A sintering ring cooling zero-emission total heat recovery steam generation system

Abstract

The present application relates to the technical field of industrial waste heat recovery and intelligent control, in particular to a sintering ring cooling zero-emission total heat recovery steam generation system, comprising a working condition parameter acquisition module for acquiring waste gas working condition parameters in real time; a risk assessment module for determining a real-time pollution risk index based on the waste gas working condition parameters acquired by the working condition parameter acquisition module and a preset risk threshold; a degradation monitoring module for determining a particle comprehensive degradation factor based on particle performance parameters collected by an online sensor; a system deviation degree processing module for determining a system deviation degree factor in combination with the real-time pollution risk index and the particle comprehensive degradation factor; and an adaptive control module for determining an adjusted particle mass circulation rate and an adjusted online cleaning system operation frequency in response to the system deviation degree factor; the present application establishes a multi-dimensional dynamic risk assessment model, significantly improving the operation reliability and survivability of the system under harsh working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial waste heat recovery and intelligent control, in particular to a sintering ring cooling zero-emission full-heat recovery steam generation system. BACKGROUND

[0002] In the field of waste heat recovery of sintering ring cooling machines, ensuring long-term stable and efficient operation of the system is crucial for energy saving and emission reduction. However, the severe fluctuations and complexity of industrial waste gas conditions pose a serious challenge to the reliability of the heat recovery system.

[0003] The existing technology mainly has the following limitations: traditional systems generally rely on static design margin and passive fault response mode, and cannot actively predict and avoid catastrophic dust accumulation risks caused by severe condition changes. The survival ability and operation reliability under harsh conditions are insufficient. There is a lack of real-time monitoring and evaluation means for the performance of core heat carrier particles. The heat transfer performance of particle media will decrease over time due to physical wear, surface contamination and thermal fatigue. The existing technology cannot effectively perceive and compensate for this gradual decline, which restricts the heat recovery efficiency of the system throughout its life cycle. The existing control strategy cannot uniformly process and cooperatively respond to external sudden pollution risks and internal component gradual performance degradation, which leads to inaccurate control response and cannot achieve a dynamic balance between ensuring equipment safety and maximizing heat recovery efficiency, limiting the intelligent level of the system. SUMMARY

[0004] The purpose of the present application is to provide a sintering ring cooling zero-emission full-heat recovery steam generation system that can actively predict and avoid dust accumulation risks caused by waste gas condition fluctuations, and real-time monitor and compensate for the performance degradation of core heat carrier particles, thereby achieving a dynamic balance between ensuring equipment safety and maximizing heat recovery efficiency. Specifically, the technical solution of the present application is as follows:

[0005] A working condition parameter acquisition module is used to acquire waste gas working condition parameters in real time, wherein the waste gas working condition parameters include waste gas inlet temperature, dust mass concentration and key chemical component mole fraction;

[0006] A risk assessment module is used to determine a real-time pollution risk index based on the waste gas working condition parameters acquired by the working condition parameter acquisition module and the preset risk threshold;

[0007] A degradation monitoring module is used to determine a particle comprehensive degradation factor based on particle performance parameters acquired by an online sensor, wherein the particle performance parameters include particle cumulative mass wear rate, surface contamination rate and specific heat capacity change rate;

[0008] The system deviation processing module is configured to determine a system deviation factor based on the real-time pollution risk index and the particle comprehensive degradation factor.

[0009] The adaptive control module is configured to determine an adjusted particle mass circulation rate and an adjusted online cleaning system operation frequency based on the system deviation factor.

[0010] Optionally, the risk assessment module is configured to determine the real-time pollution risk index by:

[0011] The exhaust gas inlet temperature, the dust mass concentration, and the molar fraction of the key chemical component are respectively subjected to ratio calculation with preset dust adhesion critical temperature, critical mass concentration, and critical molar fraction to obtain a plurality of normalized risk factors.

[0012] The plurality of normalized risk factors are subjected to linear weighted summation to generate the real-time pollution risk index.

[0013] Optionally, the degradation monitoring module is configured to determine the particle comprehensive degradation factor by:

[0014] The particle cumulative mass wear rate, the surface contamination rate, and the specific heat capacity change rate are subjected to weighted summation according to preset weight coefficients to generate the particle comprehensive degradation factor.

[0015] Optionally, the system further comprises:

[0016] The risk grading module is configured to compare the real-time pollution risk index determined by the risk assessment module with preset risk level thresholds to divide the system operation state into a safe state, a first warning state, or a second warning state.

[0017] Optionally, the system deviation processing module is configured to determine the system deviation factor by:

[0018] When the real-time pollution risk index is greater than the preset safety threshold, an external disturbance risk term is calculated based on the real-time pollution risk index;

[0019] When the real-time pollution risk index is less than or equal to the preset safety threshold, the external disturbance risk term is set to zero;

[0020] The external disturbance risk term and the weighted particle comprehensive degradation factor are summed to determine the system deviation factor.

[0021] Optionally, the adaptive control module is configured to determine the adjusted particle mass circulation rate by:

[0022] A first correction amount is generated based on the system deviation factor and a preset first control sensitivity coefficient.

[0023] Combine the first correction amount with the preset reference particle mass circulation rate to determine the adjusted particle mass circulation rate.

[0024] Optionally, the adaptive control module is configured to determine the adjusted online cleaning system operation frequency, and the method comprises:

[0025] Generate a second correction amount based on the system deviation degree factor and a preset second control sensitivity coefficient;

[0026] Combine the second correction amount with the preset reference online cleaning system operation frequency to determine the adjusted online cleaning system operation frequency.

[0027] Compared with the prior art, the present application has the following beneficial effects:

[0028] 1. The system establishes a multi-dimensional dynamic risk assessment model, which quantifies complex working condition parameters such as exhaust gas temperature, dust concentration and chemical composition into a single real-time pollution risk index, realizes active prediction and early warning of catastrophic ash accumulation risk, changes the traditional passive fault response mode into an active risk avoidance strategy, and significantly improves the operation reliability and survivability of the system under harsh working conditions;

[0029] 2. The system introduces a monitoring mechanism for the long-term performance degradation of the core heat carrier particles, which quantifies the health status of the heat transfer medium by comprehensively evaluating the physical wear, surface contamination and thermal property changes of the particles, so that the system can actively compensate for the efficiency decline caused by particle aging, realize the leap from passive maintenance to predictive maintenance, prolong the service life of the core components and maintain high heat exchange efficiency throughout the system life cycle;

[0030] 3. The system creatively builds a unified control driving factor that integrates external sudden risks and internal gradual degradation, organically integrates two types of system disturbances with completely different natures into the same mathematical framework, solves the problem that traditional systems cannot cope with multiple threats, makes the control strategy take into account both immediate risks and long-term degradation, and realizes higher levels of system adaptability and intelligence;

[0031] 4. The system implements a cooperative adaptive control strategy that can simultaneously adjust the two core operating parameters of heat management and physical intervention according to the comprehensive evaluation results of risk and system health status, and this dual synergy effect realizes the dynamic balance between equipment safety and heat exchange efficiency, ensuring that the system can maintain highly efficient and stable operation to the maximum extent when facing serious pollution risks, and the robustness is far superior to traditional single-variable control systems. BRIEF DESCRIPTION OF DRAWINGS

[0032] The present application will be further explained in conjunction with the accompanying drawings and examples:

[0033] Figure 1 This is a system block diagram of a sintering ring cooling zero-emission total heat recovery steam generation system according to the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0035] Example 1:

[0036] Please see Figure 1 A sintering ring cooling zero-emission total heat recovery steam generation system, comprising:

[0037] The operating condition parameter acquisition module is used to acquire exhaust gas operating condition parameters in real time, including exhaust gas inlet temperature, dust mass concentration, and mole fraction of key chemical components.

[0038] The risk assessment module is used to determine the real-time pollution risk index based on the exhaust gas operating parameters obtained by the operating parameter acquisition module and the preset risk threshold.

[0039] The degradation monitoring module is used to determine the comprehensive degradation factor of particles based on the particle performance parameters collected by online sensors. The particle performance parameters include the cumulative mass wear rate of particles, the surface contamination rate, and the specific heat capacity change rate.

[0040] The system deviation processing module is used to determine the system deviation factor by combining the real-time pollution risk index and the particulate comprehensive degradation factor.

[0041] An adaptive control module is used to determine the adjusted particle mass circulation rate and the adjusted online cleaning system operating frequency in response to the system deviation factor.

[0042] This embodiment provides a sintering ring cold zero-emission total heat recovery steam generation system; the core purpose of this system is to respond in real time to the heat exchange pollution risk caused by the fluctuation of exhaust gas conditions and the efficiency decline caused by the performance degradation of the core medium through a closed-loop adaptive intelligent control method, so as to ensure that the system can maintain long-term, stable and efficient heat recovery and steam production in complex and ever-changing industrial environments.

[0043] The operating condition parameter acquisition module aims to provide real-time, multi-dimensional data input for subsequent risk assessment and adaptive control. In this embodiment, the module integrates a series of industrial-grade sensors on the exhaust gas inlet pipe of the heat recovery section of the sintering ring cooler. It utilizes high-temperature thermocouples and gas flow meters to continuously acquire the exhaust gas inlet temperature. With volumetric flow rate The concentration of dust in the exhaust gas is measured in real time using an online laser dust meter. Furthermore, using an online spectrometer, the mole fraction of key chemical components in the exhaust gas that significantly affect heat exchanger scaling was analyzed. For example, the mole fraction of alkali metals; these parameters together constitute a comprehensive picture of the heat load of exhaust gas and potential pollution sources.

[0044] The risk assessment module aims to transform the collected multi-dimensional operating parameters into a single indicator that can quantify the heat exchange pollution risk currently faced by the system. This module receives real-time data from the operating parameter acquisition module, performs calculations based on a preset risk model, and finally outputs a real-time pollution risk index.

[0045] The degradation monitoring module aims to monitor and evaluate the long-term performance of the particulate medium, which serves as the core heat carrier in the system. Since the heat transfer performance of the particulate medium will decline due to physical wear, surface contamination and thermal fatigue during long-term cyclic use, this module collects various parameters reflecting the performance of the particulate medium in real time through online sensors deployed in the particulate circulation loop, and calculates and generates a comprehensive particulate degradation factor to quantify the degree of performance degradation of the particulate medium.

[0046] The purpose of the system deviation processing module is to integrate two different types of disturbances—sudden risks from the external environment and gradual degradation of internal system components—into a unified comprehensive indicator that can guide the decision-making of the control system. This module receives the real-time pollution risk index generated by the risk assessment module and the particle comprehensive degradation factor generated by the degradation monitoring module, and combines the two through a specific algorithm to finally determine a system deviation factor.

[0047] The adaptive control module aims to dynamically and proactively adjust the core operating parameters of the system based on the system deviation factor output by the system deviation processing module, in order to offset the negative impacts of risks and degradation. In this embodiment, the module mainly adjusts two key variables: the adjusted particle mass circulation rate and the adjusted online cleaning system operating frequency. In this way, the system can automatically adapt to changes in operating conditions, achieving intelligent self-protection and performance optimization.

[0048] Through the collaborative work of the above modules, this invention constructs a complete closed loop from state perception to risk assessment to decision control. It changes the traditional heat recovery system's reliance on static design margins and passive response to faults, transforming it into an intelligent system that can proactively predict and avoid catastrophic ash accumulation risks while compensating for the performance degradation of internal components. This not only greatly improves the system's survivability and operational reliability under harsh conditions, but also ensures the stability of heat recovery efficiency and steam production during long-term operation, achieving a dual improvement in economic and safety benefits.

[0049] Example 2:

[0050] The risk assessment module is used to determine the real-time pollution risk index, including:

[0051] The inlet temperature of exhaust gas, dust mass concentration, and mole fraction of key chemical components are compared with the preset critical temperature, critical mass concentration, and critical mole fraction of dust adhesion to obtain multiple normalized risk factors.

[0052] A linear weighted summation of multiple normalized risk factors is performed to generate a real-time pollution risk index;

[0053] This system also includes:

[0054] The risk classification module is used to compare the real-time pollution risk index determined by the risk assessment module with the preset risk level threshold to classify the system operation status into a safe state, a first-level warning state, or a second-level warning state.

[0055] This embodiment is a specific implementation of the risk assessment module and its associated risk classification module described in Embodiment 1; its core innovation lies in proposing a multi-factor coupled dynamic pollution risk assessment model to replace the traditional anti-pollution strategy based on static design margin.

[0056] The risk assessment module calculates normalized risk factors to determine the real-time pollution risk index. To address the technical challenge of directly comparing and integrating risk factors with different physical dimensions, this embodiment introduces normalization processing. This module uses the exhaust gas inlet temperature acquired in real-time by the operating condition parameter acquisition module. Dust mass concentration and mole fraction of key chemical components Each of these is compared to a set of preset risk thresholds;

[0057] This risk threshold is a critical parameter value that may trigger catastrophic dust accumulation, calibrated based on a large amount of historical operational data or laboratory simulation experiments. Its purpose is to provide a clear benchmark for risk assessment. In this embodiment, these thresholds include: the critical temperature for dust adhesion. Critical mass concentration (CMC) refers to the temperature at which dust particles begin to exhibit significant adhesion; it is a key physical quantity determining whether thermal adhesion and scaling occur. ; and critical mole fraction ;

[0058] After normalization, in order to comprehensively assess the coupling effect of different risk factors and generate a single, intuitive risk index, this embodiment uses the following semi-empirical model to calculate the real-time pollution risk index. :

[0059] ;

[0060] in, This is a real-time pollution risk index, dimensionless, whose value directly reflects the degree to which the system deviates from the safe operating area, and is calculated by this module.

[0061] , , These are real-time operating parameters provided by the operating parameter acquisition module.

[0062] , , It is a preset risk threshold, which is derived from statistical analysis of historical failure data or assigned by an expert system.

[0063] , , These are weighting coefficients, dimensionless, and their sum is 1. Their function is to reflect the differences in the contribution of different risk factors to the overall risk. They are determined through statistical regression analysis on a calibration dataset containing N sets of historical operating conditions. In this dataset, each operating condition is represented by a specific set of historical measurements, such as historical exhaust gas inlet temperature. Historical dust mass concentration It consists of a label, and a label indicating whether the system will eventually fail due to contamination under this operating condition;

[0064] This embodiment also includes a risk classification module, the purpose of which is to transform the continuously changing risk index into discrete operating status levels that are easy for the control system to understand and execute; this module converts the real-time pollution risk index calculated by the risk assessment module into a risk level. The system's operating status is compared in real time with preset risk level thresholds, and is divided into three categories. These risk level thresholds are determined based on statistical analysis of a large amount of historical operating data and corresponding equipment failure records. For example, threshold 1.0 represents the design baseline safety line, while threshold 1.6 corresponds to the statistical percentile of the risk index that can be reached before 95% of catastrophic dust accumulation events in history.

[0065] Safe state: When When all risk indicators are within the designed safety thresholds, the system is operating efficiently in normal mode.

[0066] Level 1 warning status: When When this occurs, it indicates that the system has entered a potential risk zone, and the control system needs to initiate mild avoidance measures.

[0067] Level 2 warning status: When This indicates that the system is facing an imminent risk of failure and requires immediate and strong intervention measures to ensure equipment safety.

[0068] This implementation provides a method for accurately and dynamically quantifying multi-dimensional, non-linear, complex pollution risks into a single indicator. This innovative approach not only enables early warning of risks but also allows for risk grading, enabling subsequent adaptive control to adopt tiered response strategies that match the severity of the risks. This avoids the resource waste and adjustment lag of traditional one-size-fits-all control, greatly improving the system's intelligence level and the accuracy and robustness in responding to sudden severe conditions. The risk grading provides intuitive warnings and situational awareness of the system status, while subsequent control modules use continuous risk indicators for precise adjustment, achieving an organic combination of graded early warning and continuous control.

[0069] Example 3:

[0070] The decline monitoring module is used to determine the comprehensive decline factor of granules, including:

[0071] The cumulative mass wear rate, surface contamination rate, and specific heat capacity change rate of particles are weighted and summed according to preset weighting coefficients to generate a comprehensive particle degradation factor.

[0072] This embodiment is a specific implementation of the degradation monitoring module described in Embodiment 1; its core purpose is to establish a model that can comprehensively evaluate the long-term performance evolution of the core heat carrier particulate medium, thereby achieving predictive maintenance of the core component.

[0073] The degradation monitoring module generates a comprehensive particle degradation factor by weighted summing of particle performance parameters collected by multi-source online sensors deployed in the particle circulation loop. Specific particle performance parameters include:

[0074] Cumulative mass wear rate of particles This refers to the ratio of the mass loss of particles due to mutual collisions and friction with the equipment wall during the circulation process to their initial total mass; it mainly reflects the degradation of the physical integrity of the particles and is obtained through online weighing or particle counting systems.

[0075] Surface contamination rate This refers to the ratio of the contaminated area caused by fine dust adhering to the surface of particles to the total surface area of ​​the particles; it directly affects the gas-particle convection heat transfer efficiency and can be monitored online through optical or image recognition sensors.

[0076] Specific heat capacity change rate : refers to the degree of deviation of the specific heat capacity, a key thermophysical property parameter of particles, from its initial value after repeated high-temperature-low-temperature cycles of thermal fatigue; it reflects the deterioration of the particles' heat storage capacity and can be obtained through a miniature sensor based on the principle of online differential scanning calorimetry.

[0077] To comprehensively assess the overall health status of particles, this embodiment constructs a custom particle comprehensive decline factor. The calculation formula is as follows:

[0078] ;

[0079] in, The particle comprehensive degradation factor is dimensionless. Its value comprehensively reflects the degree of degradation of the heat transfer capacity of the particulate medium and is calculated by this module.

[0080] , , These are real-time particle performance parameters collected by online sensors;

[0081] , , These are weighting coefficients, dimensionless, used to quantify the significance of different decay modes on the total heat transfer power of the system. They originate from sensitivity analysis based on the system's thermodynamic model. This analysis involves independently changing each decay component in the model, for example, by introducing a specific set of historical surface fouling rate values. And simulated and calculated its impact on the total heat transfer power. To determine the degree of influence, The recession patterns with the most significant impact were given the highest weight;

[0082] This embodiment adopts a linear weighted model to construct a comprehensive degradation index that is computationally efficient and easy to implement in engineering. The model assumes that the impact of each degradation mode on the total heat exchange efficiency is initially decoupled and linearly superimposed, which can provide effective trend assessment under many operating conditions. In application scenarios where higher model accuracy is required, it is also possible to further consider introducing cross-influence terms or using nonlinear functions to more finely characterize the coupling effect between each degradation mode.

[0083] This implementation method is the first to normalize and weight performance degradation phenomena of different mechanisms, generating a single index that can comprehensively reflect the heat transfer capability of particles. This enables the system control logic to perceive the health status of its core components; by... As input, the control system can predict and compensate for the efficiency decline caused by particle aging in real time, realizing a leap from passive maintenance to predictive maintenance, significantly extending the service life of particulate media, and maintaining high heat exchange efficiency throughout the system's entire life cycle.

[0084] Example 4:

[0085] The system deviation processing module is used to determine the system deviation factor, including:

[0086] When the real-time pollution risk index is greater than the preset safety threshold, the external disturbance risk term is calculated based on the real-time pollution risk index.

[0087] When the real-time pollution risk index is less than or equal to the preset safety threshold, the external disturbance risk item is set to zero.

[0088] The external disturbance risk term is summed with the weighted granular comprehensive decline factor to determine the system deviation factor.

[0089] This embodiment is a specific implementation of the system deviation processing module described in Embodiment 1; its core innovation lies in constructing a system deviation factor that can integrate external environmental risks and the health status of internal components. As the core engine driving the entire closed-loop adaptive control logic;

[0090] The system deviation processing module determines the system deviation factor through specific logical steps. This module distinguishes and processes real-time pollution risk indices. When the real-time pollution risk index If the value exceeds a preset safety threshold (1.0 in this embodiment), the system determines that the external disturbance constitutes a real threat and needs to calculate an external disturbance risk item; while when the real-time pollution risk index... When the value is less than or equal to the safety threshold, the system is in a safe state. At this time, the external disturbance risk item is forcibly set to zero to avoid unnecessary over-adjustment under safe operating conditions.

[0091] This module combines the aforementioned external disturbance risk item with a weighted granular comprehensive decline factor. Sum the results to determine the final system deviation factor. This logic is precisely achieved through the following formula:

[0092] ;

[0093] in, The system deviation factor is dimensionless and is a unified control input signal that integrates sudden risk response and slow decay compensation. It is calculated by this module.

[0094] It is a real-time pollution risk index provided by the risk assessment module;

[0095] It is a granular comprehensive decline factor provided by the decline monitoring module;

[0096] The Heaviside step function is a mathematical switching function defined as follows: the function value is 1 when the independent variable is greater than 0; the function value is 0 when the independent variable is less than or equal to 0. In this model, its role is to precisely implement the conditional logic defined in the embodiment, that is, only when the risk index... The response item to external risks is activated only when the safety threshold of 1 is exceeded;

[0097] The weight of the degradation term is dimensionless and is used to balance and adjust the relative importance of internal performance degradation in the total deviation. Its specific value is determined by analyzing historical operating data and comprehensively evaluating the relationship between the average economic loss caused by emergency shutdown due to external disturbances and the cumulative economic cost of long-term efficiency decline caused by granular performance degradation. The aim is to optimize the operating cost of the system throughout its entire life cycle.

[0098] This implementation method creatively designs a system deviation factor. The model incorporates a Heaviside step function to implement a silent activation mechanism, ensuring the stability of the control system in the safe range and its decisiveness in the risk range. More importantly, it organically unifies two distinct types of system disturbances—rapidly changing pollution risks from the outside and slowly accumulating performance degradation from the inside—under the same mathematical framework. This enables the final adaptive control strategy to simultaneously address immediate risks and compensate for long-term degradation, achieving a higher level of system adaptability and intelligence.

[0099] Example 5:

[0100] An adaptive control module, used to determine the adjusted particle mass circulation rate, includes:

[0101] Based on the system deviation factor and the preset first control sensitivity coefficient, a first correction amount is generated;

[0102] The first correction amount is combined with a preset reference particle mass circulation rate to determine the adjusted particle mass circulation rate.

[0103] The adaptive control module is used to determine the adjusted operating frequency of the online cleaning system, including:

[0104] A second correction value is generated based on the system deviation factor and the preset second control sensitivity coefficient.

[0105] The second correction amount is combined with the preset reference online cleaning system operating frequency to determine the adjusted online cleaning system operating frequency.

[0106] This embodiment is a specific implementation of the adaptive control module described in Embodiment 1; this module receives the system deviation factor determined by the system deviation processing module. Based on this, the particle mass circulation rate and the operating frequency of the online cleaning system are adjusted in a coordinated manner to form a combined risk avoidance and performance compensation strategy.

[0107] The adaptive control module is used to determine the adjusted particle mass circulation rate. Its underlying logic is based on the system deviation factor. With a preset first control sensitivity coefficient A first correction value is generated, and this first correction value is compared with a preset reference particle mass cycle rate. Combine; according to the relation A theoretical adjustment value is calculated, and considering physical constraints, the output control command is: This logic is achieved through the following relation:

[0108] ;

[0109] in, The adjusted particle mass circulation rate, measured in kg / s, is the final control command output from this module to the particle conveying system.

[0110] The reference particle mass circulation rate is a baseline operating value set under ideal operating conditions to achieve the target steam production rate.

[0111] The first control sensitivity coefficient is dimensionless, and its calibration method is as follows: apply a series of step changes in the system deviation factor to the system model. The system response is recorded, and the optimal value that ensures both speed and stability of the control behavior is tuned through system identification algorithms, such as the Ziegler-Nichols method.

[0112] It is the system deviation factor calculated by the system deviation processing module;

[0113] The adaptive control module is also used to determine the adjusted operating frequency of the online cleaning system. The steps are similar to those described above, based on the system deviation factor. With a preset second control sensitivity coefficient A second correction value is generated and compared with the preset reference online cleaning system operating frequency. Combine; according to the relation Calculate the theoretical frequency, and considering the equipment capabilities, the output control command is as follows: ,in This is the highest frequency at which the cleaning system can operate safely to prevent excessive wear and tear on the equipment. The relationship is as follows:

[0114] ;

[0115] in: The adjusted online cleaning system operating frequency, measured in Hz or times per hour, is a control command output to the dust removal device.

[0116] This is a reference frequency for the online cleaning system's operation, representing the baseline cleaning frequency under normal operating conditions.

[0117] The second control sensitivity coefficient is calibrated using the same method as... same;

[0118] It is the system deviation factor calculated by the system deviation processing module;

[0119] When the system deviation factor When the volume increases, the control system proportionally increases the particle circulation rate, thereby increasing the coolant flow rate to remove heat more quickly, thus actively suppressing the temperature rise of the particle bed and ensuring that its operating temperature is close to the critical temperature for dust adhesion. Maintaining a safe temperature difference prevents thermal adhesion at its source. Simultaneously, the system increases the operating frequency of the online cleaning system, actively removing accumulated dust by enhancing physical self-cleaning capabilities, directly combating contamination trends. This implementation achieves a dual synergistic effect of thermal management and physical intervention through coordinated adaptive control of two core operating parameters. This enables the system to possess robustness and survivability far exceeding traditional single-variable control systems when facing severe contamination risks. It maximizes efficient heat recovery operation while ensuring equipment safety, achieving a dynamic balance between safety and efficiency.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A steam generation system for zero-emission total heat recovery in a sintered ring, characterized in that, include: The operating condition parameter acquisition module is used to acquire exhaust gas operating condition parameters in real time, including exhaust gas inlet temperature, dust mass concentration, and mole fraction of key chemical components. The risk assessment module is used to determine the real-time pollution risk index based on the exhaust gas operating parameters obtained by the operating parameter acquisition module and the preset risk threshold. The degradation monitoring module is used to determine the comprehensive degradation factor of particles based on the particle performance parameters collected by online sensors. The particle performance parameters include the cumulative mass wear rate of particles, the surface contamination rate, and the specific heat capacity change rate. The system deviation processing module is used to determine the system deviation factor by combining the real-time pollution risk index and the particulate comprehensive degradation factor. The adaptive control module is used to determine the adjusted particle mass circulation rate and the adjusted online cleaning system operating frequency in response to the system deviation factor. The degradation monitoring module is used to determine the granular comprehensive degradation factor, including: The cumulative mass wear rate, surface contamination rate, and specific heat capacity change rate of particles are weighted and summed according to preset weighting coefficients to generate a comprehensive particle degradation factor.

2. The sintering ring cold zero-emission total heat recovery steam generation system according to claim 1, characterized in that, The risk assessment module is used to determine the real-time pollution risk index, including: The inlet temperature of exhaust gas, dust mass concentration, and mole fraction of key chemical components are calculated by comparing them with the preset critical temperature, critical mass concentration, and critical mole fraction of dust adhesion to obtain multiple normalized risk factors. A linear weighted summation of multiple normalized risk factors is performed to generate a real-time pollution risk index.

3. The sintering ring cold zero-emission total heat recovery steam generation system according to claim 1, characterized in that, Also includes: The risk classification module is used to compare the real-time pollution risk index determined by the risk assessment module with the preset risk level threshold to classify the system operation status into a safe state, a first-level warning state, or a second-level warning state.

4. The sintering ring cold zero-emission total heat recovery steam generation system according to claim 1, characterized in that, The system deviation processing module is used to determine the system deviation factor, including: When the real-time pollution risk index is greater than the preset safety threshold, the external disturbance risk term is calculated based on the real-time pollution risk index. When the real-time pollution risk index is less than or equal to the preset safety threshold, the external disturbance risk item is set to zero. The external disturbance risk term is summed with the weighted granular comprehensive decline factor to determine the system deviation factor.

5. The sintering ring cold zero-emission total heat recovery steam generation system according to claim 1, characterized in that, The adaptive control module is used to determine the adjusted particle mass circulation rate, including: Based on the system deviation factor and the preset first control sensitivity coefficient, a first correction amount is generated; The first correction amount is combined with a preset reference particle mass circulation rate to determine the adjusted particle mass circulation rate.

6. The sintering ring cold zero-emission total heat recovery steam generation system according to claim 1, characterized in that, The adaptive control module is used to determine the adjusted operating frequency of the online cleaning system, including: A second correction value is generated based on the system deviation factor and the preset second control sensitivity coefficient. The second correction amount is combined with the preset reference online cleaning system operating frequency to determine the adjusted online cleaning system operating frequency.

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