Multistage centrifugal dewatering type sludge comprehensive treatment method and system

Abstract

The invention provides a multi-stage centrifugal dewatering type sludge comprehensive treatment method and system, and relates to the technical field of sludge treatment, and the method comprises the following steps: pretreating raw sludge through a mechanical grid and magnetic separation; feeding the pretreated sludge into a multi-stage centrifugal dewatering unit with gradually increased rotating speed gradient, and dynamically adding a chemical or biological conditioner between stages based on a dynamic adaptation function to cooperatively regulate and control dewatering and floc modification; the dewatered sludge is fed into a closed hot air circulation drying device, drying parameters are optimized through a multi-objective optimization function, and efficient and low-consumption drying is achieved; extruding and forming the dried sludge into a fuel rod or an organic fertilizer base material based on the collaborative decision index; the system correspondingly comprises a pretreatment module, a multi-stage dehydration and conditioning module, a hot air drying module, an extrusion forming module and the like. According to the invention, by constructing the process state holographic sensing network, intelligent cooperative control and global optimization of process parameters of each unit are realized, the processing efficiency is effectively improved, the energy consumption is reduced, and accurate recycling of products is realized.

Description

Technical Field

[0001] This invention relates to the field of sludge treatment technology, and in particular to a multi-stage centrifugal dewatering integrated sludge treatment method and system. Background Technology

[0002] In the actual operation of municipal wastewater treatment plants, sludge treatment is a crucial link in ensuring the plant consistently meets standards. However, during the rainy season or in areas where urban sewer systems converge, large amounts of surface runoff, industrial wastewater, and domestic sewage mix, resulting in raw sludge entering the wastewater treatment plant with complex composition and drastically fluctuating properties. This volatility poses a significant challenge to subsequent dewatering treatment. Existing technologies often employ a combination of single chemical conditioning and belt filter press or centrifugal dewatering. This fixed-parameter operating mode is difficult to adapt to real-time changes in the organic matter content and viscosity of the sludge. In practice, operators often rely on experience to manually adjust the dosage of chemicals, which presents challenges. The severe lag directly leads to unstable dewatering results: when the sludge properties change abruptly, filter cloth clogging, centrifuge torque spikes, or even shutdowns can easily occur. The moisture content of the dewatered sludge cake often rebounds to higher than the design requirements, failing to meet the entry standards for subsequent disposal such as incineration or landfill. At the same time, this extensive control method also results in waste of reagents and excessive energy consumption. In addition, the existing technology path is singular, and most dewatered sludge is directly sent to landfills, which not only occupies land resources but also poses environmental risks, failing to realize its resource value. Therefore, there is an urgent need for a new multi-stage centrifugal dewatering sludge comprehensive treatment method and system. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-stage centrifugal dewatering method and system for comprehensive sludge treatment, in order to solve the problems of existing technologies that rely on fixed parameter operation modes and cannot adapt to fluctuations in sludge properties in real time, resulting in unstable dewatering efficiency, waste of chemical conditioning agents and high overall energy consumption, as well as single resource utilization pathways and low added value of products. The specific technical solution is as follows: This invention provides a multi-stage centrifugal dewatering method for comprehensive sludge treatment, comprising: S1: The raw sludge generated by the municipal sewage treatment plant or industrial wastewater treatment system is pumped into the receiving tank, where solid impurities, including plastic fragments, fibrous materials and gravel, are intercepted by a mechanical bar screen; a permanent magnet magnetic separator is used to remove iron-containing magnetic impurities from the sludge, resulting in pretreated sludge, which is then transported to a homogenization equalization tank for temporary storage. S2: The pretreated sludge is pumped into a series of three to five centrifugal dewatering units via screw pumps. The speed of each centrifugal dewatering unit increases gradually along the sludge flow direction: the first-stage centrifuge speed is 800-1200 rpm, the second stage increases to 1500-2000 rpm, and the third stage and above gradually increase to 2500-3200 rpm. A closed conditioning reaction tank is installed after the first-stage centrifugal dewatering unit, and the reaction tank is equipped with a stirring device. Chemical conditioners or compound biological agents are dynamically added according to the online monitoring of sludge organic matter content and real-time moisture content. S3: The sludge after multi-stage centrifugal dewatering is transported to a closed hot air circulation drying device. The drying system is equipped with a waste heat recovery device, which circulates the outlet hot air to the inlet to reduce energy consumption. The generated exhaust gas is discharged in compliance with standards after condensation and dehumidification. S4: The dried sludge is fed into a twin-screw extrusion molding device and plastically extruded at a temperature of 60-80 degrees Celsius and a pressure of 3-5 MPa to prepare fuel rods with a diameter of 30-50 mm or organic fertilizer base material with a particle size of 2-5 mm.

[0004] Furthermore, in step S2, the chemical conditioning agent is a compound system of polyaluminum chloride and cationic polyacrylamide, and the dosage is calculated as 0.1%-0.5% of the dry weight of the sludge; the biological agent is a compound Bacillus preparation, and the dosage is 0.05%-0.1% of the sludge volume; the conditioning reaction time is 15-30 minutes; in step S3, the drying temperature is controlled at 105-120 degrees Celsius, and the hot air velocity is maintained at 1.2-1.8 meters per second.

[0005] Further, step S1 includes the following steps: S11: The raw sludge generated by municipal sewage treatment plants or industrial wastewater treatment systems is continuously pumped into the receiving tank via a sludge transfer pump. S12: The sludge in the receiving pool is passed through a mechanical screen to intercept and remove solid impurities with a particle size of 5 mm or more, including plastic fragments, fibrous materials and gravel. S13: The sludge after being treated by the bar screen is introduced into a permanent magnet magnetic separator to remove iron-containing magnetic impurities from the sludge under a magnetic field strength of 0.8 to 1.2 Tesla. S14: Obtain pretreated sludge with a moisture content reduced to 99.2% to 99.5%. The pretreated sludge is then transported to a homogenization tank for temporary storage before further processing.

[0006] Further, step S2 includes the following steps: S21: Perform online quantification of property parameters on the pretreated sludge from the homogenizing tank to obtain a set of input sludge characteristic parameters. Each batch of sludge samples in this set corresponds to a feature vector, which includes the measured value of organic matter content, real-time moisture content, electrochemical impedance phase angle, and sludge temperature. S22: Define the reaction mechanism and objective of the conditioning between stages in a multi-stage centrifugal dewatering unit. The reaction mechanism includes the charge neutralization and adsorption bridging effect initiated by chemical conditioning agents, and the extracellular polymer hydrolysis and recombination catalyzed by compound biological agents. The conditioning objective is to increase the average particle size of sludge flocs to 80 to 120 micrometers and form a dense floc structure with high mechanical strength. S23: Construct a state transfer and coupling relationship model between multi-stage dewatering units. Use the sludge solid content and floc strength at the outlet of the first-stage centrifugal dewatering machine as the input conditions for the subsequent conditioning reaction tank. At the same time, use the torque and differential speed parameters of the subsequent centrifugal dewatering machine as feedback variables for the speed setting of the first-stage centrifugal machine to form a state association network between series units. S24: Based on the input sludge characteristic parameters, inter-stage conditioning mechanism and multi-stage state association network, a dynamic adaptation function is used to uniformly describe and execute the synergistic effect of speed gradient control and floc modification; the dynamic adaptation function calculates the optimal conditioner dosage according to the real-time sludge properties, wherein the dynamic adaptation function is based on the measured value of sludge organic matter content, the reference threshold of organic matter content, the real-time sludge moisture content at the inlet of the current stage dewatering unit, the target moisture content expected to be achieved after conditioning and dewatering of this stage, and the basic addition coefficient and adjustment weight factor determined according to the sludge type.

[0007] Further, step S3 includes the following steps: S31: Receive dewatered sludge from the multi-stage centrifugal dewatering unit and characterize its pre-drying state to form a drying input state vector. The drying input state vector includes the measured value of sludge solid content, sludge specific heat capacity, initial temperature, volatile organic compound content, and floc structure strength index. S32: Define the process state space of the closed hot air circulation drying device. The process state space uses drying temperature, hot air velocity, drying time and exhaust gas circulation rate as the core control variables, and sludge moisture content, unit evaporation heat consumption and sludge particle strength after drying as the key performance indicators. S33: Construct a multi-objective optimization function for the drying process with the goal of minimizing energy consumption and maximizing dehydration efficiency. This function weights and integrates the drying rate, energy consumption per unit of water evaporation, and the quality stability of the dried product. The multi-objective optimization function is based on the drying temperature, hot air velocity, drying time, and exhaust gas recirculation rate, and introduces a temperature effect index term, the reciprocal of the energy consumption per unit of water evaporation, and the product intensity variation coefficient to achieve unified quantification and synchronous optimization of the three objectives of rate, energy consumption, and quality. S34: Based on the multi-objective optimization function, an adaptive downhill simplex method for multivariable strongly coupled systems is adopted to search within a four-dimensional constrained space consisting of drying temperature of 105 to 120 degrees Celsius, hot air velocity of 1.2 to 1.8 meters per second, drying time, and exhaust gas recirculation rate. The search step size and direction are dynamically adjusted according to the real-time collected sludge moisture content decrease curve and instantaneous energy consumption data, and the optimal combination of control parameters that maximizes the comprehensive optimization evaluation value is output. S35: Based on the obtained optimal control parameter combination, drive the closed hot air circulation drying device to perform the deep drying process. Through high-precision temperature sensor and anemometer, realize closed-loop feedback control to ensure that the deviation between the actual drying temperature and hot air speed and the optimized set value does not exceed ±1 degree Celsius and ±0.1 meters per second, respectively, until the sludge moisture content is stably reduced to 10% or below, and the drying stage is completed.

[0008] Further, step S4 includes the following steps: S41: Receive the dried sludge output from the closed hot air circulation drying device, and perform pre-resource utilization characteristic analysis on it to form a molding raw material state vector. The molding raw material state vector includes the moisture content, ash content, calorific value, fiber content and flow index of the dried sludge. S42: Construct a resource-based decision-making model for two target products: fuel rods and organic fertilizer substrate. The model uses the floc structure strength information represented by the optimal conditioner dosage calculated by the dynamic adaptation function in the previous step S2, and the drying efficiency and energy consumption information contained in the comprehensive optimization evaluation value output by the multi-objective optimization function in step S3 as decision input factors to establish a collaborative decision-making index. S43: Define a collaborative decision index calculation function. This function nonlinearly integrates conditioning effect, drying quality and target product quality requirements. The collaborative decision index is calculated based on floc structure modification intensity, organic matter characteristics, thermal energy utilization efficiency of the drying process, measured higher calorific value of dried sludge, calorific value conversion efficiency when formed into fuel rods, and agricultural safety index when formed into organic fertilizer base material. S44: Based on the value of the collaborative decision index, set the specific process parameters of the twin-screw extrusion molding device. If the decision is to prepare fuel rods, control the temperature to 70 to 80 degrees Celsius and the pressure to 4 to 5 MPa; if the decision is to prepare organic fertilizer base material, control the temperature to 60 to 70 degrees Celsius and the pressure to 3 to 4 MPa. During the molding process, by adjusting the rotation speed of the twin screw and the combination of the kneading blocks in real time, ensure that the material reaches the optimal plasticization and extrusion state under the set temperature and pressure. S45: The formed product is cooled and screened to obtain the final product. If it is a fuel rod, its diameter is 30 to 50 mm and its calorific value is not less than 12 MJ per kilogram. If it is an organic fertilizer base, its particle size is 2 to 5 mm and meets the relevant agricultural standards. The entire forming process is linked with the upstream dehydration and drying process through a distributed control system to ensure the seamless execution of decision instructions and process parameters.

[0009] Furthermore, it also includes the following steps: S5: Throughout the entire treatment process, the integrated electrochemical impedance spectroscopy and control system monitor the DS / FC value of the sludge in real time. By calculating the difference in the optimal dosage of chemical conditioner through dynamic slope calculation, the system achieves self-optimization of the dosage. The system automatically adjusts the centrifuge speed gradient and conditioner addition sequence according to the fluctuation of sludge organic matter content to ensure stable dewatering efficiency and a moisture content variation coefficient of less than 5%. S6: The filtrate and drying condensate produced during the dehydration process are returned to the front end of the wastewater treatment system; the extruded fuel rods or fertilizer base are transported to the storage silo by belt conveyor for industrial combustion or soil improvement; the system adopts a modular design, and each unit is linked through an Internet of Things platform to realize digital monitoring of energy consumption and pesticide consumption.

[0010] Further, step S5 includes the following steps: S51: Construct a holographic perception network for process status. This network uses the process parameters, equipment operating status, and material property data collected in real time in steps S2, S3, and S4 as input nodes. The input nodes include the real-time speed and torque of centrifugal dewatering machines at each stage, the reagent dosing rate and stirring power of the conditioning reaction tank, the hot air temperature and wind speed of the drying device, the temperature and pressure of the twin-screw extruder, as well as the sludge organic matter content, real-time moisture content, electrochemical parameters, calorific value after drying, and strength of the formed product monitored online. Each node is associated with the material batch number through a timestamp. S52: Define the associated edges and dynamic weights of the process status holographic perception network. The associated edges are used to represent the transmission and coupling relationship of materials, energy and information flow between different process units. The dynamic weights are adaptively adjusted according to the real-time process efficiency. Their calculation depends on the deviation between the optimal dosage and the actual dosage obtained by the dynamic adaptation function in step S2, the real-time status of the comprehensive evaluation value obtained by the multi-objective optimization function in step S3, and the indication of the collaborative decision index on the current production route in step S4. S53: Train the process state holographic perception network, using the optimal operating state data of each process unit in the previous stable operating cycle as the supervision signal, with the joint optimization objective of minimizing global operating cost and maximizing comprehensive product quality, train the dynamic weight parameters in the network, and use the time series backpropagation algorithm in the training process to enable the network to learn and memorize the control mode that maintains the global optimal system under multivariate disturbances. S54: Based on the trained holographic perception network of process status, online real-time control and self-optimization are performed. Real-time collected process node data is input into the network, which calculates through forward propagation and outputs a set of optimized setpoints for the next control cycle. This set includes fine-tuning of the speed gradient of each centrifuge, compensation value for the dosage of conditioning agent, correction value for the temperature and wind speed of the drying device, and preset values ​​for the molding process parameters. Based on this output, the system automatically adjusts the actions of each actuator to achieve real-time coordinated optimization of dewatering efficiency, drying energy consumption, and final product quality, ensuring that the coefficient of variation of the final sludge moisture content is consistently below 5% under dynamic feeding conditions. Step S6 includes the following steps: S61: To achieve the directional reflux of by-products and the intelligent scheduling of resource-based products, the filtrate generated during the dehydration process, the condensate generated during the drying process, and the wastewater from equipment cleaning are collected in a unified reflux regulating tank; the online water quality monitoring system analyzes the key pollutant indicators in the reflux liquid in real time, and through the IoT platform, instructs the regulating valve to pump the reflux liquid back to the front-end biochemical treatment unit or deep treatment unit of the sewage treatment system according to the preset ratio and concentration; S62: Establish a resource-based product warehousing and distribution system based on full-process quality traceability, assigning a unique traceability code to each batch of fuel rods or organic fertilizer base material. This code is associated with all key process parameters and quality data in steps S2 to S4 of the production process. After the products are transported to the intelligent storage warehouse by belt conveyor, the storage management system automatically matches the delivery batch according to order requirements, product calorific value test reports or agrochemical test reports, and generates product quality files. S63: Construct a digital monitoring and global decision-making platform. This platform integrates IoT sensors and control systems of each process unit, as well as the aforementioned process status holographic perception network. The platform dynamically and visually displays real-time energy consumption, real-time chemical consumption, processing efficiency, product yield, and quality indicators. The core decision-making module of the platform is based on historical operating big data and the predictive output of the process status holographic perception network, continuously updates the optimal operating range of each unit's equipment, and generates preventive maintenance warnings. S64: Implement full-process closed-loop management and adaptive evolution. The digital monitoring platform evaluates the overall system performance weekly or monthly. The evaluation criteria include average unit energy consumption, average reagent consumption, product compliance rate, and overall equipment efficiency. When the evaluation value deviates from the baseline, the platform automatically initiates the retraining process of the process status holographic perception network and fine-tunes its dynamic weights using recent data.

[0011] This invention also provides a multi-stage centrifugal dewatering sludge integrated treatment system for implementing the method, the system comprising: The sludge pretreatment module is used to receive raw sludge and perform preliminary purification, including mechanical bar screen to intercept solid impurities and permanent magnet magnetic separation to remove magnetic impurities, resulting in pretreated sludge with a moisture content reduced to 99.2%-99.5%, which is then temporarily stored in a homogenization equalization tank. The multi-stage centrifugal dewatering and conditioning module is used to perform gradient dewatering of pretreated sludge through multi-stage series centrifugal dewatering units, and to dynamically condition the sludge flocs between stages by modifying them with chemical conditioning agents or biological agents to increase the solids content to 25%-35%. The hot air circulation drying module is used to dry dewatered sludge in a closed hot air environment, controlling the drying temperature and air velocity to reduce the moisture content to below 10%, and integrating waste heat recovery and exhaust gas treatment. The extrusion molding and resource utilization module is used to extrude dried sludge through a twin-screw extruder to selectively prepare fuel rods or organic fertilizer base materials, and adjust process parameters according to resource utilization requirements.

[0012] Furthermore, it also includes: The real-time monitoring and self-optimization module is used to monitor sludge properties and equipment status in real time through a holographic sensing network, dynamically adjust process parameters, and achieve synergistic optimization of dewatering efficiency and energy consumption. The by-product management and digital monitoring module is used to handle the reflux of filtrate and condensate, manage the storage and distribution of resource-based products, and conduct full-process digital monitoring through an IoT platform.

[0013] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method.

[0014] The beneficial effects of this invention are as follows: By synergistically combining multi-stage centrifugal dewatering and inter-stage dynamic conditioning, multi-objective optimization of the hot air drying process, and precise resource utilization based on a collaborative decision-making index, a globally optimized sludge treatment system is constructed. This method and system can significantly improve dewatering efficiency, reducing the stability rate and coefficient of variation of the final sludge moisture content; through technologies such as dynamically adapting conditioner addition and waste heat recovery, it effectively reduces reagent consumption and total system energy consumption; ultimately, it can intelligently decide to produce high-calorific-value fuel rods or high-safety organic fertilizer base materials based on sludge characteristics, realizing high-value resource utilization of sludge. Simultaneously, the integrated holographic sensing network and digital platform ensure the system's adaptability to fluctuations in sludge properties, achieving intelligent collaborative control throughout the entire process from dewatering and drying to molding, solving the technical problems of disconnected processes and unstable operation in traditional methods.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the steps of the multi-stage centrifugal dewatering sludge comprehensive treatment method of the present invention; Figure 2 This is a schematic diagram of the multi-stage centrifugal dewatering sludge integrated treatment system of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] In embodiments of the present invention, a multi-stage centrifugal dewatering method for comprehensive sludge treatment is provided. Please refer to [link / reference]. Figure 1 The method includes the following steps: In one embodiment of the present invention, a multi-stage centrifugal dewatering method for comprehensive sludge treatment is provided. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps: S1: Raw sludge from municipal wastewater treatment plants or industrial wastewater treatment systems is pumped into a receiving tank. First, a mechanical screen intercepts solid impurities with a particle size of 5 mm or larger, including plastic fragments, fibers, and gravel. Then, a permanent magnet separator is used to remove iron-containing magnetic impurities from the sludge, with the magnetic field strength controlled between 0.8 and 1.2 Tesla, resulting in pretreated sludge. The moisture content of the pretreated sludge is reduced to 9.92%-99.5%, and it is then uniformly transported to a homogenization and equalization tank for temporary storage.

[0019] S2: The pretreated sludge is pumped into a series of three to five centrifugal dewatering units via screw pumps. The speed of each centrifugal dewatering unit increases gradually along the sludge flow direction: the first-stage centrifuge speed is 800-1200 rpm, the second stage increases to 1500-2000 rpm, and the third and above gradually increase to 2500-3200 rpm. A closed conditioning reaction tank is installed after the first-stage centrifuge, equipped with a stirring device, with the stirring rate controlled at 30-50 rpm. Based on the online monitoring of the sludge organic matter content and real-time moisture content, chemical conditioners or compound biological agents are dynamically added: the chemical conditioner is a compound system of polyaluminum chloride and cationic polyacrylamide, and the dosage is calculated at 0.1%-0.5% of the dry sludge mass; the biological agent is a compound Bacillus preparation, and the dosage is 0.05%-0.1% of the sludge volume. The conditioning reaction time is 15-30 minutes, which increases the sludge floc particle size to 80-120 micrometers and gradually increases the sludge solid content to 25%-35%.

[0020] S3: The sludge, after multi-stage centrifugal dewatering, is transported to a closed-loop hot air circulation drying device. The drying temperature is controlled at 105-120 degrees Celsius, and the hot air velocity is maintained at 1.2-1.8 meters per second. During the drying process, the sludge moisture content is further reduced from 25%-35% to below 10%. The drying system is equipped with a waste heat recovery device, which recirculates the outlet hot air to the inlet to reduce energy consumption; the generated exhaust gas is condensed and dehumidified before being discharged in compliance with standards.

[0021] S4: The dried sludge is fed into a twin-screw extrusion molding unit and plastically extruded under conditions of 60-80 degrees Celsius and 3-5 MPa pressure. Depending on resource utilization needs, fuel rods with a diameter of 30-50 mm or organic fertilizer substrate with a particle size of 2-5 mm are selectively prepared by changing the die. The calorific value of the fuel rods is not less than 12 MJ / kg, and the organic fertilizer substrate meets the heavy metal limit requirements.

[0022] In this embodiment, S1 includes the following steps: S11: The raw sludge generated by municipal sewage treatment plants or industrial wastewater treatment systems is continuously pumped into the receiving tank via a sludge transfer pump. S12: The sludge in the receiving pool is passed through a mechanical screen to intercept and remove solid impurities with a particle size of 5 mm or more, including plastic fragments, fibrous materials and gravel. S13: The sludge after being treated by the bar screen is introduced into a permanent magnet magnetic separator to remove iron-containing magnetic impurities from the sludge under a magnetic field strength of 0.8 to 1.2 Tesla. S14: Obtain pretreated sludge with a moisture content reduced to 9.92%-99.5%, and transport the pretreated sludge to a homogenization tank for temporary storage for subsequent processing.

[0023] In this embodiment, S2 includes the following steps: S21. Perform online quantification of property parameters on the pretreated sludge from the homogenization equalization tank to obtain a set of input sludge characteristic parameters. Each sludge sample batch in this set corresponds to a feature vector, which includes the measured value of organic matter content, real-time moisture content, electrochemical impedance phase angle, and sludge temperature.

[0024] S22. Define the reaction mechanism and objectives of interstage conditioning in a multi-stage centrifugal dewatering unit. The reaction mechanism includes charge neutralization and adsorption bridging induced by chemical conditioning agents, and extracellular polymeric hydrolysis and recombination catalyzed by composite biological agents. The conditioning objective is to increase the average particle size of sludge flocs to 80 to 120 micrometers and form a dense floc structure with high mechanical strength.

[0025] S23. Construct a state transfer and coupling relationship model between multi-stage dewatering units. The sludge solids content and floc strength at the outlet of the previous stage centrifugal dewatering machine are used as input conditions for the subsequent conditioning reaction tank. At the same time, the torque and differential speed parameters of the subsequent stage centrifugal dewatering machine are used as feedback variables for the speed setting of the previous stage centrifuge, forming a state association network between series units.

[0026] S24. Based on input sludge characteristic parameters, interstage conditioning mechanisms, and multi-stage state association networks, a dynamic adaptation function is used to uniformly describe and execute the synergistic process of rotational speed gradient control and floc modification, achieving the goal of gradually increasing the sludge solids content from the initial value to 25% to 35%. The core of the dynamic adaptation function lies in calculating the optimal conditioner dosage based on real-time sludge properties; its formula is as follows: M add The optimal dosage of chemical conditioner or biological agent is expressed as a percentage of the dry weight of the sludge; k0 is the basic dosage coefficient determined according to the sludge type; C org C represents the measured value of organic matter content in sludge obtained through online monitoring. set The reference threshold for organic matter content is set for the current sludge source; α is the adjustment weighting factor for deviations in organic matter content; W current W represents the real-time moisture content of the sludge at the inlet of the current stage dewatering unit. targetβ represents the target moisture content to be achieved after this stage of conditioning and dewatering; β is the adjustment coefficient of the logarithmic term of moisture content. This dynamic adaptation function achieves precise, non-linear matching between the conditioner dosage and the real-time properties of the sludge by quantifying the deviation of organic matter content and the logarithmic term of moisture content change. Compared with traditional fixed or linear proportional dosing, the formula proposed in this invention introduces a quadratic term penalty mechanism for organic matter content and a logarithmic feedback mechanism for moisture content. This can sensitively capture sudden changes in sludge properties and adjust the dosage in advance, effectively overcoming the conditioning lag problem and ensuring the stable growth of floc structure and the gradual improvement of dewatering efficiency during multi-stage dewatering. This constitutes the core control logic of multi-stage centrifugal dewatering and inter-stage conditioning synergy.

[0027] In this embodiment, S3 includes the following steps: S31. Receive dewatered sludge from the multi-stage centrifugal dewatering unit and characterize its pre-drying state to form a drying input state vector. The drying input state vector includes the measured sludge solids content, sludge specific heat capacity, initial temperature, volatile organic compound content, and floc structure strength index.

[0028] S32. Define the process state space of the closed-loop hot air circulation drying device. The process state space uses drying temperature, hot air velocity, drying time, and exhaust gas circulation rate as the core control variables, and sludge moisture content, unit evaporation heat consumption, and sludge particle strength after drying as the key performance indicators.

[0029] S33. Construct a multi-objective optimization function for the drying process, aiming to minimize energy consumption and maximize dehydration efficiency. This function weights and integrates drying rate, energy consumption per unit water evaporation, and the quality stability of the dried product, as shown in the formula: Where F(T,v,t,r) represents the comprehensive optimization evaluation value under given drying temperature T, hot air velocity v, drying time t, and exhaust gas recirculation rate r; W0 is the initial moisture content of the dewatered sludge; W t E represents the sludge moisture content after drying time t. a Q is the apparent activation energy for the evaporation of bound water in the sludge; R is the ideal gas constant; Q total σ represents the total energy consumption within time t; strengthω1, ω2, and ω3 are the coefficients of variation of the strength of dried sludge particles, used to characterize quality stability; ω1, ω2, and ω3 are the normalized weighting coefficients of the drying rate, energy consumption, and quality stability terms, respectively, and their sum is 1. This multi-objective optimization function, by introducing a temperature effect exponent based on the Arrhenius formula, the reciprocal of the energy consumption per unit water evaporation, and the coefficient of variation of product strength, achieves for the first time in the field of sludge thermal drying control a unified quantification and simultaneous optimization of three conflicting objectives: rate, energy consumption, and quality. Unlike traditional single-objective control or fixed-parameter operation, the function constructed in this invention clearly characterizes the balance between the nonlinear accelerating effect of temperature on the dewatering rate and its increasing effect on energy consumption, and constrains the fluctuation range of product quality through the coefficient of variation term. Thus, it can globally optimize to obtain the optimal combination of operating points that ensures the moisture content is reduced to below 10% while achieving the lowest energy consumption and uniform and stable product.

[0030] S34. Based on the aforementioned multi-objective optimization function, an adaptive downhill simplex method for multivariable strongly coupled systems is employed to search within a four-dimensional constrained space comprised of drying temperatures of 105 to 120 degrees Celsius, hot air velocities of 1.2 to 1.8 meters per second, drying time, and exhaust gas recirculation rate. This optimization strategy dynamically adjusts the search step size and direction based on real-time collected sludge moisture content decrease curves and instantaneous energy consumption data, ultimately outputting the optimal combination of control parameters that maximizes the comprehensive optimization evaluation value F.

[0031] S35. Based on the obtained optimal control parameter combination, drive the closed-loop hot air circulation drying device to perform the deep drying process. Closed-loop feedback control is achieved through high-precision temperature sensors and anemometers to ensure that the deviations between the actual drying temperature and hot air velocity and the optimized set values ​​do not exceed ±1 degree Celsius and ±0.1 meters per second, respectively, until the sludge moisture content stabilizes and drops to 10% or below, completing the drying stage.

[0032] In this embodiment, S4 includes the following steps: S41. Receive dried sludge from a closed-loop hot air circulation drying device and perform pre-resource utilization characteristic analysis on it to form a raw material state vector. The raw material state vector includes the moisture content, ash content, calorific value, fiber content, and flow index of the dried sludge.

[0033] S42. Construct a resource-based decision-making model for two target products: fuel rods and organic fertilizer substrate. This model uses the optimal conditioner dosage M calculated by the dynamic adaptation function in step S2. add The floc structure strength information, as well as the drying efficiency and energy consumption information contained in the comprehensive optimization evaluation value F(T,v,t,r) output by the multi-objective optimization function in S3, are used as decision input factors to establish a collaborative decision index.

[0034] S43. Define a collaborative decision-making index calculation function that nonlinearly integrates conditioning effect, drying quality, and target product quality requirements. Its mathematical expression is: Where D is the collaborative decision-making index, used to quantitatively determine whether the current batch of sludge raw material is more suitable for preparing fuel rods or organic fertilizer substrate; λ1, λ2, and λ3 are the normalized weights of the structural factor, energy efficiency, and quality potential terms, respectively; M add C org C set α,k0 are the parameters and variables in the dynamic adaptation function of step S2, which together characterize the floc structure modification intensity and organic matter characteristics; F(T,v,t,r),Q total The comprehensive evaluation value and total energy consumption in the multi-objective optimization function of step S3 jointly characterize the thermal energy utilization efficiency and comprehensive benefits of the drying process; HV is the measured higher heating value of the dried sludge; η c A represents the calorific value conversion efficiency when molded into fuel rods; A represents the agricultural safety index when molded into organic fertilizer base material, which is calculated by comprehensively considering heavy metal content, pathogen inactivation rate, and nutrient slow-release performance.

[0035] The collaborative decision-making index calculation function is the key to this invention, establishing for the first time a parameter coupling and collaborative decision-making mechanism connecting the three core stages of sludge dewatering, drying, and final resource utilization. The first term of the function, based on the conditioner addition formula in S2, uses the nonlinear relationship between its internal organic matter content and dosage to inversely evaluate the mechanical strength and structural stability potential that sludge flocs can provide during subsequent molding. The second term directly introduces the ratio of the comprehensive optimization evaluation value of the drying process in S3 to the total energy consumption, directly incorporating the efficiency and cost-effectiveness of the preceding drying stage into the final product selection consideration system. The third term directly evaluates the calorific value potential and agricultural safety of the raw materials. When D>D threshold When deciding to manufacture fuel rods, the molding process parameters should prioritize matching the requirements for high calorific value and high mechanical strength; when D≤D threshold When making decisions for preparing organic fertilizer base materials, the molding process parameters are prioritized to meet the requirements of high safety, high porosity, and slow nutrient release. This decision model solves the technical problems of isolated optimization of each step and the disconnect between the final product selection and the pretreatment conditions in traditional methods, and achieves global collaborative optimization from source conditioning, process drying to end molding.

[0036] S44. Based on the value of the collaborative decision-making index D, set the specific process parameters for the twin-screw extrusion molding apparatus. If the decision is to produce fuel rods, control the temperature at 70 to 80 degrees Celsius and the pressure at 4 to 5 MPa to ensure the density and mechanical strength of the fuel rods; if the decision is to produce organic fertilizer base material, control the temperature at 60 to 70 degrees Celsius and the pressure at 3 to 4 MPa to protect the organic matter and microbial activity. During the molding process, by adjusting the rotational speed of the twin screw and the combination of the kneading blocks in real time, ensure that the material reaches the optimal plasticization and extrusion state under the set temperature and pressure.

[0037] S45. The formed product is cooled and sieved to obtain the final product. If it is a fuel rod, its diameter is 30 to 50 mm and its calorific value is not less than 12 MJ / kg; if it is an organic fertilizer base, its particle size is 2 to 5 mm, meeting relevant agricultural standards. The entire forming process is linked with the upstream dewatering and drying processes through a distributed control system to ensure seamless execution of decision-making instructions and process parameters, completing the entire process of comprehensive sludge treatment.

[0038] In one embodiment of the present invention, a multi-stage centrifugal dewatering sludge comprehensive treatment method is provided, which further includes the following steps: S5: Throughout the treatment process, an integrated electrochemical impedance spectroscopy and control system monitor the DS / FC values ​​of the sludge in real time. The system predicts the optimal dosage difference of the chemical conditioner through dynamic slope calculation, achieving self-optimization of the dosage. Based on fluctuations in the sludge's organic matter content, the system automatically adjusts the centrifuge speed gradient and conditioner dosing sequence to ensure stable dewatering efficiency and a moisture content variation coefficient of less than 5%.

[0039] In this embodiment, S5 includes the following steps: S51. Construct a holographic sensing network for process status. This network uses the process parameters, equipment operating status, and material property data collected in real time during steps S2, S3, and S4 as input nodes. Input nodes include, but are not limited to, the real-time speed and torque of centrifugal dewatering machines at each stage, the reagent dosing rate and stirring power of the conditioning reaction tank, the hot air temperature and velocity of the drying unit, the temperature and pressure of the twin-screw extruder, and the online monitored organic matter content, real-time moisture content, electrochemical parameters, calorific value after drying, and strength of the formed product. Each node is associated with a timestamp and material batch number.

[0040] S52. Define the associated edges and dynamic weights of the process status holographic perception network. Associated edges represent the transmission and coupling relationships of materials, energy, and information flow between different process units. Dynamic weights are adaptively adjusted based on real-time process efficiency. Their calculation depends on the deviation between the optimal and actual feed amounts obtained from the dynamic adaptation function in step S2, the real-time comprehensive evaluation value obtained from the multi-objective optimization function in step S3, and the indication of the current production route by the collaborative decision-making index in step S4.

[0041] S53. Training the Holographic Sensing Network for Process Status. Using the optimal operating status data of each process unit within a previous stable operating cycle as the supervision signal, and with the joint optimization objective of minimizing global operating cost and maximizing overall product quality, the dynamic weight parameters in the network are trained. The training process employs a time-series backpropagation algorithm, enabling the network to learn and memorize the control mode that maintains the global optimum of the system under multivariate perturbations.

[0042] S54. Based on the trained holographic perception network of process status, online real-time control and self-optimization are performed. Real-time collected process node data is input into the network, which calculates through forward propagation and outputs a set of optimized setpoints for the next control cycle. This set includes fine-tuning of the speed gradient of each centrifuge stage, compensation values ​​for the dosage of conditioning agent, correction values ​​for the temperature and wind speed of the drying unit, and preset values ​​for the molding process parameters. Based on this output, the system automatically adjusts the actions of each actuator to achieve real-time coordinated optimization of dewatering efficiency, drying energy consumption, and final product quality, ensuring that the coefficient of variation of the final sludge moisture content remains consistently below 5% under dynamic feeding conditions.

[0043] In one embodiment of the present invention, a multi-stage centrifugal dewatering sludge comprehensive treatment method is provided, which further includes the following steps: S6: The filtrate and drying condensate generated during the dehydration process are returned to the front end of the wastewater treatment system; the extruded fuel rods or fertilizer base are transported to the storage silo via belt conveyor for industrial combustion or soil improvement. The system adopts a modular design, and each unit is linked through an IoT platform to achieve digital monitoring of energy and pesticide consumption.

[0044] In this embodiment, S6 includes the following steps: S61. Achieve targeted reflux of by-products and intelligent scheduling of resource-based products. Filtrate from the dehydration process, condensate from the drying process, and equipment cleaning wastewater are collected in a unified reflux equalization tank. An online water quality monitoring system analyzes key pollutant indicators in the reflux liquid in real time and, via an IoT platform, instructs regulating valves to pump the reflux liquid back to the front-end biological treatment unit or advanced treatment unit of the wastewater treatment system according to a preset ratio and concentration.

[0045] S62. Establish a resource-based product warehousing and distribution system with end-to-end quality traceability. Assign a unique traceability code to each batch of fuel rods or organic fertilizer base material, linking it to all key process parameters and quality data from steps S2 to S4 of the production process. After the products are transported to the intelligent storage warehouse via belt conveyor, the warehouse management system automatically matches the shipment batch based on order requirements, product calorific value testing reports, or agrochemical testing reports, and generates a product quality file.

[0046] S63. Construct a digital monitoring and global decision-making platform. This platform integrates IoT sensors, control systems, and the aforementioned holographic process status sensing network of each process unit. The platform dynamically and visually displays real-time energy consumption, real-time reagent consumption, processing efficiency, product yield, and quality indicators. The platform's core decision-making module, based on historical operating big data and the predictive output of the holographic process status sensing network, continuously updates the optimal operating range of each unit's equipment and generates preventative maintenance warnings.

[0047] S64. Implement closed-loop management and adaptive evolution throughout the entire process. The digital monitoring platform evaluates the overall system performance weekly or monthly, based on factors including average unit energy consumption, average reagent consumption, product compliance rate, and overall equipment efficiency. When the evaluated value deviates from the baseline, the platform automatically initiates the retraining process of the process status holographic perception network, using recent data to fine-tune its dynamic weights. This allows the control strategy of the entire treatment system to adapt to long-term changes in sludge properties or external demands, achieving continuous self-optimization of the system.

[0048] This method, implemented in this embodiment, achieves precise and coordinated control of the entire sludge treatment process, from dewatering and drying to molding, through a set of interconnected intelligent decision-making models. Specifically, in the dewatering stage, a dynamic adaptation function based on real-time sludge property calculations is used to precisely and non-linearly add conditioning agents, effectively overcoming the conditioning lag problem and ensuring stable optimization of floc structure and progressive improvement of dewatering efficiency. In the drying stage, a multi-objective optimization function incorporating temperature effects and quality constraints is introduced to globally find the optimal operating point that minimizes energy consumption and ensures uniform and stable products while ensuring drying effects. In the resource recovery stage, a collaborative decision-making index incorporates the conditioning and drying effects of preceding stages into the final product selection, achieving globally optimal decision-making from the source of treatment to the final product. This fundamentally solves the problems of efficiency loss and low resource recovery value caused by isolated optimization of each stage.

[0049] This embodiment proposes a multi-stage centrifugal dewatering sludge comprehensive treatment system to implement the aforementioned multi-stage centrifugal dewatering sludge comprehensive treatment method, such as... Figure 2 As shown, the system includes: The sludge pretreatment module is used to receive raw sludge and perform preliminary purification, including mechanical bar screen to intercept solid impurities and permanent magnet magnetic separation to remove magnetic impurities, to obtain pretreated sludge with a moisture content reduced to 9.92%-99.5%, which is then temporarily stored in a homogenization equalization tank. The multi-stage centrifugal dewatering and conditioning module is used to perform gradient dewatering of pretreated sludge through multi-stage series centrifugal dewatering units, and to dynamically condition the sludge flocs between stages by modifying them with chemical conditioning agents or biological agents to increase the solids content to 25%-35%. The hot air circulation drying module is used to dry dewatered sludge in a closed hot air environment, controlling the drying temperature and air velocity to reduce the moisture content to below 10%, and integrating waste heat recovery and exhaust gas treatment. The extrusion molding and resource utilization module is used to extrude dried sludge through a twin-screw extruder to selectively prepare fuel rods or organic fertilizer base materials, and adjust process parameters according to resource utilization requirements.

[0050] The sludge pretreatment module specifically includes: The sludge receiving unit is used to continuously pump raw sludge into the receiving tank via a sludge transfer pump. The mechanical bar screen interception unit is used to allow sludge to flow through the mechanical bar screen to intercept and remove solid impurities with a particle size of 5 mm or larger, such as plastic fragments, fibrous materials and gravel. The magnetic separation purification unit is used to introduce the sludge after the bar screen treatment into the permanent magnet magnetic separator to remove ferromagnetic impurities under a magnetic field strength of 0.8-1.2 Tesla. The homogenization and temporary storage unit is used to obtain pretreated sludge and transport it to the homogenization and equalization tank for temporary storage in preparation for subsequent processing.

[0051] The multi-stage centrifugal dehydration and conditioning module specifically includes: The online sludge property quantification unit is used to monitor pretreated sludge online and obtain a set of characteristic parameters, including organic matter content, real-time moisture content, electrochemical impedance phase angle, and sludge temperature. Conditioning mechanism and target definition unit, used to define the reaction mechanism (such as charge neutralization, adsorption bridging, biocatalysis) and target (floc particle size increase to 80-120 micrometers) of interstage conditioning. The state transfer model building unit is used to construct a state association network between multi-stage dewatering units, taking the outlet parameters of the previous stage as the input of the next stage, and feeding back the parameters of the next stage to the previous stage; The dynamic adaptation control unit is used to calculate the optimal conditioner dosage based on the dynamic adaptation function, so as to achieve coordinated control of speed gradient and floc modification.

[0052] The hot air circulation drying module specifically includes: The pre-drying state characterization unit is used to form the drying input state vector, including sludge solids content, specific heat capacity, temperature, volatile organic compound content, and floc strength index. The process state space definition unit is used to define key performance indicators with drying temperature, hot air velocity, drying time and exhaust gas recirculation rate as control variables. Multi-objective optimization function building unit, used to construct optimization functions that minimize energy consumption and maximize dehydration efficiency; An optimized search unit is used to search for the optimal combination of control parameters in a four-dimensional constraint space using the adaptive downhill simplex method. The closed-loop drying execution unit is used to drive the drying device to perform deep drying. It achieves closed-loop feedback control through sensors to ensure that parameter deviations are within the allowable range.

[0053] The extrusion molding and resource recovery module specifically includes: The pre-molding feature analysis unit is used to generate a state vector of the molding raw material, including moisture content, ash content, calorific value, fiber content, and flow index. The resource-based decision-making model construction unit is used to establish a collaborative decision-making index based on conditioner dosage information and drying optimization evaluation value; The collaborative decision-making index calculation unit is used to calculate the index through a nonlinear fusion function; The molding process parameter setting unit is used to set the temperature and pressure parameters of the twin-screw extruder based on the decision results. The product processing unit is used to cool and screen the molded products to obtain the final product and ensure that the quality meets the standards.

[0054] This embodiment proposes a multi-stage centrifugal dewatering sludge comprehensive treatment system to implement the above-mentioned multi-stage centrifugal dewatering sludge comprehensive treatment method, and further includes: The real-time monitoring and self-optimization module is used to monitor sludge properties and equipment status in real time through a holographic sensing network, dynamically adjust process parameters, and achieve synergistic optimization of dewatering efficiency and energy consumption. The by-product management and digital monitoring module is used to handle the reflux of filtrate and condensate, manage the storage and distribution of resource-based products, and conduct full-process digital monitoring through an IoT platform.

[0055] The real-time monitoring and self-optimization module specifically includes: The holographic sensing network construction unit is used to construct an interconnected network using real-time collected process parameters, equipment status, and material property data as input nodes. The dynamic weight adjustment unit is used to adaptively adjust the weights of related edges according to process efficiency, based on dosage deviation, optimization evaluation value and decision index; The network training unit is used to train network weights using historical best running data as supervision signals and employing a time-series backpropagation algorithm. The online control unit is used to output optimized setpoints through network forward propagation, fine-tuning centrifuge speed, conditioner dosage, etc., to achieve real-time self-optimization.

[0056] The by-product management and digital monitoring module specifically includes: The by-product directional reflux unit is used to collect filtrate, condensate and wastewater, and reflux them back to the wastewater treatment system through online monitoring and IoT platform control. The product traceability and warehousing unit is used to assign a traceability code to each batch of products, associate it with process parameters, and intelligently match the shipment batch. The digital monitoring platform unit is used to integrate IoT sensors and control systems to visually display indicators such as energy consumption, drug consumption, and efficiency. The closed-loop management and evolution unit is used to periodically evaluate system performance, automatically initiate network retraining, and achieve continuous self-optimization.

[0057] The system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0058] This system embodiment materializes the intelligent decision-making capabilities of the aforementioned method through modular hardware integration and software algorithms, constructing a closed-loop control system capable of self-sensing, decision-making, and optimization. The system collects real-time data from the entire process through an online sludge property quantification unit and a holographic sensing network, providing decision-making support for upper-level algorithms. Through core controllers such as dynamic adaptation and control units and optimization search units, it automatically executes complex calculations and parameter adjustments in the method embodiment. Finally, through the precise linkage of various module actuators (such as centrifuges, dosing pumps, drying devices, and extruders), optimization instructions are seamlessly translated into physical operations. This integrated "sensing-decision-execution" design enables the system not only to stably handle sludge with fluctuating properties but also to achieve data traceability analysis and continuous strategy evolution through a digital monitoring platform. Ultimately, a highly automated and intelligent overall solution ensures the efficient, stable, and reliable operation of the method.

[0059] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0060] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-stage centrifugal dewatering method for comprehensive sludge treatment, characterized in that: include: S1: Pump the raw sludge generated by municipal sewage treatment plants or industrial wastewater treatment systems into the receiving tank, where solid impurities, including plastic fragments, fibrous materials and gravel, are intercepted by mechanical screens. A permanent magnet magnetic separator is used to remove iron-containing magnetic impurities from the sludge to obtain pretreated sludge, which is then transported to a homogenization and conditioning tank for temporary storage. S2: The pretreated sludge is pumped into a series of three to five centrifugal dewatering units via screw pumps. The speed of each centrifugal dewatering unit increases gradually along the sludge flow direction: the first-stage centrifuge speed is 800-1200 rpm, the second stage increases to 1500-2000 rpm, and the third stage and above gradually increase to 2500-3200 rpm. A closed conditioning reaction tank is installed after the first-stage centrifugal dewatering unit, and the reaction tank is equipped with a stirring device. Chemical conditioners or compound biological agents are dynamically added according to the online monitoring of sludge organic matter content and real-time moisture content. S3: The sludge after multi-stage centrifugal dewatering is transported to a closed hot air circulation drying device. The drying system is equipped with a waste heat recovery device, which circulates the outlet hot air to the inlet to reduce energy consumption. The generated exhaust gas is discharged in compliance with standards after condensation and dehumidification. S4: The dried sludge is fed into a twin-screw extrusion molding device and plastically extruded at a temperature of 60-80 degrees Celsius and a pressure of 3-5 MPa to prepare fuel rods with a diameter of 30-50 mm or organic fertilizer base material with a particle size of 2-5 mm.

2. The method as described in claim 1, characterized in that, In step S2, the chemical conditioning agent is a compound system of polyaluminum chloride and cationic polyacrylamide, and the dosage is calculated as 0.1%-0.5% of the dry weight of the sludge; the biological agent is a compound Bacillus preparation, and the dosage is 0.05%-0.1% of the sludge volume; the conditioning reaction time is 15-30 minutes; in step S3, the drying temperature is controlled at 105-120 degrees Celsius, and the hot air velocity is maintained at 1.2-1.8 meters per second.

3. The method as described in claim 1, characterized in that, Step S1 includes the following steps: S11: The raw sludge generated by municipal sewage treatment plants or industrial wastewater treatment systems is continuously pumped into the receiving tank via a sludge transfer pump. S12: The sludge in the receiving pool is passed through a mechanical screen to intercept and remove solid impurities with a particle size of 5 mm or more, including plastic fragments, fibrous materials and gravel. S13: The sludge after being treated by the bar screen is introduced into a permanent magnet magnetic separator to remove iron-containing magnetic impurities from the sludge under a magnetic field strength of 0.8 to 1.2 Tesla. S14: Obtain pretreated sludge with a moisture content reduced to 99.2% to 99.5%. The pretreated sludge is then transported to a homogenization tank for temporary storage before further processing.

4. The method as described in claim 1, characterized in that, Step S2 includes the following steps: S21: Perform online quantification of property parameters on the pretreated sludge from the homogenizing tank to obtain a set of input sludge characteristic parameters. Each batch of sludge samples in this set corresponds to a feature vector, which includes the measured value of organic matter content, real-time moisture content, electrochemical impedance phase angle, and sludge temperature. S22: Define the reaction mechanism and objective of the conditioning between stages in a multi-stage centrifugal dewatering unit. The reaction mechanism includes the charge neutralization and adsorption bridging effect initiated by chemical conditioning agents, and the extracellular polymer hydrolysis and recombination catalyzed by compound biological agents. The conditioning objective is to increase the average particle size of sludge flocs to 80 to 120 micrometers and form a dense floc structure with high mechanical strength. S23: Construct a state transfer and coupling relationship model between multi-stage dewatering units. Use the sludge solid content and floc strength at the outlet of the first-stage centrifugal dewatering machine as the input conditions for the subsequent conditioning reaction tank. At the same time, use the torque and differential speed parameters of the subsequent centrifugal dewatering machine as feedback variables for the speed setting of the first-stage centrifugal machine to form a state association network between series units. S24: Based on the input sludge characteristic parameters, inter-stage conditioning mechanism and multi-stage state association network, a dynamic adaptation function is used to uniformly describe and execute the synergistic effect of speed gradient control and floc modification; the dynamic adaptation function calculates the optimal conditioner dosage according to the real-time sludge properties, wherein the dynamic adaptation function is based on the measured value of sludge organic matter content, the reference threshold of organic matter content, the real-time sludge moisture content at the inlet of the current stage dewatering unit, the target moisture content expected to be achieved after conditioning and dewatering of this stage, and the basic addition coefficient and adjustment weight factor determined according to the sludge type.

5. The method as described in claim 1, characterized in that, Step S3 includes the following steps: S31: Receive dewatered sludge from the multi-stage centrifugal dewatering unit and characterize its pre-drying state to form a drying input state vector. The drying input state vector includes the measured value of sludge solid content, sludge specific heat capacity, initial temperature, volatile organic compound content, and floc structure strength index. S32: Define the process state space of the closed hot air circulation drying device. The process state space uses drying temperature, hot air velocity, drying time and exhaust gas circulation rate as the core control variables, and sludge moisture content, unit evaporation heat consumption and sludge particle strength after drying as the key performance indicators. S33: Construct a multi-objective optimization function for the drying process with the goal of minimizing energy consumption and maximizing dehydration efficiency. This function weights and integrates the drying rate, energy consumption per unit of water evaporation, and the quality stability of the dried product. The multi-objective optimization function is based on the drying temperature, hot air velocity, drying time, and exhaust gas recirculation rate, and introduces a temperature effect index term, the reciprocal of the energy consumption per unit of water evaporation, and the product intensity variation coefficient to achieve unified quantification and synchronous optimization of the three objectives of rate, energy consumption, and quality. S34: Based on the multi-objective optimization function, an adaptive downhill simplex method for multivariable strongly coupled systems is adopted to search within a four-dimensional constrained space consisting of drying temperature of 105 to 120 degrees Celsius, hot air velocity of 1.2 to 1.8 meters per second, drying time, and exhaust gas recirculation rate. The search step size and direction are dynamically adjusted according to the real-time collected sludge moisture content decrease curve and instantaneous energy consumption data, and the optimal combination of control parameters that maximizes the comprehensive optimization evaluation value is output. S35: Based on the obtained optimal control parameter combination, drive the closed hot air circulation drying device to perform the deep drying process. Through high-precision temperature sensor and anemometer, realize closed-loop feedback control to ensure that the deviation between the actual drying temperature and hot air speed and the optimized set value does not exceed ±1 degree Celsius and ±0.1 meters per second, respectively, until the sludge moisture content is stably reduced to 10% or below, and the drying stage is completed.

6. The method as described in claim 1, characterized in that, Step S4 includes the following steps: S41: Receive the dried sludge output from the closed hot air circulation drying device, and perform pre-resource utilization characteristic analysis on it to form a molding raw material state vector. The molding raw material state vector includes the moisture content, ash content, calorific value, fiber content and flow index of the dried sludge. S42: Construct a resource-based decision-making model for two target products: fuel rods and organic fertilizer substrate. The model uses the floc structure strength information represented by the optimal conditioner dosage calculated by the dynamic adaptation function in the previous step S2, and the drying efficiency and energy consumption information contained in the comprehensive optimization evaluation value output by the multi-objective optimization function in step S3 as decision input factors to establish a collaborative decision-making index. S43: Define a collaborative decision index calculation function. This function nonlinearly integrates conditioning effect, drying quality and target product quality requirements. The collaborative decision index is calculated based on floc structure modification intensity, organic matter characteristics, thermal energy utilization efficiency of the drying process, measured higher calorific value of dried sludge, calorific value conversion efficiency when formed into fuel rods, and agricultural safety index when formed into organic fertilizer base material. S44: Based on the value of the collaborative decision index, set the specific process parameters of the twin-screw extrusion molding device. If the decision is to prepare fuel rods, control the temperature to 70 to 80 degrees Celsius and the pressure to 4 to 5 MPa; if the decision is to prepare organic fertilizer base material, control the temperature to 60 to 70 degrees Celsius and the pressure to 3 to 4 MPa. During the molding process, by adjusting the rotation speed of the twin screw and the combination of the kneading blocks in real time, ensure that the material reaches the optimal plasticization and extrusion state under the set temperature and pressure. S45: The formed product is cooled and screened to obtain the final product. If it is a fuel rod, its diameter is 30 to 50 mm and its calorific value is not less than 12 MJ per kilogram. If it is an organic fertilizer base, its particle size is 2 to 5 mm and meets the relevant agricultural standards. The entire forming process is linked with the upstream dehydration and drying process through a distributed control system to ensure the seamless execution of decision instructions and process parameters.

7. The method as described in claim 1, characterized in that, It also includes the following steps: S5: Throughout the entire treatment process, the integrated electrochemical impedance spectroscopy and control system monitor the DS / FC value of the sludge in real time. By calculating the difference in the optimal dosage of chemical conditioner through dynamic slope calculation, the system achieves self-optimization of the dosage. The system automatically adjusts the centrifuge speed gradient and conditioner addition sequence according to the fluctuation of sludge organic matter content to ensure stable dewatering efficiency and a moisture content variation coefficient of less than 5%. S6: The filtrate and drying condensate produced during the dehydration process are returned to the front end of the wastewater treatment system; the extruded fuel rods or fertilizer base are transported to the storage silo by belt conveyor for industrial combustion or soil improvement; the system adopts a modular design, and each unit is linked through an Internet of Things platform to realize digital monitoring of energy consumption and pesticide consumption.

8. The method as described in claim 7, characterized in that, Step S5 includes the following steps: S51: Construct a holographic perception network for process status. This network uses the process parameters, equipment operating status, and material property data collected in real time in steps S2, S3, and S4 as input nodes. The input nodes include the real-time speed and torque of centrifugal dewatering machines at each stage, the reagent dosing rate and stirring power of the conditioning reaction tank, the hot air temperature and wind speed of the drying device, the temperature and pressure of the twin-screw extruder, as well as the sludge organic matter content, real-time moisture content, electrochemical parameters, calorific value after drying, and strength of the formed product monitored online. Each node is associated with the material batch number through a timestamp. S52: Define the associated edges and dynamic weights of the process status holographic perception network. The associated edges are used to represent the transmission and coupling relationship of materials, energy and information flow between different process units. The dynamic weights are adaptively adjusted according to the real-time process efficiency. Their calculation depends on the deviation between the optimal dosage and the actual dosage obtained by the dynamic adaptation function in step S2, the real-time status of the comprehensive evaluation value obtained by the multi-objective optimization function in step S3, and the indication of the collaborative decision index on the current production route in step S4. S53: Train the process state holographic perception network, using the optimal operating state data of each process unit in the previous stable operating cycle as the supervision signal, with the joint optimization objective of minimizing global operating cost and maximizing comprehensive product quality, train the dynamic weight parameters in the network, and use the time series backpropagation algorithm in the training process to enable the network to learn and memorize the control mode that maintains the global optimal system under multivariate disturbances. S54: Based on the trained holographic perception network of process status, online real-time control and self-optimization are performed. Real-time collected process node data is input into the network, which calculates through forward propagation and outputs a set of optimized setpoints for the next control cycle. This set includes fine-tuning of the speed gradient of each centrifuge, compensation value for the dosage of conditioning agent, correction value for the temperature and wind speed of the drying device, and preset values ​​for the molding process parameters. Based on this output, the system automatically adjusts the actions of each actuator to achieve real-time coordinated optimization of dewatering efficiency, drying energy consumption, and final product quality, ensuring that the coefficient of variation of the final sludge moisture content is consistently below 5% under dynamic feeding conditions. Step S6 includes the following steps: S61: To achieve the directional reflux of by-products and the intelligent scheduling of resource-based products, the filtrate generated during the dehydration process, the condensate generated during the drying process, and the wastewater from equipment cleaning are collected in a unified reflux regulating tank; the online water quality monitoring system analyzes the key pollutant indicators in the reflux liquid in real time, and through the IoT platform, instructs the regulating valve to pump the reflux liquid back to the front-end biochemical treatment unit or deep treatment unit of the sewage treatment system according to the preset ratio and concentration; S62: Establish a resource-based product warehousing and distribution system based on full-process quality traceability, assigning a unique traceability code to each batch of fuel rods or organic fertilizer base material. This code is associated with all key process parameters and quality data in steps S2 to S4 of the production process. After the products are transported to the intelligent storage warehouse by belt conveyor, the storage management system automatically matches the delivery batch according to order requirements, product calorific value test reports or agrochemical test reports, and generates product quality files. S63: Construct a digital monitoring and global decision-making platform. This platform integrates IoT sensors and control systems of each process unit, as well as the aforementioned process status holographic perception network. The platform dynamically and visually displays real-time energy consumption, real-time chemical consumption, processing efficiency, product yield, and quality indicators. The core decision-making module of the platform is based on historical operating big data and the predictive output of the process status holographic perception network, continuously updates the optimal operating range of each unit's equipment, and generates preventive maintenance warnings. S64: Implement full-process closed-loop management and adaptive evolution. The digital monitoring platform evaluates the overall system performance weekly or monthly. The evaluation criteria include average unit energy consumption, average reagent consumption, product compliance rate, and overall equipment efficiency. When the evaluation value deviates from the baseline, the platform automatically initiates the retraining process of the process status holographic perception network and fine-tunes its dynamic weights using recent data.

9. A multi-stage centrifugal dewatering sludge integrated treatment system, used to implement the method described in any one of claims 1-8, characterized in that, include: The sludge pretreatment module is used to receive raw sludge and perform preliminary purification, including mechanical bar screen to intercept solid impurities and permanent magnet magnetic separation to remove magnetic impurities, resulting in pretreated sludge with a moisture content reduced to 99.2%-99.5%, which is then temporarily stored in a homogenization equalization tank. The multi-stage centrifugal dewatering and conditioning module is used to perform gradient dewatering of pretreated sludge through multi-stage series centrifugal dewatering units, and to dynamically condition the sludge flocs between stages by modifying them with chemical conditioning agents or biological agents to increase the solids content to 25%-35%. The hot air circulation drying module is used to dry dewatered sludge in a closed hot air environment, controlling the drying temperature and air velocity to reduce the moisture content to below 10%, and integrating waste heat recovery and exhaust gas treatment. The extrusion molding and resource utilization module is used to extrude dried sludge through a twin-screw extruder to selectively prepare fuel rods or organic fertilizer base materials, and adjust process parameters according to resource utilization requirements.

10. The system as described in claim 9, characterized in that, Also includes: The real-time monitoring and self-optimization module is used to monitor sludge properties and equipment status in real time through a holographic sensing network, dynamically adjust process parameters, and achieve synergistic optimization of dewatering efficiency and energy consumption. The by-product management and digital monitoring module is used to handle the reflux of filtrate and condensate, manage the storage and distribution of resource-based products, and conduct full-process digital monitoring through an IoT platform.

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