, namely anthracite, activated filter media and quartz sand. The anthracite has a porosity of more than 50% and excellent adsorption performance. The activated filter media can remove more than 90% of particles with a diameter of 4μm and has both heavy metal adsorption capacity and anti-biofouling properties. The quartz sand filter media is pressure-resistant, wear-resistant and has strong interception capacity. The combination of the three filter media forms a multi-stage interception system, which has a better dirt holding capacity than a single filter media.

[0023] Automatic backwash control: The PLC control system monitors the pressure difference between the inlet and outlet water of the micro-flocculation filter in real time via a differential pressure transmitter. When the pressure difference reaches 0.05 Bar, the backwash program is automatically started. The backwash process consists of air washing and a backwash intensity of 20 L / m. 2 • s, lasting 5 min, water and air washing simultaneously, backwash intensity 5 L / m 2 • 5 minutes for continuous washing, and 5 minutes for backwashing to ensure clean regeneration of filter media, maintain stable effluent quality and treatment capacity, and extend equipment life.

[0024] Please see Figures 1-4 As shown, the present invention provides a PHG countercurrent bridging reaction system for wastewater pretreatment, comprising: Specifically, the reaction monitoring module includes an optical emitting unit, a light receiving unit, and an image acquisition unit.

[0025] In implementation, a stable, uniform, and spatially distributed parallel detection light curtain is projected onto the reaction area via an optical emission unit. It is understood that monochromatic point light sources with a single wavelength and small divergence angle, such as laser diodes with a wavelength of 650 nm, are used, arranged at equal intervals along a vertical axis, preferably with a spacing of 10 mm. All point light sources are driven synchronously to ensure that the emitted beams remain spatially parallel, thus forming a grating or light curtain composed of multiple parallel rays within the reaction area. Any filaments passing through this area will modulate the beams, thereby encoding the spatial distribution information of the filaments into the optical signal. The use of monochromatic light avoids the signal complexity caused by polychromatic light scattering, and the equal-interval arrangement ensures uniform sampling of the reaction area in the vertical direction.

[0026] The purpose of setting up the optical receiving unit is to synchronously and independently capture the transmitted light and side-scattered light modulated by the flocs, in order to obtain complementary information reflecting the different physical properties of the flocs. It is understood that, based on the different mechanisms of light-particle interaction, the attenuation of transmitted light intensity can reflect the overall concentration and size of the flocs, while the intensity of side-scattered light is more sensitive to small particles and can reflect the surface characteristics and particle size distribution of the flocs. Therefore, the transmitted light receiver is precisely positioned directly opposite the optical emitting unit to directly receive the light intensity after penetrating the reaction region. Preferably, a large-area silicon photodiode is used for the transmitted light receiver. The side-scattered light receiver array is a one-dimensional sensor array, positioned at a predetermined angle to the beam axis, specifically for receiving photons side-scattered by the particles. The angle is chosen to maximize sensitivity to the microscopic scattering characteristics of the flocs while ensuring sufficient signal strength. Based on maximizing the scattered light signal intensity, 60° is determined. Generally, the initial preset angle is 45°±10°. The optical receiving unit can simultaneously generate two optical sequence signals with different physical meanings, laying the foundation for subsequent differentiation between overall concentration changes and microparticles.

[0027] The image acquisition unit is designed to directly acquire visual morphological images of the flocs, serving as an important supplement and verification to the optical signals. It employs a high-resolution, high-frame-rate industrial camera, ensuring its lens optical axis is orthogonal to the aforementioned detection light plane. Simultaneously, to overcome potential insufficient light or backlighting issues within the reaction vessel, a coaxial illumination source is provided to the camera, ensuring the illumination light is emitted along the lens optical axis, thereby minimizing shadows and obtaining images with clear floc outlines. In practice, the camera's angle of view and focal length must be precisely adjusted to ensure its field of view completely covers the reaction area traversed by the optical detection light. This guarantees that the flocs observed in the image and the flocs in the modulated light signal belong to the same group, ensuring temporal and spatial synchronization. This provides the most intuitive microscopic morphological parameters such as the equivalent diameter, shape, and density of the flocs, which are then fused and compared with the macroscopic optical signals, significantly enhancing the dimensionality and reliability of state perception.

[0028] In this invention, the reaction monitoring module constructs a standard detection field through an optical emission unit, acquires macroscopic and continuous physical optical signals through a light receiving unit, and acquires microscopic and instantaneous morphological image signals through an image acquisition unit, thereby achieving spatial alignment and temporal synchronization. Together, they form a three-dimensional sensing system, providing comprehensive, real, and traceable flocculent process data for the entire PHG anisotropic bridging reaction system, which is a prerequisite for achieving precise closed-loop control.

[0029] Specifically, the data analysis module extracts temporal trend features reflecting the initial changes in floc settling state and concentration from the scattered light signal. It can be understood that changes in the population size, movement speed, and number of flocs during formation, growth, and settling directly cause temporal fluctuations in the intensity of the scattered light signal with specific patterns. By performing time-domain analysis on the scattered light signal, calculating its rate of change, fluctuation frequency, or fitting the slope of its attenuation curve within a sliding time window, temporal trend features reflecting the initial changes in floc settling state and concentration can be extracted. This transforms the scattered light intensity sequence into a trend indicator with clear physical meaning, thereby capturing early abnormal tendencies in the process before significant changes in floc morphology occur that are visually or visually identifiable, thus serving as an early warning system.

[0030] Specifically, a vertically parallel array of light beams forms a vertical detection light curtain within the reaction zone. As the flocs settle from top to bottom under gravity and pass through beams at different heights sequentially, corresponding time-series pulses are generated in the scattered light signal. These pulses are identified through signal processing, and the time difference between two adjacent beams passing through the same group of flocs is accurately calculated. Given the fixed spacing between the beams, the instantaneous settling velocity of the flocs can be calculated. Statistical analysis of a large number of instantaneous velocity values ​​acquired over a continuous period yields characteristic values ​​such as the average settling rate and velocity distribution range. This allows for the quantitative and direct measurement of the actual settling performance of the flocs, providing a core basis for judging floc density and separation effectiveness.

[0031] The control system periodically issues commands to stop the agitator for a very short time, creating a brief and controlled settling observation window. Typically, this command cycle is 20–30 minutes, the agitator is stopped for 2–5 seconds, and the settling observation window lasts 10–30 seconds. During this window, the curve of scattered light intensity changing over time is collected at high frequency. Because agitation stops, the flocs settle freely in still water, causing the scattered light intensity to decrease systematically. A linear or exponential fit is performed on the initial stage of this attenuation curve; the resulting initial attenuation slope is the time-series trend characteristic. This characteristic can comprehensively evaluate the natural settling efficiency and overall stability of the floc population after being freed from hydraulic disturbance.

[0032] The data analysis module utilizes edge detection algorithms to separate and identify individual flocs from the background and water body in the image sequence signal, and performs calculation and analysis on the contour of each identified floc. Due to their physical properties of refractive index or density, flocs typically exhibit abrupt changes in grayscale compared to the surrounding water background. The edge detection algorithm locates these abrupt changes by calculating the intensity and direction of grayscale changes in the neighborhood of each pixel in the image. In practice, the acquired color image sequence is first converted to a grayscale image to simplify calculations. Subsequently, the grayscale image is convolved using classic edge detection operators such as the Canny operator or other operators. These operators highlight areas with large gradient values ​​in the image, i.e., the edge contours of the flocs, while suppressing background noise. For example, the Canny operator uses Gaussian filtering to smooth the image, calculates gradient magnitude and direction, performs non-maximum suppression to refine edges, and performs double threshold detection and edge connection steps, ultimately outputting a binary contour map composed of clear, single-pixel wide edges.

[0033] This transforms the original image, which was originally full of complex information, into a simplified image containing only the outlines of the flocs. Based on this binary outline image, the system can automatically identify each closed outline using connected component analysis. Each closed outline is identified as an independent floc individual and assigned a unique identifier.

[0034] The acquired visual feature parameters include the equivalent diameter and the fractal dimension of the contour. Specifically, the equivalent diameter is obtained by converting the projected area of ​​the irregular floc into the diameter of a circle of equal area, which is used to visually represent the size of the floc. The fractal dimension of the contour is determined by assessing the complexity and irregularity of the floc contour. A higher fractal dimension indicates a more tortuous contour and a looser, more porous structure, while a lower fractal dimension indicates a smoother contour and a denser structure. The determination of visual feature parameters can transform the qualitative, overall image impression into quantitative morphological parameters specific to individual flocs, enabling the precise measurement and continuous tracking of the two key state dimensions: size and structural density.

[0035] The purpose of generating the light intensity distribution sequence map in the data analysis module is to construct a light intensity distribution profile of the reaction region along the direction of transmitted light based on the real-time light intensity data collected by the transmitted light receiver array. This allows for visualization and quantification of the macroscopic distribution of flocs across the cross-section of the reaction region. Specifically, a series of photosensitive units arranged vertically in the transmitted light receiver correspond to a specific spatial coordinate. When parallel detection light passes through the reaction region, each unit synchronously receives the light signal at its corresponding position and outputs a real-time light intensity value. The system maps the light intensity values ​​of all units at the same moment to their spatial coordinates into a light intensity distribution curve, which is the light intensity distribution sequence map. This curve intuitively reflects the spatial distribution characteristics of floc concentration along the beam direction at that moment. By analyzing the uniformity, fluctuation pattern, and trend of the light intensity distribution in this sequence map, it is possible to globally assess whether the spatial distribution of the floc cloud is uniform, whether there are abnormalities such as sedimentation short-circuiting or floating accumulation, thus providing a direct basis for judging whether the hydraulic stirring conditions are suitable.

[0036] In this invention, the data analysis module, through the above-mentioned parallel three-way processing flow, completes the in-depth processing and feature extraction of the original data from three orthogonal dimensions: time dynamics, individual morphology, and spatial distribution. This constructs a multi-dimensional digital profile of the floc state for the system, so that subsequent state diagnosis is no longer based on a single, fuzzy signal, but on a comprehensive quantitative feature set that integrates time-series trends, micro-morphology, and macro-distribution, thus laying the foundation for intelligent decision-making in the system.

[0037] Specifically, the data analysis module compares the equivalent diameter of the flocs and the fractal dimension of the contour with the corresponding normal range thresholds to make a preliminary state determination. If the preliminary determination result is that at least one visual feature parameter exceeds its corresponding normal range threshold, it is determined to be an abnormal state and triggers the final state diagnosis combined with the light intensity distribution sequence map; otherwise, it is a normal state.

[0038] The initial state determination based on visual feature parameters aims to achieve rapid and automated preliminary screening and anomaly alarm for floc conditions. The normal range thresholds for the two parameters are calibrated through a limited number of standard operating condition tests. For example, for specific water quality and treatment targets, the lower limit of the equivalent diameter threshold can be calibrated to 50 micrometers, and the upper limit of the fractal dimension threshold to 1.7. The thresholds are typically determined by statistical analysis of a large number of floc images collected during historical stable operation, taking specific quantiles of their feature parameter distributions and fine-tuning them. Generally, the initial equivalent diameter is taken as the 10th quantile, and the initial fractal dimension as the 90th quantile. During operation, the data analysis module compares the real-time calculated equivalent diameter and fractal dimension of each floc with the aforementioned preset thresholds, instantly completing compliance checks on the two core quality dimensions: whether the current floc size is large enough and whether the structure is sufficiently dense, thus achieving immediate detection of abnormal states.

[0039] The mechanism linking anomaly triggering and final diagnosis aims to construct an intelligent diagnostic chain that progresses from simple to complex and is activated on demand, thereby optimizing the allocation of system computing resources. When the initial judgment finds that all visual feature parameters are within the normal threshold range, the system determines it to be in a normal state, maintains the current operating parameters, and does not need to initiate deeper analysis. Once at least one visual feature parameter exceeds its corresponding normal threshold, for example, if the average equivalent diameter of the flocs is consistently below 50 micrometers, it is immediately determined to be in an abnormal state. This means that the system will call upon two-dimensional dynamic information characterizing the macroscopic spatial distribution and migration trend of the flocs to cross-verify the cause of the anomaly, whether it is due to uneven local mixing leading to abnormal distribution or a deterioration in overall settling performance.

[0040] In this invention, the data analysis module uses computationally efficient visual feature parameters for rapid initial screening, no longer limited to microscopic images, but combining the light intensity distribution sequence map for final state diagnosis. If anomalies are suspected, computational resources are then mobilized for deeper data for precise authentication, thereby improving the accuracy of overall diagnosis and the reliability of decision-making while ensuring the real-time response of the system.

[0041] Specifically, after initial screening for anomalies using visual parameters, the data analysis module uses macroscopic spatial distribution and dynamic temporal information obtained from the scattered light receiver array to precisely locate and differentiate the root causes of the anomalies. This concretizes the general anomaly state into specific fault modes that can directly guide control operations. The macroscopic movement and distribution morphology of flocs within the reaction area directly and sensitively reflect the combined effect of hydraulic stirring conditions and flocculation chemical reactions.

[0042] The analysis and diagnosis of spatial distribution uniformity is crucial for assessing hydraulic conditions. Specifically, the output signals of each sensing unit in the side-scattering light receiver array are simultaneously acquired, and the signal values ​​of each unit at the same time are used to construct a spatial matrix of scattering intensity reflecting the spatial concentration distribution of flocs at that moment. The spatial distribution uniformity index is quantified by calculating the statistical characteristics of this matrix; generally, the coefficient of variation is used. A preset distribution threshold can be determined through calibration experiments during stable system operation. For example, statistical analysis can be performed on matrices acquired under numerous compliant conditions, and the mean minus twice the standard deviation can be used as the threshold. If the current uniformity index is below this threshold, it indicates uneven floc distribution, and the diagnosis is abnormal hydraulic mixing conditions.

[0043] Analyzing the overall migration trend is crucial for evaluating the flocculation process. During the baseline learning period, the system continuously collects and stores dynamic scattering distribution maps under compliant conditions, i.e., a spatial matrix of scattering intensity arranged in a time series. By analyzing the displacement of floc clouds within the continuous matrix in the maps, trend characteristic parameters such as average settling velocity are calculated, and a historical baseline is established. In real-time operation, the system processes the latest generated dynamic maps using the same algorithm to extract real-time migration trend parameters. If these parameters deviate significantly from the historical baseline, it indicates abnormal floc settling performance. Combined with the determination of uniform spatial distribution, the diagnosis can be an abnormal flocculant response.

[0044] For example, an array consisting of 16 sensing units arranged in 4 rows × 4 columns might generate the following matrix after a single synchronous sampling, where each value represents the real-time scattered light intensity at the corresponding spatial coordinate point: [ [105, 110, 108, 102], [215, 85, 90, 205], [95, 220, 78, 215], [100, 105, 212, 98] ]. In this matrix, the differences in values ​​intuitively reflect the spatial distribution of floc concentration. For example, higher values, such as 220 and 215, may indicate local floc aggregation, while lower values, such as 85 and 78, correspond to areas with lower concentrations.

[0045] In subsequent real-time operation, the system uses the same algorithm to process the scattered light signal within the current window period, extracting the real-time average settling velocity or mainstream angle as a migration trend feature parameter. The current window period is preferably the most recent 30 seconds.

[0046] During the final diagnosis, if the spatial distribution uniformity is lower than a preset distribution threshold, the diagnosis result is abnormal hydraulic mixing conditions; if the spatial distribution uniformity is not lower than the preset distribution threshold and the overall migration trend characteristic parameters are abnormal, the diagnosis result is abnormal flocculant response. It can be understood that uniform distribution with an abnormal migration trend indicates that the hydraulic conditions within the reactor are basically uniform, but the properties of the flocs themselves (such as density and strength) have not met expectations, thus pointing to abnormal flocculant response as the root cause of the fault. The effect of this implementation is that it enables the system to have process self-adaptive capabilities, with its diagnostic criteria dynamically calibrated according to the optimal operating state. This allows for more sensitive and accurate detection of subtle changes in the process state and the provision of intelligent diagnoses that conform to the current operating conditions.

[0047] For example, in areas where gravity settling occurs primarily, if a persistent and significant upward trend is detected, or if the settling velocity is significantly lower than the historical baseline under similar operating conditions, it is determined that the overall migration trend characteristic parameters are abnormal. When the system determines that the spatial distribution uniformity meets the standard, but this migration trend is abnormal, it indicates that the hydraulic mixing conditions are basically normal, but the floc characteristics themselves, such as density and size, have not met expectations. The root cause of the problem is more likely to be the effect of coagulant addition, so the diagnosis is abnormal flocculant response.

[0048] In this invention, by introducing and analyzing the macroscopic spatial distribution and migration trend characteristics, the root cause of primary abnormal alarms is traced, and a single abnormal alarm is accurately diverted to two completely different process control dimensions: stirring and mixing or reagent addition. This provides a decisive basis for the subsequent implementation of differentiated and precise control strategies.

[0049] Specifically, the strategy execution module is configured to, after completing the final state diagnosis, if the diagnosis result is abnormal hydraulic mixing conditions, generate control strategy instructions to adjust the parameters of the mixing device speed and / or operating mode; if the diagnosis result is abnormal flocculant response, generate control strategy instructions to adjust the parameters of the coagulant or coagulant aid dosage ratio.

[0050] In implementation, the purpose of generating commands for abnormal hydraulic mixing conditions is to address the problem of uneven floc distribution within the reaction zone. When such an anomaly is diagnosed, the core of the control strategy command generated by the system is to adjust the rotational speed of the agitator. The adjustment method is based on the deviation between the spatial distribution uniformity index calculated from the scattered light signal and a preset distribution threshold. The adjustment amount is typically determined based on data obtained from hydraulic simulations and flow field tests of a specific reactor at different rotational speeds. It is understood that there is a strong correlation between the uniformity of floc distribution within the reaction zone and the agitator rotational speed. Increasing the agitator rotational speed directly increases the fluid shear rate and turbulent kinetic energy, thereby enhancing macroscopic and microscopic mixing and improving uneven distribution. Therefore, the system linearly determines the rotational speed adjustment range based on the negative deviation between the spatial distribution uniformity index calculated in real-time from the scattered light signal and its preset distribution threshold. The system determines the adjustment range using an initial linear proportional relationship of 0.5. For example, if the measured uniformity index is 10% lower than the threshold, the command is to increase the agitator rotational speed by 5%, achieving a balanced strategy of on-demand compensation.

[0051] Understandably, the adjustment range should be proportional to the severity of the problem. This avoids over-adjustment and energy waste caused by small fluctuations, while ensuring sufficient corrective force when there is significant uneven distribution. The proportionality coefficient is usually determined by conducting cold-state flow field tests on the specific reactor, computational fluid dynamics simulations or tracer experiments, observing the gradient of mixing uniformity at different rotational speeds, and calibrating and simplifying it in conjunction with system stability requirements.

[0052] When generating instructions for abnormal flocculant response, the aim is to correct problems such as poor floc growth or poor settling performance caused by improper floc dosing. When such anomalies are diagnosed, the core of the control strategy instructions generated by the system is to adjust the dosing parameters of the coagulant or coagulant aid. The adjustment method is also based on quantitative analysis. If the anomaly is triggered by insufficient equivalent diameter, the instruction is to increase the dosage of the coagulant (such as PAC); if the anomaly is triggered by excessively high fractal dimension, the instruction may be to adjust the dosage ratio of the coagulant aid (such as PAM) or switch the agent type. The adjustment amount is typically determined by first calculating a base dosage based on the current influent flow rate and pollutant concentration, and then calculating an additional compensation dosage based on the deviation between the measured equivalent diameter and the target value using a proportional-integral (PI) controller. The compensation coefficient, or the gain parameter of the PI controller, is obtained through prior calibration and optimization using beaker tests and dynamic simulation tests for this water quality. This allows for dynamic and precise matching of the needs of reagent dosing and real-time water quality changes, ensuring that the flocculation chemical reaction is always within the optimal range while avoiding reagent waste.

[0053] In this invention, the control strategy generation module does not simply output on or off instructions, but is an adaptive intelligent decision-maker that translates abstract diagnostic conclusions into instructions that the device can understand and execute. This translates the intelligent perception capabilities of the entire system into verifiable and optimizable physical control actions, forming a complete technical closed loop from monitoring and diagnosis to execution.

[0054] Specifically, after the strategy execution module performs the dosing or stirring adjustment operation, the system waits for the process response cycle, i.e., the preset cycle, typically 5 minutes, to allow the new chemical or hydraulic conditions to fully act on the entire reaction system and form an observable steady state. Subsequently, the system instructs the reaction monitoring module to re-acquire the light intensity distribution sequence map. The light intensity distribution sequence map is a two-dimensional projection of the floc concentration field on the reactor cross-section; areas of low light intensity correspond to high floc concentration, where light is blocked or scattered, while areas of high light intensity correspond to low floc concentration. The flocculation state characterization quantity represents the uniformity of floc distribution. Its calculation process involves in-depth statistical analysis of the light intensity values ​​of all pixels in a single frame image, calculating the average deviation and the average value, and using the ratio of the average deviation to the average value as the flocculation state characterization quantity.

[0055] Understandably, for an ideal, homogeneous mixture, its concentration field should be homogeneous, reflected in the light intensity map as all pixel values ​​being similar, with a small standard deviation and therefore a low coefficient of variation. Conversely, when short-circuit flow, dead zones, or floc accumulation occur, areas with significant differences in brightness will appear in the image, leading to an increase in the standard deviation and the coefficient of variation. Therefore, the flocculation state characterization is a highly sensitive criterion for judging spatial distribution anomalies.

[0056] Specifically, the strategy execution module determines the control effect of the adjustment operation based on the flocculation state characterization quantity. The uniformity target threshold is obtained through systematic calibration experiments. In the initial stage of device operation or during the stable operating phase, operators, while ensuring that the effluent water quality continuously meets the standards, run the system and collect a large number of light intensity distribution sequence maps, calculating the corresponding flocculation state characterization quantity, thus forming a dataset representing the health status. Statistical analysis is performed on this dataset, for example, taking its 80th percentile or mean plus one standard deviation as a candidate value for the initial threshold. After determining this threshold, the system's judgment logic is established, and after each adjustment, the newly calculated index is compared with this threshold in real time.

[0057] If the flocculation state characterization quantity reaches or exceeds the threshold, the control effect is deemed to have met the standard. Simultaneously, the control parameters and result indices are stored in a success case library to reinforce the system's memory of effective control strategies for that operating condition. If the index does not reach the threshold, the control effect is deemed to have failed to meet the standard. The underlying logic of this judgment lies in acknowledging the limitations of a single control strategy and the complexity of the process.

[0058] Specifically, upon receiving a signal indicating that the control effect has not met the requirements, the early warning processing module issues an early warning and autonomously initiates at least one of the advanced processing procedures. In implementation, the module continuously monitors for control effect failure signals from the strategy execution module. This signal is a Boolean value (yes / no). Once the early warning processing module receives this signal, it immediately interrupts the normal loop and elevates the system state from optimized operation to fault response level. First, a preset level early warning is issued, such as an audible and visual alarm or a status notification from the host computer, its direct purpose being to notify the system that it has entered an abnormal state. Then, according to pre-set logic, the module autonomously initiates at least one advanced processing procedure without manual intervention. This achieves a seamless transition from failure detection to response initiation, minimizing the waiting time when the system is in a suboptimal state and creating conditions for rapid recovery.

[0059] Specifically, after fine-tuning the stirring parameters in the advanced processing program, the system re-monitors and evaluates the results, generates a readjustment instruction containing the fine-tuning parameters and sends it to the strategy execution module for execution, and triggers the reaction monitoring module and data analysis module to re-collect and evaluate the status after execution. It is understandable that the control effect may not meet the target due to insufficient precision in the magnitude or direction of the previous adjustment. The principle of fine-tuning and re-evaluation is to adopt a conservative, tentative strategy. The early warning processing module has a pre-set simple fine-tuning parameter generation rule. Using the gradient descent idea, it generates a re-adjustment instruction with a fixed proportion or fixed step size based on the adjustment instruction issued by the previous strategy execution module. Preferably, the proportion is 10% to 20%. This instruction is then sent to the strategy execution module for mandatory execution. After execution, the process triggers the reaction monitoring module and data analysis module to complete a completely new monitoring-analysis cycle to re-collect and evaluate the system status. This provides the system with a rapid adjustment attempt based on new parameters, aiming to break through possible local optima or slow response through automated fine-tuning.

[0060] The system self-test and parameter reset process sequentially checks the operational status of the optical emission unit, light receiving unit, and image acquisition unit of the reaction monitoring module, and resets the system's key control parameters to preset initial values. Control failures may stem from distorted monitoring data or drift in core parameters. The principle behind the system self-test and parameter reset process is to quickly verify and restore the sensing system and control foundation. After the process is initiated, each unit in the reaction monitoring module performs a self-test in sequence: the optical emission unit checks the light source drive current and brightness, the light receiving unit checks the signal baseline and gain, and the image acquisition unit checks focus and illumination to determine if there are any obvious abnormalities in the sensing link. Simultaneously, the process batch resets the system's key control parameters to preset initial values, such as various thresholds in the data analysis module and adjustment coefficients in the strategy execution module. This quickly eliminates soft faults caused by sensor drift or accidental parameter errors, restoring the system to a known and stable baseline state.

[0061] In this invention, the implementation of the early warning processing module and the advanced processing program together constitute an intelligent fault response system. When the conventional closed-loop control loop cannot bring the system state back to the target range, the system will not stagnate or rely entirely on manual intervention. Instead, it can actively call the embedded processing method and try different automated recovery strategies, thereby significantly improving the system's autonomy and reliability under complex real-world conditions.

[0062] This invention integrates parallel light curtain detection, lateral scattered light reception, and machine vision imaging to construct a multi-dimensional, high-precision floc state perception system, achieving comprehensive synchronous monitoring from microscopic morphology to macroscopic distribution. The system performs initial anomaly screening based on visual features and accurately diagnoses the root cause of anomalies using scattered light signals, effectively distinguishing between abnormal hydraulic stirring conditions and flocculant response anomalies. This generates differentiated control commands, enabling intelligent adjustment of stirring or dosing parameters. Furthermore, after adjustment, the system evaluates the control effect based on light intensity distribution sequence diagrams, forming a closed-loop control mechanism. Equipped with early warning and advanced self-processing functions, it significantly improves the system's adaptability and fault response capabilities. Ultimately, this system ensures stable effluent quality while reducing reliance on manual intervention, improving the control precision and operational reliability of the flocculation process, and is suitable for complex and variable wastewater pretreatment scenarios.

[0063] Tests showed that this embodiment has a significant removal effect on wastewater turbidity, iron content, and hardness. Actual operation data showed that when the influent turbidity was 12-35 NTU, the effluent turbidity was stable between 0.1-0.5 NTU, with an average removal rate of over 98%; when the influent iron concentration was 0.1-1.0 mg / L, the effluent iron concentration was controlled between 0.05-0.125 mg / L, with an average removal rate of over 60%; at the same time, it also has a certain removal effect on hard wastewater, laying a good water quality foundation for subsequent reaction monitoring and precise control.

[0064] The results of the turbidity test are shown in the table below: Table 1. Results of Turbidity Operation Test The results of the iron removal operation test are shown in the table below: Table 2 Results of Iron Removal Operation Test The results of the hardness test are shown in the table below: Table 3. Results of Hardness Test The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the 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. A PHG countercurrent bridging reaction system for wastewater pretreatment, characterized in that, include: The reaction monitoring module is used to project several beams of detection light parallel in the vertical direction onto the reaction area, receive scattered light to generate a scattered light signal reflecting the overall distribution state of the flocs, receive transmitted light signals to generate transmitted light signals, and take pictures of the reaction area to obtain image sequence signals of the flocs. The data analysis module is used to extract visual feature parameters that characterize the microscopic morphology of flocs from the image sequence signal in order to determine whether the initial state is abnormal. The scattered light signal is used to extract the trend characteristics reflecting the macroscopic distribution of flocs to generate the final state diagnosis result, and the transmitted light signal is used to generate a light intensity distribution sequence map of the reaction area based on its brightness distribution. The strategy execution module is used to generate control strategy instructions based on the final state diagnosis results, drive the corresponding dosing device or stirring device to perform adjustment operations, and determine the flocculation state characterization quantity based on the light intensity distribution sequence diagram after the adjustment operation is completed, and evaluate the control effect based on the new data after a preset period. The early warning processing module issues an early warning based on the diagnostic results of the data analysis module and autonomously initiates at least one advanced processing procedure, wherein the advanced processing procedure includes fine-tuning the stirring parameters and then re-monitoring and evaluating or triggering a system self-check and parameter reset process.

2. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 1, characterized in that, The reaction monitoring module includes: The optical emission unit consists of several monochromatic point light sources arranged at equal intervals along the vertical direction, used to project several beams of detection light parallel in the vertical direction onto the reaction area; The light receiving unit includes a transmitted light receiver disposed opposite to the optical emitting unit, and an array of side-scattered light receivers disposed at a predetermined angle to the axis of the detection light beam, which are used to receive transmitted light and side-scattered light respectively to generate the transmitted light signal and the scattered light signal. The image acquisition unit includes an industrial camera and a coaxial illumination source integrated therewith. The optical axis of the lens of the industrial camera is orthogonally arranged to the detection light plane, so as to capture the reaction area to obtain the image sequence signal. The field of view of the image acquisition unit covers the reaction area through which the detection light passes.

3. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 2, characterized in that, The data analysis module extracts the temporal trend features reflecting the sedimentation state and initial concentration changes of flocs from the scattered light signal, segments and identifies the floc images in the image sequence signal, calculates and obtains visual feature parameters characterizing the microscopic morphology of individual flocs, and receives the transmitted light signal. Based on the spatial position coordinates of each sensing unit in the transmitted light receiver and its output light intensity value, it generates a light intensity distribution sequence map reflecting the spatial distribution of light intensity in the reaction area at that moment. The visual feature parameters include the equivalent diameter of the floc and the fractal dimension of the contour.

4. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 3, characterized in that, The data analysis module compares the equivalent diameter of the floc and the fractal dimension of the profile with the corresponding normal range threshold to determine the initial state. If the initial determination result is that at least one visual feature parameter exceeds its corresponding normal range threshold, it is determined to be an abnormal state, and a final state diagnosis based on the scattered light signal is triggered.

5. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 4, characterized in that, The data analysis module is configured to analyze the scattered light signal in response to the initial state determination result being an abnormal state, and extract characteristic parameters reflecting the spatial distribution uniformity and overall migration trend of the flocs for final state diagnosis: If the spatial distribution uniformity is lower than the preset distribution threshold, the diagnosis result is abnormal hydraulic stirring conditions; If the spatial distribution uniformity is not lower than a preset distribution threshold and the overall migration trend characteristic parameters are abnormal, the diagnosis result is that the flocculant response is abnormal.

6. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 5, characterized in that, The strategy execution module is configured to generate control strategy instructions corresponding to the diagnosis results after completing the final state diagnosis, wherein: If the diagnosis result indicates abnormal hydraulic mixing conditions, the generated control strategy command will be to adjust the parameters of the mixing device speed and / or operating mode. If the diagnosis result indicates an abnormal flocculant response, the generated control strategy instruction will be to adjust the parameters of the coagulant or coagulant aid dosage ratio.

7. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 6, characterized in that, The strategy execution module is configured to determine the flocculation state characterization quantity based on the acquired light intensity distribution sequence map at a preset period after the adjustment operation is completed. The flocculation state characterization quantity is calculated and determined based on the light intensity value of each spatial coordinate point in the light intensity distribution sequence diagram, in order to characterize the uniformity of floc distribution within the adjusted reaction area.

8. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 7, characterized in that, The strategy execution module determines the control effect of the adjustment operation based on the flocculation state characterization quantity: If the flocculation state characterization quantity does not reach the uniformity target threshold, the control effect is determined to be substandard, and a control effect substandard signal is sent to the early warning processing module.

9. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 8, characterized in that, The early warning processing module is configured to issue an early warning and automatically initiate at least one of the advanced processing procedures upon receiving a signal that the control effect has not met the standard.

10. The PHG countercurrent bridging reaction system for wastewater pretreatment according to claim 9, characterized in that, The advanced processing procedure initiated by the early warning processing module includes: After fine-tuning the stirring parameters, the system re-monitors and evaluates the data, generates a readjustment command containing the fine-tuning parameters, sends it to the strategy execution module for execution, and triggers the reaction monitoring module and data analysis module to re-collect and evaluate the state after execution. The system self-test and parameter reset process sequentially performs self-tests on the operating status of the optical emission unit, light receiving unit, and image acquisition unit of the reaction monitoring module, and resets the system's key control parameters to preset initial values.

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