A sludge dewatering agent self-adaptive dosing method and system

Abstract

The application discloses a sludge dewatering agent self-adaptive dosing method and system, which comprises the following steps: obtaining sludge operation state parameters, i.e., sludge flow, sludge concentration, original sludge charge characteristic parameter and original sludge apparent viscosity; calculating inorganic coagulant basic dosing quantity and polymer flocculant basic dosing quantity according to the sludge operation state parameters; adding inorganic coagulant to the sludge to perform primary conditioning, then detecting the intermediate residual potential of the sludge after the primary conditioning, and then performing feedback correction to obtain the inorganic coagulant optimal dosing quantity; adding polymer flocculant to the sludge after the primary conditioning to perform secondary conditioning, then detecting the conditioning sludge apparent viscosity of the sludge after the secondary conditioning, and then performing feedback correction to obtain the polymer flocculant optimal dosing quantity; and continuously adding the agent to the subsequent sludge according to the optimal dosing quantity until the next round of optimization triggering condition is met, and then returning to the first step.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment technology, and relates to sludge treatment and automated control, specifically to an adaptive dosing method and system for sludge dewatering agents. Background Technology

[0002] Sludge dewatering is a crucial step in wastewater treatment. Its core objective is to reduce the water content of sludge through physical or chemical means, thereby reducing sludge volume and lowering subsequent transportation and disposal costs. Among these methods, chemical conditioning has become one of the most widely used pretreatment methods for municipal and industrial sludge treatment due to its flexibility, adaptability, and significant dewatering effect.

[0003] In the chemical conditioning process, inorganic coagulants (such as ferric chloride and polyaluminum chloride) and polymeric flocculants (such as polyacrylamide (PAM) and modified PAM) need to be added. Inorganic coagulants destabilize sludge colloidal particles through charge neutralization, while polymeric flocculants promote the aggregation of destabilized particles into larger flocs through adsorption bridging. The synergistic effect of the two can significantly improve the filtration and dewatering performance of sludge. However, the composition of sludge is complex and variable, and its concentration, particle surface charge characteristics, rheological behavior, and dewatering performance all fluctuate significantly due to factors such as influent water quality, treatment process, seasonal changes, and sludge age. Under these complex conditions, accurately determining the optimal dosage of inorganic coagulants and polymeric flocculants based on real-time changes in sludge properties has always been a technical challenge in the field of sludge dewatering.

[0004] Currently, the dosage of chemicals in sludge dewatering is mostly determined manually based on experience. Operators estimate the dosage based on sludge appearance, processing capacity, and past operational experience, then repeatedly adjust it through beaker tests or sludge discharge results. This method heavily relies on personal experience, is highly subjective, and has inherent drawbacks such as delayed response and large dosage deviations. It is difficult to adapt to the complex operating conditions of fluctuating sludge properties, frequently resulting in insufficient chemical dosage leading to substandard dewatering, or excessive chemical dosage leading to waste, increased operating costs, and deterioration of sludge rheological properties.

[0005] Chinese patent CN118459033A discloses a front-end control system for dewatering a sludge treatment tank. This system includes a sludge treatment tank, a temperature control module, a feeding module, a dewatering module (stirring + plate and frame filter press), a detection module, and a control module. The detection module uses an industrial camera and machine vision (bilateral filtering, contour extraction, binarization, and other image processing) to collect real-time data on parameters such as wastewater level, interface (for calculating settling velocity), temperature, pH, sludge viscosity, particle length / area, and cake solids content. Based on this data and a pre-set orthogonal parameter calibration table, the control module automatically matches the optimal chemical dosage (sludge solids content 5-20%), stirring time (0.5-2 hours), and stirring speed (200-800 rpm) using the "minimum difference summation" method. Temperature control prioritizes maintaining ≥25℃, supports static sedimentation and supernatant discharge, and can iteratively update the calibration table. The system achieves precise feeding, real-time operating condition control, and visual monitoring, overcoming reliance on manual experience and improving dewatering performance and intelligence. This patent emphasizes the innovative combination of multi-parameter coupled control and machine vision detection, applicable to the front-end conditioning stage of wastewater treatment plants. However, the excessive introduction of multiple parameters such as temperature, pH, and particle morphology, along with machine vision detection systems, leads to a significant increase in initial investment and maintenance costs (industrial cameras and sensors are easily fouled and deactivated in corrosive sludge environments, requiring frequent calibration and cleaning), which is difficult for small and medium-sized wastewater treatment plants to bear. Under complex conditions such as turbidity, uneven lighting, and bubble interference, the robustness of image processing (such as bilateral filtering and contour extraction) of machine vision is insufficient, easily generating errors in settling interfaces and particle recognition, thus affecting the accuracy of dosing and stirring parameters. The "minimum difference summation" matching method relies on a finite orthogonal calibration table, which has poor adaptability when the actual sludge composition is complex and variable, and the effect is unstable in the early stages of iterative updates, with high costs for extended calibration. The overall system complexity also amplifies the risk of fault propagation, increases energy consumption and operation and maintenance skill thresholds, makes debugging difficult, and makes it difficult to operate stably under extreme conditions. These problems stem from the pursuit of high integration and precise control of multiple parameters, the difficult trade-offs between extracting key parameters, improving algorithm robustness, and practical verification, and the significant economic and technical challenges to implementation and promotion.

[0006] Chinese patent CN116655214A discloses a sludge conditioning method, apparatus, electronic equipment, and storage medium, focusing on the accurate determination of the reaction endpoint in the sludge chemical conditioning process. The core idea is to monitor the initial viscosity data of the sludge during stirring after adding conditioning agents (PAM, chitosan, starch, etc.) in real-time online, calculate the viscosity fluctuation value, and determine that the reaction is complete and automatically stop stirring when the fluctuation value is less than a set threshold and persists for a certain period. Multiple sets of experiments have been conducted beforehand to verify the strong correlation between viscosity and capillary dewatering time, ensuring viscosity as a reliable proxy indicator. The method also includes: monitoring the initial viscosity stability before stirring to ensure uniform mixing; accurately determining the amount of conditioning agent added based on the sludge solids content; and automating the method using electronic equipment / storage media. Compared to traditional manual experience or simple dewatering tests, this method avoids insufficient reaction (poor dewatering) or excessive stirring (high energy consumption), significantly improving dewatering effect and reducing energy consumption. The patent provides a complete process, apparatus structure, and software implementation, applicable to various sludge conditioning scenarios, and has strong practicality and scalability. However, while this patent provides an automated method for determining the endpoint of sludge conditioning reactions, it also has the following shortcomings: This method is essentially designed for intermittent batch processes, only used to determine whether the reagent and sludge have fully reacted and to stop stirring; it cannot be directly adapted to continuous flow processes. More importantly, the different states of the raw sludge feed make it difficult to reliably indicate the quality of the final dewatering effect solely based on the viscosity data after reagent addition, as viscosity mainly reflects the degree of reaction completion during the current stirring process. Furthermore, under conditions of vigorous stirring, high precision, response speed, and long-term stability of the viscosity sensor are required. In actual sludge environments with corrosion, scaling, and bubble interference, measurement drift or malfunction is prone to occur, leading to misjudgment of the reaction endpoint (premature cessation resulting in insufficient reaction, or prolonged stirring increasing energy consumption).

[0007] Chinese patent CN217351160U discloses a flocculant dosing device with viscosity monitoring and control function. This patent addresses the viscosity control challenge in the preparation of flocculants (such as PAM) by designing a simple dosing device. The core components include a mixing chamber, a stirring chamber, and a storage chamber within the mixing shell; a storage tank with an inlet pipe for feeding, and an outlet pipe (with a metering pump) for output; a built-in viscosity sensor in the storage chamber for real-time monitoring. The innovation lies in the cylindrical flow regulation chamber on the inlet pipe, containing a flow regulation column. The column position is manually adjusted via a top threaded rod and a turntable, changing the inlet flow area and thus quickly and accurately controlling the inlet water volume to adjust the final agent viscosity to match the sludge concentration requirements. The device also includes auxiliary components such as a switching valve. When the viscosity sensor detects a failure, the discharge is paused, and the conveying process resumes after a quick adjustment by operating the turntable. This design is simple to operate, highly efficient in adjustment, reduces downtime, ensures the continuity of processes such as screw press dewatering, and improves overall sludge dewatering efficiency. This patent focuses on hardware structure optimization, is low-cost, easy to implement, and represents a targeted improvement for the front end of the dosing process. However, this method only monitors the viscosity of the prepared PAM reagent solution itself, rather than the viscosity of the sludge mixture after dosing or the final dewatering effect, and cannot directly guide or optimize the reaction endpoint and dewatering performance of sludge-reagent. Summary of the Invention

[0008] In view of the above-mentioned shortcomings of the existing technology, the purpose of this invention is to provide an adaptive dosing method and system for sludge dewatering agents, which aims to solve the problems of slow response to changes in sludge properties, reliance on manual experience for agent dosage which is difficult to accurately match with actual working conditions, and poor stability of dewatering effect in the existing sludge dewatering process.

[0009] The technical solution of this invention is implemented as follows:

[0010] An adaptive dosing method for sludge dewatering agents specifically includes the following steps:

[0011] S1: Obtain sludge operating status parameters, including sludge flow rate. sludge concentration Original mud charge characteristic parameters and the apparent viscosity of the original mud ;

[0012] S2: The basic dosage of inorganic coagulant is calculated based on the sludge operating status parameters. and the basic dosage of polymeric flocculant ;

[0013] S3: Add inorganic coagulant to the sludge according to the basic dosage to perform primary conditioning of the sludge, and then test the intermediate residual potential of the sludge after primary conditioning. Then, the basic dosage of inorganic coagulant is corrected based on the intermediate residual potential to obtain the optimized dosage of inorganic coagulant. ;

[0014] S4: Add polymeric flocculant to the primary conditioned sludge according to the basic dosage to perform secondary conditioned sludge, and then test the apparent viscosity of the conditioned sludge after secondary conditioned sludge. Then, the basic dosage of polymeric flocculant is adjusted based on the apparent viscosity of the conditioned sludge to obtain the optimized dosage of polymeric flocculant. ;

[0015] S5: Continuously add the agents to the subsequent sludge according to the optimized dosage of inorganic coagulant and polymeric flocculant until the conditions for the next round of optimization are met, then return to step S1.

[0016] Further, in step S2, the basic dosage of the inorganic coagulant... The calculation formula is as follows, obtained from the pre-constructed multivariate regression feedforward prediction model A:

[0017]

[0018] in, For dry solid load, ; , , , These are the coefficients of the regression model.

[0019] Further, in step S2, the basic dosage of the polymeric flocculant... The calculation formula is obtained from the pre-constructed multivariate regression feedforward prediction model B, as follows:

[0020]

[0021] in, For dry solid load, ; , , , , These are the coefficients of the regression model.

[0022] Furthermore, in step S3, when the basic dosage of inorganic coagulant is corrected based on the intermediate residual potential, the specific steps include:

[0023] The target potential window for conditioning with inorganic coagulants is defined as follows: .

[0024] when At that time, the inorganic coagulant feedback correction amount .

[0025] when or The inorganic coagulant feedback correction amount is calculated using the following formula:

[0026]

[0027] in, This is the feedback gain coefficient, with a value ranging from 0.05 to 2.00. This is the normalized feedback function for the potential deviation; Residual potential Potential deviation relative to the target potential window.

[0028] The optimized dosage of the inorganic coagulant .

[0029] Furthermore, potential deviation Defined as:

[0030]

[0031] but .

[0032] Furthermore, in step S4, when the basic dosage of polymeric flocculant is corrected based on the apparent viscosity of the conditioned sludge, the specific steps include:

[0033] The target rheological window for conditioning with polymeric flocculants is defined as: .

[0034] when At that time, the feedback correction amount of polymeric flocculant .

[0035] when or The feedback correction amount for polymeric flocculants is calculated using the following formula:

[0036]

[0037] in, This is the viscosity deviation normalization feedback function; This is the feedback gain coefficient, with a value ranging from 0.05 to 2.00. The apparent viscosity of the conditioned sludge Viscosity deviation relative to the target rheological window.

[0038] The optimized dosage of the polymeric flocculant .

[0039] Furthermore, viscosity deviation Defined as:

[0040]

[0041] but

[0042] Furthermore, in step S4, the next round of optimization triggering conditions include time triggering conditions and load change triggering conditions. A new round of optimization cycle is started when either of the two triggering conditions is met.

[0043] The time triggering condition is: continuous running time T ≥ preset time interval. The Take 0.5-4 hours.

[0044] The load change trigger condition is: sludge flow rate. sludge concentration or dry solid load The rate of change of any parameter relative to the previous optimization cycle satisfies ,in: These are the current running parameters; These are the parameter values ​​corresponding to the previous optimization cycle; The preset change threshold is set to 10%-30%.

[0045] The present invention also provides an adaptive dosing system for sludge dewatering agents, used to execute the aforementioned adaptive dosing method for sludge dewatering agents, including a mixing reaction unit and a dewatering device. The mixing reaction unit is connected to a sludge inlet main pipe to introduce sludge, and the mixing reaction unit is connected to the dewatering device through a pipe. It also includes a raw sludge detection unit, an agent dosing unit, a conditioning status detection unit, and a control unit.

[0046] The raw sludge detection unit includes an online detection module and a bypass intermittent detection module. The online detection module is located on the main sludge inlet pipe and is used for real-time sludge flow. and sludge concentration The bypass intermittent detection module is located on the main sludge inlet pipe downstream of the online detection module, and is used to intermittently acquire the charge characteristic parameters of the raw sludge. and the apparent viscosity of the original mud .

[0047] The reagent dosing unit includes an inorganic coagulant dosing device and a polymeric flocculant dosing device, which are used to add inorganic coagulants and polymeric flocculants to the sludge, respectively.

[0048] The mixing reaction unit includes a primary mixing reaction zone and a secondary mixing reaction zone. The primary mixing reaction zone is used for the mixing reaction of inorganic coagulant and sludge, and the secondary mixing reaction zone is used for the mixing reaction of polymeric flocculant and sludge conditioned by inorganic coagulant.

[0049] The conditioning state detection unit includes an intermediate state potential detection module located downstream of the primary mixing reaction zone and a conditioning viscosity detection module located downstream of the secondary mixing reaction zone, which are used to detect the residual potential of the intermediate sludge, respectively. and conditioning the apparent viscosity of sludge .

[0050] The control unit is communicatively connected to the raw mud detection unit, the agent dosing unit, and the conditioning status detection unit, respectively, and is used to receive detection data, perform optimization trigger judgment, and output inorganic coagulant and polymer flocculant dosing control commands.

[0051] Furthermore, the control unit includes a load detection module, a trigger determination module, an optimized cycle execution module, an inorganic coagulant optimized control module, a polymeric flocculant optimized control module, a reagent execution module, and a maintenance module.

[0052] The load detection module is used to continuously acquire sludge flow rate. and sludge concentration And calculate the dry solids load. .

[0053] The trigger determination module is used to determine whether to start a new round of optimization cycle based on time trigger conditions and / or load change trigger conditions.

[0054] The optimization cycle execution module is used to perform the entire process of raw mud bypass sampling and detection, inorganic coagulant addition and feedback correction, polymer flocculant addition and feedback correction, and operation data saving after the trigger judgment module outputs the optimization command.

[0055] The inorganic coagulant optimization control module is used to calculate the basic dosage of inorganic coagulant based on sludge operating status parameters. And based on the intermediate state residual potential Feedback corrections were performed to obtain the optimized dosage of inorganic coagulant. .

[0056] The polymeric flocculant optimization control module is used to calculate the basic dosage of polymeric flocculant based on sludge operating parameters. And based on the apparent viscosity of the conditioned sludge Feedback corrections were performed to obtain the optimal dosage of polymeric flocculant. .

[0057] The drug delivery module is used to optimize the dosage based on the inorganic coagulant. Optimize the dosage of polymeric flocculants Generate dosing control commands to drive the dosing unit to perform dosing.

[0058] The operation-maintaining module is used to maintain the current dosage output after the optimization cycle until the next optimization cycle starts.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. This invention achieves synergistic adaptive optimization of inorganic coagulants and polymeric flocculants through dual-parameter, dual-closed-loop agent optimization control based on the synergistic characterization of sludge charge state and rheological state. Specifically, this invention uses the charge characteristic parameters of the raw sludge as the optimization control variable for inorganic coagulants and the rheological parameters of the raw sludge as the optimization control variable for polymeric flocculants, establishing corresponding feedforward prediction models for each. Furthermore, it constructs independent feedback control loops by combining the intermediate residual potential and the apparent viscosity of the conditioned sludge, achieving decoupled optimization control of inorganic coagulants and polymeric flocculants in stages and with different objectives. Compared to traditional methods that rely on manual experience or single detection indicators to adjust agents, this invention can more accurately reflect the state changes at different stages of sludge conditioning, improving the targeting and control precision of agent dosing.

[0061] 2. This invention employs a control strategy combining raw sludge state prediction, conditioning state feedback, and event-triggered optimization to achieve dynamic adaptive adjustment of sludge dewatering agents. This invention only initiates bypass sampling and optimization calculations when the operating time reaches a set cycle or when the sludge operating load changes significantly. It predicts the basic dosage using raw sludge state parameters and then uses intermediate residual potential and the apparent viscosity of the conditioned sludge for feedback correction. After optimization, it maintains the current dosage and continues operation, retaining only continuous monitoring of sludge flow rate and sludge concentration. This improves the system's adaptability to changes in sludge flow rate, concentration, and properties, while avoiding system oscillations caused by continuous monitoring and frequent adjustments, reducing the operating load on monitoring equipment, and improving system stability and reliability.

[0062] 3. This invention can improve reagent utilization efficiency and reduce operating costs while ensuring or improving sludge dewatering effect, and has good engineering application value. Engineering case studies have verified that, under the same sludge source, the same dewatering equipment, and the same operating conditions, this invention reduces the dosage of inorganic coagulants by more than 15% and the dosage of polymeric flocculants by more than 20% compared to traditional manual addition, while improving sludge dewatering effect by more than 3%. This effectively improves sludge dewatering performance while reducing reagent consumption. Furthermore, this invention does not require changes to the existing sludge dewatering process; it only requires the addition of a bypass detection unit and the configuration of a corresponding control algorithm. It can be easily integrated into automatic control systems such as PLCs, DCSs, industrial computers, or edge controllers, and is applicable to various sludge treatment scenarios, including municipal sludge and industrial sludge, demonstrating good versatility, compatibility, and engineering promotion value. Attached Figure Description

[0063] Figure 1 - A schematic diagram of the system described in this invention.

[0064] The components are: 1-Online continuous detection module; 2-Bypass intermittent detection module; 3-Inorganic coagulant dosing device; 4-Primary mixing reaction zone; 5-Polymer flocculant dosing device; 6-Secondary mixing reaction zone; 7-Dehydration device; 8-Intermediate potential detection module; 9-Conditioned viscosity detection module; 10-Control unit. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0066] Existing sludge dewatering processes suffer from problems such as delayed response to changes in sludge properties, reliance on manual experience for reagent dosage which is difficult to accurately match with actual working conditions, and poor stability of dewatering effect.

[0067] Based on this, the present invention provides an adaptive dosing system for sludge dewatering agents, see [link to relevant documentation]. Figure 1 The system includes a mixing reaction unit and a dewatering device. The mixing reaction unit is connected to a sludge inlet main pipe to introduce sludge into the mixing reaction unit for conditioning. The mixing reaction unit is connected to the dewatering device through a pipeline, and the dewatering device is used to dewater the conditioned sludge. It also includes a raw sludge detection unit, a reagent dosing unit, a conditioning status detection unit, and a control unit 10.

[0068] (1) Raw mud detection unit

[0069] The raw sludge detection unit is used to acquire sludge operating status parameters, including an online continuous detection module 1 and a bypass intermittent detection module 2.

[0070] (1.1) Online continuous detection module 1: installed on the main sludge inlet pipeline, used to acquire sludge flow rate in real time. and sludge concentration (MLSS). Among them, sludge flow rate The sludge concentration was continuously measured using a flow meter. Continuous measurements were taken using an online MLSS analyzer.

[0071] (1.2) Bypass intermittent detection module 2: Located on the main sludge inlet pipe downstream of the online continuous detection module 1, it uses a bypass sampling method to obtain the charge characteristic parameters of the raw sludge. and the apparent viscosity of the original mud To avoid impacting the main process operation during the detection process and to ensure that the sampling results reflect the current state of the raw mud, a fresh raw mud sample is introduced after the control unit issues a detection command, and the apparent viscosity of the raw mud is measured simultaneously. and charge characteristic parameters The detection process is as follows: After the detection is completed, the control unit controls the bypass sampling circuit to empty the sampled sludge and closes the bypass, waiting for the next detection cycle. The apparent viscosity of the raw sludge is... and charge characteristic parameters All parameters originate from the same bypass sampling and are used as the initial state parameters for the feedforward control in this optimization cycle.

[0072] (2) Drug dosing unit

[0073] The reagent dosing unit includes an inorganic coagulant dosing device 3 and a polymeric flocculant dosing device 5, which are used to add inorganic coagulant and polymeric flocculant to the sludge, respectively, thus forming an inorganic coagulant dosing point and a polymeric flocculant dosing point.

[0074] In specific applications, the system also includes storage devices for storing inorganic coagulants and polymeric flocculants. The inorganic coagulants are preferably cationic inorganic electrolytes, including but not limited to one or more of ferric chloride, polyferric chloride (PFC), polyferric sulfate (PFS), polyaluminum chloride (PAC), and polyaluminum ferric chloride (PAFC). The polymeric flocculants are preferably one or more of polyacrylamide (PAM) or modified PAM. The inorganic coagulants and polymeric flocculants are prepared into solutions of preset concentrations and stored separately in their respective storage devices. Their concentrations are predetermined based on the sludge properties, reagent type, and engineering operation requirements, and remain constant during normal operation.

[0075] (3) Mixed reaction unit

[0076] The mixing reaction unit includes a primary mixing reaction zone 4 and a secondary mixing reaction zone 6.

[0077] The primary mixing reaction zone is located downstream of the inorganic coagulant addition point. It is used to achieve rapid mixing and reaction between the inorganic coagulant and the raw sludge, so that the sludge particles can complete charge neutralization and destabilization, forming intermediate sludge after conditioning with inorganic coagulant. Its control objective is to bring the charge state of the intermediate sludge into the preset target potential window.

[0078] The secondary mixing reaction zone is located downstream of the primary mixing reaction zone, and the polymer flocculant dosing point is located between the secondary and primary mixing reaction zones. The secondary mixing reaction zone is used to achieve full mixing and flocculation reaction between the polymer flocculant and intermediate sludge. Through adsorption bridging, it promotes the formation of stable floc structures in sludge particles to obtain the final conditioned sludge. Its control objective is to make the apparent viscosity of the conditioned sludge enter the preset target rheological window, thereby obtaining the best dewatering performance.

[0079] (4) Conditioning status detection unit

[0080] The conditioning state detection unit includes the intermediate potential detection module 8 and the conditioning viscosity detection module 9.

[0081] (4.1) Intermediate potential detection module 8: The intermediate potential detection module 8 is located downstream of the primary mixing reaction zone 4 and before the polymer flocculant addition point, and is used to detect the intermediate residual potential after conditioning with inorganic coagulant. The test results serve as input parameters for the inorganic coagulant feedback control module.

[0082] (4.2) Conditioning viscosity detection module 9: The conditioning viscosity detection module is located after the secondary mixing reaction zone and before the dewatering device 7, and is used to detect the apparent viscosity of the final conditioning sludge. The test results serve as input parameters for the polymer flocculant feedback control module.

[0083] The two detection modules of the conditioning status detection unit operate in a bypass intermittent sampling mode to acquire sludge state parameters at different stages of the conditioning process, which serve as the data source for feedback control. After the detection is completed, the sampled sludge is emptied and the bypass sampling loop is closed, awaiting the next optimization cycle.

[0084] (5) Control unit

[0085] The control unit 10 can be implemented using a PLC, DCS, industrial computer, or edge controller, and is connected to the raw sludge detection unit, the reagent dosing unit, and the conditioning status detection unit respectively. It is used to receive sludge operating status parameters, execute optimization cycle trigger judgment, complete the calculation of optimized dosage of inorganic coagulant and polymer flocculant, and output reagent dosing control commands to realize adaptive optimization control of sludge dewatering agents.

[0086] The control unit includes a load monitoring module, a trigger determination module, an optimized cycle execution module, an inorganic coagulant optimized control module, a polymeric flocculant optimized control module, and a reagent execution module.

[0087] (5.1) Load detection module: The load detection module is used to continuously receive sludge operating status parameters, including sludge flow rate. and sludge concentration The current dry solids load is calculated based on the real-time acquired sludge operating status parameters.

[0088]

[0089] It continuously records the current running status and the running status parameters corresponding to the previous optimization cycle, providing a basis for determining the triggering of the optimization cycle.

[0090] (5.2) Trigger determination module: used to determine whether to start a new round of optimization cycle based on time trigger conditions and / or load change trigger conditions.

[0091] Under normal operating conditions, the control unit maintains the current final optimized dosage of inorganic coagulant and polymeric flocculant and does not activate raw sludge detection or conditioning status detection.

[0092] When the preset trigger conditions are met, the control unit starts a new optimization cycle.

[0093] The triggering conditions include time-based triggering conditions and load change triggering conditions. The two triggering methods adopt a parallel and independent triggering mechanism, and a new round of optimization cycle can be started when either triggering condition is met.

[0094] (a) Time-triggered conditions

[0095] When the system runs continuously At that time, the optimization cycle is initiated. Among them, The preferred time is 0.5-4 h.

[0096] (b) Load change triggering conditions

[0097] When sludge flow sludge concentration or dry solid load The rate of change of any parameter relative to the previous optimization cycle satisfies At that time, the optimization cycle is initiated. Among them: These are the current running parameters; These are the parameter values ​​corresponding to the previous optimization cycle; They can be represented separately , or ; The preset change threshold is preferably 10%-30%.

[0098] (5.3) Optimization Cycle Execution Module: After the trigger judgment module outputs an optimization command, the control unit starts a complete optimization cycle, including:

[0099] Initiate bypass sampling and introduce fresh raw mud.

[0100] Simultaneous detection of apparent viscosity of raw mud and charge characteristic parameters .

[0101] The basic dosage of inorganic coagulant is calculated based on the charge state and operating parameters of the original mud.

[0102] Update the inorganic coagulant dosage and perform the dosing.

[0103] Detection of intermediate residual potential after conditioning with inorganic coagulant .

[0104] according to Feedback correction is performed on the dosage of inorganic coagulant to obtain the final optimized dosage and maintain the output.

[0105] The basic dosage of polymeric flocculant is calculated based on the rheological state of the original sludge and the operating parameters.

[0106] Update the dosage of polymeric flocculant and perform the dosing.

[0107] Test the apparent viscosity of the conditioned sludge .

[0108] according to Feedback corrections are made to the dosage of polymeric flocculant to obtain the final optimized dosage and maintain the output.

[0109] Save the running parameters, control results, and system status data for this optimization cycle to the running database.

[0110] (5.4) Inorganic coagulant optimization control module: used to calculate the basic dosage of inorganic coagulant based on sludge operating status parameters. And based on the intermediate state residual potential Feedback corrections were performed to obtain the optimized dosage of inorganic coagulant. The basic dosage of inorganic coagulant is predicted based on the charge state of the raw sludge and the sludge operation parameters. Online feedback correction is then performed based on the charge state of the sludge after conditioning with inorganic coagulant, achieving adaptive optimization control of the dosage of inorganic coagulant and maintaining the surface charge of sludge particles within the preset target potential window.

[0111] (5.5) Polymer flocculant optimization control module: used to calculate the basic dosage of polymer flocculant based on sludge operating parameters. And based on apparent viscosity Feedback corrections were performed to obtain the optimal dosage of polymeric flocculant. .

[0112] (5.6) Reagent Execution Module: Used to determine the dosage of inorganic coagulant. and the dosage of polymeric flocculant The system generates dosing control commands to drive the dosing units (inorganic coagulant dosing device and polymeric flocculant dosing device) to execute dosing. Specifically, the dosing is first performed according to the baseline dosing amounts of inorganic coagulant and polymeric flocculant calculated by feedforward. After dosing, the measured... and Feedback calculations yield the optimal dosage of inorganic coagulant and polymeric flocculant, and the dosage is modified accordingly (if the base dosage already brings the potential and viscosity into the target range, then no modification is needed).

[0113] (5.7) Maintenance Module: This module maintains the final dosage of inorganic coagulant and polymeric flocculant obtained from the current optimization cycle until the next optimization cycle begins, after the optimization cycle ends. During the maintenance phase, only the sludge flow rate is continuously collected. and sludge concentration Stop the bypass detection of raw sludge, intermediate potential bypass detection, and viscosity bypass detection of conditioned sludge, and only perform optimized cycle trigger condition monitoring to avoid system oscillation caused by frequent adjustments and reduce the operating load of the detection equipment.

[0114] The aforementioned system records operational data for each optimization cycle during system operation and establishes an operational database to provide data support for system operation analysis, process optimization, and offline maintenance of the feedforward model. The operational data includes raw mud operating status parameters (…). , , , ), reagent dosing parameters ( , Conditioning status parameters ( , ) and dehydration effect parameters (moisture content of mud cake) (Preferably, offline detection methods are used to obtain the data). Preferably, the feedforward prediction model can be recalibrated offline or its parameters corrected based on a long-term accumulated operational database to improve the model's adaptability to different sludge operating conditions.

[0115] Meanwhile, the present invention also provides an adaptive dosing method for sludge dewatering agents, which specifically includes the following steps:

[0116] S1: Obtain sludge operating status parameters, including sludge flow rate. sludge concentration Original mud charge characteristic parameters and the apparent viscosity of the original mud Among these, the sludge flow rate is acquired in real time through an online continuous monitoring module. and sludge concentration The charge characteristic parameters of the raw mud are acquired in real time through an online continuous detection module. and the apparent viscosity of the original mud It is obtained through the bypass intermittent detection module after the optimization cycle triggering conditions are met.

[0117] The optimization cycle triggering conditions include time triggering conditions and load triggering conditions. The two triggering conditions adopt a parallel independent triggering mechanism, and the optimization cycle can be started when either triggering condition is met.

[0118] The time triggering condition is: when the continuous running time T ≥ the preset time interval. The Take for 0.5-4 hours;

[0119] The load change trigger condition is: sludge flow rate. sludge concentration or dry solid load The rate of change of any parameter relative to the previous optimization cycle satisfies ,in: These are the current running parameters; These are the parameter values ​​corresponding to the previous optimization cycle; The preset change threshold is set to 10%-30%.

[0120] Step S2: Calculate the basic dosage of inorganic coagulant based on the sludge operating status parameters. and the basic dosage of polymeric flocculant .

[0121] The basic dosage of the inorganic coagulant is as follows. The calculation formula is as follows, obtained from the pre-constructed multivariate regression feedforward prediction model A:

[0122]

[0123] In the formula, For dry solid load, ; , , , These are the coefficients of the regression model.

[0124] Here, the multiple regression feedforward prediction model A is established using an offline modeling method. Specifically, the model is established by collecting no fewer than 50 sets of experimental data covering different working conditions through pilot-scale tests and actual engineering operations, using the original mud charge characteristic parameters... Dry solid load sludge flow and sludge concentration Using the optimal inorganic coagulant dosage under the corresponding working conditions as the output variable and employing multiple regression analysis, a feedforward prediction model A is established. The model establishment process includes: experimental data collection, outlier removal and data preprocessing, multiple linear regression fitting, least squares method for solving regression coefficients, and five-fold cross-validation to evaluate model accuracy and optimize model fit. .

[0125] The basic dosage of the polymeric flocculant The calculation formula is obtained from the pre-constructed multivariate regression feedforward prediction model B, as follows:

[0126]

[0127] In the formula, , , , , These are the coefficients of the regression model.

[0128] The multivariate regression feedforward prediction model B here is established using an offline modeling method. Specifically, the method involves collecting no fewer than 50 sets of experimental data covering different working conditions through pilot-scale experiments and actual engineering operations, using the apparent viscosity of the original mud as a reference. Dry solid load sludge flow and sludge concentration Using the optimal dosage of polymeric flocculant under the corresponding operating conditions (the dosage at which the apparent viscosity of the conditioned sludge reaches the target rheological window and the moisture content of the sludge cake is lowest) as the input variable, a feedforward prediction model B is established using multiple regression analysis. The model establishment process includes experimental data acquisition, outlier removal and data preprocessing, multiple linear regression fitting, least squares method to solve for regression coefficients, and five-fold cross-validation to evaluate model accuracy and select the best model fit. .

[0129] S3: Add inorganic coagulant to the sludge according to the basic dosage to perform primary conditioning of the sludge, and then test the intermediate residual potential of the sludge after primary conditioning. Then, the basic dosage of inorganic coagulant is corrected based on the intermediate residual potential to obtain the optimized dosage of inorganic coagulant. .

[0130] Specifically, the target potential window for inorganic coagulant conditioning is defined as: .

[0131] when At this point, it is assumed that the surface charge of the sludge particles has reached the optimal neutralization state, and the current inorganic coagulant dosage should remain unchanged, i.e., the inorganic coagulant feedback correction amount. .

[0132] when When the sludge particles still have a high negative charge on their surface, the inorganic coagulant dosage is insufficient, and the dosage of inorganic coagulant should be appropriately increased; when If the dosage is too high, it indicates that the inorganic coagulant has been added excessively, and the dosage should be appropriately reduced. Therefore, when or The inorganic coagulant feedback correction amount is calculated using the following formula:

[0133]

[0134] in, The feedback gain coefficient is adaptively determined based on the potential deviation amplitude, and its value ranges from 0.05 to 2.00. When the potential deviation is small, a smaller feedback gain is used to improve control stability; when the potential deviation is large, a larger feedback gain is used to improve the system response speed. This is the normalized feedback function for the potential deviation; Residual potential Potential deviation relative to the target potential window. Potential deviation Defined as:

[0135]

[0136] The normalized feedback function is then expressed as: .

[0137] The optimized dosage of the inorganic coagulant .

[0138] In this way, no feedback correction is performed when the intermediate residual potential is within the target potential window; feedback correction is only performed when the intermediate residual potential exceeds the target potential window. This avoids frequent adjustments due to detection noise or small fluctuations, thereby improving the system's operational stability.

[0139] S4: Add polymeric flocculant to the primary conditioned sludge according to the basic dosage to perform secondary conditioned sludge, and then test the apparent viscosity of the conditioned sludge after secondary conditioned sludge. Then, the basic dosage of polymeric flocculant is adjusted based on the apparent viscosity of the conditioned sludge to obtain the optimized dosage of polymeric flocculant. .

[0140] Specifically, the target rheological window for conditioning with polymeric flocculants is defined as: .

[0141] when At that time, it is considered that the sludge floc structure and rheological properties have reached the optimal conditioning state, and the dosage of polymeric flocculant should be kept constant, i.e., the polymeric flocculant feedback correction amount. ;

[0142] when If the dosage of polymeric flocculant is insufficient, the flocculation effect is inadequate, and the rheological properties of the sludge after conditioning do not meet the target requirements, the dosage of polymeric flocculant should be appropriately increased. If the viscosity of the sludge system is too high, it indicates that the polymeric flocculant has been added in excess, and the dosage of the polymeric flocculant should be appropriately reduced. Therefore, when... or The feedback correction amount for polymeric flocculants is calculated using the following formula:

[0143]

[0144] in, This is the viscosity deviation normalization feedback function; The feedback gain coefficient is adaptively matched according to the viscosity deviation amplitude, and its value ranges from 0.05 to 2.00. When the viscosity deviation is small, a smaller feedback gain is used to improve control stability; when the viscosity deviation is large, a larger feedback gain is used to improve the system response speed. The apparent viscosity of the conditioned sludge Viscosity deviation relative to the target rheological window. Defined as:

[0145]

[0146] Normalized feedback function Represented as:

[0147] The optimized dosage of the polymeric flocculant .

[0148] In this way, when the apparent viscosity of the conditioned sludge is within the target rheological window, no feedback correction is performed. Feedback correction is only performed when the apparent viscosity of the conditioned sludge exceeds the target rheological window, and the dosage of polymeric flocculant is updated based on the feedback correction result. Otherwise, the final dosage of polymeric flocculant determined in the previous optimization cycle remains unchanged, so as to avoid frequent adjustments caused by detection noise or small fluctuations and improve the stability of system operation.

[0149] S5: Continuously add the agents to the subsequent sludge according to the optimized dosage of inorganic coagulant and polymeric flocculant until the conditions for the next round of optimization are met, then return to step S1.

[0150] Specifically, after the optimization cycle, the agents are continuously added to the subsequent sludge according to the optimized dosages of inorganic coagulant and polymeric flocculant. During the maintenance phase, only sludge flow rate and sludge concentration are continuously collected. Stop the bypass detection of raw sludge, intermediate potential detection and conditioned sludge viscosity bypass detection, and only perform optimization cycle trigger condition monitoring until the next round of optimization trigger conditions are met, then return to step S1.

[0151] Example

[0152] This embodiment uses concentrated sludge from a municipal wastewater treatment plant as the treatment target. After concentration, the sludge enters the dewatering system. During the operation of the dewatering system, the sludge flow rate is 20-30 m³ / h, and the sludge concentration (MLSS) is 25-40 g / L.

[0153] In this embodiment, ferric chloride (FeCl3) is used as an inorganic coagulant, and cationic polyacrylamide (CPAM) is used as a polymeric flocculant. The FeCl3 solution and CPAM solution are prepared at preset concentrations and stored separately in reagent storage tanks, then transported to the sludge pipeline via corresponding dosing pumps.

[0154] 1. Sludge parameter detection

[0155] This embodiment uses a combination of continuous detection and bypass intermittent detection to obtain sludge operating status parameters.

[0156] (1) Continuous operation parameter detection

[0157] An electromagnetic flowmeter and an online MLSS analyzer are installed on the main sludge feed pipeline for continuous monitoring of sludge flow. and sludge concentration .

[0158] The control system calculates the dry solids load entering the system per unit time based on the detection data: .

[0159] (2) Detection of charge and rheological parameters of raw mud

[0160] Once the control system meets the optimized cycle triggering conditions, the bypass sampling module is activated to introduce fresh raw mud samples. A potential detector is used to measure the surface charge state of the raw mud particles, obtaining the characteristic parameters of the raw mud charge. In this embodiment, the potential range of the original mud is: .

[0161] The apparent viscosity of the raw mud was determined using a viscometer under the following conditions: room temperature and a viscometer rotation speed of 60 rpm. The viscosity of the raw mud was obtained as follows: In this embodiment, the viscosity range of the original mud is: .

[0162] 2. Optimized control of inorganic coagulants

[0163] (1) Establishment of feedforward prediction model

[0164] An inorganic coagulant feedforward prediction model was established based on no fewer than 50 sets of pilot-scale and field operation data. (The model is described below.) , , , The FeCl3 dosage required to achieve the optimal charge conditioning state is used as the input variable and the output variable is used as the output variable.

[0165] The results obtained through multiple regression analysis are as follows:

[0166] The accuracy of the model was evaluated using 5-fold cross-validation, and the coefficient of determination was obtained as follows: It meets the requirements of engineering applications.

[0167] (2) Feedback correction control

[0168] After the FeCl3 addition is completed, intermediate sludge samples are collected through a bypass detection module located downstream of the inorganic coagulant addition point and upstream of the polymeric flocculant addition point.

[0169] The residual potential after conditioning was measured using a potential detector. In this embodiment, the preferred target potential window after conditioning is set as follows: .

[0170] when hour: .

[0171] when When the sludge particles still have a high negative charge, the FeCl3 dosage should be increased.

[0172] when If the dosage is too high, it indicates that FeCl3 has been added in excess, and the amount of FeCl3 added should be reduced.

[0173] Feedback correction amount is based on: Calculate; where, The feedback gain coefficient is determined based on the absolute value of the normalized feedback function. The size is set in stages to balance control stability and response speed. Preferably, the value selection rule is as follows (this stage only applies to the case study factory; other cases should be adjusted according to specific circumstances). The value can be adjusted between 0.05 and 2, and the specific settings for different levels also need to be determined.

[0174]

[0175] The final FeCl3 dosage is: .

[0176] 3. Optimized control of polymeric flocculants

[0177] (1) Establishment of feedforward prediction model

[0178] An inorganic coagulant feedforward prediction model was established based on no fewer than 50 sets of pilot-scale and field operation data. The apparent viscosity of the raw mud was used as the criterion. Dry solids load sludge flow sludge concentration The CPAM dosage at which optimal dehydration performance is achieved is used as the input variable and the output variable.

[0179] Establish a feedforward model for polymeric flocculants:

[0180] Model determination coefficient: .

[0181] (2) Feedback correction control

[0182] After CPAM addition, the final conditioned sludge is collected via a bypass detection module located at the front end of the dewatering equipment. The apparent viscosity of the conditioned sludge is measured using a rotational viscometer. This embodiment sets the target rheological window:

[0183] when hour: .

[0184] when If the concentration of CPAM is insufficient, the dosage should be increased.

[0185] when If the concentration of CPAM is too high, reduce the CPAM dosage.

[0186] Feedback correction amount: In this embodiment, the feedback gain coefficient Based on the normalized value of the apparent viscosity deviation of the conditioned sludge The magnitude of the deviation is adaptively set in stages. When the deviation is small, a smaller feedback gain is used to avoid frequent system adjustments; when the deviation is large, a larger feedback gain is used to improve the control response speed. Preferably, the corresponding relationship is set as follows (this stage is only applicable to the case plant; other cases should be adjusted according to specific circumstances). The value can be adjusted between 0.05 and 2, and the specific settings for different levels also need to be determined.

[0187]

[0188] final: .

[0189] Specific operational data

[0190] In the actual operation of a wastewater treatment plant, four representative operating conditions (S1-S4) were selected for verification, and the calculation results are shown in the table below:

[0191]

[0192] As shown in the table above, under the four operating conditions S1-S4, the raw sludge flow rate changed from 20.2 m³ / h to 26.9 m³ / h, the sludge concentration changed from 25.1 kg / m³ to 39.7 kg / m³, the dry solids load increased from 507.02 kg / h to 1067.93 kg / h, the apparent viscosity of the raw sludge increased from 720.8 mPa·s to 1169.4 mPa·s, and the charge characteristic parameter of the raw sludge changed from −22.1 mV to −30.8 mV. This indicates that the verification conditions covered different sludge loads and sludge property changes. Within this range, the feedforward control model can automatically calculate the basic dosage based on the raw sludge state parameters. The basic dosage of inorganic coagulant is 86.9-221.6 L / h, and the basic dosage of polymeric flocculant is 282.6-453.9 L / h, indicating that the feedforward model can respond reasonably to changes in the sludge operating state.

[0193] Furthermore, based on feedforward control, this invention obtains the intermediate residual potential after conditioning with inorganic coagulant through bypass sampling. And the apparent viscosity of the conditioned sludge after conditioning with polymeric flocculants The system employs feedback control to dynamically correct the basic dosage. When the intermediate residual potential is within the target potential window (e.g., under conditions S1 and S4), the feedback correction is zero, and the system maintains the output of the feedforward calculation results. When the residual potential exceeds the target window (e.g., under conditions S2 and S3), the control unit calculates the feedback correction based on the potential deviation, increasing the inorganic coagulant dosage by 3.4 L / h and decreasing it by 3.6 L / h, respectively, so that the final dosage can be corrected to a more optimal level in a timely manner.

[0194] For the control of polymeric flocculants, when the apparent viscosity of the conditioned sludge is within the target rheological window (condition S1), the feedback correction remains zero; when the apparent viscosity of the conditioned sludge is higher than the target window (conditions S2 and S4), the system determines that the polymeric flocculant dosage is insufficient and adjusts the dosage accordingly; when the apparent viscosity of the conditioned sludge is lower than the target window (condition S3), the system determines that the flocculant dosage is excessive and improves the dosage accuracy through feedback correction. Therefore, this invention can achieve closed-loop regulation of the polymeric flocculant dosage based on changes in the sludge rheological state, making the dosage more consistent with actual conditioning needs.

[0195] Compared to the operation of the same wastewater treatment plant before the implementation of this invention, which relied on manual, experience-based dosing, the dosages of inorganic coagulant and polymeric flocculant were approximately 200 L / h and 500 L / h, respectively, under the same dewatering equipment and similar operating conditions. After adopting this invention, the average consumption of inorganic coagulant and polymeric flocculant decreased by approximately 22% and 28%, respectively, maintaining stable sludge conditioning effects while reducing chemical consumption. Furthermore, before implementing this invention, the moisture content of the dewatered sludge cake at the plant was typically stable between 81% and 83%; after adopting this invention, the moisture content of the dewatered sludge cake under four typical operating conditions was 78.2%, 78.9%, 77.4%, and 78.7%, respectively, generally stable within the range of 77%-79%, a reduction of more than 3 percentage points compared to the original operating level. The above results show that the present invention can optimize the dosage of inorganic coagulant and polymeric flocculant in real time according to the charge state and rheological state of sludge, which can significantly reduce the consumption of reagents, effectively improve the sludge dewatering performance, and improve the stability and economy of system operation.

[0196] Finally, it should be noted that the above embodiments of the present invention are merely illustrative examples and not intended to limit the implementation of the invention. Those skilled in the art can make other variations and modifications based on the above description. It is impossible to exhaustively list all possible implementations here. All obvious variations or modifications derived from the technical solutions of this invention are still within the scope of protection of this invention.

Claims

1. A method for adaptive dosing of sludge dewatering agents, characterized in that, Specifically, the following steps are included: S1: Obtain sludge operating status parameters, including sludge flow rate. sludge concentration Original mud charge characteristic parameters and the apparent viscosity of the original mud ; S2: The basic dosage of inorganic coagulant is calculated based on the sludge operating status parameters. And the basic dosage of polymeric flocculant ; S3: Add inorganic coagulant to the sludge according to the basic dosage to perform primary conditioning of the sludge, and then test the intermediate residual potential of the sludge after primary conditioning. Then, the basic dosage of inorganic coagulant is corrected based on the intermediate residual potential to obtain the optimized dosage of inorganic coagulant. ; S4: Add polymeric flocculant to the primary conditioned sludge according to the basic dosage to perform secondary conditioned sludge, and then test the apparent viscosity of the conditioned sludge after secondary conditioned sludge. Then, the basic dosage of polymeric flocculant is adjusted based on the apparent viscosity of the conditioned sludge to obtain the optimized dosage of polymeric flocculant. ; S5: Continuously add the agents to the subsequent sludge according to the optimized dosage of inorganic coagulant and polymeric flocculant until the conditions for the next round of optimization are met, then return to step S1.

2. The adaptive dosing method for sludge dewatering agents according to claim 1, characterized in that, In step S2, the basic dosage of the inorganic coagulant is... The calculation formula is as follows, obtained from the pre-constructed multivariate regression feedforward prediction model A: in, For dry solid load, ; , , , These are the coefficients of the regression model.

3. The adaptive dosing method for sludge dewatering agents according to claim 1 or 2, characterized in that, In step S2, the basic dosage of the polymeric flocculant is... The calculation formula is as follows, obtained from the pre-constructed multivariate regression feedforward prediction model B: in, For dry solid load, ; , , , , These are the coefficients of the regression model.

4. The adaptive dosing method for sludge dewatering agents according to claim 1 or 2, characterized in that, In step S3, when the basic dosage of inorganic coagulant is corrected based on the intermediate residual potential, the specific steps include: The target potential window for conditioning with inorganic coagulants is defined as follows: ; when At that time, the inorganic coagulant feedback correction amount ; when or The inorganic coagulant feedback correction amount is calculated using the following formula: in, This is the feedback gain coefficient, with a value ranging from 0.05 to 2.

00. This is the normalized feedback function for the potential deviation; Residual potential Potential deviation relative to the target potential window; The optimized dosage of the inorganic coagulant .

5. The adaptive dosing method for sludge dewatering agents according to claim 4, characterized in that, Potential deviation Defined as: but .

6. The adaptive dosing method for sludge dewatering agents according to claim 1 or 3, characterized in that, In step S4, when the basic dosage of polymeric flocculant is adjusted based on the apparent viscosity of the conditioned sludge, the specific steps include: The target rheological window for conditioning with polymeric flocculants is defined as: ; when At that time, the feedback correction amount of polymeric flocculant ; when or The feedback correction amount for polymeric flocculants is calculated using the following formula: in, This is the viscosity deviation normalization feedback function; This is the feedback gain coefficient, with a value ranging from 0.05 to 2.

00. The apparent viscosity of the conditioned sludge Viscosity deviation relative to the target rheological window; The optimized dosage of the polymeric flocculant .

7. The adaptive dosing method for sludge dewatering agents according to claim 6, characterized in that, Viscosity deviation Defined as: but 8. The adaptive dosing method for sludge dewatering agents according to claim 1, characterized in that, In step S4, the next round of optimization triggering conditions include time triggering conditions and load change triggering conditions. A new round of optimization cycle will start when either of the two triggering conditions is met. The time triggering condition is: continuous running time T ≥ preset time interval. The Take for 0.5-4 hours; The load change trigger condition is: sludge flow rate. sludge concentration or dry solid load The rate of change of any parameter relative to the previous optimization cycle satisfies ,in: These are the current running parameters; These are the parameter values ​​corresponding to the previous optimization cycle; The preset change threshold is set to 10%-30%.

9. An adaptive dosing system for sludge dewatering agents, used to execute the adaptive dosing method for sludge dewatering agents according to any one of claims 1-8, comprising a mixing reaction unit and a dewatering device, wherein the mixing reaction unit is connected to a sludge inlet main pipe for introducing sludge, and the mixing reaction unit is connected to the dewatering device via a pipe; characterized in that, It also includes a raw mud detection unit, a chemical dosing unit, a conditioning status detection unit, and a control unit; The raw sludge detection unit includes an online detection module and a bypass intermittent detection module. The online detection module is located on the main sludge inlet pipe and is used for real-time sludge flow. and sludge concentration The bypass intermittent detection module is located on the main sludge inlet pipe downstream of the online detection module, and is used to intermittently acquire the charge characteristic parameters of the raw sludge. and the apparent viscosity of the original mud ; The agent dosing unit includes an inorganic coagulant dosing device and a polymeric flocculant dosing device, which are used to add inorganic coagulant and polymeric flocculant to sludge, respectively. The mixing reaction unit includes a primary mixing reaction zone and a secondary mixing reaction zone. The primary mixing reaction zone is used for the mixing reaction of inorganic coagulant and sludge, and the secondary mixing reaction zone is used for the mixing reaction of polymeric flocculant and sludge conditioned by inorganic coagulant. The conditioning state detection unit includes an intermediate state potential detection module located downstream of the primary mixing reaction zone and a conditioning viscosity detection module located downstream of the secondary mixing reaction zone, which are used to detect the residual potential of the intermediate sludge, respectively. and conditioning the apparent viscosity of sludge ; The control unit is communicatively connected to the raw mud detection unit, the agent dosing unit, and the conditioning status detection unit, respectively, and is used to receive detection data, perform optimization trigger judgment, and output inorganic coagulant and polymer flocculant dosing control commands.

10. The adaptive dosing system for sludge dewatering agents according to claim 9, characterized in that, The control unit includes a load detection module, a trigger determination module, an optimized cycle execution module, an inorganic coagulant optimized control module, a polymeric flocculant optimized control module, a reagent execution module, and a maintenance module; The load detection module is used to continuously acquire sludge flow rate. and sludge concentration And calculate the dry solids load. ; The trigger determination module is used to determine whether to start a new round of optimization cycle based on time trigger conditions and / or load change trigger conditions; The optimization cycle execution module is used to perform the entire process of raw mud bypass sampling and detection, inorganic coagulant addition and feedback correction, polymer flocculant addition and feedback correction, and operation data saving after the trigger judgment module outputs the optimization command. The inorganic coagulant optimization control module is used to calculate the basic dosage of inorganic coagulant based on sludge operating status parameters. And based on the intermediate state residual potential Feedback corrections were performed to obtain the optimized dosage of inorganic coagulant. ; The polymeric flocculant optimization control module is used to calculate the basic dosage of polymeric flocculant based on sludge operating parameters. And based on the apparent viscosity of the conditioned sludge Feedback corrections were performed to obtain the optimal dosage of polymeric flocculant. ; The drug delivery module is used to optimize the dosage based on the inorganic coagulant. Optimize the dosage of polymeric flocculants Generate dosing control commands to drive the dosing unit to perform dosing; The operation-maintaining module is used to maintain the current dosage output after the optimization cycle until the next optimization cycle starts.

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