Intelligent temperature control system and method for sludge dewatering

Abstract

The invention relates to a sludge dewatering intelligent temperature control system and method applied to the technical field of sludge treatment. The system is composed of a multi-mode sensing layer, a decision control center, a PCM energy management module and an integrated execution layer. The multi-mode sensing layer is used for collecting sludge characteristics, process energy and mud cake water content parameters in real time; the decision control center runs an XGBoost and MPC deep fusion algorithm, an XGBoost module generates initial process parameters according to sludge characteristics and dynamically corrects based on PCM residual heat capacity, and an MPC module performs rolling optimization by using a global objective function and outputting a synchronous cooperative control instruction by using a sludge-PCM coupling model; the PCM energy management module realizes heat energy storage and efficient utilization through a shell and tube type energy storage heat exchanger and a cascade waste heat recovery loop; and the integrated execution layer accurately executes the instruction and cooperatively completes the dehydration process. The problems that a traditional system is passive in control and low in energy efficiency are solved, and accurate, efficient and stable operation of the dehydration process is achieved.

Description

Technical Field

[0001] This invention relates to a temperature control system, and more particularly to an intelligent temperature control system and method for sludge dewatering applied in the field of sludge treatment technology. Background Technology

[0002] Industrial sludge dewatering is a crucial link and energy-intensive focus in the sludge treatment and disposal chain. Thermal conditioning technology, by heating and destroying the colloidal structure and microbial cells of sludge to release bound water, can significantly improve the efficiency of subsequent mechanical dewatering and is one of the key technologies for achieving sludge reduction and resource utilization.

[0003] Existing systems generally employ external heat sources such as steam and thermal oil, relying on traditional PID control strategies to maintain dewatering temperatures. However, this control method has a fundamental flaw: an irreconcilable contradiction exists between the passive nature of the control and the dynamic nature of process requirements. On the one hand, the heat source itself has inherent pressure and temperature fluctuations; on the other hand, the composition of sludge (such as organic matter content and viscosity) varies significantly depending on its source and the season, resulting in the required optimal dewatering temperature not being constant. Traditional PID control, as a "post-event" feedback mechanism, cannot proactively mitigate heat source disturbances, nor can it intelligently adapt to changes in sludge characteristics, remaining in a state of "passive response" and "following fluctuations."

[0004] This inherent deficiency at the control level directly leads to unstable dewatering temperature, high system energy consumption, and large fluctuations in the final sludge cake moisture content, which has become the core bottleneck restricting the upgrading of sludge dewatering systems towards high efficiency, stability, and intelligence. Summary of the Invention

[0005] In view of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to overcome the fundamental contradiction between the "passivity of control" and the "dynamic nature of process requirements" in the existing sludge dewatering temperature control system, so that the system can be transformed from the current state of passive response and following fluctuations to an intelligent control mode that can predict ahead, intervene actively and optimize globally, thereby fundamentally improving the stability, energy efficiency and economy of the dewatering process.

[0006] To solve the above problems, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides an intelligent temperature control system for sludge dewatering, comprising: a multimodal sensing layer, a decision control center, a PCM energy management module, and an integrated execution layer; wherein the multimodal sensing layer provides data input, the decision control center generates collaborative instructions through a deep fusion algorithm, the PCM energy management module realizes intelligent thermal energy scheduling, and the integrated execution layer implements control actions. The four components work together to overcome the shortcomings of passive control in existing systems, as detailed below:

[0008] The multimodal sensing layer is used to collect sludge characteristic parameters, process and energy state parameters, and product quality parameters in real time. Among them, the sludge characteristic parameters include at least the organic component content, viscosity, and solids content of the sludge; the process and energy state parameters include at least the state parameters of the phase change material (PCM); the PCM state parameters include at least the residual heat capacity, instantaneous heat transfer power, and phase change completion degree; and the product quality parameter is the moisture content of the dewatered sludge cake.

[0009] The decision control center communicates with the multimodal sensing layer and runs a deep fusion algorithm architecture, including an XGBoost prediction module and an MPC control module. The XGBoost prediction module is configured to generate an initial temperature set range and flocculant dosing recommendations based on sludge characteristic parameters, and dynamically increase or conservatively correct the upper limit of the initial temperature set range based on the remaining heat capacity in the PCM state parameters (i.e., decrease the upper limit of the initial temperature set range). The MPC control module has a built-in sludge-PCM coupled heat transfer model, and its optimization objective function includes thermal energy cost, reagent cost, and PCM heat storage state deviation terms, which are used to output collaborative control commands.

[0010] The PCM energy management module communicates with the decision control center and includes a PCM energy storage heat exchanger and a cascade waste heat recovery loop. The PCM energy storage heat exchanger is used to store and release heat, and the cascade waste heat recovery loop switches the heat utilization path according to the coordinated control command.

[0011] The integrated execution layer communicates with the decision control center and includes a temperature control unit, a chemical dosing unit, and a mechanical dehydration unit. The temperature control unit receives collaborative control commands to adjust the supply of heat medium, the chemical dosing unit receives collaborative control commands to adjust the type and dosage of flocculant, and the mechanical dehydration unit works in collaboration with the temperature control unit and the chemical dosing unit.

[0012] The decision control center synchronously predicts sludge temperature changes and PCM heat storage and release behavior through a sludge-PCM coupled heat transfer model, and performs rolling optimization based on the optimization objective function. This outputs time-synchronized coordinated control commands for controlling the temperature control unit, dosing unit, and PCM energy management module, thereby achieving global optimization of the sludge dewatering process.

[0013] Furthermore, the PCM state parameters are acquired by the PCM state sensing subsystem, which includes a distributed temperature sensor array and a high-precision flow meter. The distributed temperature sensor array is arranged in an equilateral triangle pattern across the shell-side cross-section of the PCM energy storage heat exchanger. The high-precision flow meter is used to detect the volumetric flow rate F of the heat exchange medium flowing through the PCM energy storage heat exchanger. The remaining heat capacity is calculated by performing temperature distribution inversion and flow integral algorithms, and the calculation formula is as follows:

[0014] Qremaining =∫(c p ·ρ·F·ΔT)dt+m·ΔH·(1-η);

[0015] Where F is the volumetric flow rate of the heat exchange medium measured by a high-precision flow meter, and c p ρ is the isobaric specific heat capacity of the heat exchange medium, ρ is the density of the heat exchange medium, ΔT is the inlet and outlet temperature difference of the heat exchange medium, m is the mass of the PCM, ΔH is the latent heat of phase change of the PCM, and η is the degree of phase change completion calculated by temperature distribution inversion through a distributed temperature sensor array.

[0016] Furthermore, the optimization objective function of the MPC control module is:

[0017] J = Σ (Electricity consumption + Heat transfer medium cost + α·(Temperature tracking error)) 2 +β·(PCM thermal storage state deviation) 2 +γ·(Drug Cost));

[0018] α, β, and γ are weighting coefficients, whose values ​​are determined by orthogonal experimentation, and the specific values ​​satisfy α∈[0.8,1.2], β∈[0.5,0.9], and γ∈[0.3,0.7]. The coordinated control commands are issued with a unified timing reference to ensure that the response time difference of temperature control, dosing, and PCM actions is ≤200ms.

[0019] Furthermore, the XGBoost prediction module incorporates multiple sludge adaptation modes, automatically switching based on the sludge organic component content and viscosity detected by the multimodal sensing layer. A smooth transition algorithm is used during mode switching: new temperature setting range = current temperature range × k1 + target temperature range × k2, where k1 and k2 are preset weight coefficients, and k1 + k2 = 1 to avoid sudden temperature changes. The trigger threshold for each mode is determined by a sludge characteristic clustering algorithm, specifically the K-means clustering algorithm, whose clustering features include the polysaccharide to protein content ratio, viscosity, and solids content.

[0020] Furthermore, the intelligent temperature control system also includes an early warning linkage module. This module communicates in real time with the decision control center and the PCM energy management module. It includes a gas concentration sensor, a pressure sensor, and a tiered linkage unit. The gas concentration sensor is used to detect the concentration of methane and hydrogen sulfide. When an anomaly is detected, the tiered linkage unit first instructs the decision control center to adjust the heat release rate of the PCM energy storage heat exchanger and the heat distribution of the tiered waste heat recovery loop for emergency regulation. If the anomaly is not resolved within a preset time window, then shutdown or fire-fighting linkage measures are triggered.

[0021] Furthermore, the integrated execution layer adopts a modular distributed structure, including at least two independent temperature control-dewatering functional modules. Each functional module is adapted to belt filter press, centrifugal dewatering machine or plate and frame filter press through a standardized interface. The decision control center schedules the start-up and shutdown status and operating parameters of each functional module according to the sludge treatment volume and heat load requirements.

[0022] Furthermore, the PCM energy storage heat exchanger has a shell-and-tube structure, filled with a composite phase change material. The composite phase change material is a composite material of medium-temperature eutectic salt and expanded graphite, with a phase change melting point of 55-70℃ and a latent heat of phase change ≥180kJ / kg. The nominal diameter of the PCM energy storage heat exchanger is DN600-DN1000, with 4-6 tube passes and 1-2 shell passes.

[0023] Furthermore, the cascade waste heat recovery loop includes a primary heat exchanger, a secondary heat exchanger, and a heat pump unit. The primary heat exchanger extracts high-temperature waste heat from the dried tail gas at a temperature of 80°C to 120°C. The secondary heat exchanger uses the high-temperature waste heat to preheat the wet sludge, raising the temperature of the wet sludge from 20°C to 40-50°C. The heat pump unit raises the low-grade waste heat from 30-60°C after the secondary heat exchange to 80-100°C, which is then reused in the heat storage stage of the temperature control unit or the PCM energy storage heat exchanger.

[0024] Furthermore, the rolling optimization cycle of the MPC control module is 10 to 30 seconds, with built-in safety constraints: the upper limit of the temperature control unit is ≤120℃, and the heating rate is ≤5℃ / min; the sampling frequency of the multimodal sensing layer is 1 time / second, the product quality parameter is the moisture content of the mud cake, and the decision control center stably controls the moisture content of the mud cake at 30% to 40%.

[0025] Secondly, this invention provides an intelligent temperature control method for sludge dewatering, applied to the aforementioned intelligent temperature control system for sludge dewatering, comprising the following steps:

[0026] S1: The multimodal sensing layer collects sludge characteristic parameters, process and energy state parameters, and product quality parameters in real time and transmits them to the decision control center; among them, the PCM state parameters are collected by a distributed temperature sensor array and a high-precision flow meter arranged in an equilateral triangle, and the remaining heat capacity is calculated by temperature distribution inversion and flow integral algorithms.

[0027] S2: The XGBoost prediction module receives sludge characteristic parameters, determines the sludge type and corresponding adaptation mode through the K-means clustering algorithm, generates an initial temperature setting range and flocculant addition suggestions, dynamically corrects the upper limit of the initial temperature setting range based on the remaining heat capacity in the PCM state parameters, uses a smooth transition algorithm to avoid temperature abrupt changes during correction, and outputs the corrected process parameter set.

[0028] S3: The MPC control module calls the sludge-PCM coupled heat transfer model, inputs the corrected set of process parameters, PCM state parameters and product quality parameters, and performs rolling optimization based on the optimization objective function that includes thermal energy cost, reagent cost and PCM heat storage state deviation term, to generate collaborative control instructions; the collaborative control instructions are issued with a unified timing reference to ensure the synchronization of multiple unit actions;

[0029] S4: The PCM energy management module and the integrated execution layer respond to the coordinated control commands, and synchronously adjust the heat medium supply, PCM heat storage and release status, flocculant dosing parameters and mechanical dehydration unit operating parameters; if the early warning linkage module detects an abnormality, it will prioritize emergency regulation by adjusting the PCM heat release rate and waste heat distribution.

[0030] S5: Execute steps S1 to S4 in a loop with the same cycle as the rolling optimization cycle of the MPC control module, and continuously correct the collaborative control instructions through product quality parameters to achieve global collaborative optimization of the sludge dewatering process.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. This invention embeds the real-time dynamic state of phase change material (PCM) into the rolling optimization of model predictive control (MPC), enabling the system to have forward-looking energy management and scheduling capabilities. It can actively suppress heat source fluctuations and adapt to changes in sludge characteristics, upgrading the control mode from "passive response" to "active intervention", thereby achieving precise and stable control of dewatering temperature.

[0033] 2. This invention, through active control mode, combined with cascade waste heat recovery technology and synergistic optimization decision-making on thermal energy cost and reagent cost, breaks the control island of energy flow and material flow in traditional systems, and achieves a leap from local stability to global optimization.

[0034] 3. Through modular execution layer design and integrated early warning linkage mechanism, this invention ensures that the system has the ability to respond quickly and adapt adaptively when facing abnormal operating conditions, minimizing the impact of single equipment failures or process disturbances on the overall stability of the system, and greatly improving the continuous and reliable operation capability of the system. Attached Figure Description

[0035] Figure 1 This is a system structure block diagram of the intelligent temperature control system for sludge dewatering of the present invention;

[0036] Figure 2 This is a flowchart of the intelligent temperature control method for sludge dewatering according to the present invention. Detailed Implementation

[0037] The two embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0038] First implementation method:

[0039] Figure 1 An intelligent temperature control system for sludge dewatering is shown, comprising: a multimodal sensing layer, a decision control center, a PCM energy management module, and an integrated execution layer.

[0040] The multimodal sensing layer is the sensing foundation of the system, used to collect sludge characteristic parameters, process and energy state parameters, and product quality parameters in real time. Among them, the sludge characteristic parameters include at least the organic component content, viscosity, and solids content of the sludge; the process and energy state parameters include at least the state parameters of the phase change material (PCM); the PCM state parameters include at least the residual heat capacity, instantaneous heat transfer power, and phase change completion degree; and the product quality parameter is the moisture content of the dewatered sludge cake.

[0041] Specifically, the multimodal sensing layer deploys various sensors to comprehensively capture process information. An online near-infrared spectrometer is installed on the sludge conveying pipeline. This instrument emits near-infrared light of specific wavelengths and receives the transmission or reflection spectra of the sludge. Through a built-in chemometric model, it can analyze the relative content of key organic components such as polysaccharides and proteins in the sludge within seconds. Simultaneously, an online sludge viscometer is installed at the same workstation. Using a rotational or vibrational principle, it measures the apparent viscosity of the sludge in real time to reflect the stability of its colloidal structure. A microwave concentration meter is installed on the main sludge pipeline. By measuring the energy attenuation of microwaves passing through the sludge, it accurately calculates the solids content of the sludge. To monitor energy status, temperature, pressure, and flow sensor groups are distributed across the system's heat transfer medium pipelines, sludge flow channels, and exhaust gas pipelines. In particular, a dedicated PCM state sensing subsystem is set up to accurately grasp the real-time state of the phase change material (PCM). This subsystem includes a high-precision flow meter installed on the heat exchange medium inlet pipe of the PCM energy storage heat exchanger to accurately measure the volumetric flow rate (denoted as F) of the medium flowing through the heat exchanger. On the shell side (i.e., the PCM side) of the PCM energy storage heat exchanger, a distributed temperature sensor array is arranged in a three-dimensional equilateral triangle lattice. This arrangement most effectively captures the phase transition front of the PCM in space. The system uses the temperature data read from this array and employs an inversion algorithm to calculate the macroscopic phase transition completion degree (denoted as η) of the entire PCM unit. The final product quality is monitored by an online cake moisture meter installed at the outlet of dewatering equipment (such as a plate and frame filter press). This instrument can measure the moisture content of the cake in real time without contact, based on near-infrared or microwave principles.

[0042] The decision control center communicates with the multimodal sensing layer, and all data collected by the multimodal sensing layer is transmitted to the decision control center in real time. The decision control center runs a deep fusion algorithm architecture, including an XGBoost prediction module and an MPC control module. The XGBoost prediction module is configured to generate an initial temperature setpoint range and flocculant dosing recommendations based on sludge characteristic parameters, and dynamically increase or conservatively correct the upper limit of the initial temperature setpoint range based on the remaining heat capacity in the PCM state parameters. The MPC control module has a built-in sludge-PCM coupled heat transfer model, and its optimization objective function includes thermal energy cost, reagent cost, and PCM heat storage state deviation terms, which are used to output collaborative control commands.

[0043] Specifically, first, the XGBoost prediction module begins operation. This module is pre-trained using a large amount of historical operational data (including sludge characteristics and corresponding optimal process parameters) to form a powerful prediction model. The module receives real-time collected sludge characteristic parameters (polysaccharide to protein ratio, viscosity, and solids content) and uses the built-in K-means clustering algorithm to quickly analyze these features. The clustering algorithm uses the polysaccharide to protein ratio, viscosity, and solids content as core features to classify the sludge into categories such as "high polysaccharide, difficult to dewater" and "high ash, easy to dewater." Once the category is determined, the module calls the process parameter model corresponding to that category and outputs an initial temperature setpoint range (e.g., 85-95℃) and recommendations for the type and dosage of flocculant.

[0044] Next, the XGBoost prediction module performs a crucial deep fusion step: it queries the PCM state sensing subsystem to obtain the current remaining heat capacity. The remaining heat capacity is calculated using the formula Q. remaining =∫(c p The expression ·ρ·F·ΔT)dt+m·ΔH·(1-η) is calculated. Where c p Let ρ be the isobaric specific heat capacity of the heat exchange medium, ρ be the density of the heat exchange medium, ΔT be the inlet and outlet temperature difference of the heat exchange medium (measured by inlet and outlet temperature sensors), m be the mass of the PCM, and ΔH be the latent heat of phase change of the PCM. Based on the same set of real-time data, the system calculates the instantaneous heat transfer power using the formula: instantaneous heat transfer power = c p The instantaneous heat transfer power between the PCM and the heat transfer medium is calculated using ρ·F·ΔT, thus obtaining a complete picture of the dynamic energy state of the PCM. If Q remaining If the energy storage capacity is greater than 75% of the rated capacity, it is considered sufficient. The module will dynamically increase the upper limit of the initial temperature range (e.g., from 95℃ to 98℃) to utilize excess heat energy to accelerate dehydration; if Q remainingIf the temperature is less than 30% of the rated capacity, a conservative adjustment is made (e.g., the upper limit is lowered to 90°C) to ensure the system can operate continuously. When switching modes, a smooth transition algorithm is used: new temperature setting range = current temperature range × k1 + target temperature range × k2, where k1 and k2 are preset weighting coefficients, and k1 + k2 = 1, to avoid abrupt changes in the temperature setting value. In a preferred embodiment, k1 is 0.7 and k2 is 0.3.

[0045] Then, the Model Predictive Control (MPC) module takes over for real-time rolling optimization. This module incorporates a sludge-PCM coupled heat transfer model, which can simultaneously predict the temperature change trajectory of the sludge and the heat storage / release dynamics of the PCM over a future period. The optimization objective function J of the MPC is defined as: J = Σ (Electricity consumption + Heat transfer medium cost + α·(Temperature tracking error)) 2 +β·(PCM thermal storage state deviation) 2 +γ·(Drug Cost)).

[0046] Wherein, α, β, and γ are weighting coefficients, with preferred values ​​ranging from α ∈ [0.8, 1.2], β ∈ [0.5, 0.9], and γ ∈ [0.3, 0.7]. Through prior orthogonal experimental design, a set of preferred values ​​was found to be α = 1.0, β = 0.7, and γ = 0.5. This objective function optimizes precise temperature control, PCM energy scheduling, and reagent economy within a unified framework. The MPC module performs rolling optimization every configurable period (e.g., 20 seconds), with a typical period setting between 10 and 30 seconds. The optimal coordinated control command sequence is issued using a unified timing reference. To ensure the coordination of actions among multiple execution units such as temperature control, dosing, and PCM energy management, and to avoid process parameter mismatch due to response delays, the system strictly controls the response time difference of each unit to within 200 milliseconds through high-precision clock synchronization and real-time communication protocols. This design specification, determined through system dynamics simulation, is crucial for ensuring the overall optimization effect. Furthermore, during the rolling optimization process, the MPC control module's solution process is strictly limited by a set of built-in safety constraints to ensure absolute system safety. These constraints include, but are not limited to: the upper limit of the temperature control unit's temperature must not exceed 120°C, and the system's heating rate must not exceed 5°C / minute. Simultaneously, the multimodal sensing layer collects data at a fixed sampling frequency of 1 time / second, providing stable and reliable real-time data to the control system. The core control objective of the entire system, namely the product quality parameter—the moisture content of the mud cake—is set within the range of 30% to 40%. Through the aforementioned closed-loop optimization mechanism, the decision control center can stably control the final moisture content of the mud cake within this target range.

[0047] The integrated execution layer communicates with the decision control center and includes a temperature control unit, a chemical dosing unit, and a mechanical dehydration unit. The temperature control unit receives collaborative control commands to adjust the supply of heat medium, the chemical dosing unit receives collaborative control commands to adjust the type and dosage of flocculant, and the mechanical dehydration unit works in collaboration with the temperature control unit and the chemical dosing unit.

[0048] Specifically, the integrated execution layer receives instructions and coordinates its actions. The temperature control unit adjusts the opening of the regulating valves on the steam or heat medium pipelines according to the instructions. The dosing unit selects the designated flocculant chamber according to the instructions and adjusts the speed of the variable frequency dosing pump to precisely control the dosage. The mechanical dewatering unit (such as a plate and frame filter press) works closely with the temperature control and dosing processes in terms of timing.

[0049] The PCM energy management module is the energy hub of the system, communicating with the decision control center. It includes a PCM energy storage heat exchanger and a cascade waste heat recovery loop. The PCM energy storage heat exchanger is used to store and release heat, and the cascade waste heat recovery loop switches the heat utilization path according to the coordinated control command.

[0050] The PCM energy storage heat exchanger has a shell-and-tube structure and is filled with a composite phase change material. The composite phase change material is a composite material of intermediate-temperature eutectic salt and expanded graphite, with a phase change melting point of 55-70℃ and a latent heat of phase change ≥180kJ / kg. The nominal diameter of the PCM energy storage heat exchanger is DN600-DN1000, with 4-6 tube passes and 1-2 shell passes.

[0051] In a preferred embodiment, the shell-and-tube PCM energy storage heat exchanger has a nominal diameter of DN800, 4 tube passes, and 1 shell pass. The composite phase change material filled inside has a phase change melting point of 65°C and a latent heat of phase change ≥180kJ / kg, and is prepared from a medium-temperature eutectic salt and expanded graphite.

[0052] The cascaded waste heat recovery loop achieves efficient cascaded utilization of energy: the first-stage heat exchanger recovers high-temperature waste heat from the dried tail gas at a temperature of about 80°C to 120°C; the second-stage heat exchanger uses this heat to preheat the original wet sludge from the ambient temperature (e.g., 20°C) to 40-50°C; the heat pump unit further extracts the low-grade waste heat of 30-60°C after the second-stage heat exchange, raises its grade to 80-100°C, and then reuses it in the main heating stage of the temperature control unit or stores heat in the PCM energy storage heat exchanger.

[0053] The system also integrates an early warning and linkage module. This module communicates in real time with the decision control center and the PCM energy management module, and includes a gas concentration sensor, a pressure sensor, and a tiered linkage unit. The gas concentration sensor is used to detect methane and hydrogen sulfide concentrations. When the gas concentration sensor detects an abnormal methane or hydrogen sulfide concentration (i.e., the concentration exceeds a preset safety threshold), the tiered linkage unit first instructs the decision control center to adjust the heat release rate of the PCM energy storage heat exchanger and the heat distribution of the cascade waste heat recovery loop for emergency control. If the abnormality is not resolved within a preset time window (e.g., the methane or hydrogen sulfide concentration does not decrease below the safety threshold), then shutdown or fire-fighting linkage measures are triggered.

[0054] The integrated execution layer adopts a modular distributed structure. The system may include at least two independent temperature control-dewatering functional modules, each compatible with different types of mechanical dewatering units such as belt filter presses, centrifugal dewatering machines, or plate and frame filter presses via standardized interfaces. The decision control center dynamically schedules the start / stop status and operating parameters of each functional module based on real-time sludge processing volume and heat load requirements, achieving flexible capacity allocation and load balancing.

[0055] Second implementation method:

[0056] Figure 2 This paper presents an intelligent temperature control method for sludge dewatering, comprising the following steps:

[0057] S1. Comprehensive perception and data fusion: After the system is started, the multimodal perception layer collects sludge characteristic parameters (organic component content, viscosity and solids content), process and energy state parameters (temperature, pressure, flow rate and PCM state parameters) and product quality parameters (sludge cake moisture content) in real time. Among them, the PCM state parameters are collected by a distributed temperature sensor array and a high-precision flow meter arranged in an equilateral triangle, and the remaining heat capacity is calculated by temperature distribution inversion and flow rate integration algorithms.

[0058] S2. Intelligent Prediction and Parameter Correction: The XGBoost prediction module receives real-time sludge characteristic parameters, determines the sludge type and corresponding adaptation mode through K-means clustering algorithm, generates the initial temperature setting range and flocculant addition suggestions, and then dynamically increases or conservatively corrects the upper limit of the initial temperature setting range based on the residual heat capacity in the PCM state parameters. A smooth transition algorithm is used during correction to avoid temperature abrupt changes, and finally outputs the corrected process parameter set.

[0059] S3. Collaborative Decision Making and Rolling Optimization: The MPC control module calls the sludge-PCM coupled heat transfer model, inputs the modified process parameter set, PCM state parameters and sludge cake moisture content, and performs rolling optimization based on the optimization objective function J, which includes thermal energy cost, chemical cost and PCM heat storage state deviation term. The rolling optimization cycle of MPC can be configured within 10 to 30 seconds. In a preferred embodiment, the cycle is set to 20 seconds. The collaborative control command generated after optimization is issued with a unified timing reference to ensure that the response time difference of temperature control, chemical dosing and PCM action is ≤200ms, so as to realize the synchronization of multi-unit action.

[0060] S4. Linked Execution and Emergency Control: The PCM energy management module and the integrated execution layer (temperature control unit, dosing unit, mechanical dehydration unit) respond to coordinated control commands and synchronously adjust parameters such as heat medium supply, PCM heat storage and release status, flocculant type and dosage. During the entire process, if the early warning linkage module detects an abnormality, it will prioritize emergency control by adjusting the PCM heat release rate and waste heat distribution.

[0061] S5. Closed-loop evolution and continuous optimization: Steps S1 to S4 are executed cyclically with the same cycle as the rolling optimization cycle of the MPC control module. The sludge cake moisture content is used as the core feedback and is continuously used to correct the collaborative control commands. Through this closed-loop mechanism, the system can stably control the sludge cake moisture content within the target range of 30% to 40% and achieve global collaborative optimization of the sludge dewatering process.

[0062] In light of current practical needs, the above-described embodiments of this invention are not limited to these specific implementations. Any changes made within the scope of knowledge possessed by those skilled in the art, without departing from the concept of this invention, still fall within the protection scope of this invention.

Claims

1. A sludge dewatering intelligent temperature control system, characterized in that, include: A multimodal sensing layer is used to collect sludge characteristic parameters, process and energy state parameters, and product quality parameters in real time. The sludge characteristic parameters include at least the organic component content, viscosity, and solids content of the sludge. The process and energy state parameters include at least the state parameters of the phase change material (PCM). The PCM state parameters include at least the residual heat capacity, instantaneous heat transfer power, and phase change completion degree. The product quality parameter is the moisture content of the dewatered sludge cake. The decision control center is communicatively connected to the multimodal perception layer. The decision control center runs a deep fusion algorithm architecture, including an XGBoost prediction module and an MPC control module. The XGBoost prediction module is configured to generate an initial temperature set range and flocculant dosing recommendations based on the sludge characteristic parameters, and dynamically increase or conservatively correct the upper limit of the initial temperature set range based on the remaining heat capacity in the PCM state parameters. The MPC control module has a built-in sludge-PCM coupled heat transfer model, and its optimization objective function includes thermal energy cost, reagent cost, and PCM heat storage state deviation terms, which are used to output collaborative control commands. The PCM energy management module, which is communicatively connected to the decision control center, includes a PCM energy storage heat exchanger and a cascade waste heat recovery loop. The PCM energy storage heat exchanger is used to store and release heat, and the cascade waste heat recovery loop switches the heat utilization path according to the coordinated control command. The integrated execution layer, which is communicatively connected to the decision control center, includes a temperature control unit, a dosing unit, and a mechanical dehydration unit. The temperature control unit receives collaborative control commands to adjust the supply of heat medium, the dosing unit receives collaborative control commands to adjust the type and dosage of flocculant, and the mechanical dehydration unit works collaboratively with the temperature control unit and the dosing unit. The decision control center synchronously predicts sludge temperature changes and PCM heat storage and release behavior through the sludge-PCM coupled heat transfer model, and performs rolling optimization based on the optimization objective function, thereby outputting time-synchronized coordinated control commands for controlling the temperature control unit, the dosing unit and the PCM energy management module, so as to achieve global optimization of the sludge dewatering process.

2. The intelligent temperature control system for sludge dewatering according to claim 1, wherein The PCM state parameters are acquired by the PCM state sensing subsystem, which includes a distributed temperature sensor array and a high-precision flow meter. The distributed temperature sensor array is arranged inside the PCM energy storage heat exchanger in an equilateral triangle distribution on the shell-side cross-section. The high-precision flow meter is used to detect the volumetric flow rate F of the heat exchange medium flowing through the PCM energy storage heat exchanger; the remaining heat capacity is calculated by performing temperature distribution inversion and flow integral algorithms, and the calculation formula is as follows: Q remaining =∫(c p ·ρ·F·ΔT)dt+m·ΔH·(1-η); wherein F is the volume flow rate of the heat exchange medium measured by the high-precision flow meter, c p is the specific heat capacity of the heat exchange medium at constant pressure, p is the density of the heat exchange medium, AT is the temperature difference between the inlet and outlet of the heat exchange medium, m is the mass of the PCM, AH is the latent heat of phase change of the PCM, and η is the degree of phase change completion calculated by temperature distribution inversion of the distributed temperature sensor array.

3. The intelligent temperature control system for sludge dewatering of claim 1, wherein, The optimization objective function of the MPC control module is: J =∑(electric energy consumption + heat medium cost + a · (temperature tracking error) 2 + β · (PCM storage state deviation) 2 + γ · (medicament cost)) Wherein, α, β, γ are weight coefficients, the values of which are determined by orthogonal test method, and the specific values meet α∈[0.8, 1.2], β∈[0.5, 0.9], γ∈[0.3, 0.7]; the cooperative control instruction is issued with a unified timing reference, ensuring that the response time difference of temperature control, dosing, and PCM action is ≤200 ms.

4. The intelligent temperature control system for sludge dewatering of claim 1, wherein, The XGBoost prediction module has multiple sludge adaptation modes built-in, automatically switches according to the organic component content and viscosity of sludge detected by the multi-modal perception layer, and uses a smooth transition algorithm when switching modes: new temperature setting interval = current temperature interval × k1 + target temperature interval × k2, k1 and k2 are preset weight coefficients, and k1 + k2 = 1, to avoid temperature mutation; the triggering threshold of each mode is determined by a sludge characteristic clustering algorithm, which is a K-means clustering algorithm, and the clustering characteristics include the polysaccharide-to-protein content ratio, viscosity, and solid content.

5. The intelligent temperature control system for sludge dewatering of claim 1, wherein, It also includes a pre-warning linkage module that communicates in real time with the decision control center and the PCM energy management module, including a gas concentration sensor, a pressure sensor, and a hierarchical linkage unit, the gas concentration sensor is used to detect methane and hydrogen sulfide concentrations, when an anomaly is detected, the hierarchical linkage unit preferentially instructs the decision control center to adjust the heat release rate of the PCM energy storage heat exchanger and the heat distribution of the gradient waste heat recovery circuit for emergency control, if the anomaly is not resolved within a preset time window, then trigger the shutdown or fire-fighting linkage measures.

6. The intelligent temperature control system for sludge dewatering of claim 1, wherein, The integrated execution layer adopts a modular distributed structure, including at least two independent temperature control-dewatering function modules, each function module is adapted to a belt filter, a centrifugal dewatering machine, or a plate-and-frame filter through a standardized interface, and the decision control center adjusts the start-stop state and operating parameters of each function module according to the sludge treatment capacity and heat load demand.

7. The intelligent temperature control system for sludge dewatering of claim 1, wherein, The PCM energy storage heat exchanger is of a shell-and-tube structure, filled with a composite phase change material, the composite phase change material is a composite material of a medium-temperature eutectic salt and expanded graphite, the phase change melting point is 55-70℃, and the phase change latent heat is ≥180kJ / kg; the nominal diameter of the PCM energy storage heat exchanger is DN600-DN1000, the number of tube passes is 4-6, and the number of shell passes is 1-2.

8. The intelligent temperature control system for sludge dewatering of claim 1, wherein, The gradient waste heat recovery circuit includes a primary heat exchanger, a secondary heat exchanger, and a heat pump unit, the primary heat exchanger extracts high-temperature waste heat of dried tail gas at a temperature of 80-120℃, the secondary heat exchanger uses the high-temperature waste heat to preheat wet sludge, increasing the temperature of the wet sludge from 20℃ to 40-50℃, and the heat pump unit increases the low-grade waste heat at 30-60℃ after the secondary heat exchanger to 80-100℃, which is used for the heat storage link of the temperature control unit or the PCM energy storage heat exchanger.

9. The intelligent temperature control system for sludge dewatering of claim 1, wherein, The rolling optimization period of the MPC control module is 10-30 seconds, with safety constraints: the upper limit of the temperature of the temperature control unit is ≤120℃, and the temperature rise rate is ≤5℃ / min; the sampling frequency of the multi-modal perception layer is 1 time / second, the product quality parameter is the moisture content of the sludge cake, and the decision control center stably controls the moisture content of the sludge cake at 30%-40%.

10. A sludge dewatering intelligent temperature control method applied to the sludge dewatering intelligent temperature control system of any one of claims 1-9, characterized in that, The method comprises the following steps: S1: The multi-modal perception layer collects sludge characteristic parameters, process and energy state parameters, and product quality parameters in real time, and transmits them to the decision control center; wherein the PCM state parameters are collected by a distributed temperature sensor array in an equilateral triangle distribution and a high-precision flowmeter, and the residual heat capacity is calculated by temperature distribution inversion and flow integration algorithm; S2: The XGBoost prediction module receives the sludge characteristic parameters, determines the sludge type and the corresponding adaptive mode by the K-means clustering algorithm, generates the initial temperature setting interval and the flocculant addition suggestion, and dynamically corrects the upper limit value of the initial temperature setting interval based on the residual heat capacity in the PCM state parameter; when correcting, a smooth transition algorithm is used to avoid temperature mutation, and the corrected process parameter set is output; S3: The MPC control module calls the sludge-PCM coupled heat transfer model, inputs the corrected process parameter set, the PCM state parameter and the product quality parameter, and performs rolling optimization based on the optimization objective function including the heat energy cost, the reagent cost and the PCM heat storage state bias term, to generate the collaborative control instruction; the collaborative control instruction is issued under a unified time sequence reference to ensure the synchronization of multiple unit actions; S4: The PCM energy management module and the integrated execution layer respond to the collaborative control instruction, and synchronously adjust the heat medium supply, the PCM heat storage and release state, the flocculant addition parameter and the mechanical dewatering unit operation parameter; if the early warning linkage module detects an abnormality, emergency control is performed by adjusting the PCM heat release rate and the residual heat distribution; S5: Steps S1 to S4 are executed in the same period as the rolling optimization period of the MPC control module, and the product quality parameter is continuously corrected to realize the global collaborative optimization of the sludge dewatering process.

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