Sludge dewatering system based on screw extrusion and multi-stage filtration

Abstract

The present application discloses a sludge dewatering system based on spiral extrusion and multi-stage filtration, relates to the technical field of sewage treatment, collects the particle size distribution, extracellular polymer concentration and surface cohesion coefficient of sludge, calculates the plugging tendency index, constructs a filter channel induction parameter set, pre-expands and disturbs the sludge to form a filter channel microstructure, obtains a first stage of sludge cake and filtrate through spiral extrusion, sends the sludge cake into a multi-stage filtration system, dynamically switches the filter path based on the pressure difference and the cleanliness of the filtrate, obtains a second stage of sludge cake and filtrate, and according to the conditions, implements airflow backflushing or hot air surface drying and edge pressing treatment on the sludge cake, and outputs the final sludge cake; the method realizes efficient dewatering and intelligent control of high-viscosity and high-plugging-risk sludge, improves the system operation stability and sludge cake quality, and is suitable for deep dewatering treatment in complex sludge scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sewage treatment, in particular to a sludge dewatering system based on spiral extrusion and multi-stage filtration. BACKGROUND

[0002] In the operation process of industrial parks, pharmaceutical factories and biological fermentation sewage treatment stations, high-viscosity, low-particle-content but complex-organic-component residual sludge is often produced. Due to strong adhesion and little free water, the traditional plate-and-frame filter press or belt filter press has low dewatering efficiency, the filter cloth is frequently blocked, and even it is difficult to form a cake for discharge. Especially in winter when the temperature is low, the internal moisture of the sludge is not easy to release, and the moisture content often stays above 85% for a long time, which makes it difficult to enter the subsequent incineration or landfill process, seriously affecting the treatment capacity and operation stability of the sewage plant.

[0003] At present, although spiral extrusion or centrifugal dewatering equipment is applied in some scenarios, when facing sludge containing a large amount of extracellular polymeric substance (EPS) and in flocculent block shape, and lacking of supporting particles, the filter liquid channel in the spiral extrusion stage is easily closed, causing poor water outflow or even backflow under reverse pressure, which further aggravates the blockage. SUMMARY

[0004] The purpose of the present application is to provide a sludge dewatering system based on spiral extrusion and multi-stage filtration to solve the problems in the background art.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical solution: a sludge dewatering system based on spiral extrusion and multi-stage filtration, comprising:

[0006] A data acquisition module acquires particle size distribution parameters P, extracellular polymeric substance concentration parameters E and surface adhesion coefficient μ of the sludge to be treated, and establishes a sludge blockage tendency index T according to the combination of P, E and μ;

[0007] A filter channel generation parameter module generates and calls a set of preset filter channel induction generation parameter set D according to the blockage tendency index T, including initial shear disturbance frequency ω, transient gas-liquid ratio α and induction time window Δt;

[0008] A disturbance application module applies short-time low-pressure disturbance to the sludge based on the parameter set D to obtain a first-stage sludge cake M1 and a first filtrate L1;

[0009] A filtration control module performs multi-stage filtration on M1, including dynamically switching filter path based on pressure difference change and filtrate cleanliness, and sequentially dewatering in different filter layers F1, F2 and F3 to obtain a second-stage sludge cake M2 and a filtrate L2;

[0010] A judging module, which judges whether to enter the pulse cleaning step by performing structural scanning on M2, extracting its density p and residual water content q, and combining with the self-identifying grading feedback condition: if p>threshold value and q>threshold value , then enter the air flow back flushing module, otherwise enter the integrated processing module;

[0011] The air flow back flushing module applies an air flow back flushing sequence G to M2, which is used to control the reverse pulse intensity and interval time of each filter layer, and outputs the final sludge cake M3.

[0012] The integrated processing module implements the integrated processing of hot air surface drying and edge pressing on M2 which does not meet the condition of entering the air flow back flushing module, and outputs the sludge cake M4 which can be directly incinerated or landfilled.

[0013] Preferably, a sludge clogging tendency index T is established according to the combination of P, E and μ, including:

[0014] P, E and μ are normalized, and the sludge clogging tendency index T is calculated by using a weighted linear model of normalized P, E and μ.

[0015] Preferably, a set of preset filter channel induction generation parameter set D is generated and called, including:

[0016] According to the numerical level of the clogging tendency index T, the shear disturbance frequency ω of the corresponding level is called to regulate the responsiveness of the sludge floc microstructure;

[0017] Based on the initial gas content and water content of the sludge, the optimal transient gas-liquid ratio a is calculated to control the formation of short filtration paths of induced bubbles in the sludge;

[0018] The induction time window At is set, and its value is adjusted according to the sludge temperature and EPS adhesion;

[0019] ω, a and At parameters form a preset parameter set D.

[0020] Preferably, the filter direction path is dynamically switched based on the differential pressure change and the filtrate cleanliness, including:

[0021] The instantaneous differential pressure value ΔP of the inlet and outlet of the filter unit is collected in real time, and the instantaneous differential pressure reflection index is calculated after analysis;

[0022] The filtrate cleanliness parameter C in a fixed time period is monitored, and the filtrate cleanliness fluctuation index is calculated after analysis;

[0023] The built-in filter direction switching decision model FCM is called, which uses the instantaneous differential pressure reflection index and the filtrate cleanliness fluctuation index as joint input variables, and uses a weight weighting function to output the filter direction switching instruction;

[0024] According to the switching instruction output by the decision model, the flow direction of the filtering path channel is controlled through the electrically controlled three-way valve, and the dynamic switching of the filtrate flow direction is completed.

[0025] Preferably, the calculation method of the instantaneous pressure difference reflection index is:

[0026] Based on the continuously collected ΔP data within T seconds, a ΔP change curve is constructed, and a time differential algorithm is used to calculate the change rate d(ΔP) / dt of ΔP;

[0027] The change rate of ΔP is combined with the current ΔP value to construct an instantaneous pressure difference reflection index R_p, and the calculation formula is: .

[0028] Preferably, the calculation method of the filtrate cleanliness fluctuation index is:

[0029] Within a fixed time period T1, the filtrate cleanliness parameter C is continuously collected with a sampling period of 1 second, and the suspended solid concentration in the filtrate is used as an example;

[0030] A time sequence C(t) is constructed for all sampling points within the time period T1, and the sliding window average method is used to calculate the mean value of the filtrate cleanliness And the instantaneous deviation ΔC(t), the dynamic fluctuation curve of the filtrate cleanliness is obtained;

[0031] Based on the standard deviation and average change rate of ΔC(t), the filtrate cleanliness fluctuation index R_c is defined, and the calculation expression is: Where σ(C) represents the standard deviation of the filtrate cleanliness, The average value of the cleanliness change rate per unit time.

[0032] Preferably, the air flow backflush sequence G applied to M2 includes:

[0033] According to the permeability of each stage filter layer F1, F2, F3 and the current residual pressure difference, set the target parameters of the staged backflush, including the reverse pulse air pressure value and the corresponding pulse action time;

[0034] Inject compressed gas at the set pressure into F1, F2, and F3 in turn according to the order from high-porosity filter layer to low-porosity filter layer;

[0035] During the gas injection process, the backflush pulse intensity and interval time are adjusted in real time according to the response state of each stage filter layer, and the response state includes the filter layer backflow, pressure difference recovery speed and filtrate turbidity fluctuation;

[0036] After completing the backflush sequence G, monitor the surface structure change and moisture content of the mud cake M2, and judge whether the target dry threshold is reached, if not, repeat the optimized G sequence for one round.

[0037] Preferably, the M2 which does not meet the backflush module condition of the entering gas flow is subjected to the integrated treatment of hot air surface drying and edge pressing, comprising:

[0038] The surface layer temperature and drying rate of the mud cake are monitored in real time, and a PID control algorithm is used to automatically adjust the hot air flow rate and temperature;

[0039] The mechanical constraint of the synchronous edge pressing mechanism is applied to the mud cake edge during hot air drying, and the edge pressing mechanism adopts a curved surface self-adaptive pressing plate structure.

[0040] After the integrated surface drying and edge pressing treatment is completed, the overall thickness shrinkage ratio and edge integrity of the mud cake are detected, and if the target indicators are met, the output is the third stage mud cake M4.

[0041] In the above technical solution, the technical effects and advantages provided by the present application are as follows:

[0042] 1. By introducing the plugging tendency index T based on the sludge characteristic parameters (such as particle size distribution, extracellular polymer concentration and surface cohesion coefficient), and the dynamic construction of the filter channel induction parameter set D, the present application realizes the adaptive control of the whole process from sludge pretreatment, spiral extrusion to multi-stage filtration, effectively solves the problems of poor filtration performance of high-viscosity sludge and easy plugging of traditional dewatering equipment. At the same time, combined with the real-time calculation of the instantaneous pressure difference reflection index and the filter cleanliness fluctuation index, the filter dynamic switching model is driven to realize intelligent response and path control of the multi-stage filtration state, which significantly improves the filtration efficiency and system operation stability.

[0043] 2. The present application provides differentiated drying strategies for mud cakes under different structural characteristics and moisture conditions through the air backflush sequence G and the integrated hot air surface drying-edge pressing module. This module has the ability of process closed-loop feedback, adaptive edge pressing control and intelligent judgment of drying effect, which not only effectively improves the structural integrity and drying quality of the final mud cake, but also enhances the adaptability of the system to complex sludge working conditions, and has good industrial promotion value. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments or prior art of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0045] Figure 1 The system module flowchart of the present application. DETAILED DESCRIPTION

[0046] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0047] Embodiments, please refer to Figure 1 The sludge dewatering system based on spiral extrusion and multi-stage filtration described in the embodiments includes:

[0048] The data acquisition module acquires the particle size distribution parameter P, the extracellular polymer concentration parameter E and the surface cohesion coefficient μ of the sludge to be treated, and establishes a sludge clogging tendency index T according to the combination of the P, E and μ;

[0049] The filter channel generation parameter module generates and calls a set of preset filter channel induction generation parameter set D according to the clogging tendency index T, including the initial shear disturbance frequency ω, the transient gas-liquid ratio α and the induction time window Δt;

[0050] The disturbance application module applies a short-time low-pressure disturbance to the sludge based on the parameter set D to obtain a first-stage sludge cake M1 and a first filtrate L1;

[0051] The filtration control module performs multi-stage filtration on M1, including dynamically switching the filter path based on the pressure difference change and the filtrate cleanliness, and sequentially dewatering in different filter layers F1, F2 and F3 to obtain a second-stage sludge cake M2 and a filtrate L2;

[0052] The judgment module performs structural scanning on M2, extracts its density ρ and residual water content θ, and combines with the sludge cake self-identification grading feedback condition to judge whether to enter the pulse cleaning step: if ρ>threshold value and θ>threshold value , then enter the airflow backflush module, otherwise enter the integrated processing module;

[0053] The airflow backflush module applies an airflow backflush sequence G to M2, and the sequence G is used to control the reverse pulse intensity and interval time of each filter layer, and outputs a final sludge cake M3;

[0054] The integrated processing module performs hot air surface drying and edge pressing integrated processing on M2 that does not meet the condition of entering the airflow backflush module, and outputs a sludge cake M4, which can be directly incinerated or landfilled.

[0055] In the present application, the sludge to be treated is first subjected to particle size distribution monitoring by an online particle size analyzer. The analyzer uses laser diffraction to continuously scan the size of solid particles in the sludge sample and outputs a particle size distribution curve. To more accurately reflect the impact of fine particles on filter material blockage, the system performs weighted correction on the proportion of fine particles with a particle size ranging from 0.1 microns to 50 microns. The correction method uses a high weight factor to exponentially weight the particles in this range, forming a final particle size parameter P for modeling. This parameter is expressed in terms of fine particle volume fraction and has a unit of percentage (%).

[0056] The extracellular polymer concentration E is measured by an infrared fluorescence colorimetric sensor. The sensor uses a 405 nm excitation light source to detect the fluorescence intensity of ribosomes and polysaccharides in the sludge sample to reflect the concentration level of EPS. To eliminate the impact of different environmental temperatures on the test results, a temperature compensation algorithm is used to correct the measured fluorescence intensity in real time. The final E value is expressed in milligrams per liter (mg / L) and reflects the concentration of EPS per unit volume of sludge.

[0057] The surface adhesion of the sludge sample is measured by a contact adhesion force test probe. During the test, the probe is in contact with the sludge sample and is pulled away, and the maximum pull-off force is recorded and converted to adhesion per unit area as the original adhesion coefficient μ. This value is further converted to a standard adhesion energy value (expressed in millijoules per square meter) for unified modeling with other parameters.

[0058] The sludge blockage tendency index T is a comprehensive evaluation index of the risk of sludge blockage in porous media during filtration. Its calculation method is based on the normalized results of the above parameters P, E, and μ, and uses a weighted linear model for combination. The expression is: ; where a, b, and c are the weight coefficients corresponding to each parameter, and the initial values are set according to historical experimental data experience, such as a = 0.4, b = 0.35, and c = 0.25.

[0059] To adapt to different sludge sources and changing conditions, the present application introduces a machine learning regression model (such as ridge regression or support vector regression) to dynamically correct coefficients a, b, and c based on historical dewatering results and blockage event feedback, achieving continuous optimization of the model.

[0060] The blockage tendency index T is dimensionless, with a relative numerical range of 0 to 100. The reference threshold value = 60 is set, and when T is greater than this threshold value , it is determined that the sludge has a high blockage risk, and the filter channel induced generation program is started; if T is less than the threshold value , the standard extrusion path is entered.

[0061] In the present application, in order to realize the filter channel induction pretreatment of sludge before dewatering, the corresponding shear disturbance frequency ω is selected according to the numerical level of the clogging tendency index T, which is used to drive the responsive reorganization of the sludge internal floc structure. The numerical range of the clogging tendency index T is set to 0-100, and the present application divides it into three level intervals: low clogging risk (T≤40), medium clogging risk (40<T≤70) and high clogging risk (T>70). For different levels, the preset disturbance frequency range is called: low risk: disturbance frequency ω is 5-10 Hz, mainly used for slightly decoupled flocs; medium risk: disturbance frequency ω is 10-20 Hz, which strengthens the internal shear reconstruction of the floc; high risk: disturbance frequency ω is 20-30 Hz, which is used to break the high adhesion structure.

[0062] The disturbance frequency is controlled by a high-frequency servo vibrator, and the sludge flow rate and residence time are synchronized to avoid excessive disturbance causing structural damage. The frequency setting can be optimized by machine learning according to historical operation data to realize adaptive disturbance response. The unit of ω is hertz (Hz), and ω is transmitted to the vibration actuator of the filter channel induction device to realize precise shearing.

[0063] In order to further enhance the filter channel generation effect in the process of internal structure disturbance of sludge, the present application introduces a transient bubble injection mechanism, calculates the initial gas content and water content of the sludge, generates the optimal transient gas-liquid ratio α, ensures the short existence of bubbles in the sludge, and forms a percolation channel.

[0064] The initial gas content is measured by an online gas content sensor, with a unit of volume fraction (%), and the initial water content is measured by an infrared dry-wet analyzer. An empirical calculation model is set up to output the α value according to the following logic: if the sludge gas content is less than 1% and the water content is greater than 95%, set α=0.25; if the gas content is between 1%-3% and the water content is between 90%-95%, set α=0.15; if the original gas content of the sludge is high or the water content is less than 90%, set α=0.05-0.10 to prevent the formation of excess bubbles interfering with the structure. α is defined as the volume ratio of injected gas to liquid per unit time, with a unit of dimensionless ratio value. The transient gas injection is completed by a precision micro-gas pump and a metering pump, and a round of short-time injection (less than 3 seconds) is carried out in the filter channel induction stage.

[0065] The filter channel induction intervention needs to be completed within a limited time window to prevent excessive disturbance or insufficient disturbance from affecting structure formation. The present application sets an induction time window Δt, which is determined according to the actual temperature T of the sludge (unit: ) and the EPS cohesive coefficient μ (unit: ).

[0066] The setting rules are as follows: if ≥ 25 °C and μ ≤ 8 mJ / m², set Δt = 3-5 seconds; if 15-25 °C and μ is 8-12 mJ / m², set Δt = 6-10 seconds; if ≤ 15 °C or μ ≥ 12 mJ / m², set Δt = 10-15 seconds to ensure that the floc has sufficient response time.

[0067] Temperature value The temperature value is collected by the temperature sensor in the pipeline, and the adhesion coefficient μ can be measured by the contact adhesion force probe. Through table lookup matching based on the rule engine parameter table, Δt is quickly output. The unit is second (s), and the time window is used to control the vibration and gas injection linkage execution period to ensure that stable and through microchannels are formed under specific environmental conditions without damaging the overall floc skeleton. Δt exceeding the recommended range may cause floc disorder or induction failure, so it is one of the key adjustable control parameters.

[0068] After obtaining the three parameters of shear disturbance frequency ω, transient gas-liquid ratio α and induction time window Δt, the system combines them to form a complete set of filter channel induction generation parameter set D. The parameter set structure is a three-tuple, D = {ω, α, Δt}, and is encoded in JSON format and sent to the pre-expansion induction device execution unit through the communication bus.

[0069] In the present application, the disturbance application module is used to apply short-time low-pressure disturbance to the sludge sample of the filter channel generation parameter set D (containing shear disturbance frequency ω, transient gas-liquid ratio α and induction time window Δt) to induce controllable deformation and pore generation of its internal structure, to release part of the free water in advance and form a preliminary dewatering path, thereby obtaining the first stage cake M1 and the first filtrate L1.

[0070] Specifically, the raw sludge is sent into the disturbance reaction cavity after preliminary conditioning, the control system calls parameter set D, and initializes the disturbance logic. The execution device prepares the servo drive and the gas injection path, and the gas and liquid are premixed according to the proportion of α and then enter the standby state. Start the servo motor to drive the disturbance cavity to perform periodic high-frequency shear in the circumferential direction at a frequency of ω. The disturbance amplitude is set to 1-3 mm, and the disturbance direction is the transverse tangent direction to prevent longitudinal compaction of the floc. During the vibration process, the internal structure of the floc is subjected to periodic shear stress, and part of the EPS structure is broken, and micro gaps are formed. After about 1-2 seconds of disturbance, the control system starts the micro gas-liquid injection system, and injects the mixed gas-liquid into the sludge according to the proportion of α. The injection speed is controlled at 5-10 mL / s, and the duration is not more than 2 seconds, ensuring that the bubbles are uniformly distributed and not aggregated. The bubbles act as temporary support structures, promoting the separation of micro-flocs on both sides of the shear path, forming a filter channel prototype. All disturbance and injection processes are completed within a time window of Δt. The system strictly controls the duration of the disturbance, and stops immediately after the time reaches Δt. Too long may damage the floc structure, and too short cannot complete the induction target. After the disturbance is completed, a micro pressure plate is provided at the bottom of the reaction cavity, which applies a constant pressure head (<0.02 MPa) at the moment the disturbance ends to promote the downward flow of free liquid. The filtrate L1 is guided into the filtrate collector through the liquid discharge channel, and the remaining floc is naturally settled and preliminarily compacted to form the first stage mud cake M1.

[0071] The filtration regulation module is used for multi-stage filtration treatment of the first stage mud cake M1, including filter layers F1, F2 and F3 with three different pore sizes, to perform sequential dewatering operation in progressive pore size order. During the filtration process, the differential pressure change and the filtrate cleanliness parameters of each stage of the filtration unit are monitored in real time, and through the filter direction dynamic switching control mechanism, based on the calculated instantaneous differential pressure reflection index and the filtrate cleanliness fluctuation index, the filter direction switching decision model is called to automatically adjust the filtrate flow direction. Finally, the second stage mud cake M2 and the filtrate L2 are obtained.

[0072] Specifically, the instantaneous differential pressure value ΔP of the inlet and outlet of the filtration unit is collected in real time, and the instantaneous differential pressure reflection index is calculated after analysis;

[0073] A set of high-precision pressure sensors is installed at the inlet and outlet of the filtration unit, with a sampling frequency of not less than 10 Hz, to collect pressure data of the inlet and outlet in real time, and calculate the instantaneous differential pressure ΔP, with the unit of kilopascal (kPa).

[0074] Within a set time window T (the recommended range is 10-30 seconds), continuous ΔP sampling data is collected to construct a time series ΔP(t); the time differential algorithm is applied to ΔP(t) to calculate the instantaneous change rate d(ΔP) / dt, with the unit of kPa / s; combined with the current ΔP value, the instantaneous differential pressure reflection index is calculated , which is defined as: ; wherein log(1+ΔP) is a logarithm function with natural constant as base, used to enhance the sensitivity of the exponential under high pressure difference condition. The unit is kPa / s, representing the pressure difference growth trend per unit time. If It sharply increases, indicating that the filter layer is rapidly clogged, which will trigger the warning or filter adjustment logic.

[0075] Monitor the filtrate cleanliness parameter C in a fixed time period, and calculate the filtrate cleanliness fluctuation index after analysis;

[0076] Integrate an online turbidity sensor in the filtrate outlet channel to monitor the suspended solid content in the filtrate in real time as the filtrate cleanliness parameter C, with the unit of milligrams per liter (mg / L).

[0077] In a fixed time period T1 (recommended 30-60 seconds), continuously collect C values with 1 second as the sampling period to construct the time series C(t). Then perform the following processing:

[0078] Calculate the cleanliness average value in the time period using the sliding window average method ; Calculate the instantaneous deviation for each sampling point; Calculate the cleanliness standard deviation based on the fluctuation characteristics of ΔC(t) ; Calculate the change rate of C per unit time using the first-order difference method, and take its average value, denoted as AVG[dC / dt], with the unit of mg / L / s.

[0079] Based on the above data, define the filtrate cleanliness fluctuation index R_c, whose expression is as follows: ; R_c comprehensively reflects the fluctuation intensity and rate change of the filtrate quality.

[0080] Call the built-in filter channel switching decision model FCM, which takes the instantaneous pressure difference reflection index and the filtrate cleanliness fluctuation index as joint input variables, and uses a weight weighting function to output the filter channel switching instruction;

[0081] Use the embedded filter channel switching decision model FCM (Filter Channel Model), whose inputs are the above two key dynamic indexes: and R_c. FCM uses a weighted linear combination model to combine the real-time values and change trends of the two input indicators, and outputs the filter channel switching instruction S. The value of S includes: S=0: maintain the current forward filtration; S=1: switch to reverse filtration; S=2: execute the alternating pulse filtration mode. The expression structure of the decision model is: wherein and are empirical weight parameters, with the default value of =0.6, = 0.4; can be trained and updated by running historical data sets to adapt to different sludge characteristics and filter response behavior. If the model output value exceeds the set action threshold, the switching action is triggered immediately.

[0082] According to the switching instruction output by the decision model, the flow direction of the filter path channel is controlled by the electrically controlled three-way valve to complete the dynamic switching of the filtrate flow direction.

[0083] Specifically, when the decision model outputs S≠0, the switching instruction is issued to the execution layer to control the electrically controlled three-way valve group to switch the liquid flow direction. The three-way valve can realize: forward flow (liquid in → filter layer → liquid out); reverse flushing (outlet reverse injection → filter layer → original liquid discharge); periodic alternating flow (alternating positive and negative switching).

[0084] After being processed by the multi-stage filtration system, the first stage cake M1 is further compacted under the dehydration effect of the filter layers F1, F2 and F3, and the free water and part of the bound water are effectively removed, forming a second stage cake M2 with more stable structure and lower water content. At the same time, the liquid filtered by each stage of filter layer converges to form filtrate L2.

[0085] In the present application, the density p is a structural parameter for measuring the compaction degree of the cake, with units of grams per cubic centimeter ( ). Non-contact detection is performed by an ultrasonic echo density sensor installed on the cake conveying track in combination with a carrier weight sensor. The specific steps are as follows:

[0086] The ultrasonic sensor sends a detection signal to obtain the reflection characteristics of the cross section of the cake and calculate the volume information; the weight value output by the load sensor on the conveying section is read synchronously; the density formula p=m / V is used, where m is the mass of the cake and V is the calculated volume, to obtain the real-time density p value; the measured p value is compared with the system set threshold (recommended initial setting ) as one of the structural determination bases.

[0087] The residual water content θ represents the proportion of residual water in the cake, with units of percentage (%). The system uses an infrared moisture analyzer for rapid determination: representative samples of M2 are sent into the analyzer and irradiated by an infrared light source; the absorption characteristics of the sample at a specific wavelength are analyzed and compared with the calibrated dry-wet conversion model; the water content θ of the current sample is output; the measured value is compared with the set threshold (such as 70%) as a dehydration depth judgment basis. and The system can be dynamically adjusted according to the type of sludge, process target and energy consumption control strategy, and has a manual intervention interface.

[0088] The judgment module is embedded with a self-identification grading feedback logic, which performs the following conditional judgment in combination with parameters p and q: if and , it indicates that M2 has reached a higher compaction state but the water content is still high, indicating that there is internal bound water that has not been released, and it is suitable to enter the air flow backflushing module for deep desorption;

[0089] If any of the above conditions is not met, it indicates that the dewatering potential of M2 has approached the critical point, and it is not suitable to apply high-energy intervention, and it will be guided to the integrated processing module to perform surface hot air drying and edge compaction processing.

[0090] The judgment result will directly determine the subsequent processing path of the mud cake: if it enters the air flow backflushing module, the preset pulse sequence parameters G will be immediately called and the programmable backflushing system will be started; if it enters the integrated processing module, the control signal triggers the hot air blowing and edge pressing linkage unit to complete the final dewatering step.

[0091] In the present application, based on the permeability index of each level of filter layer F1, F2, F3 and the currently measured residual pressure difference, the corresponding backflushing parameters are set. The permeability is calculated from the relationship between the filtrate flow rate and the pressure drop of the filter layer, with the unit being liters per minute per square centimeter (L / (min·cm2)); the residual pressure difference is obtained in real time by a pressure difference sensor at both ends of the filter cavity, with the unit being kilopascals (kPa). According to the empirical model or the training data set, the backflushing parameter pairs of each level of filter layer are generated, including: the reverse pulse gas pressure value

[0092] (n=1, 2, 3), the recommended range is 20-80 kPa; the pulse action time , the recommended range is 1-5 seconds. For example, if F1 has the strongest permeability but the highest residual pressure, set =50 kPa, =3 seconds; if F3 has weak permeability and low residual pressure, set =30 kPa, =2 seconds. According to the arrangement logic of the filter layer pore size from large to small, the system controls the backflushing program to inject compressed gas at the set pressure to F1, F2, F3 in sequence. This sequence can effectively avoid the reverse pressure accumulation of the downstream filter layer caused by the interference of the upstream backflushing water flow.

[0093] The gas is injected into the back pressure cavity of each level of filter layer through an independent pulse valve channel, forming a short-time high-pressure pulse flow, which promotes the reverse desorption of residual liquid and residue retained in the filter layer pores and surface, thereby restoring the filter material filtration channel.

[0094] During the backflushing process, the control system continuously monitors the following three response state indicators: the filter layer backflow

[0095] ​, unit: L / min, obtained by a flowmeter; differential pressure recovery speed v_ΔP, unit: kPa / s, calculated by differential pressure change rate; filtrate turbidity fluctuation σ_C, unit: mg / L, standard deviation obtained by turbidity sensor within a time window.

[0096] When the backflow amount is detected to be continuously lower than the reference threshold (e.g. <1 L / min) or the differential pressure recovery speed is slow (e.g. v_ΔP < 0.5 kPa / s), it indicates that the filter layer is not sufficiently cleaned, and the system will automatically increase the backflush air pressure of the current filter layer or extend the action time; otherwise, it will be appropriately reduced to avoid excessive structural disturbance. This dynamic adjustment is realized by a fuzzy control algorithm or a PID self-tuning control logic, so that the backflush sequence G has self-adaptive ability to different working conditions.

[0097] After completing the execution of the G sequence, the surface state and moisture content of the mud cake M2 are detected in real time. The surface structure change is captured by a structured light scanner to determine the degree of flatness, and the moisture content is detected by a microwave moisture meter, with the unit being %.

[0098] A target drying threshold is set (e.g. 60% moisture content), and if the detection result shows that the current moisture content of the mud cake is , the final mud cake M3 is output; if the detection result shows that the current moisture content of the mud cake is , it is judged that the dehydration is not up to standard, and the original G sequence parameters are automatically optimized (e.g. adjusted to , ) and the backflush process is repeated once, until the moisture content is lower than the target or the maximum number of cycles is reached.

[0099] In the present application, the second-stage mud cake M2 is sent into a closed hot air drying cavity after multi-stage filtration. The system sets a hot air outlet above the cavity through a hot air circulating heater, and opens a partition temperature control system to apply hot air flow with a temperature of 45-65 degrees Celsius to the surface of the mud cake.

[0100] The hot air temperature adopts a middle-high and low-edge partition control strategy, i.e. the central hot air temperature is higher (e.g. 60℃) and the edge area is slightly lower (e.g. 50℃), to avoid cracking due to over-drying at the edge. The hot air speed control range is 0.5-2.5 meters / second, forming a stable laminar flow to reduce disturbance.

[0101] The surface layer temperature of the mud cake is obtained in real time (unit: ℃), and compared with the target temperature (e.g. 55℃) to build a PID control algorithm to adjust the hot air output: deviation ;

[0102] Control variable Kp, Ki, Kd are proportional, integral, and differential coefficients, which can be automatically matched and adjusted according to the thickness of the mud cake to control the surface dry depth to be between 2-5 mm, and prevent the structure disturbance caused by the migration of deep water.

[0103] In the hot air drying process, the edge stabilizing mechanism is started at the same time to implement physical stabilizing pressing on the edge of the mud cake. The edge stabilizing mechanism adopts a curved surface self-adaptive pressing plate structure, which includes a plurality of flexible arc-shaped pressing claws, and can automatically adjust the contact surface angle according to the height difference of the edge of the mud cake, so as to realize uniform pressing and prevent the edge from warping, breaking or cracking caused by hot drying.

[0104] The edge pressing pressure is set to be in the range of 5-15 kPa, and is applied to the periphery of the mud cake through a stepping motor precision control transmission system, and the continuous pressing time is synchronized with the hot drying, so as to ensure that the edge of the mud cake does not produce structure discontinuity in the heating process.

[0105] The edge stabilizing control system monitors the edge stabilizing response in real time through the mud cake edge boundary deformation sensor, and if the elastic deformation of the edge exceeds 2 mm, the pressure is automatically increased by 10% and the pressing plate angle is adjusted to maintain the edge boundary stability.

[0106] After the integrated surface drying and edge stabilizing treatment is completed, the following two key indicators are used to determine whether the mud cake meets the output conditions:

[0107] Thickness shrinkage ratio : set to , wherein is the initial thickness, is the thickness after treatment. If ≥ 0.15, it indicates that the mud cake has undergone significant dehydration shrinkage;

[0108] Edge integrity index : the edge crack monitoring sensor collects the number of boundary breakage n and the deformation amplitude δ, and calculates , wherein N is the total number of boundary detection points, is the maximum allowed edge warping. If ≥ 0.90, it indicates that the boundary integrity meets the requirements.

[0109] When the above two indicators both meet the preset threshold, it is determined that the mud cake M2 has completed the integrated treatment process, and the output is the third stage mud cake M4, whose moisture content is less than 65%.

[0110] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A sludge dewatering system based on screw extrusion and multi-stage filtration, characterized by: The method comprises the following steps: A data acquisition module is used to acquire the particle size distribution parameter P, the extracellular polymeric substance concentration parameter E and the surface cohesion coefficient μ of the sludge to be treated, and a sludge clogging tendency index T is established according to the combination of P, E and μ; the sludge clogging tendency index T is established according to the combination of P, E and μ, which comprises: P, E and μ are normalized, and the normalized P, E and μ are used to calculate the sludge clogging tendency index T by using a weighted linear model; A filter channel generation parameter module is used to generate and call a set of preset filter channel induction generation parameter set D according to the clogging tendency index T, which comprises an initial shear disturbance frequency ω, a transient gas-liquid ratio α and an induction time window Δt; the generation and calling of the set of preset filter channel induction generation parameter set D comprises: according to the numerical level of the clogging tendency index T, the shear disturbance frequency ω of the corresponding level is called to regulate the responsiveness of the recombination of the sludge floc microstructure; the optimal transient gas-liquid ratio α is calculated based on the initial gas content and the water content of the sludge to control the formation of a short-time filtration path of induced bubbles in the sludge; the filter channel induction intervention needs to be completed within a limited time window, and the induction time window Δt is set, the value of which is adjusted according to the sludge temperature and the EPS cohesion; ω, α and Δt are combined to form the preset parameter set D; A disturbance application module is used to apply a short-time low-pressure disturbance to the sludge based on the parameter set D to obtain a first-stage sludge cake M1 and a first filtrate L1; the original sludge is sent into a disturbance reaction cavity after being preliminarily conditioned, a servo motor is started, and the disturbance cavity is driven to perform periodic high-frequency shearing in the circumferential direction at a frequency ω; after the disturbance is performed for 1-2 seconds, the control system starts a micro gas-liquid injection system, and mixed gas-liquid is injected into the sludge according to the proportion of α; the bubbles act as temporary support structures to promote the separation of micro-flocs on both sides of the shearing path, and a filter channel prototype is formed; after the disturbance is completed, a micro pressure plate is arranged at the bottom of the reaction cavity to apply a constant pressure head at the moment when the disturbance is completed to promote the downward flow of free liquid; the filtrate L1 is guided into a filtrate collector through a liquid discharge channel, and the remaining flocs are naturally settled and preliminarily compacted to form a first-stage sludge cake M1; A filtration regulation module is used to perform multi-stage filtration on M1, which comprises dynamically switching the filtration path based on the pressure difference change and the filtrate cleanliness, and dynamically switching the flow direction of the filtrate by controlling the flow direction of the filtration path channel through an electrically controlled three-way valve according to the switching instruction output by the decision model to complete the dynamic switching of the flow direction of the filtrate; and the M1 is dehydrated in the different filter layers F1, F2 and F3 in turn to obtain a second-stage sludge cake M2 and a filtrate L2; A judging module judges whether to enter the pulse cleaning step according to the compactness ρ and the residual water content θ of M2 and the self-identified grading feedback condition of the mud cake: if ρ > threshold value and θ > threshold value , the gas flow backflush module is entered, otherwise the integrated processing module is entered; An air flow backflushing module is used to apply an air flow backflushing sequence G to M2, and the sequence G is used to control the reverse pulse intensity and interval time of each filter layer to output a final sludge cake M3; An integrated treatment module is used to implement integrated treatment of hot air surface drying and edge pressing on M2 that does not meet the conditions for entering the air flow backflushing module to output a sludge cake M4 for direct incineration or landfill treatment.

2. The sludge dewatering system based on screw pressing and multi-stage filtration according to claim 1, characterized in that: The application of the air flow backflushing sequence G to M2 comprises: According to the permeability of each filter layer F1, F2 and F3 and the current residual pressure difference, the target parameters of the graded backflushing are set, which comprise a reverse pulse air pressure value and a corresponding pulse action time. Injecting compressed gas with set pressure into F1, F2, F3 in turn from high-porosity filter layer to low-porosity filter layer; During the gas injection process, the strength and interval time of the backflush pulse are adjusted in real time according to the response state of each filter layer, including the filter layer backflow, pressure difference recovery speed and filtrate turbidity fluctuation; After the completion of the backflush sequence G, the surface structure change and moisture content of the mud cake M2 are monitored to determine whether the target drying threshold is reached, and if not, the optimized G sequence is repeated for one round.

3. The sludge dewatering system based on screw pressing and multi-stage filtration according to claim 1, characterized in that: The M2 that does not meet the conditions of the backflush module of the entering gas flow is subjected to hot air surface drying and edge pressing integrated processing, including: Real-time monitoring of the surface layer temperature and drying rate of the mud cake, and automatic adjustment of the hot air flow rate and temperature using a PID control algorithm; At the same time of hot air drying, a synchronous edge pressing mechanism is applied to the edge of the mud cake, and the pressing mechanism adopts a curved surface self-adaptive pressing plate structure; After the integrated surface drying and edge pressing processing is completed, the overall thickness shrinkage ratio and edge integrity of the mud cake are detected, and if the target indicators are met, the output is the third stage mud cake M4.

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