Energy-saving and environment-friendly tail gas treatment control system and method based on cremator

Abstract

The present application relates to the technical field of intelligent emission control, and particularly relates to an energy-saving and environment-friendly tail gas treatment control system and method based on a cremator. The method comprises the following steps: collecting tail gas emitted by the cremator and performing pollutant concentration detection to obtain pollutant concentration data; performing pollution level division based on the pollutant concentration data to obtain first pollution level data and second pollution level data; performing multi-stage tail gas purification simulation according to the first pollution level data to obtain multi-stage tail gas purification data; performing high-energy-consumption tail gas treatment equipment detection according to the multi-stage tail gas purification data to obtain high-energy-consumption tail gas treatment equipment data; and performing fan fault diagnosis based on the high-energy-consumption tail gas treatment equipment data to obtain fan fault data. The present application improves the tail gas purification efficiency of the cremator and the precision and automation level of energy-saving and environment-friendly control based on intelligent emission control technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent emission control, in particular to an energy-saving and environmentally-friendly tail gas treatment control system and method based on a cremator. BACKGROUND

[0002] The traditional energy-saving and environmentally-friendly tail gas treatment control method based on a cremator relies on a single pollutant concentration monitoring method, lacks comprehensive perception of the diversity and dynamic changes of tail gas components, and thus cannot accurately classify pollution levels, thereby affecting the subsequent purification treatment effect. The tail gas purification process is usually a preset fixed process, lacking differentiation and dynamic adjustment capabilities for different pollution levels, making it difficult to achieve efficient and energy-saving tail gas treatment, resulting in energy waste and poor treatment effect. The existing method does not thoroughly monitor high-energy-consumption tail gas treatment equipment, often only focusing on the surface operating parameters of the equipment, lacking intelligent diagnosis of internal conditions such as filter bag blockage, dust adhesion, and filter aging, resulting in delayed fault warning and maintenance strategies, increasing equipment failure risk and maintenance costs. Filter core blockage detection relies on simple differential pressure monitoring, ignoring the influence of filter core internal microstructure changes and particle morphology on blockage, making it difficult to accurately determine the blockage location and severity, affecting dust removal efficiency and system stable operation. The energy-saving control strategy is usually a single-dimensional start-stop control, lacking comprehensive linkage adjustment of the fan, filter core, and purification parameters, and cannot achieve optimal energy consumption management and environmental protection goals during tail gas treatment, ultimately resulting in low overall system energy efficiency and difficulty in meeting environmental emission standards. SUMMARY

[0003] Therefore, it is necessary to provide an energy-saving and environmentally-friendly tail gas treatment control system and method based on a cremator to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, an energy-saving and environmentally-friendly tail gas treatment control method based on a cremator includes the following steps:

[0005] Step S1: Collecting the tail gas emitted by the cremator and detecting the pollutant concentration to obtain pollutant concentration data; based on the pollutant concentration data, classifying the pollution level to obtain first pollution level data and second pollution level data;

[0006] Step S2: According to the first pollution level data, performing multi-stage tail gas purification simulation to obtain multi-stage tail gas purification data; according to the multi-stage tail gas purification data, detecting high-energy-consumption tail gas treatment equipment to obtain high-energy-consumption tail gas treatment equipment data; based on the high-energy-consumption tail gas treatment equipment data, diagnosing fan faults to obtain fan fault data;

[0007] Step S3: According to the second pollution level data, a medium-intensity tail gas purification simulation is performed to obtain medium-intensity tail gas purification data; based on the medium-intensity tail gas purification data, dust removal efficiency analysis is performed to obtain dust removal efficiency data; according to the dust removal efficiency data, filter core blockage detection is performed to obtain filter core blockage data;

[0008] Step S4: According to the fan fault data, a redundant fan start-stop strategy control is performed to obtain redundant fan start-stop control data; according to the filter core blockage data, tail gas treatment parameter dynamic adjustment is performed to obtain tail gas adjustment treatment parameters; according to the tail gas adjustment treatment parameters and the redundant fan start-stop control data, tail gas energy-saving and environment-friendly control is performed to obtain tail gas treatment control data.

[0009] The present application improves the recognition ability of tail gas composition complexity and dynamic change by introducing the fine detection and grade division mechanism of pollutant concentration data, realizes the accurate classification of tail gas pollution level, and lays the foundation for subsequent differentiated treatment. Through multi-stage tail gas purification simulation and medium-intensity tail gas purification simulation, the purification process can be dynamically adjusted according to different pollution levels, avoiding resource waste caused by fixed process, and realizing more targeted and efficient purification control. The monitoring of high-energy consumption tail gas treatment equipment starts from energy consumption data and extends to in-depth diagnosis of internal running state, including fan failure, filter bag aging, dust adhesion and other dimensions, which can timely find equipment abnormalities and effectively reduce system failure rate and maintenance cost. In the dust removal efficiency analysis process, particle morphology analysis and filter core blockage mechanism recognition are introduced to improve the multi-angle perception ability of filter core state, break through the limitation of traditional differential pressure method, accurately judge the blockage position and its severity, and thus ensure the continuous and stable operation of the dust removal system. Redundant fan start-stop control realizes dynamic response combined with fan fault data, improves the redundant reliability of system operation through load regulation and precise execution of start-stop strategy. The tail gas treatment parameter adjustment is based on the filter core blockage state to further improve the real-time response and energy matching ability of tail gas treatment. Finally, the tail gas treatment control integrates redundant fan control and parameter dynamic adjustment to realize the collaborative optimization control of energy consumption, environmental protection and stability, greatly improving the overall energy efficiency and pollutant treatment capacity of the system.

[0010] Preferably, the present specification also provides an energy-saving and environment-friendly tail gas treatment control system based on a cremator, which is used to execute the energy-saving and environment-friendly tail gas treatment control method based on a cremator as described above, and the energy-saving and environment-friendly tail gas treatment control system based on a cremator comprises:

[0011] A pollution level division module is configured to collect tail gas discharged by the cremator and perform pollutant concentration detection to obtain pollutant concentration data; based on the pollutant concentration data, pollution level division is performed to obtain first pollution level data and second pollution level data;

[0012] The high-energy consumption tail gas treatment equipment detection module is used for multi-stage tail gas purification simulation according to the first pollution level data, obtaining multi-stage tail gas purification data; high-energy consumption tail gas treatment equipment detection is carried out according to the multi-stage tail gas purification data, obtaining high-energy consumption tail gas treatment equipment data; fan fault diagnosis is carried out based on the high-energy consumption tail gas treatment equipment data, obtaining fan fault data;

[0013] The dust removal efficiency analysis module is used for middle-intensity tail gas purification simulation according to the second pollution level data, obtaining middle-intensity tail gas purification data; dust removal efficiency analysis is carried out based on the middle-intensity tail gas purification data, obtaining dust removal efficiency data; filter core blockage detection is carried out according to the dust removal efficiency data, obtaining filter core blockage data;

[0014] The tail gas energy-saving and environmental protection control module is used for redundancy fan start-stop strategy control according to the fan fault data, obtaining redundancy fan start-stop control data; tail gas treatment parameter dynamic adjustment is carried out according to the filter core blockage data, obtaining tail gas adjustment treatment parameter; tail gas energy-saving and environmental protection control is carried out according to the tail gas adjustment treatment parameter and the redundancy fan start-stop control data, obtaining tail gas treatment control data.

[0015] The energy-saving and environmental protection tail gas treatment control system based on the cremator of the application can realize any one of the energy-saving and environmental protection tail gas treatment control methods based on the cremator, is used as a medium for joint operation and signal transmission between modules, and is used for completing the energy-saving and environmental protection tail gas treatment control method based on the cremator. The internal modules of the system cooperate with each other, improve the cremator tail gas purification efficiency, and improve the precision and automation level of energy-saving and environmental protection control. BRIEF DESCRIPTION OF DRAWINGS

[0016] Other characteristics, objects and advantages of the application will become more apparent from the following detailed description of non-restrictive embodiments made with reference to the accompanying drawings:

[0017] Fig. 1 A step flowchart of the energy-saving and environmental protection tail gas treatment control method based on the cremator of the application is shown in the figure;

[0018] Fig. 2 A detailed step flowchart of step S1 in the application is shown in the figure;

[0019] Fig. 3 A tail gas treatment equipment schematic diagram in the application is shown in the figure;

[0020] The implementation of the object of the application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0021] The technical method of the present application will be described clearly and completely in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0022] In addition, the drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, and do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] To achieve the above-mentioned purpose, please refer to Figs. 1 to 3 The present application provides an energy-saving and environment-friendly tail gas treatment control method based on a cremator, which comprises the following steps:

[0025] Step S1: collecting the exhaust gas of the cremator and detecting the pollutant concentration to obtain pollutant concentration data; based on the pollutant concentration data, pollution level classification is performed to obtain first pollution level data and second pollution level data;

[0026] In this embodiment, the exhaust gas of the cremator is collected, and a gas sampler is used to continuously sample the exhaust gas in the exhaust pipe of the cremator. The sampling frequency is set to 1 time per minute, and the sampling time is not less than 10 seconds to ensure the representativeness of the sampling sample. The sampling device is equipped with multiple sensors, including but not limited to non-dispersive infrared sensor (NDIR) for CO and CO2 concentration detection, electrochemical sensor for NOx and SO2 concentration detection, and laser scattering particulate matter sensor for particulate matter (PM2.5 and PM10) concentration detection. The sensor measurement data is collected to the data acquisition unit after signal amplification and filtering processing to eliminate environmental interference. The pollutant concentration data is sent to the pollution level division module through the real-time data transmission module. The first pollution level threshold is set as CO concentration not less than 100 mg / m 3 , NOx concentration not less than 80 mg / m 3 , and particulate matter concentration not less than 50 mg / m 3 , and the second pollution level threshold is set as CO concentration less than 100 mg / m 3 and higher than 50 mg / m 3 , NOx concentration less than 80 mg / m 3 and higher than 40 mg / m 3 , and particulate matter concentration less than 50 mg / m 3 and higher than 25 mg / m 3 . The pollution level division is realized by threshold comparison of multiple pollutant concentration data. The calculation process uses a logic judgment module to compare each pollutant concentration with the corresponding threshold one by one, and calculates the overall pollution index by combining the weighted average algorithm. According to the index size, the data is divided into first pollution level data and second pollution level data, and finally the pollution level data is output to the subsequent tail gas purification simulation module.

[0027] Step S2: According to the first pollution level data, multi-stage tail gas purification simulation is carried out to obtain multi-stage tail gas purification data; according to the multi-stage tail gas purification data, high energy consumption tail gas treatment equipment detection is carried out to obtain high energy consumption tail gas treatment equipment data; based on the high energy consumption tail gas treatment equipment data, fan fault diagnosis is carried out to obtain fan fault data;

[0028] In this embodiment, according to the pollution level data, the operation of the bag dust removal module is triggered by control instructions. The working pressure of this module is controlled at 20 kPa, the filter bag filtering area is set to 200 square meters, the filtering wind speed is limited to 1.2 m / s-1.5 m / s, and the particulate matter concentration is monitored in real time to ensure that the particulate matter purification rate reaches more than 80%. Subsequently, the desulfurization and denitrification module is triggered according to the output data of the dust removal module, and the desulfurizing agent addition concentration is set to 1500 mg / m 3, the desulfurization and denitration catalyst activity temperature is maintained at 350°C ± 10°C, the catalytic reaction time is 2 seconds, the desulfurization and denitration efficiency is collected in real time by an online flue gas analyzer, and gaseous pollutant purification data is generated. Then start the catalytic oxidation module, the catalyst type of this module is platinum-rhodium alloy catalyst, the catalytic temperature is controlled at 400°C, the reactor residence time is set to 1.5 seconds, and the catalytic efficiency is calculated by measuring the change of total organic matter (TVOC). Finally, the output data of each stage of the purification module is real-time aggregated and the overall multi-stage tail gas purification data is calculated using numerical integration method. Then, according to the multi-stage purification data, the power consumption of the smoke dust filtering equipment is counted, the power analyzer is used to measure the equipment power, and the power threshold is set to 2kW. If the power consumption exceeds the threshold, it is determined as a high-power smoke dust filtering equipment. The selected equipment is subjected to bag filter clogging detection, the differential pressure sensor is used to measure the pressure difference before and after the filter bag, and the threshold is set to 1500Pa. If the pressure difference exceeds the threshold, it is determined as clogging. The dust adhesion force at the clogging site is measured by a laser particle size analyzer to determine the particle size and morphology characteristics of particulate matter, and the adhesion force data is calculated by combining the particulate matter adhesion model. The abnormality of filter material micropore is observed by scanning electron microscope (SEM) to observe the change of filter bag micropore structure, and aging evaluation is carried out by combining pore size distribution statistical analysis. According to the filter bag aging degree and power data, the contribution degree of high energy consumption is calculated, and the threshold is set to more than 10% of the net power loss. Finally, the high energy consumption tail gas treatment equipment is determined by comprehensive analysis, and the high energy consumption tail gas treatment equipment data is output.

[0029] Step S3: simulate medium-intensity tail gas purification according to the second pollution level data to obtain medium-intensity tail gas purification data; analyze dust removal efficiency based on the medium-intensity tail gas purification data to obtain dust removal efficiency data; and detect filter core clogging according to the dust removal efficiency data to obtain filter core clogging data;

[0030] In this embodiment, the second pollution level data is uploaded to the medium-intensity tail gas purification simulation platform, and the simulation environment parameters are set, including a purification agent dosage of 0.3 kg / min, a reactor catalyst activity temperature of 300°C, a tail gas flow rate controlled at 5 m / s, a flow field simulation refresh period of 3 seconds, and a kinetic calculation time step of 0.5 seconds. The flow field simulation module simulates the tail gas flow and pollutant reaction process by computational fluid dynamics (CFD) method to output medium-intensity tail gas purification data. Then, a laser particle size analyzer is used to measure the particle size distribution of smoke dust in the tail gas, the particle size range is set to 0.1 μm to 10 μm, and the particle size data is used for morphology division to extract fibrous and spherical particles respectively. The fibrous smoke dust is subjected to fiber winding state analysis by a fiber winding detector to measure the pressure drop change of the fiber net and calculate the pressure drop change value to evaluate the clogging trend. The spherical smoke dust is subjected to settling velocity calculation based on Stokes law, the settling velocity is related to the particle size and density, the gravitational acceleration in the calculation formula is 9.81 m / s 2 , and the fluid viscosity adopts the air dynamic viscosity value of 1.81 × 10-5 Pa s, the residence time of particles in the purification system is calculated and its removal rate is estimated. The overall dedusting efficiency data is calculated by using the weighted average method to integrate the dedusting efficiency of fibrous and spherical smoke dust. Finally, the filter core blockage detection is started according to the dedusting efficiency data, and the local pressure difference of the filter core is measured by using the pressure difference sensors before and after the smoke channel. The threshold is set to 1200 Pa or more for blockage warning, and the filter core blockage data is output.

[0031] Step S4: Redundant fan start-stop strategy control is performed according to the fan fault data to obtain redundant fan start-stop control data; tail gas treatment parameter dynamic adjustment is performed according to the filter core blockage data to obtain tail gas adjustment treatment parameters; tail gas energy-saving and environmentally-friendly control is performed according to the tail gas adjustment treatment parameters and the redundant fan start-stop control data to obtain tail gas treatment control data.

[0032] In this embodiment, based on the fan fault data, a PLC controller is used to execute the redundant fan start-stop strategy, and the redundant fan start threshold is set to start the standby fan when the main fan operating power is lower than 75% and the vibration sensor detects that the vibration amplitude exceeds 20 mm / s, and the fan temperature exceeds 80℃. The fan start-stop signal is transmitted in real time through the digital IO port to realize automatic control of the redundant fan. According to the filter core blockage data, the tail gas treatment parameters are dynamically adjusted, including fan speed, purifying agent dosage and filtering air speed. The fan speed adjustment range is 800 rpm to 1500 rpm, the purifying agent dosage range is 0.1 kg / min to 1.0 kg / min, and the filtering air speed is adjusted between 1.0 m / s to 1.5 m / s. The adjustment strategy is based on a closed-loop control algorithm, and a PID controller is used to calculate the deviation in real time and adjust the parameters to ensure that the tail gas treatment system operates in the optimal state. Finally, the system uses industrial control bus (such as Modbus TCP) to synchronously send tail gas energy-saving and environmentally-friendly control instructions according to the tail gas adjustment treatment parameters and the redundant fan start-stop control data, completes the generation and execution of the overall tail gas treatment control data, and realizes the energy-saving and environmentally-friendly treatment process of the cremator tail gas.

[0033] Especially important is that step S4 includes the following steps:

[0034] Step S41: Extract abnormal condition trigger parameters according to fan fault data;

[0035] In this embodiment, three-axis acceleration data (unit: m / s 2), and real-time RPM (revolutions per minute) data is obtained in combination with the fan speed sensor. The vibration intensity signal is subjected to frequency spectrum analysis by fast Fourier transform (FFT), and the main peak amplitude variation in the 300 Hz to 1200 Hz frequency band is extracted; if the main peak frequency shifts by more than ± 10 Hz and the amplitude rises by more than 30% of the baseline mean value, it is marked as an abnormal shaft system imbalance. At the same time, temperature data is collected by the bearing temperature sensor, and if the temperature exceeds 75°C (set threshold) and remains above 5 minutes, it is marked as a lubrication abnormality. Finally, four abnormal working condition triggering parameters are extracted, including “frequency offset (Hz)”, “vibration mean amplitude (m / s 2 )”, “bearing temperature (°C)”, and “axial transient acceleration deviation rate”.

[0036] Step S42: matching the redundant fan configuration strategy based on the abnormal working condition triggering parameters;

[0037] In this embodiment, the abnormal working condition parameters are compared with a preset fault response strategy table, which is divided into three response intervals according to different fault levels. For example, when the frequency offset is greater than ± 20 Hz, the vibration amplitude is greater than 3.0 m / s 2 , the bearing temperature is greater than 80°C, and the vibration trend shows a continuous growth pattern, the third level redundant fan replacement scheme is activated (simultaneously activating the standby fan No. 1 and the air volume feedback control device). According to the matching result, the corresponding redundant fan configuration strategy is selected, including specific parameters such as “enabled fan number”, “air volume setting (m 3 / h)”, “intervention delay (seconds)”, “parallel operation time period (minutes)”, etc. The strategy is read by the control unit and cached in the Flash storage area of the device control board.

[0038] Step S43: fan load distribution based on the redundant fan configuration strategy, to obtain redundant fan load control data;

[0039] In this embodiment, the main control PLC system sends a frequency adjustment instruction (unit: Hz) to the fan frequency converter, and the air volume target set in the configuration strategy is used for air speed matching. The air volume is obtained by real-time joint calculation of the air speed sensor and the ventilation cross-sectional area (unit: m 2 ). For example, when the target air volume is 1200 m 3 / h, the ventilation channel cross-sectional area is 0.6 m 2 , and the expected air speed is 5.56 m / s. The main fan and the redundant fan are divided into loads in a 70:30 or 50:50 ratio, and the output frequency is adjusted by the frequency converter to achieve precise control. Finally, a set of data is formed, including “main fan speed (RPM)”, “redundant fan speed (RPM)”, “air volume distribution ratio (%)”, and “load adjustment time point (seconds)”, which is used to record the fan load distribution state.

[0040] Step S44: Execute start-stop logic judgment based on redundant fan load regulation data to obtain redundant fan start-stop control data.

[0041] In this embodiment, after reading the redundant fan load regulation data, the start-stop logic judgment is executed. If the redundant fan operating load is continuously lower than 20% of the rated load for 5 minutes, and the main fan fault flag is cleared (0 signal), the redundant fan shutdown instruction is issued; if the load is stable at more than 50% and the fault signal exists, it continues to run. The start-stop control logic is executed based on time as the reference period (the period is set to 10 seconds), and the load ratio and running state identification bit are calculated every period. The judgment variables include "load ratio threshold = 20%" "continuous ultra-low load time = 300s" "main fan fault bit = 1 / 0" and the like. Finally, the redundant fan start-stop control data is obtained, including "start-stop signal (on = 1 / off = 0)" "control execution time (ms)" "control target fan number".

[0042] Step S45: According to the filter core blockage data, the tail gas treatment parameter is dynamically adjusted to obtain the tail gas adjustment treatment parameter.

[0043] In this embodiment, based on the filter core differential pressure value (unit: Pa) and the blockage level (1-5) contained in the filter core blockage data, the backflushing period and backflushing pressure are set. If the filter core differential pressure is higher than 3000Pa, and the blockage level is above 4, the backflushing interval is set not to exceed 5 minutes, and the backflushing pressure needs to reach 0.6MPa. The control system will read the pressure sensor and time controller data in real time, and dynamically adjust the working time sequence of the dust removal module combined with the historical blockage trend curve, including the key parameters such as "backflushing frequency (times / h)" "backflushing duration (seconds)" "air valve opening and closing time ratio (%)". The above parameters are dynamically adjusted by the programmable controller to control the electromagnetic valve and air valve execution device, and the complete tail gas adjustment treatment parameter set is output.

[0044] Step S46: According to the tail gas adjustment treatment parameter and the redundant fan start-stop control data, the tail gas energy-saving and environmental protection control is performed to obtain the tail gas treatment control data.

[0045] In this embodiment, the exhaust gas treatment parameters output in step S45 and the redundant fan start / stop control data from step S44 are input to the exhaust gas energy-saving control unit. The control logic judgment module is invoked, and exhaust gas treatment control operations are executed in conjunction with the fan operating status and dust removal system status. Specifically, the execution process includes: when the redundant fan is activated and the backflushing cycle is shortened to less than 3 minutes, the pressure value of the main exhaust gas treatment duct is simultaneously reduced by 5%-10% to prevent overpressure; if the fan is shut down, the residence time of the treated gas is automatically extended to more than 7 seconds. The exhaust gas emission delay parameter, purifier addition adjustment coefficient, and exhaust gas guide valve opening (controlled within the range of 30%-80%) set by the control module are all used as core execution control variables. All adjustment results form an exhaust gas treatment control dataset, including "purification flow rate (m³ / s)". 3 " / h)" "Backflush execution status" "Guide valve angle (°)" "Exhaust gas delay emission setting (s)".

[0046] Preferably, step S1 includes the following steps:

[0047] Step S11: Collect exhaust gas from the cremator to obtain exhaust gas sampling data;

[0048] In this embodiment, exhaust gas from the cremator is collected using a gas sampling system installed inside the cremator's exhaust pipe. The sampling probe is made of high-temperature resistant stainless steel, with a temperature range of -40℃ to 600℃. The sampling pipeline is equipped with a condenser to control the temperature at 5℃, preventing water vapor from affecting the sample. The sampling frequency is set to once per minute, with a single sampling duration of 10 seconds. The sampling flow rate is stabilized at 0.5 L / min, and a constant volume sampling method is used to ensure consistent sampling volume. The sampling data is transmitted to the data acquisition module via wired or fiber optic cable. The sampling data includes gaseous pollutant components and particulate matter concentration, and the data format is real-time digital signal. The sampling system is calibrated periodically, with the calibration gas concentration according to the national standard GB / T16157-1996, to ensure the accuracy and stability of the sampling data.

[0049] Step S12: Perform pretreatment of pollutants based on exhaust gas sampling data to obtain purification pretreatment data;

[0050] In this embodiment, according to the tail gas sampling data, the water vapor content in the tail gas is reduced to below 5% by using an automatic condensation and dehydration device to avoid the influence of water on subsequent analysis. Then, a multi-stage filter system is used, with a coarse filter mesh size of 10 μm and a fine filter mesh size of 1 μm to remove large particle suspensions. The purification pretreatment data consists of water content, particulate matter concentration, and preliminary separation data of gas components. Chemical pretreatment is performed on gaseous pollutants, and absorption method is used to neutralize SO2 and part of NOx by alkaline liquid spraying device, with the spraying liquid pH value controlled at 9.0±0.2, and the spraying liquid flow rate dynamically adjusted according to the tail gas flow rate to ensure effective absorption of pollutants. The parameters in the pretreatment process are monitored and fed back in real time by sensors to ensure stable operation of the pretreatment and output of standardized purification pretreatment data.

[0051] Step S13: Based on the purification pretreatment data, a plurality of pollutant separations are performed to obtain pollutant separation data;

[0052] In this embodiment, based on the purification pretreatment data, a plurality of pollutant separations are performed, and the pollutants in the tail gas are separated into a plurality of components by using gas separation technology. A membrane separation device is used, with a polymeric composite membrane as the membrane material, a membrane with high gas permeability and selectivity to SO2 and NOx is selected, the working pressure is controlled at 0.1 MPa to 0.3 MPa, and the membrane surface area is designed to be 50 square meters to ensure that the treatment capacity meets the tail gas flow demand. Low-temperature condensation technology is used to physically separate heavy metals and organic matter, with a condensation temperature of -10°C and a fixed condensation time of 60 seconds to ensure that the pollutants are fully condensed and precipitated. The multi-group pollutant separation data includes gas component concentration, separation efficiency, and residual concentration, which are detected in real time by an online gas chromatograph with a detection accuracy of ±0.1 ppm, and the data is transmitted in real time to the data processing unit.

[0053] Step S14: Based on the pollutant separation data, concentration detection is performed to obtain pollutant concentration data;

[0054] In this embodiment, based on the pollutant separation data, concentration detection is performed, and high-precision gas analyzers are used to determine the concentration of each group of pollutants. The instruments include a non-dispersive infrared detector (NDIR) for CO and CO2, a chemiluminescence detector for NOx, a ultraviolet fluorescence detector for SO2, and a laser light scattering method for particulate matter PM2.5 and PM10. The sampling period of each detection instrument is set to 5 seconds, and the data resolution reaches 0.01 mg / m 3The instrument ensures stable measurement data through a built-in temperature and pressure compensation system, and the detection data is transmitted to the pollution level classification system through an industrial Ethernet. Before use, the instrument is calibrated according to the relevant national standard GB / T16157-1996, and the calibration period is once a week. The concentration detection data includes single pollutant concentration and combined concentration of multiple indicators, and all data are recorded in real-time digital form.

[0055] Step S15: Classifying pollution levels based on pollutant concentration data to obtain first pollution level data and second pollution level data.

[0056] In this embodiment, the pollution level is classified based on the pollutant concentration data, and the pollutant concentration is compared with the standard limit value to perform item-by-item threshold judgment. The judgment conditions of the first pollution level are CO concentration ≥ 100 mg / m 3 , NOx concentration ≥ 80 mg / m 3 , and particulate matter concentration ≥ 50 mg / m 3 ; the judgment conditions of the second pollution level are 50 mg / m 3 ≤ CO concentration < 100 mg / m 3 , 40 mg / m 3 ≤ NOx concentration < 80 mg / m 3 , and 25 mg / m 3 ≤ particulate matter concentration < 50 mg / m 3 . The weighted pollution index calculation formula is used, with CO weight of 40%, NOx weight of 35%, and particulate matter weight of 25%. The pollution index is obtained by multiplying the concentration of each pollutant by the corresponding weight and summing. According to the pollution index, the threshold is set, and the pollution index ≥ 0.7 is the first pollution level, and 0.4 ≤ pollution index < 0.7 is the second pollution level. The pollution level classification result is output in digital signal form for subsequent tail gas purification simulation.

[0057] Preferably, step S15 comprises the following steps:

[0058] Step S151: determining a target pollution factor based on the pollutant concentration data;

[0059] In this embodiment, the target pollution factor is determined based on the pollutant concentration data, and the real-time pollutant concentration data collected by the sensor network is used as input, mainly including five main pollutant indicators such as CO, carbon monoxide, NOx, nitrogen oxides, SO2, particulate matter PM2.5 and PM10. For example, the CO limit value is 100 mg / m 3 , the NOx limit value is 80 mg / m 3 , the SO2 limit value is 60 mg / m 3 , and the PM2.5 limit value is 35 mg / m 3The concentration of the pollutant is compared to determine whether it exceeds the limit value. If the concentration of the pollutant exceeds the limit value, the pollutant is selected as the target pollution factor. At the same time, if the concentration of the pollutant is above 80% of the limit value but does not reach the limit value, the pollutant is also listed as a target pollution factor of concern. This process is automatically executed by setting a threshold value unit, and the specific numerical value of the threshold value is the above-mentioned national standard limit value and the corresponding percentage. The result of determining the target pollution factor is output in the form of pollutant category and concentration value data, providing an accurate basis for subsequent weight allocation.

[0060] Step S152: allocating weights to pollutants based on the target pollution factors to obtain weighted pollution index data;

[0061] In this embodiment, the weights of the pollutants are allocated based on the target pollution factors, and the target pollution factors are assigned weights using the weighted method, and the sum of the weights is 1. The weights of the pollutants are determined according to the results of the environmental impact and human health risk assessment, and the weight standard is based on the environmental risk assessment report and historical emission data published by the national environmental protection department. For example, the weight of CO is set to 0.4, the weight of NOx is 0.35, the weight of particulate matter PM2.5 is 0.15, and the weight of SO2 is 0.1. The weight allocation process uses weighted summation calculation, which multiplies the current concentration of each target pollution factor by the corresponding weight to calculate the weighted pollution index. The calculation formula of the weighted pollution index is: index = ∑(pollutant concentration x weight). The pollutant concentration value is measured in real time, and the weight value is pre-set and fixed in the system database. The result is output in numerical form as input for pollution level classification.

[0062] Step S153: classifying pollution levels according to the weighted pollution index data to obtain first pollution level data and second pollution level data.

[0063] In this embodiment, the pollution levels are classified according to the weighted pollution index data. First, the pollution level threshold standard is set, and the weighted pollution index is divided into three intervals: weighted index ≥ 0.7 is defined as the first pollution level, indicating high concentration of pollutants; weighted index between 0.4 and 0.7 is defined as the second pollution level, indicating medium concentration of pollutants; and weighted index below 0.4 is the low pollution level and is not processed. During the classification process, the system reads the weighted pollution index in real time, calculates and compares the threshold intervals hour by hour. For index data of different time periods, time series statistics are performed to ensure the stability and accuracy of the pollution level classification results. The pollution level classification results are transmitted to the exhaust gas purification simulation module in the form of digital signals to start the corresponding processing process. This step includes reading the weighted pollution index value, applying the pre-set threshold condition judgment, and formatting the result into standard pollution level codes, such as "1" for the first level and "2" for the second level, for subsequent processing by the system.

[0064] Preferably, the multi-stage tail gas purification simulation according to the first pollution level data in step S2 comprises:

[0065] The purification process is determined according to the first pollution level data, thereby generating a multi-stage purification process starting instruction;

[0066] In this embodiment, the system receives the first pollution level data obtained by dividing the pollution level, which is represented by a digital coding method, and the range is 1-3 levels, wherein "1" represents a high pollution level. According to the data, the preset control logic is used to determine the purification process level to be started. The specific determination rule is: if the first pollution level data is equal to 1, the three-stage purification process is started; if it is equal to 2, the two-stage purification process is started; if it is equal to 3, the one-stage purification process is started. The determination logic is realized by the built-in judgment module of the programmable logic controller (PLC), and the determination result is converted into the corresponding starting instruction code as an output signal. The starting instruction is sent to the tail gas treatment equipment control unit in the form of a standard digital signal, and the instruction includes the start-stop signal and the operating parameter setting of the corresponding purification module, such as the operating air volume set to 5000 cubic meters per hour, the air speed set to 5 meters per second, and the temperature maintained above 150℃, to ensure the normal operation of the subsequent module.

[0067] Based on the multi-stage purification process starting instruction, the bag dust removal module is activated to obtain primary particulate matter purification data;

[0068] In this embodiment, the bag dust removal module is activated based on the multi-stage purification process starting instruction to obtain primary particulate matter purification data. This step is realized by receiving the multi-stage purification process starting instruction, opening the electric control valve of the bag dust collector, and starting the pulse blowing system. The operating parameters of the bag dust removal module include the filtering air volume (4000-6000 cubic meters / hour), the filtering speed (1.5-2.5 meters / minute), the blowing pressure (0.4-0.6 megaPascal), and the blowing period (30-60 seconds). A differential pressure sensor is used to monitor the pressure difference before and after the bag filter area in real time, and the pressure difference range is controlled within 250-500 Pascal, and the blowing ash removal program is automatically triggered when the range is exceeded. During the operation of the bag dust removal module, a laser particle counter and a light scattering sensor are used to measure the particulate matter concentration in the inlet and outlet flue gas, collect data such as particle size distribution and particulate matter mass concentration, and upload the data through a data acquisition system to form primary particulate matter purification data. The data is in mg / m 3 , and the time sampling period is 10 seconds. The value is used for subsequent working condition adjustment of the desulfurization and denitrification module.

[0069] Based on the primary particulate matter purification data, the desulfurization and denitrification module is started to obtain gaseous pollutant purification data;

[0070] In this embodiment, the desulfurization and denitrification module is started based on the primary particulate matter purification data, and gaseous pollutant purification data is obtained. The desulfurization and denitrification module startup condition is that the particulate matter concentration in the primary particulate matter purification data is less than 80 mg / m 3 , and the inlet SO2 concentration is higher than 60 mg / m 3 . The system automatically starts the desulfurization spray tower and the selective catalytic reduction (SCR) device. In the desulfurization spray tower, the spray liquid pH value is controlled at 7.0-8.0, the spray liquid flow rate is 2-5 liters / minute, and the spray pressure is maintained at 0.2-0.5 megapascals. The operating temperature of the SCR device is controlled at 320-400°C, and the ammonia gas dosage is adjusted in real time according to the NOx concentration, usually 5-20 mg / m 3 . The gaseous pollutant concentration is detected in real time by the continuous emission monitoring system (CEMS), including the concentrations of SO2, NOx, NH3, etc., with a detection frequency of 1 second. The collected gaseous pollutant concentration data are measured in ppm or mg / m 3 , and the data are transmitted to the control system through the acquisition device to generate gaseous pollutant purification data for reference by the catalytic oxidation module.

[0071] According to the gaseous pollutant purification data, the catalytic oxidation module is started, and organic matter treatment data is obtained.

[0072] In this embodiment, the catalytic oxidation module is started when the VOC concentration in the gaseous pollutant purification data exceeds 20 mg / m 3 , and the reactor temperature is maintained at 300-450°C during operation. The catalyst type is platinum-palladium-based, and the catalyst loading density is 0.5-0.8 g / cm 3 . The reactor gas residence time is set to 0.5-1 second, and the air volume is controlled at 4000 cubic meters / hour. The module startup is signaled by the control unit to power on the electric heating device, and the heating power is adjusted by the feedback of the temperature sensor. The organic matter treatment data is detected by the gas chromatograph to measure the change in VOC concentration before and after the reaction, with a data sampling period of every 5 minutes. The measurement data are expressed in percentage reduction of concentration, recorded and uploaded to the data management system for subsequent full-process tail gas output monitoring.

[0073] According to the organic matter treatment data, full-process tail gas output monitoring is performed, and multi-stage tail gas purification data is obtained.

[0074] In this embodiment, the whole-process tail gas output monitoring collects CO, NOx, SO2, particulate matter and other indicators at the end of the tail gas through a multi-point flue gas sampling system, uses electrochemical sensors and optical scattering method sensors, and the monitoring data acquisition frequency is once per second. The tail gas temperature is monitored and controlled at 120-180°C, the flue gas flow rate is measured in the range of 0-20 meters / second, and the flue gas flow is measured by a vortex flowmeter. The collected tail gas indicators are transmitted to the control center in real time, and the data is recorded using a time series database. According to the comparison of the concentration of each component of the tail gas and the emission limit value, a multi-stage tail gas purification data report is formed, which includes the fields of time stamp, pollutant name, concentration value, treatment process state, etc. The data is used for feedback control of the overall tail gas treatment process and subsequent equipment maintenance decision-making.

[0075] Preferably, the high-energy tail gas treatment equipment detection according to the multi-stage tail gas purification data in step S2 includes:

[0076] According to the multi-stage tail gas purification data, the power of the smoke dust filtering equipment is counted;

[0077] In this embodiment, the real-time current, voltage and running time data of each smoke dust filtering equipment in the multi-stage tail gas purification system are collected, and the power monitoring instrument is used to monitor the power consumption of each equipment in real time. The actual power consumption of each smoke dust filtering equipment is calculated through the power calculation formula (wherein U is the voltage, I is the current, is the power factor, the value range is 0.85-0.95), the actual power consumption of each smoke dust filtering equipment is calculated. The data acquisition period is once per second, and the hourly and daily power consumption is accumulated. During the counting process, the data is corrected in combination with the equipment operating condition parameters (such as the filtering air volume, the inlet and outlet pressure difference), the abnormal power values during non-normal start-up or maintenance period are excluded, and the accuracy of the statistical results is ensured. Finally, the power data table of each equipment is generated, with the unit of kilowatt (kW), as the basic data for subsequent equipment energy consumption analysis.

[0078] Based on the power of the smoke dust filtering equipment, high-power smoke dust filtering equipment is identified, and high-power smoke dust filtering equipment data is obtained;

[0079] In this embodiment, for the counted smoke dust filtering equipment power data, the high-power threshold is set to be more than 85% of the rated power of the equipment, and the rated power parameter library of the equipment is used as the standard basis. By comparing the real-time power data with the rated power threshold, the equipment whose power continuously exceeds the threshold and whose running time exceeds 30 minutes is selected. The selection is executed through the PLC automatic judgment logic, and the list of high-power equipment is output, including the equipment number, power value, duration and other information. The system generates a high-power smoke dust filtering equipment data report based on the selection results, which provides a target equipment range for subsequent bag clogging detection.

[0080] According to the high-power smoke dust filtering equipment data, the bag blocking detection is performed to obtain bag blocking data;

[0081] In this embodiment, for the identified high-power equipment, the differential pressure transmitter is used to measure the flue gas pressure difference before and after the filter bag of the equipment, and the differential pressure range is set to 50-500 Pa. The filter bag blocking state judgment rule is: when the differential pressure continuously exceeds 400 Pa and the maintenance time exceeds 10 minutes, it is determined that the bag is blocked. The differential pressure value is recorded in real time through the data acquisition system, and the sampling frequency of the differential pressure sensor is 1 second / time. Combined with the flue gas flow data and temperature and humidity parameters, the environmental factor interference is eliminated, and the filter bag blocking position and blocking severity are accurately located. The bag blocking data is generated, including the blocking position number, the blocking degree level (mild, moderate, severe) and the time stamp.

[0082] According to the bag blocking data, dust adhesion force analysis is performed to obtain dust adhesion force data;

[0083] In this embodiment, the dust samples collected from the filter bag blocking position are tested for physical parameters using a surface tension tester and a dust adhesion force measuring device. The test parameters include adhesion strength, the measurement range is 0.1 to 10 Newton, the test environment temperature is controlled at 25℃±2℃, and the humidity is controlled at 50%±5%. The static contact angle method is used to measure the contact angle of dust and filter material surface, the value range is 30°-120°, reflecting the dust adhesion property. During the test process, the data of adhesion force changing with time is collected to form the dust adhesion force time sequence curve. The average adhesion force and its standard deviation are calculated by data analysis software to generate the dust adhesion force data report.

[0084] Based on the dust adhesion force data, filter material micro-abnormality detection is performed to obtain filter material micro-pore abnormality data;

[0085] In this embodiment, high-resolution scanning electron microscope (SEM) is used to image the filter material micro-pore structure of the filter bag, with a resolution of nanometer level, to detect the size, shape and porosity change of the filter material surface micro-pores. By comparing the normal filter material micro-pore structure parameters (pore size average diameter 3-5 microns, porosity 45%-55%), the abnormal micro-pore area is detected, and the abnormal conditions such as blockage, deformation and damage are identified. The image processing algorithm is used to quantitatively analyze the area proportion of the abnormal area, and the area exceeding 5% is defined as filter material micro-pore abnormality. The filter material micro-pore abnormality data is formed, including the abnormal type, the abnormal degree level and the corresponding position coordinates.

[0086] According to the filter material micro-pore abnormality data, the filter bag aging evaluation is performed to obtain filter bag aging data;

[0087] In this embodiment, according to the type and degree of filter material micropore abnormalities, combined with filter bag service time and operating conditions, the filter bag aging evaluation standard is used for evaluation. The evaluation index includes micropore blockage rate, porosity reduction percentage, filter material strength loss percentage (measured by tensile test, standard tensile strength 10 MPa, after aging, reduced to 7 MPa or less is judged as obvious aging). The data acquisition system records the aging parameters, combined with equipment maintenance records and running time for comprehensive analysis, outputs the filter bag aging grade, divided into mild (0-20% aging), moderate (20-50% aging), severe (more than 50% aging). Form a filter bag aging data report, including aging grade, aging area and recommended replacement time.

[0088] Based on the filter bag aging data, the high energy consumption contribution of the smoke dust filtration equipment is analyzed, the high energy consumption contribution data is obtained, and the high energy consumption tail gas treatment equipment is determined according to the high energy consumption contribution data, and the high energy consumption tail gas treatment equipment data is obtained.

[0089] In this embodiment, the influence of pressure difference increase caused by filter bag aging on equipment power consumption is calculated, and the power-pressure difference relationship formula ΔP increase = K x ΔP x Q is used, wherein ΔP is the pressure difference change (unit Pa), Q is the filter air volume (cubic meters / second), and K is the equipment coefficient (value 0.7-1.0). Combined with the actual operation data, separate the additional power consumption part caused by filter bag aging. Compare the high energy consumption contribution value with the total power of the equipment, and confirm that the equipment whose proportion exceeds 20% is the high energy consumption tail gas treatment equipment. The determination result includes equipment number, high energy consumption contribution percentage, operating parameters and aging index, etc., as the basis for subsequent fan fault diagnosis and maintenance plan, and generates high energy consumption tail gas treatment equipment data report.

[0090] Preferably, the fan fault diagnosis based on the high energy consumption tail gas treatment equipment data in step S2 comprises:

[0091] Based on the high energy consumption tail gas treatment equipment data, bearing lubricating oil pollution detection is carried out, and bearing lubricating oil pollution data is obtained;

[0092] In this embodiment, a lubricating oil sampling device is installed at the bearing of the high-energy exhaust gas treatment equipment using a field acquisition system to automatically extract lubricating oil samples at regular intervals, with a sampling frequency set to once every 12 hours. The collected lubricating oil samples are sent to a spectral analyzer for element composition detection, using infrared spectroscopy (FTIR) and Raman spectroscopy to analyze the content of water, metal particles, oxides, and other contaminants in the lubricating oil. The water content threshold is set at 0.1% (mass ratio), and the metal particle concentration threshold is set at 10 ppm (parts per million by weight). By comparing the data obtained by the analyzer with the threshold, the part exceeding the threshold is determined as lubricating oil contamination. The collection process is strictly in accordance with the lubricating oil sampling standards specified by the equipment manufacturer, ensuring that the sampling position is accurate and that the contaminant concentration data is representative, and finally outputting bearing lubricating oil contamination data, including the type, concentration, and timestamp of the contaminants.

[0093] Metallic particle data is obtained by identifying metallic particles from the bearing lubricating oil contamination data.

[0094] In this embodiment, the lubricating oil sample is processed through a magnetic separator, and high-sensitivity magnetic detectors are used to separate iron and non-iron metal particles, with a detection particle size range set to 0.5 microns to 100 microns. Subsequently, a scanning electron microscope (SEM) is used to analyze the morphology and composition of the separated metal particles, with a resolution of nanometers, focusing on identifying the shape (such as particles, flakes, and fibers) and elemental composition (Fe, Cu, Al, Cr, etc.) of the particles. The particle morphology data is correlated with the bearing operating state, and the particle size distribution and number density are statistically analyzed to determine the activity and wear type of the metal particles. The data collection frequency is synchronized with the lubricating oil sampling, and the analysis results are output in the form of particle concentration (units: pieces per milliliter of lubricating oil) and particle size distribution chart, forming the metal particle data.

[0095] Impeller wear data is obtained by evaluating impeller wear based on the metal particle data.

[0096] In this embodiment, the particle elemental composition and morphological characteristics in the metal particle data are used in combination with a historical impeller material composition and wear mechanism database to determine the wear type (such as pitting, abrasive wear, and fatigue spalling). The wear severity threshold is set at a particle concentration exceeding 5000 pieces per milliliter and an iron element proportion exceeding 70%, serving as a high-wear warning. By calculating the particle concentration change rate per unit time, the wear intensification trend is evaluated. Comprehensive analysis is conducted in combination with impeller operating condition parameters (speed, load, temperature) to form an impeller wear level (mild, moderate, severe) and wear location estimation. The output impeller wear data includes wear level, predicted wear location, and historical trend data.

[0097] Blade bending data is obtained by measuring blade bending based on the impeller wear data.

[0098] In this embodiment, a laser scanning measurement system is used to perform three-dimensional surface scanning on the impeller blades, with a measurement accuracy of ±0.01 mm. By comparing with the reference blade CAD model, the deformation of the blades in each coordinate axis is calculated, and the blade curvature is mainly detected. The threshold of blade curvature is set to 0.1 mm beyond the design tolerance, which is considered as abnormal. The measurement data is converted into bending angle and maximum deformation distance, combined with the blade running speed and load data, and the deformation trend is analyzed. The measurement process needs to be carried out in the stopped state of the equipment to ensure the stability of the measurement, and the blade bending data report is output, including blade number, maximum bending angle, deformation position and measurement time.

[0099] Based on the blade bending data, the rotating unbalance data is obtained;

[0100] In this embodiment, a high-precision three-axis acceleration sensor installed at the bearing of the fan is used to collect vibration signals, and the sampling frequency is set to 5 kHz for time domain and frequency domain analysis. The vibration amplitude corresponding to the rotating frequency is identified by fast Fourier transform (FFT), and if the vibration amplitude exceeds the standard limit value of 0.5g (gravity acceleration) of the equipment operation, it is determined that there is rotating unbalance. Combined with the blade bending data, the vibration characteristics and their correlation with blade deformation are analyzed, and the unbalance mass and unbalance moment are calculated. The rotating unbalance data is output, including unbalance amplitude, unbalance phase angle, frequency spectrum and quantitative relationship of corresponding blade deformation.

[0101] Based on the rotating unbalance data, the fan fault diagnosis is carried out, and the fan fault data is obtained.

[0102] In this embodiment, the vibration analysis results, impeller wear and blade bending data are integrated, and fault recognition is performed according to the preset fault threshold. The fault classification criteria include bearing abnormality (vibration frequency deviation ±5%), impeller wear (particle concentration exceeding threshold), blade bending (deformation exceeding 0.1 mm), and rotating unbalance (vibration exceeding 0.5g). A fault report is automatically generated by the fault diagnosis algorithm, and the report content covers fault type, severity level, fault start time and warning level. The fan fault data is output, including diagnosis conclusion, fault time node and corresponding parameter value, which provides basis for subsequent maintenance.

[0103] Preferably, step S3 comprises the following steps:

[0104] Step S31: uploading the second pollution level data to the medium-intensity tail gas purification simulation platform;

[0105] In this embodiment, the second pollution level data is imported into the data interface of the tail gas purification simulation platform in a structured format (such as JSON or XML). The uploaded data includes the type of pollutants, concentration value and timestamp, the data collection interval is 1 minute, and the accuracy of the pollutant concentration reaches 0.01 mg / m 3 . Ensure data integrity and accuracy, check the format and range of uploaded data through the data verification module to ensure that it meets the platform requirements. After uploading, the data is archived in the platform database for subsequent simulation calls.

[0106] Step S32: Set the purification agent dosage range to 0.1 kg / min-1.0 kg / min, and the reaction temperature adjustment range of the reactor catalyst activity to 200℃-400℃;

[0107] In this embodiment, the purification agent dosage range is set to 0.1 kg / min to 1.0 kg / min, and the reaction temperature adjustment range of the reactor catalyst activity is set to 200℃ to 400℃. According to the composition and concentration characteristics of the tail gas, the dosage rate of the automatic dosing system of the purification agent is configured, and the flowmeter is used to monitor the dosage in real time, ensuring that the dosage error is controlled within ±0.01 kg / min. The reactor is equipped with a thermocouple array to monitor the catalyst activity reaction temperature in real time, and the temperature controller adjusts the reactor heating system, with a temperature adjustment accuracy of ±2℃. The above parameters are input through the control interface, and the parameter range is locked to prevent misoperation.

[0108] Step S33: Set the tail gas flow field simulation refresh period to 1s-5s, and the purification reaction kinetics calculation time step to 0.1s-1s;

[0109] In this embodiment, the tail gas flow field simulation refresh period is set to 1 second to 5 seconds, and the purification reaction kinetics calculation time step is set to 0.1 second to 1 second. The simulation platform collects input tail gas parameters in real time, updates the flow field state according to the set refresh period, and ensures that the simulation data reflects the latest working condition. The explicit time integration method is used for kinetics calculation, and the calculation time step is set to 0.1 second to ensure numerical stability, and the maximum is not more than 1 second to avoid the decline of simulation accuracy. The setting of time step and refresh period is executed through the simulation system parameter configuration module and stored in the system configuration file.

[0110] Step S34: Run the flow field simulation module in the tail gas purification simulation platform to obtain medium-intensity tail gas purification data;

[0111] In this embodiment, the flow field simulation module in the tail gas purification simulation platform is run, and the numerical calculation method is used to simulate the tail gas flow and reaction process. The finite volume method is used to discretize the three-dimensional flow field of the tail gas in the reactor, and the number of calculation grids is about 500,000 to 1,000,000 units. The boundary conditions are set according to the actual equipment inlet and outlet parameters, and the inlet tail gas temperature, pressure, flow rate and pollutant concentration data are derived from real-time monitoring. During the simulation process, the purification agent concentration distribution, temperature field and reactant concentration are dynamically calculated, and the purification efficiency, pollutant distribution and reaction temperature data are output to form the medium-intensity tail gas purification data.

[0112] Step S35: Dust removal efficiency analysis based on medium-intensity tail gas purification data, to obtain dust removal efficiency data;

[0113] In this embodiment, dust removal efficiency analysis is performed based on medium-intensity tail gas purification data, and dust removal efficiency calculation is based on the change rate of captured particulate matter concentration. A particle size distribution analyzer is used to measure the particle size and number of particulate matter before and after purification, with a particle size range of 0.1 microns to 10 microns and a sampling frequency of once per minute. The dust removal efficiency is calculated according to the formula E=(C_in-C_out) / C_in*100%, where C_in is the particulate matter concentration before purification and C_out is the particulate matter concentration after purification. The analysis results include dust removal efficiency at different particle size segments, and the data is recorded in real time to form the dust removal efficiency data.

[0114] Step S36: Filter core clogging detection according to dust removal efficiency data, to obtain filter core clogging data.

[0115] In this embodiment, filter core clogging detection is performed according to dust removal efficiency data, and the degree of filter core clogging is obtained by measuring the pressure difference between the inlet and outlet of the smoke dust channel through a pressure difference sensor. The pressure difference threshold is set to 1200Pa, and if the value exceeds this value, it is determined that the filter core is clogged. The performance degradation speed of the filter core is analyzed in combination with the trend of the dust removal efficiency data, and the differential analysis method is used to identify the critical point of efficiency decline. The filter core clogging data includes the current pressure difference value, clogging level (mild, moderate, severe), filter core usage time and historical clogging record, and the data is uploaded to the control system in real time to support filter core replacement and maintenance decision-making.

[0116] Preferably, step S35 includes the following steps:

[0117] Step S351: Smoke dust collection based on medium-intensity tail gas purification data, to obtain smoke dust data;

[0118] In this embodiment, a high-precision smoke sampler is installed at the sampling point in the flue gas outlet of the tail gas purification device. The sampling frequency is set to automatically collect once every 1 minute, and the sampling time lasts not less than 10 seconds to ensure that the sampling volume is stable and representative. The sampler uses a filter membrane to capture smoke, and the filter membrane aperture is 0.3 microns to ensure effective capture of fine particulate matter. After sampling, the mass difference before and after the filter membrane is measured by the weight method to calculate the smoke mass concentration in the unit volume of tail gas. During data collection, the tail gas temperature, humidity and flow rate are recorded in real time to correct the sampling volume and ensure data accuracy. The smoke data includes sampling time, sampling volume, smoke mass concentration and related environmental parameters.

[0119] Step S352: Perform particle size analysis based on the smoke data to obtain smoke particle size data;

[0120] In this embodiment, a laser particle size analyzer (such as a laser scattering particle size analyzer) is used to measure the particle size distribution of the collected smoke samples. The measurement range covers 0.1 microns to 10 microns, and the particle size distribution is divided into 20 particle size segments. The sample is dried to control the humidity below 5% to avoid moisture affecting the measurement. The laser particle size analyzer is configured with a light source wavelength of 650 nm, and the measurement is repeated not less than 3 times, and the average value is taken. The volume distribution and number distribution of each particle size segment are recorded during analysis. The particle size data corresponds to the sampling time one by one and is stored in the database as basic data for subsequent morphology division.

[0121] Step S353: Morphology division based on smoke particle size data to obtain fibrous smoke data and spherical smoke data;

[0122] In this embodiment, according to the particle size data and morphology characteristic database, particles with a particle size in the range of 0.1 microns to 1 micron and a large aspect ratio (aspect ratio ≥ 3) are defined as fibrous smoke, and particles with a particle size in the range of 0.1 microns to 10 microns and an aspect ratio close to 1 (0.9 to 1.1) are defined as spherical smoke. A scanning electron microscope (SEM) is used to verify the morphology of typical sampling samples, with a scanning magnification of 2000x to 5000x and a resolution of 10 nanometers. The particle morphology is identified and matched with the particle size data. According to the morphology characteristic database and the particle size threshold, the smoke data is classified and counted to obtain fibrous smoke data and spherical smoke data, including particle number, mass concentration and size distribution.

[0123] Step S354: Fibrous winding detection based on fibrous smoke data to obtain fibrous winding data; pressure drop change evaluation based on fibrous winding data to obtain pressure drop change data; and fibrous smoke removal rate calculation based on pressure drop change data;

[0124] In this embodiment, the collected filter cross-section fiber samples are scanned by high-resolution optical microscopy combined with image analysis software, with a magnification setting of 1000 times and an image resolution of 2048x2048 pixels. The number and density of fiber intersections are identified by image processing algorithms, and the winding degree index is calculated. The winding index range is defined as 0 to 1, with 0 representing no winding and 1 representing complete winding. Combined with the winding index, the pressure difference between the two sides of the filter is measured by a differential pressure sensor with an accuracy of ±1 Pa. The pressure difference change and the winding index establish a linear corresponding relationship model to obtain the pressure drop change data. According to the pressure drop change data, the fibrous soot removal rate is calculated by the formula E_f = 1―(ΔP_measured÷ΔP_initial), where ΔP_measured is the current pressure drop and ΔP_initial is the pressure drop of the unblocked filter. The removal rate is expressed in percentage.

[0125] Step S355: Calculate the settling velocity based on the spherical soot data; determine the deposition position according to the settling velocity; statistically analyze the particle residence time based on the deposition position; calculate the spherical soot removal rate according to the particle residence time;

[0126] In this embodiment, the settling velocity of spherical particles is calculated according to Stokes' law, using the formula v = (2 / 9) × (ρ_p―ρ_f) × g × d^2 ÷ μ, where ρ_p is the particle density (2500 kg / m 3 ), ρ_f is the exhaust gas density (1.2 kg / m 3 ), g is the acceleration of gravity 9.81 m / s 2 , d is the particle diameter, and μ is the dynamic viscosity of the exhaust gas (1.8 × 10^-5 Pa·s). The settling velocity of spherical soot with different particle sizes is calculated, with the unit of settling velocity being m / s. According to the settling velocity, combined with the geometric parameters of the exhaust gas pipeline, the particle deposition position is determined, which is expressed as the length position coordinate of the pipeline. Using the particle deposition position, the residence time of the particles in the deposition zone is calculated, which is based on the air flow velocity (2 m / s) and the distance of the deposition position. Through the particle residence time, the particle removal efficiency formula E_s = 1―exp(―k × t) is used, where k is the removal rate constant and t is the residence time, to obtain the spherical soot removal rate.

[0127] Step S356: Integrate the fibrous soot removal rate and the spherical soot removal rate to obtain the dust removal efficiency data.

[0128] In this embodiment, the fibrous soot removal rate E_f and the spherical soot removal rate E_s are weighted and averaged according to the respective soot mass concentration weight, and the calculation formula is E_total = (C_f * E_f + C_s * E_s) ÷ (C_f + C_s), where C_f and C_s are the mass concentrations of fibrous and spherical soot respectively. The calculation process uses real-time collected mass concentration data, and the weight proportion is dynamically adjusted. The final dust removal efficiency data is output in percentage form, and the data includes a timestamp and the corresponding efficiency value, which is used for subsequent filter core blockage detection and tail gas treatment control.

[0129] Preferably, step S36 includes the following steps:

[0130] Step S361: determining a low dust removal efficiency period according to the dust removal efficiency data;

[0131] In this embodiment, real-time or historical dust removal efficiency data is obtained, and the data format includes a timestamp and the corresponding dust removal efficiency percentage. The data sampling frequency is set to once per minute. Using the set low efficiency threshold of 30%, the threshold is used as the judgment standard. When the dust removal efficiency is lower than 30%, the corresponding time period is recorded as a low dust removal efficiency period. The time period lasts at least 5 minutes to be recognized as valid, avoiding instantaneous fluctuation misjudgment. The dust removal efficiency data is processed by a sliding window average, and the window size is set to 3 minutes to smooth abnormal fluctuations. The starting and ending times of the low efficiency period are determined according to the sliding average. The low dust removal efficiency period data structure includes the starting time, the ending time and the lowest dust removal efficiency value, which is used for subsequent targeted analysis.

[0132] Step S362: detecting the soot channel pressure difference based on the low dust removal efficiency period;

[0133] In this embodiment, a pressure difference sensor is installed on the flue on both sides of the soot filter inlet and outlet. The sensor model should meet the industrial standard, the measurement range is 0-5000 Pascal, and the accuracy is ±1%. The sensor collects pressure difference data in real time, and the sampling frequency is set to 10 seconds. Ensure that the pressure difference trend is captured. In the identified low dust removal efficiency period, collect the pressure difference data in the corresponding time period. The environmental temperature and tail gas flow rate parameters are recorded synchronously through the data acquisition system, and the parameters are used for pressure difference data correction. When the pressure difference value is greater than the preset normal running upper limit of 500 Pascal, it is determined that there is an abnormal load in the soot channel. The pressure difference data storage format includes a timestamp, a pressure difference value and a corrected pressure difference value, which provides data support for subsequent load pressure calculation.

[0134] Step S363: determining the filter channel load pressure according to the soot channel pressure difference;

[0135] In this embodiment, according to the pressure difference data of the flue gas channel, the load pressure of each filter channel is calculated through the pressure distribution model. The filter structure parameters are preset as follows: the total number of filter cartridges is 100, the cross-sectional area of a single filter channel is 0.005 square meters, and the overall filter area of the filter cartridge is 0.5 square meters. The load pressure calculation formula is P_load = ΔP / N, where ΔP is the collected pressure difference value, N is the number of filter cartridges, and P_load is the load pressure of a single filter channel. During the calculation process, the pressure distribution is corrected in combination with the flue gas concentration and flow rate, and the tail gas flow rate data (normal working range of 2-5 m / s) is used for pressure correction. The load pressure of the filter channel is in pascal, and the data is recorded in time series to ensure dynamic monitoring of the load change of the filter cartridge.

[0136] Step S364: identifying the high-pressure area of the filter cartridge according to the load pressure of the filter channel;

[0137] In this embodiment, all filter channel load pressures are monitored in real time, and the high-pressure determination threshold is set to 100 pascals. The filter channel with a load pressure exceeding 100 pascals is marked as a high-pressure area. A distributed pressure sensing network is used to transmit the pressure data of each filter channel to a centralized control unit for parallel calculation. By comparing the load pressure distribution of all filter channels, the spatial position of the high-pressure area is identified. The spatial position is represented by the filter cartridge number and its coordinates in the filter cartridge array, and the data structure includes the filter cartridge number, load pressure value, and coordinate information. The high-pressure area state is refreshed every 10 seconds, and the high-pressure area list is dynamically updated to provide real-time clogging risk prompts.

[0138] Step S365: performing filter cartridge clogging detection based on the high-pressure area of the filter cartridge to obtain filter cartridge clogging data.

[0139] In this embodiment, the filter cartridges in the high-pressure area are subjected to clogging detection, and the differential pressure method and impedance method are combined to evaluate the clogging condition. The differential pressure method utilizes the pressure difference between the inlet and outlet of the filter cartridge, and the clogging determination threshold is set to 150 pascals. If the threshold is exceeded, the filter cartridge is determined to be clogged. The impedance method detects particle clogging through the change in the electrical resistance of the filter cartridge. A resistance sensor is used to measure the surface resistance value of the filter cartridge, and the normal resistance range is 10-50 ohms. If the resistance exceeds 70 ohms, it is determined to be clogged. The filter cartridge clogging data includes the clogged filter cartridge number, the clogging level (mild, moderate, and severe, which are distinguished according to the pressure and resistance threshold values), the clogging time, and the position coordinates. The clogging detection results are uploaded to the control system in real time to guide the filter cartridge cleaning or replacement operation.

[0140] Especially important is that step S365 includes:

[0141] performing filter cartridge pressure difference determination based on the high-pressure area of the filter cartridge to obtain filter cartridge pressure difference data;

[0142] In this embodiment, first use the differential pressure sensor distributed in the filter core both ends to collect differential pressure. Select a capacitive differential pressure sensor (for example, ± 500 Pa range, sensitivity 0.1 Pa), its acquisition frequency is set to 1 Hz, and the acquisition time is set to 60 seconds continuously. The differential pressure value is transmitted to the data acquisition module after being collected by the sensor, and an analog-to-digital converter with 12-bit A / D conversion accuracy is used to complete the conversion from analog to digital. The data transmission path communicates stably through the CAN bus. To improve data stability, a Savitzky-Golay filter is introduced in the system to suppress noise in the original differential pressure data, with a sliding window of 11 and an order of 3. The final output differential pressure data is saved in the form of time series, and forms a "filter differential pressure data array", with data unit Pa and time unit second.

[0143] According to the filter differential pressure data, the pressure loss change data is calculated.

[0144] In this embodiment, after obtaining the differential pressure time series data of the filter, the first-order difference method is used to calculate the change rate of adjacent differential pressure values. The differential pressure change rate per second is represented by ΔP(t) = P(t) - P(t-1). If the average change rate in 10 seconds is greater than 5 Pa / s, it is determined as "pressure loss mutation". At the same time, multi-point linear regression is introduced to fit the slope of the differential pressure curve trend, and the fitting window is set to 10 seconds. Determine whether the fitting slope continues to rise more than 0.3 Pa / s 2 . The pressure loss change data is finally output in three forms of sliding window mean, slope value and difference fluctuation rate, with units of Pa / s and Pa / s 2 , which is used for subsequent particle deposition trend judgment.

[0145] According to the pressure loss change data, the particle deposition trend data is obtained.

[0146] In this embodiment, during the particle deposition trend analysis process, first extract the continuously rising section in the pressure loss change, and calculate the peak growth amplitude and frequency using the envelope analysis method. The envelope calculation uses the Hilbert transform method to construct the instantaneous amplitude sequence. The goal of trend analysis is to identify the timing and location characteristics of particle deposition, so the differential pressure growth section is mapped with the filter core space region combined with the spatial index of the filter core. Introduce the deposition factor α = (differential pressure rising rate x rising duration) / filter core cross-sectional area (unit: Pa-s / cm 2 ) for normalized calculation, when α exceeds 2.5 Pa-s / cm 2 , it is determined as a high deposition trend area. The output result is marked with the deposition trend level (high, medium, low) and time stamp of each space section in the form of a two-dimensional table.

[0147] According to the deposition trend data, filter core blockage feature recognition is performed to obtain blockage feature data;

[0148] In this embodiment, based on the deposition trend grade division, combined with the filter core space structure (divided into 10 segments in the length direction, and each segment is further divided into 3 layers), a blockage feature index matrix is established in each region. The blockage feature indexes include: the maximum pressure difference rise value in the region, the rise slope, the duration, and the pressure difference fall time difference. Each index is described in a matrix form, and a comprehensive blockage factor β is calculated, which is defined as: β = max (ΔP) Slope T_up / T_down, wherein T_up is the pressure difference rise duration, and T_down is the fall time. The threshold value β≥250 (based on experimental calibration data) is set as the severe blockage recognition standard. The output result includes the β value and the state identification (normal / mild blockage / severe blockage) of each region.

[0149] According to the blockage feature data, blockage degree clustering recognition is performed to obtain regional blockage degree data;

[0150] In this embodiment, the blockage degree clustering adopts the K-Means unsupervised clustering algorithm, and a one-dimensional array composed of all the region blockage feature β values is input into the clustering model. The clustering number k is set to 3, representing three categories of mild blockage, moderate blockage, and severe blockage. Before clustering, the β value is subjected to Min-Max normalization processing, and the mapping range is [0, 1]. The upper limit of the iteration number is set to 100, and the clustering convergence standard is that the center point change amount is less than 0.001. After clustering, each region is assigned to a specific category, and a regional blockage degree data table is formed, including: filter core segment number, category number, category centroid value, and distance measure. The data is saved in JSON structure for calling by the visualization module.

[0151] According to the regional blockage degree data, the whole core blockage state is quantitatively evaluated to obtain filter core blockage data.

[0152] In this embodiment, the whole core state quantitative evaluation adopts a weighted cumulative method, and different weights are assigned to each blockage category: the weight of mild blockage is 0.2, the weight of moderate blockage is 0.6, and the weight of severe blockage is 1.0. The whole core blockage index γ = ∑ (W_i × N_i) / N_total, wherein W_i is the weight of the i-th category, N_i is the number of regions of the category, and N_total is the total number of regions. When γ≥0.65, it is judged as “overall blockage critical state”, and when γ≥0.85, it is judged as “overall severe blockage”. The output filter core blockage data includes: the whole core blockage index γ, the blockage level identification (normal / warning / severe), the blockage region list and the corresponding weight value. The data result is used to trigger the redundant filter core switching or automatic ash cleaning control logic.

[0153] Preferably, the present specification also provides an energy-saving and environment-friendly tail gas treatment control system based on a cremator, which is used to execute the energy-saving and environment-friendly tail gas treatment control method based on a cremator as described above, and the energy-saving and environment-friendly tail gas treatment control system based on a cremator comprises:

[0154] A pollution level division module is configured to collect tail gas discharged by the cremator, detect the concentration of pollutants, and obtain pollutant concentration data; and divide the pollution level based on the pollutant concentration data to obtain first pollution level data and second pollution level data.

[0155] A high-energy-consumption tail gas treatment equipment detection module is configured to perform multi-stage tail gas purification simulation based on the first pollution level data to obtain multi-stage tail gas purification data; perform high-energy-consumption tail gas treatment equipment detection based on the multi-stage tail gas purification data to obtain high-energy-consumption tail gas treatment equipment data; and perform fan fault diagnosis based on the high-energy-consumption tail gas treatment equipment data to obtain fan fault data.

[0156] A dust removal efficiency analysis module is configured to perform medium-intensity tail gas purification simulation based on the second pollution level data to obtain medium-intensity tail gas purification data; perform dust removal efficiency analysis based on the medium-intensity tail gas purification data to obtain dust removal efficiency data; and perform filter core blockage detection based on the dust removal efficiency data to obtain filter core blockage data.

[0157] A tail gas energy-saving and environment-friendly control module is configured to perform redundant fan start-stop strategy control based on the fan fault data to obtain redundant fan start-stop control data; perform dynamic adjustment of tail gas treatment parameters based on the filter core blockage data to obtain tail gas adjustment treatment parameters; and perform tail gas energy-saving and environment-friendly control based on the tail gas adjustment treatment parameters and the redundant fan start-stop control data to obtain tail gas treatment control data.

[0158] Therefore, from any viewpoint, the embodiments should be considered as exemplary and non-limiting, the scope of the present application being defined by the appended claims and not by the above description, and it is intended to embrace all variations falling within the meaning and range of equivalents of the elements of the claims.

[0159] The above description is merely one specific implementation of the application, which enables a person skilled in the art to understand or implement the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application should not be limited to the embodiments shown herein, but should be consistent with the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator, characterized in that, Includes the following steps: Step S1: Collect exhaust gas from the cremator and detect pollutant concentrations to obtain pollutant concentration data; classify the pollution level based on the pollutant concentration data to obtain first pollution level data and second pollution level data; Step S2: Perform multi-stage exhaust gas purification simulation based on the first pollution level data to obtain multi-stage exhaust gas purification data; High-energy-consuming exhaust gas treatment equipment was tested based on multi-stage exhaust gas purification data, resulting in the following data: The power of the dust filtration equipment was statistically analyzed based on multi-stage exhaust gas purification data. Based on the power identification of dust filtration equipment, high-power dust filtration equipment is obtained to acquire data on high-power dust filtration equipment. Bag clogging data is obtained by detecting bag clogging based on data from high-power dust filtration equipment. Dust adhesion force analysis was performed based on the bag clogging data to obtain dust adhesion force data; Filter media micropore anomaly detection was performed based on dust adhesion force data to obtain filter media micropore anomaly data; The aging of the filter bags was evaluated based on the abnormal micropore data of the filter media, and the aging data of the filter bags was obtained. Based on filter bag aging data, a high energy consumption contribution analysis was performed on the power of the dust filtration equipment to obtain high energy consumption contribution data. Based on the high energy consumption contribution data, high energy consumption exhaust gas treatment equipment was determined, and high energy consumption exhaust gas treatment equipment data was obtained. Based on data from high-energy-consuming exhaust gas treatment equipment, fan fault diagnosis was performed, resulting in fan fault data, including: Bearing lubricating oil contamination data were obtained by detecting bearing lubricating oil contamination based on data from high-energy-consuming exhaust gas treatment equipment. Metal particle identification was performed based on bearing lubricating oil contamination data to obtain metal particle data. Impeller wear data is obtained by evaluating the metal particle data; Blade bending data is obtained by measuring blade bending based on impeller wear data; Rotational imbalance analysis was performed based on blade bending data to obtain rotational imbalance data; Based on rotational imbalance data, wind turbine fault diagnosis is performed to obtain wind turbine fault data; Step S3: Perform medium-intensity exhaust gas purification simulation based on the second pollution level data to obtain medium-intensity exhaust gas purification data; perform dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; perform filter element clogging detection based on the dust removal efficiency data to obtain filter element clogging data. Step S4: Perform redundant fan start-stop strategy control based on fan fault data to obtain redundant fan start-stop control data; dynamically adjust exhaust gas treatment parameters based on filter blockage data to obtain exhaust gas adjustment treatment parameters; perform exhaust gas energy-saving and environmental protection control based on exhaust gas adjustment treatment parameters and redundant fan start-stop control data to obtain exhaust gas treatment control data.

2. The energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator as described in claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect exhaust gas from the cremator to obtain exhaust gas sampling data; Step S12: Perform pretreatment of pollutants based on exhaust gas sampling data to obtain purification pretreatment data; Step S13: Based on the purification pretreatment data, perform multiple pollutant separations to obtain pollutant separation data; Step S14: Based on the pollutant separation data, perform concentration detection to obtain pollutant concentration data; Step S15: Based on the pollutant concentration data, classify the pollution level to obtain the first pollution level data and the second pollution level data.

3. The energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator as described in claim 2, characterized in that, Step S15 includes the following steps: Step S151: Determine the target pollutant based on pollutant concentration data; Step S152: Assign pollutant weights based on the target pollutant factors to obtain weighted pollution index data; Step S153: Divide the pollution levels according to the weighted pollution index data to obtain the first pollution level data and the second pollution level data.

4. The energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator as described in claim 1, characterized in that, Step S2, which involves simulating multi-stage exhaust gas purification based on the first pollution level data, includes: The purification process is determined based on the first pollution level data, thereby generating multi-level purification process start instructions; The bag filter module is activated based on the multi-stage purification process start command to obtain primary particulate matter purification data. The desulfurization and denitrification module is started based on the primary particulate matter purification data to obtain gaseous pollutant purification data. The catalytic oxidation module is activated based on the gaseous pollutant purification data to obtain organic matter treatment data; Based on the organic matter treatment data, the entire process of exhaust gas output is monitored to obtain multi-stage exhaust gas purification data.

5. The energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator as described in claim 1, characterized in that, Step S3 includes the following steps: Step S31: Upload the second pollution level data to the medium-intensity exhaust gas purification simulation platform; Step S32: Set the dosage range of the purifying agent to 0.1 kg / min-1.0 kg / min, and adjust the reaction temperature range of the reactor catalyst activity to 200°C-400°C; Step S33: Set the exhaust gas field simulation refresh cycle to 1s-5s, and the purification reaction kinetics calculation time step to 0.1s-1s; Step S34: Run the flow field simulation module in the exhaust gas purification simulation platform to obtain medium-intensity exhaust gas purification data; Step S35: Analyze the dust removal efficiency based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; Step S36: Detect filter clogging based on dust removal efficiency data to obtain filter clogging data.

6. The energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator according to claim 5, characterized in that, Step S35 includes the following steps: Step S351: Collect smoke and dust data based on medium-intensity exhaust gas purification data; Step S352: Perform particle size analysis based on the dust data to obtain dust particle size data; Step S353: Based on the particle size data, classify the morphology to obtain data on fibrous dust and spherical dust. Step S354: Detect fiber entanglement based on fibrous dust data to obtain fiber entanglement data; evaluate pressure drop change based on fiber entanglement data to obtain pressure drop change data; calculate fibrous dust removal rate based on pressure drop change data; Step S355: Calculate the settling rate based on the spherical dust data; determine the deposition location based on the settling rate; calculate the particle residence time based on the deposition location; calculate the spherical dust removal rate based on the particle residence time. Step S356: Integrate the removal rates of fibrous dust and spherical dust to obtain dust removal efficiency data.

7. The energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator according to claim 5, characterized in that, Step S36 includes the following steps: Step S361: Determine the period of low dust removal efficiency based on the dust removal efficiency data; Step S362: Detect the pressure difference in the dust removal channel based on the period of low dust removal efficiency; Step S363: Determine the filter channel load pressure based on the pressure difference in the dust and smoke channel; Step S364: Identify the high-pressure area of ​​the filter element based on the filter channel load pressure; Step S365: Detect filter element blockage based on the high-pressure area of ​​the filter element to obtain filter element blockage data.

8. An energy-saving and environmentally friendly exhaust gas treatment and control system based on a cremator, characterized in that, For executing the energy-saving and environmentally friendly exhaust gas treatment and control method based on a cremator as described in claim 1, the energy-saving and environmentally friendly exhaust gas treatment and control system based on a cremator includes: The pollution level classification module is used to collect exhaust gas from crematoriums and detect pollutant concentrations to obtain pollutant concentration data; based on the pollutant concentration data, the pollution level is classified to obtain first pollution level data and second pollution level data. The high-energy-consumption exhaust gas treatment equipment detection module is used to simulate multi-stage exhaust gas purification based on the first pollution level data to obtain multi-stage exhaust gas purification data; to detect the high-energy-consumption exhaust gas treatment equipment based on the multi-stage exhaust gas purification data to obtain high-energy-consumption exhaust gas treatment equipment data; and to diagnose fan faults based on the high-energy-consumption exhaust gas treatment equipment data to obtain fan fault data. The dust removal efficiency analysis module is used to simulate medium-intensity exhaust gas purification based on the second pollution level data to obtain medium-intensity exhaust gas purification data; to perform dust removal efficiency analysis based on the medium-intensity exhaust gas purification data to obtain dust removal efficiency data; and to perform filter element clogging detection based on the dust removal efficiency data to obtain filter element clogging data. The exhaust gas energy-saving and environmental protection control module is used to perform redundant fan start-stop strategy control based on fan fault data to obtain redundant fan start-stop control data; to dynamically adjust exhaust gas treatment parameters based on filter blockage data to obtain exhaust gas adjustment treatment parameters; and to perform exhaust gas energy-saving and environmental protection control based on exhaust gas adjustment treatment parameters and redundant fan start-stop control data to obtain exhaust gas treatment control data.

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