A portable multi-channel flue gas analyzer

Abstract

The application discloses a kind of portable multi-channel flue gas analyzers, it is related to flue gas monitoring technical field, including flue gas filtration system, flue gas analysis system and control system, by scene configuration module constructs dynamic flue gas analysis scene model, quantifies the adsorption efficiency factor of different particulate matter components to specific gas and pressure difference-integral coefficient, data compensation module then utilizes this model, combined with the pressure difference change of filtration system real-time monitoring calculates particulate matter effective adsorption integral, and according to this, the gas content data of analysis system detection is accurately compensated, and the output corrected true concentration, the instrument integrates multiple analysis modules in portable box, with multi-channel synchronous measurement capability and intelligent diagnosis function, can be through data correlation analysis early warning filter membrane breakage or process abnormality, finally significantly improves the measurement accuracy, reliability and intelligent level of portable equipment under complex working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flue gas monitoring, in particular to a portable multi-channel flue gas analyzer. BACKGROUND

[0002] The flue gas analyzer is a key equipment for environmental monitoring, industrial process control and combustion efficiency evaluation, which is used for quantitative analysis of various gas component concentrations in flue gas, such as sulfur dioxide, nitrogen oxides, carbon monoxide, oxygen, etc. According to the use mode, it is mainly divided into fixed continuous emission monitoring system (CEMS) and portable flue gas analyzer.

[0003] The traditional portable flue gas analyzer usually adopts a draw-out sampling mode, and its basic working principle is: the flue gas is drawn out from the flue through a sampling pump and a probe, and after pretreatment such as dust filtration and moisture removal, it is sent to a sensor unit for analysis. Common sensor technologies include non-dispersive infrared (NDIR), electrochemical (ECD), paramagnetic oxygen (PD), etc.

[0004] However, the existing portable flue gas analyzer has several urgent problems and limitations in practical application:

[0005] 1. Measurement error problem caused by particulate matter adsorption: This is the most significant and universal source of systematic error. In order to protect the precision sensor, all flue gas analyzers are equipped with a filter device at the front end of the sampling to intercept particulate matter. These intercepted particulate matter (especially fly ash, carbon black, etc. with high specific surface area and activity) are not chemically inert, and they can cause significant physical or chemical adsorption of certain gas components in the flue gas, which leads to the gas concentration reaching the sensor being lower than the actual concentration in the flue gas, causing a continuous measurement negative deviation. The existing technology generally lacks effective compensation means for this phenomenon, and cannot adapt to the actual situation of changing particulate matter composition and adsorption characteristics under different fuels and different processes, and there are inherent defects in the authenticity and accuracy of the measurement data;

[0006] 2. Single function and insufficient data dimensionality: Traditional devices are usually positioned for "gas concentration measurement", with a single function. Although most devices are equipped with a differential pressure sensor, its purpose is limited to monitoring whether the filter membrane is blocked to prompt the user to replace it, and it does not deeply explore the rich information behind the differential pressure data, such as being unable to convert differential pressure changes into particulate matter mass concentration information, and being unable to correlate gas concentration with particulate matter state for analysis. The data dimensionality is narrow and the value is limited.

[0007] 3. Low degree of intelligence, lack of diagnostic ability: traditional analyzers only provide simple alarms when the concentration exceeds the standard, and cannot perform in-depth diagnosis on data anomalies. When the measurement value fluctuates, it is difficult for the operation and maintenance personnel to quickly distinguish whether it is a change in the emission source itself, a sampling system failure (such as filter membrane damage, pipeline leakage), or an instrument problem. It needs to rely on manual experience to check one by one, which is time-consuming and laborious, and is easy to misjudge. SUMMARY

[0008] The purpose of the present application is to provide a portable multi-channel flue gas analyzer that can perform intelligent adsorption compensation, has advanced diagnostic functions, and can adapt to various working conditions, to solve the problems raised in the above background art.

[0009] To achieve the above purpose, the present application provides the following technical solution: a portable multi-channel flue gas analyzer, comprising: a flue gas filtration system, a flue gas analysis system and a control system;

[0010] The control system includes a data compensation module and a scene configuration module. The flue gas component data and particulate matter component data in the analysis environment are input into the scene configuration module to configure the corresponding flue gas analysis scene model. The data compensation module constructs an adsorption compensation strategy for data compensation of the particulate matter adsorbed flue gas component part according to the configured flue gas analysis scene model;

[0011] The flue gas filtration system includes a filtration module for filtering particulate matter in flue gas and a differential pressure detection module. The differential pressure detection module obtains the pressure difference on both sides of the filtration module at the detection time node, and transmits the obtained pressure difference and corresponding time node data to the flue gas analysis scene model. The particulate matter accumulation data intercepted between any two time nodes is obtained according to the pressure difference change between the two time nodes;

[0012] The flue gas analysis system includes a flue gas analysis module. The flue gas analysis module detects the gas component content in the flue gas and outputs the gas content data detected at the corresponding time node;

[0013] The adsorption compensation strategy includes obtaining the detected gas content data and the corresponding time node, extracting the particulate matter accumulation data between the time node and the previous time node, calling the adsorption coefficient of the corresponding particulate matter for the flue gas component using the configured flue gas analysis scene model, and outputting the gas content adsorbed by the particulate matter as the adsorption compensation value to supplement the detected gas content data to form the corrected gas content data output.

[0014] As preferred, the specific method of configuring the flue gas analysis scene model in the scene configuration module comprises establishing an adsorption mapping table between particulate matter components and flue gas components, embedding an adsorption efficiency factor of particulate matter components on the adsorption of components in flue gas, extracting the particulate matter component with the largest proportion in particulate matter, and according to the characteristics of the particulate matter component with the largest proportion, assigning a corresponding measured differential pressure-accumulation relationship coefficient α to form a flue gas analysis scene model with the adsorption efficiency factor and α.

[0015] As preferred, the specific method of obtaining the intercepted particulate matter accumulation data between two time nodes in the flue gas analysis scene model comprises:

[0016] Receiving pressure difference and corresponding time node data to construct differential pressure-time data pair (ΔP, T), wherein (ΔP, T) is a data set {(ΔP0, T0), (ΔP1, T1), (ΔP2, T2)…(ΔP n ,T n )}, the intercepted particulate matter accumulation Δm between any two time nodes is Δm = α*(ΔP n - ΔP n-i ), the intercepted particulate matter accumulation Δm is corrected by using the adsorption efficiency factor of the flue gas analysis scene model, and the effective adsorption accumulation Δm_eff of particulate matter is obtained.

[0017] As preferred, the generation method of the corrected gas content data comprises the following steps:

[0018] Establishing the detected gas content data and the corresponding time node data set {(T0, C0), (T1, C1), (T2, C2)…(T n ,C n )}, wherein the gas content data C0 is the first detection data;

[0019] Obtaining the gas content data C n detected at the current time node, the time node T n and the effective adsorption accumulation Δm_eff of particulate matter at the time node T n-1 , calling the adsorption coefficient β of the detected gas component, thereby obtaining the adsorption compensation value ΔQ = β*Δm_eff;

[0020] Converting the obtained adsorption compensation value ΔQ into the same data format as the gas content data detected and output by the flue gas analysis module according to the corresponding adsorbed gas, and finally supplementing the gas content data C n to form corrected gas content data.

[0021] As preferred, the control system further comprises a safety warning module, which sets a difference threshold K for the detected gas content data, takes the first detected gas content data C0 as the reference value, C n When the difference between C0 and C exceeds the set difference threshold K, the corresponding time node T is extracted n When the difference between C0 and C exceeds the set difference threshold K, the corresponding time node T is extracted n-i ;

[0022] The particulate matter accumulation data between T n-i and T n is obtained, and a particulate matter accumulation change curve in the time period T n-i and T n is constructed, and the curve characteristics are associated and analyzed to issue a corresponding warning report.

[0023] As preferred, the method of associating and analyzing the curve characteristics comprises associating and analyzing the detected gas content data C n and the shape of the particulate matter accumulation change curve, constructing an analysis logic strategy, which comprises:

[0024] When the gas content data C n suddenly drops, the corresponding step-type growth curve shape and the accelerated growth-type curve shape trigger an emergency warning and a high-level warning report, respectively, and the dust removal facility is detected;

[0025] When the gas content data C n changes smoothly, the corresponding curve shape of smooth but abnormal growth rate triggers an ordinary warning report, and the process operation state is detected, and the scene model parameters are manually updated if necessary;

[0026] When the gas content data C n recovers after a transient spike in the time period, the corresponding pulse-type curve shape triggers a prompt warning report, and an event record is made.

[0027] As preferred, in the adsorption mapping table, for a complex particulate matter system containing multiple particulate matters with significant adsorption performance, the comprehensive adsorption performance factor ξ_total is calculated using a weighted average algorithm based on the mass proportion of each component (ωi): ξ_total = Σ(ωi * ξi), where i is the component number of the particulate matter, and the effective adsorption accumulation Δm_eff is calculated using the value of ξ_total.

[0028] As preferred, the flue gas analysis system further comprises an automatic calibration unit, and the adsorption compensation strategy automatically suspends data compensation before and after the flue gas analysis module performs zero-point or span automatic calibration, and updates the reference value in the data set with a new calibration reference value C0 after calibration is completed, and then re-enables the data compensation function

[0029] As preferred, the safety warning module synchronously outputs a correlation data snapshot when issuing a warning report, and the snapshot comprises a comparison chart of the gas content data change curve and the particulate matter accumulation data change curve in the abnormal time period [T n-i , T n ].

[0030] As preferred, a suitcase is further included, and the interior of the suitcase is in a layered structure, and the first layer panel, the second layer panel and the third layer panel are arranged from top to bottom.

[0031] A plurality of integrated modules composed of the control system and the flue gas filtering system are arranged side by side on the first layer panel.

[0032] A flue gas analysis system corresponding in number to the first layer panel modules is arranged on the second layer panel.

[0033] A battery for supplying power to the whole device, an electrical supply integrated system and a battery and electrical control integrated system are arranged on the third layer panel.

[0034] Ventilation openings are formed in the side surface of the suitcase, and heat dissipation fans for dissipating heat of the device are arranged.

[0035] Therefore, the portable flue gas analyzer has the advantages that:

[0036] The portable flue gas analyzer has the advantages that: BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below only represent some of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application. The drawings are all simplified schematic views, and only show the basic structure of the present application.

[0038] Figure 1 It is a schematic diagram of the overall structure of a portable multi-channel flue gas analyzer of the present application.

[0039] Figure 2 It is a schematic diagram of the internal first layer panel structure of a portable multi-channel flue gas analyzer of the present application.

[0040] Figure 3 It is a schematic diagram of the internal second layer panel structure of a portable multi-channel flue gas analyzer of the present application.

[0041] Figure 4 It is a schematic diagram of the flue gas analysis system structure of a portable multi-channel flue gas analyzer of the present application.

[0042] Figure 5 It is a schematic diagram of the internal third layer panel structure of a portable multi-channel flue gas analyzer of the present application.

[0043] Figure 6 It is a schematic diagram of the internal panel layer structure of a portable multi-channel flue gas analyzer of the present application.

[0044] Reference signs:

[0045] 1. Radiating fan; 2. Briefcase; 3. Control system; 4. Flue gas filtering system; 5. Switch; 7. Flue gas analysis system; 8. Electrical wiring; 9. Circuit integration system module; 10. Flue gas analysis module; 11. Flue gas gas path pipeline; 12. Gas path air extraction module; 13. Battery; 14. Electrical supply integration system; 15. Battery and electrical control integration system; 16. First layer panel; 17. Second layer panel; 18. Third layer panel. DETAILED DESCRIPTION

[0046] The present application will now be further described in conjunction with the drawings, obviously, the described embodiments only represent some of the embodiments of the present application, and not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application. The drawings are all simplified schematic views, and only show the basic structure of the present application.

[0047] To facilitate understanding of the present invention, a more complete description of the invention will be given below with reference to the accompanying drawings, which illustrate several embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the invention will be more thorough and complete.

[0048] All features disclosed in this specification, or steps in all methods or processes disclosed herein, may be combined in any way, except for mutually exclusive features and / or steps.

[0049] Any feature disclosed in this specification (including any appended claims, abstract, and drawings) may be replaced by other equivalent or similar features for a similar purpose, unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is merely one example of a series of equivalent or similar features.

[0050] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; they can refer to the internal communication of at least two elements or the interaction relationship of at least two elements, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0051] The following is combined Figures 1-6 The present invention provides a detailed description of one embodiment: a portable multi-channel flue gas analyzer, including an external carrying case 2, with the entire device housed inside the carrying case 2. A cooling fan 1 is installed on the side of the carrying case 2 for heat dissipation. The interior of the carrying case 2 is configured from top to bottom as a first panel 16, a second panel 17, and a third panel 18. The first panel 16 houses a module consisting of six control systems 3 and flue gas filtration systems 4. The second panel 17 houses six flue gas analysis systems 7, each corresponding to one of the control systems 3 and flue gas filtration systems 4 modules in the first panel 16. The third panel 18 houses a battery 13 for powering the device, an electrical supply integrated system 14, and a battery and electrical control integrated system 15.

[0052] The control system 3 includes a data compensation module and a scene configuration module;

[0053] The scenario configuration module constructs a flue gas analysis scenario model. The core task of this module is to "profile" the currently measured flue gas and define a specific and quantifiable analysis context.

[0054] The flue gas composition data and particulate matter composition data in the analysis environment are input into the scene configuration module to configure the corresponding flue gas analysis scene model. First, the flue gas data in the environment to be monitored is extracted from the factory side or autonomously analyzed to obtain the composition data in the flue gas and the corresponding particulate matter composition data (such as the composition in the flue gas: SO2, NOx, CO, CO2, and other substance compositions, and the particulate matter composition: SiO2, Al2O3, CaO, Na2O, K2O, and unburned carbon), establish an adsorption mapping table between the particulate matter composition and the flue gas composition, and define the adsorption affinity relationship between different particulate matter compositions and different flue gas compositions, for example:

[0055] CaO (calcium oxide) has very strong chemical adsorption (generating calcium sulfate, calcium chloride, etc.) to acidic gases such as SO2, HCl, and HF.

[0056] Unburned carbon (C) has strong physical adsorption (through van der Waals force) to VOCs (volatile organic compounds) and Hg (mercury).

[0057] SiO2 and Al2O3 (silicon dioxide and aluminum oxide) are chemically inert and have weak adsorption capacity.

[0058] On the basis of the above qualitative relationship, a quantitative adsorption efficiency factor ξ value is assigned to each pair of “particulate matter-gas” relationship. ξ is a dimensionless coefficient representing the adsorption capacity multiple of the component particulate matter relative to the standard reference (such as standard activated carbon), for example:

[0059] ξ(CaO-SO2) = 3.5 (very strong adsorption capacity);

[0060] ξ(C-Hg) = 18 (very strong adsorption capacity to mercury);

[0061] ξ(SiO2-SO2) = 0.1 (very weak adsorption capacity).

[0062] The largest proportion of particulate matter composition in the particulate matter is extracted, and the corresponding measured pressure difference-accumulation relationship coefficient α is assigned according to the characteristics of the largest proportion of particulate matter composition, forming a flue gas analysis scene model with adsorption efficiency factor and α;

[0063] Specifically, the system analyzes the input particulate matter composition data, identifies several components with the largest mass proportion and / or the highest adsorption efficiency factor, and defines them as “main effective components”.

[0064] For example, the ingredient data is: CaO: 45%, SiO2: 15%, Al2O3: 15%, C: 10%, then CaO is determined as the main effective ingredient, and the built-in knowledge base of the module stores the α values of different types of particulate matter, which are obtained through laboratory calibration.

[0065] α defines the physical mass (mg) corresponding to the unit pressure difference (Pa) generated by a specific type of particulate matter, that is, Δm = α * Δ(ΔP), and the α value varies with the characteristics of the particulate matter. Porous and lightweight carbon black particles (small α value) and dense and heavy mineral particles (large α value) have completely different actual interception masses when generating the same pressure difference. The system calls the corresponding α value (for example, α_CaO = 0.15 mg / Pa) from the library according to the determined main effective ingredient (such as CaO).

[0066] Finally, all parameters are integrated into a dynamic data structure to form an instant, computable flue gas analysis scene model.

[0067] Model output example:

[0068] Scene name: "domestic waste incineration-dry desulfurization-high calcium fly ash";

[0069] Adsorption mapping matrix (ξ_matrix): {CaO: {SO2: 3.5, HCl: 4.2}, C: {Hg: 18.0,...},...};

[0070] Pressure difference-accumulation coefficient (α): 0.15 mg / Pa;

[0071] Reference temperature and humidity: 150°C, 20%;

[0072] Timestamp: 2023-10-27 10:00:00.

[0073] The data compensation module constructs an adsorption compensation strategy for data compensation of the adsorption of flue gas components by particulate matter according to the configured flue gas analysis scene model.

[0074] The flue gas filtration system 4 includes a filtration module for filtering particulate matter in flue gas and a pressure difference detection module. The pressure difference detection module obtains the pressure difference between the two sides of the filtration module at the detection time node, and transmits the obtained pressure difference and the corresponding time node data to the flue gas analysis scene model. The pressure difference between any two time nodes is obtained to obtain the particulate matter accumulation data intercepted between the two time nodes.

[0075] Specifically, the following steps are included:

[0076] Step 1: Data synchronization and collection

[0077] High-frequency data collection: The differential pressure sensor samples the pressure values upstream and downstream of the filter membrane in real time at a high frequency (e.g., 1 Hz) and calculates the instantaneous differential pressure ΔP.

[0078] Timestamping: Each differential pressure data ΔP is labeled with a high-precision timestamp T, forming a strictly corresponding differential pressure-time data pair (ΔP, T).

[0079] Data set construction: The system continuously runs, forming a complete data set arranged in chronological order: {(ΔP0, T0), (ΔP1, T1), (ΔP2, T2) … (ΔP n , T n )}. Where (ΔP0, T0) is usually the initial baseline value when the filter membrane is clean.

[0080] Step two: Calculate the physical particulate mass accumulation (Δm)

[0081] Request calculation: When the system needs to know the amount of particulate matter intercepted in the time interval [T n-i , T n ], it will make a request to the scene model.

[0082] Get differential pressure change: The scene model queries the differential pressure values ΔP n-i and ΔP n corresponding to the time nodes from the data set and calculates the differential pressure change: Δ(ΔP) = ΔP n - ΔP n-i .

[0083] This differential pressure change directly reflects the increase in flow resistance due to the accumulation of new particulate matter.

[0084] Using the differential pressure-mass conversion coefficient (α) pre-stored in the scene model, calculate the physical mass of particulate matter: Δm = α * Δ(ΔP).

[0085] Step three: Correct to effective adsorption mass (Δm_eff)

[0086] Call adsorption efficiency factor (ξ): The system reads the pre-defined adsorption efficiency factor ξ from the currently activated flue gas analysis scene model, and calculates the effective adsorption mass: Δm_eff = Δm * ξ.

[0087] The adsorption mapping table, for a complex particle system containing multiple particles with significant adsorption performance, uses a weighted average algorithm based on the mass proportion of each component (ωi) to calculate the comprehensive adsorption performance factor ξ_total: ξ_total = Σ(ωi * ξi), where i is the component number, and uses the ξ_total value to calculate the effective adsorption amount Δm_eff.

[0088] Δm_eff is no longer a simple physical mass of particles, but is converted into an equivalent standard adsorbent amount, which answers the question "how much standard adsorption capacity does this pile of particles correspond to", thereby providing a unique and reliable basis for subsequent precise chemical adsorption compensation.

[0089] The flue gas analysis system includes a flue gas analysis module 10, a circuit integration system module 9, a gas path air extraction module 12, and a flue gas path pipeline 11. The flue gas analysis module detects the gas component content in the flue gas and outputs the detected gas content data at the corresponding time node,

[0090] The adsorption compensation strategy includes obtaining the detected gas content data and the corresponding time node, extracting the particle mass data between the time node and the previous time node, using the configured flue gas analysis scene model to call the adsorption coefficient of the corresponding particle for the flue gas component, outputting the gas content adsorbed by the particle matter as the adsorption compensation value, and supplementing it into the detected gas content data to form corrected gas content data output. The flue gas analysis system also includes an automatic calibration unit. The adsorption compensation strategy automatically pauses data compensation before and after the zero point or span automatic calibration of the flue gas analysis module, and updates the reference value in the data set with a new calibration reference value C0 after calibration is complete, and then re-enables the data compensation function;

[0091] Specifically, the following steps are included:

[0092] Step 1: Establish a time-gas content data set

[0093] Data synchronization record:

[0094] The flue gas analysis module 10 outputs a concentration value C n (e.g. SO2 = 80.5ppm) for each gas concentration measurement, and simultaneously stamps a high-precision, strictly synchronized time stamp T n for each concentration value C n .

[0095] The system organizes these data into an ordered set: {(T0,C0), (T1,C1), (T2,C2)……(T n ,C n )}.

[0096] Special meaning of Co: usually refers to the first measurement value after system startup, when the filter membrane is clean, or the reference baseline value set at a specific time (such as after calibration), which may be one of the reference values for subsequent compensation calculation.

[0097] Step two: calculate the adsorption compensation value (ΔQ)

[0098] Get the effective adsorption mass (Δm_eff):

[0099] The compensation algorithm needs to calculate the adsorption compensation value in the time interval [T n-1 , T n ], which sends a request to the flue gas analysis scene model: "Please provide the effective adsorption mass in the time period from T n-1 to T n ".

[0100] After the scene model receives the request, the following sub-steps are executed:

[0101] Query the differential pressure history data to get ΔP n - ΔP n-1 .

[0102] Calculate the physical mass: Δm = α * (ΔP n - ΔP n-1 ).

[0103] Call the adsorption efficiency factor (ξ) of the current model for correction: Δm_eff = Δm * ξ.

[0104] The scene model returns Δm_eff to the compensation algorithm.

[0105] Call the adsorption coefficient (β):

[0106] The compensation algorithm calls the scene model again to request the adsorption coefficient β of the current particulate matter to the target gas (i.e. the gas corresponding to the concentration value C n , such as SO2).

[0107] Physical meaning of β: it represents "how many milligrams of gas can be adsorbed by one milligram of effective adsorption mass of particulate matter", and this coefficient is obtained through laboratory adsorption isotherm test.

[0108] Calculate the adsorption compensation value (ΔQ):

[0109] Calculate the absolute mass of the adsorbed gas: ΔQ = β * Δm_eff.

[0110] The unit of ΔQ is mg (milligrams), which represents the amount of gas adsorbed in the time interval [T n-1 , T nThe total mass of target gas adsorbed by the newly added particulate matter during this period.

[0111] Step 3: Data Formatting and Compensation

[0112] Unit conversion and formatting:

[0113] The concentration value C output by the flue gas analysis module n The unit is usually ppm (volume concentration) or mg / m³ (mass concentration).

[0114] Therefore, the adsorbed gas mass ΔQ (mg) must be converted to a value related to C. n For a concentration value ΔC in the same unit, the adsorbed mass ΔQ is converted into the volume concentration loss ΔC under standard conditions using the ideal gas law and molar mass. The calculated concentration loss value ΔC is then added back to the original measured value C. n The final output corrected gas content data is the value that the system finally releases, which minimizes the interference of particulate matter adsorption and is closest to the true concentration of flue gas.

[0115] It should be noted that, in this embodiment, the control system 3 further includes a safety early warning module. This module includes setting a threshold value K for the difference in detected gas content data, using the initial detected gas content data C0 as a reference value. n When the difference between C0 and C0 exceeds the set difference threshold K, the corresponding time node T is extracted. n The time point T when the gas content data first changed n-i ;

[0116] Get time node T n-i With T n The particulate matter accumulation data between them are used to construct T n-i With T n The curve of particulate matter accumulation over a time period is analyzed for correlation and corresponding early warning reports are issued.

[0117] Specifically, it includes the following steps:

[0118] Step 1: Exception Triggering and Data Extraction

[0119] Anomaly detection:

[0120] Continuously monitor the gas content data for each test C n .

[0121] Calculate the absolute difference between the value and the reference value C0: |C n - C0|.

[0122] When |C n- Co | > K (threshold of difference, trigger early warning analysis process.

[0123] Time node locking:

[0124] System records the current abnormal time node T n .

[0125] The system traces back the historical data and finds the time node T n-i when the gas content data first began to change persistently. This can be determined by calculating the slope or continuous difference in a sliding window to avoid false triggering due to a single noise point.

[0126] Second step: Feature extraction of particulate matter accumulation change curve

[0127] The system obtains high-resolution particulate matter accumulation data (usually effective adsorption accumulation Δm_eff) within the T n-i to T n period, and extracts the following key features:

[0128] Slope or change rate: Calculate the increase in particulate matter accumulation per unit time, which directly reflects the growth rate of particulate matter load.

[0129] Acceleration: Calculate the rate of change of the change rate itself, to determine whether the problem is sudden or gradually worsening. If the acceleration is large, it indicates a sudden event.

[0130] Curve shape:

[0131] Smooth: Accumulation increases steadily and steadily, which is normal operating condition.

[0132] Step type: At a certain time point, the accumulation suddenly increases significantly, and then remains stable at a new level.

[0133] Impulse type: Accumulation suddenly surges and then quickly falls.

[0134] Accelerating growth type: The rate of accumulation growth is getting faster and faster.

[0135] Third step: Correlation analysis and root cause inference

[0136] Correlate gas concentration anomaly type and particulate matter accumulation change characteristics, build analysis logic matrix, and infer the most likely root cause.

[0137] 1. Concentration suddenly decreases (C n far less than C0), step type growth curve shape, trigger emergency warning;

[0138] Particulate matter load suddenly jumps, causing adsorption capacity to increase sharply, and a large amount of gas is adsorbed.

[0139] Cause: Filter membrane damage, large particles in high-temperature flue gas directly penetrate the damaged part, impact on the pipeline or sensor downstream of the filter membrane, form a new, unmeasured adsorption layer, cause the measurement value to be severely distorted. This is one of the most dangerous faults, for example, an emergency warning case is as follows:

[0140] Emergency warning: Suspected filter membrane damage

[0141] Time: T n .

[0142] Phenomenon: SO2 concentration drops sharply, particulate matter accumulation curve appears step jump.

[0143] Diagnosis: Filter membrane may be damaged, causing particulate matter pollution gas path, measurement data severely distorted.

[0144] Suggestion: Immediately perform system self-check (such as zero point and span check), prepare to shut down and replace the filter membrane.

[0145] 2, the concentration suddenly decreased (C n Much smaller than C0), accelerated growth curve pattern, trigger advanced warning;

[0146] Particulate matter load grows abnormally, adsorbs excessive gas.

[0147] Dust removal equipment failure, such as bag filter bag breakage, electrostatic precipitator power failure, etc., resulting in particulate matter concentration in flue gas far exceeding the design value, filter membrane overload, for example, an advanced warning case is as follows:

[0148] Advanced warning: Dust removal efficiency significantly decreased

[0149] Time: T n .

[0150] Phenomenon: Gas concentration continues to decline, particulate matter accumulation rate continues to accelerate.

[0151] Diagnosis: Upstream dust removal facilities (bag / electric precipitator) may fail, particulate matter emission concentration increases.

[0152] Suggestion: Check the dust collector operating parameters (pressure difference, current, dust cleaning program).

[0153] 3, the concentration slowly drifts (C n Slowly changing) corresponds to a smooth but abnormal growth rate curve pattern, triggering ordinary warning;

[0154] Particulate matter load growth rate does not match historical same period or set value, but the pattern is normal.

[0155] Cause: Process fluctuation or fuel change. For example, a coal-fired power plant burns coal from different origins (coal quality change), or a waste incinerator changes the composition of waste, resulting in changes in fly ash characteristics (such as adsorption ξ). For example, a common early warning case is as follows:

[0156] Common early warning: change in particulate matter emission characteristics;

[0157] Time: T n .

[0158] Phenomenon: Gas concentration drift, particulate matter accumulation rate deviates from baseline.

[0159] Diagnosis: Changes in particulate matter concentration or composition in the inlet flue gas, possibly related to fuel or process adjustment.

[0160] Suggestion: Pay attention to process operation status, and manually update scene model parameters if necessary.

[0161] 4, Concentration instantaneous spike recovery corresponds to pulse type curve shape, triggering prompt early warning:

[0162] A short, high-intensity particulate matter impact event.

[0163] Cause: Operational event. For example, the blowback cleaning process of the dust collector, resulting in a large amount of particulate matter being temporarily blown to the sampling probe, forming a "smoke plume" impact, a prompt early warning case is as follows:

[0164] Prompt early warning: operational event interference;

[0165] Time: T n .

[0166] Phenomenon: Instantaneous change in gas concentration and rapid recovery, with a synchronous pulse peak in particulate matter accumulation.

[0167] Diagnosis: Data anomalies are highly related to upstream cleaning and other operational events, and the system has automatically compensated.

[0168] Suggestion: No operation is required, only event record.

[0169] Step 4: Early warning report generation and output

[0170] The system automatically generates a structured early warning report based on the correlation analysis results, including:

[0171] Early warning level: urgent, high-level, common, prompt.

[0172] Trigger time: T n .

[0173] Abnormal phenomenon description: summary of gas and particulate matter data anomalies.

[0174] Diagnosis conclusion: the root cause inferred by the system.

[0175] Treatment suggestion: specific action guidelines provided to the operator.

[0176] Correlation data snapshot: attach T n-1 to T n Gas concentration and particulate matter accumulation change curve diagram in a time period.

[0177] Through the above steps, the safety warning module realizes the leap from "phenomenon monitoring" to "root cause diagnosis", and instead of simply saying "concentration exceeds the limit", it can analyze and say "concentration exceeds the limit because the filter membrane is likely to be broken, which is the evidence (particulate matter step growth)", greatly improving the intelligent level and operation and maintenance efficiency of the system, and providing strong data support for preventive maintenance.

[0178] In summary, the portable multi-channel flue gas analyzer provided by the application combines highly integrated hardware design with intelligent software algorithm, bringing revolutionary technological progress and significant benefits, mainly in the following aspects:

[0179] 1. Revolutionary improvement of measurement accuracy and reliability:

[0180] Traditional portable flue gas analyzers regard particulate matter as a simple interference, and its adsorption effect will cause unavoidable systematic negative deviation in gas concentration measurement value, with low data reliability. The application innovatively embeds an adsorption compensation model in the portable device, dynamically quantifies the physical properties (alpha coefficient) and chemical adsorption properties (xi, beta coefficient) of particulate matter through a flue gas analysis scene model, can accurately calculate the amount of gas adsorbed by particulate matter, and real-time compensate it, finally output corrected gas content data close to the real concentration of flue gas. This fundamentally solves the precision problem that has long plagued the flue gas monitoring industry, and makes the data quality of the portable analyzer reach the level comparable to high-end fixed CEMS, providing the only accurate data basis for environmental law enforcement, pollution accounting and process diagnosis.

[0181] 2. Great leap in device intelligence and diagnosis function:

[0182] This device goes beyond the category of "measuring tool", evolving into an intelligent diagnostic system. Its unique safety warning module is no longer limited to issuing a simple "concentration exceeds limit" alert, but can perform in-depth root cause analysis by correlating gas concentration anomalies with particulate matter accumulation curve characteristics. For example, it can accurately distinguish between "filter membrane damage", "dust collector failure", "process fluctuation", and other completely different but serious faults, and provide graded warnings and specific handling recommendations. This is equivalent to providing the operator with an experienced expert, enabling a shift from passive maintenance to predictive maintenance, greatly improving operational efficiency and avoiding decision-making errors caused by measurement distortion.

[0183] 3. Excellent portability and versatility brought by integrated and modular design:

[0184] Six complete analysis modules (gas circuit, electrical circuit, analysis module) are highly integrated into a suitcase, enabling multi-channel parallel measurement while ensuring portability. This design allows simultaneous and efficient monitoring of different points or multiple pollutants at the same point, with much higher efficiency than traditional methods of carrying multiple single-function devices.

[0185] Layered layout and modular design optimize heat dissipation (through side fans), power supply (dedicated battery layer), and gas circuit isolation, ensuring stable and reliable operation. A single device can complete gas concentration, particulate matter concentration (converted by differential pressure), and multiple parameter monitoring, making it powerful and cost-effective.

[0186] 4. Strong adaptability and wide scene applicability:

[0187] Traditional analyzers often lose accuracy when changing measurement scenarios (e.g., from coal-fired power plants to waste incineration plants) due to changes in particulate matter characteristics. The scene configuration module of this invention allows users to select or input fuel and process information to load pre-configured scene models containing specific alpha, xi, and beta parameters, enabling the system to instantly adapt to new flue gas environments and ensure consistent measurement accuracy in any scenario. This "flexible" design greatly expands the device's application range, enabling it to comfortably handle complex and varied industrial field monitoring requirements.

[0188] In summary, the portable multi-channel flue gas analyzer successfully condenses the precise analysis capability of laboratory level and the continuous diagnosis function of fixed system in the portable platform through the collaborative innovation of software and hardware, not only realizes a qualitative leap in measurement accuracy, but also sets a new industry benchmark in equipment intelligence, function integration and wide application, and has great market application value and promotion prospect. The above is only a specific embodiment of the invention, but the protection scope of the invention is not limited thereto, any change or replacement without creative labor should be covered within the protection scope of the invention. Therefore, the protection scope of the invention should be limited by the protection scope defined in the claims.

Claims

1. A portable multi-channel flue gas analyzer, characterized in that: This includes flue gas filtration systems, flue gas analysis systems, and control systems; The control system includes a data compensation module and a scene configuration module. The data compensation module acquires flue gas composition data and particulate matter composition data in the analysis environment and inputs them into the scene configuration module to configure the corresponding flue gas analysis scene model. The data compensation module constructs an adsorption compensation strategy for the particulate matter adsorbing the flue gas composition based on the configured flue gas analysis scene model to compensate for the data. The flue gas filtration system includes a filtration module for filtering particulate matter in flue gas and a differential pressure detection module. The differential pressure detection module acquires the pressure difference between the two sides of the filtration module at the detection time node, and transmits the acquired pressure difference and the corresponding time node data to the flue gas analysis scenario model. Based on the change in pressure difference between any two time nodes, the accumulated amount of particulate matter intercepted between the two time nodes is obtained. The flue gas analysis system includes a flue gas analysis module, which detects the content of gas components in the flue gas and outputs the gas content data detected at the corresponding time point. The adsorption compensation strategy includes acquiring the detected gas content data and the corresponding time node, extracting the particulate matter accumulation data between the time node and the previous time node, using the configured flue gas analysis scenario model to call the adsorption coefficient of the corresponding particulate matter for the flue gas components, and outputting the gas content adsorbed by the particulate matter as the adsorption compensation value to supplement the detected gas content data to form the corrected gas content data output.

2. The portable multi-channel flue gas analyzer according to claim 1, characterized in that: The specific method for configuring the flue gas analysis scenario model in the scenario configuration module includes establishing an adsorption mapping table between particulate matter components and flue gas components, embedding the adsorption efficiency factor of particulate matter components on the adsorption of components in flue gas, extracting the particulate matter component with the largest proportion in particulate matter, and assigning the corresponding measured pressure difference-volume relationship coefficient α according to the characteristics of the particulate matter component with the largest proportion, thus forming a flue gas analysis scenario model with adsorption efficiency factor and α.

3. A portable multi-channel flue gas analyzer according to claim 2, characterized in that: The specific method for the flue gas analysis scenario model to obtain the cumulative particulate matter data intercepted between two time points includes: The pressure difference is received and the corresponding time point data are used to construct a pressure difference-time data pair (ΔP,T), where (ΔP,T) is the data set {(ΔP0,T0), (ΔP1,T1), (ΔP2,T2)...(ΔP...T1)}. n ,T n The volume of particulate matter intercepted between any two time points is Δm = α*(ΔP) n - ΔP n-i The adsorption efficiency factor of the flue gas analysis scenario model is used to correct the intercepted particulate matter volume Δm to obtain the effective adsorption volume Δm_eff of particulate matter.

4. A portable multi-channel flue gas analyzer according to claim 3, characterized in that: The method for generating the corrected gas content data includes the following steps: The set of detected gas content data and corresponding time points is established as {(T0,C0), (T1,C1), (T2,C2)……(T…)}. n C n The gas content data C0 is the initial detection data; Obtain the gas content data C detected at the current time point. n With time node T n and T n-1 The effective adsorption product of time-bound particulate matter Δm_eff is obtained by taking the adsorption coefficient β of the detected gas component, and thus obtaining the adsorption compensation value ΔQ=β*Δm_eff. The obtained adsorption compensation value ΔQ is converted into the same data format as the gas content data detected and output by the flue gas analysis module, according to the corresponding adsorbed gas, and finally the gas content data C is added. n Generate corrected gas content data.

5. A portable multi-channel flue gas analyzer according to claim 4, characterized in that: The control system further includes a safety early warning module, which sets a threshold value K for the difference in detected gas content data, using the initial detected gas content data C0 as a reference value. n When the difference between C0 and C0 exceeds the set difference threshold K, the corresponding time node T is extracted. n The time point T when the gas content data first changed n-i ; Get time node T n-i With T n The particulate matter accumulation data between them are used to construct T n-i With T n The curve of particulate matter accumulation over a time period is analyzed for correlation and corresponding early warning reports are issued.

6. A portable multi-channel flue gas analyzer according to claim 5, characterized in that: Methods for correlating and analyzing curve features include analyzing the detected gas content data C n The morphology of the particulate matter accumulation change curve is correlated with the analysis to construct an analytical logic strategy, which includes: Gas content data C n The step-type growth curve shape and the accelerated growth curve shape corresponding to the sudden decline trigger emergency warning and advanced warning reports respectively, and detect dust removal facilities. Gas content data C n A curve shape that is stable but has an abnormal growth rate, corresponding to a steady change, triggers a normal early warning report, detects the process operation status, and manually updates the scenario model parameters when necessary. Gas content data C n After a momentary spike within a time period, the curve returns to its corresponding pulse-like shape, triggering an alert report and recording the event.

7. A portable multi-channel flue gas analyzer according to claim 4, characterized in that: For complex particulate matter systems containing multiple particles with significant adsorption efficiencies, the comprehensive adsorption efficiency factor in the adsorption mapping table is calculated using a weighted average algorithm based on the mass percentage of each component, and the effective adsorption volume Δm_eff is calculated using the comprehensive adsorption efficiency factor value.

8. A portable multi-channel flue gas analyzer according to claim 5, characterized in that: The flue gas analysis system also includes an automatic calibration unit. The adsorption compensation strategy automatically pauses data compensation before and after the flue gas analysis module performs zero-point or span automatic calibration, and updates the reference value in the data set with a new calibration benchmark value C0 after calibration is completed, and then re-enables the data compensation function.

9. A portable multi-channel flue gas analyzer according to claim 6, characterized in that: When issuing an early warning report, the security early warning module simultaneously outputs a related data snapshot, which includes the abnormal time period [T]. n-i , T n A comparison chart of the gas content data change curve and the particulate matter volume data change curve within the [data range].

10. A portable multi-channel flue gas analyzer according to claim 1, characterized in that: It also includes a suitcase, the interior of which has a layered structure, with a first panel, a second panel and a third panel arranged from top to bottom; Several integrated modules consisting of the control system and the flue gas filtration system are installed side by side on the first layer panel. The second layer panel is equipped with a flue gas analysis system corresponding to the number of modules in the first layer panel; The third-layer panel is equipped with a battery, an electrical supply integration system, and a battery and electrical control integration system for powering the entire device.

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