Portable multichannel flue gas analyzer

Abstract

The invention discloses a portable multi-channel flue gas analyzer, and relates to the technical field of flue gas monitoring, the portable multi-channel flue gas analyzer comprises a flue gas filtering system, a flue gas analysis system and a control system, a dynamic flue gas analysis scene model is constructed through a scene configuration module, adsorption efficiency factors and pressure difference-volume coefficients of different particulate matter components to specific gas are quantified, and the specific gas is analyzed. The data compensation module is used for calculating the effective adsorption volume of the particulate matters by utilizing the model and combining the pressure difference change monitored by the filtering system in real time, accurately compensating the gas content data detected by the analysis system according to the effective adsorption volume of the particulate matters, and outputting the corrected real concentration. The system has a multi-channel synchronous measurement capability and an intelligent diagnosis function, can perform early warning on filter membrane damage or process abnormity through data association analysis, and finally remarkably improves the measurement precision, reliability and intelligent level of portable equipment under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of flue gas monitoring technology, specifically a portable multi-channel flue gas analyzer. Background Technology

[0002] Flue gas analyzers are key equipment for environmental monitoring, industrial process control, and combustion efficiency assessment. They are used to quantitatively analyze the concentration of various gaseous components in flue gas, such as sulfur dioxide, nitrogen oxides, carbon monoxide, and oxygen. According to their usage, they are mainly divided into stationary continuous emission monitoring systems (CEMS) and portable flue gas analyzers.

[0003] Traditional portable flue gas analyzers typically employ an extraction sampling method. Their basic working principle involves drawing flue gas from the flue using a sampling pump and probe, pre-treating it through dust filtration and dehumidification, and then sending it to the sensor unit for analysis. Commonly used sensor technologies include non-dispersive infrared (NDIR), electrochemical (ECD), and paramagnetic oxygen (PD).

[0004] However, existing portable flue gas analyzers have several pain points and limitations that urgently need to be addressed in practical applications:

[0005] 1. Measurement error caused by particulate matter adsorption: This is the most significant and common source of systematic error. To protect precision sensors, all flue gas analyzers are equipped with filters at the sampling front end to intercept particulate matter. These intercepted particulate matter (especially fly ash, carbon black, etc., which have high specific surface area and activity) are not chemically inert. They can significantly physical or chemically adsorb specific gas components in the flue gas. This results in the gas concentration that finally reaches the sensor being lower than the actual concentration in the flue gas, causing a continuous negative measurement bias. Existing technologies generally lack effective compensation methods for this phenomenon and cannot adapt to the ever-changing particulate matter composition and adsorption characteristics under different fuels and processes. The authenticity and accuracy of the measurement data have inherent defects.

[0006] 2. Limited functionality and insufficient data dimensions: Traditional equipment is usually positioned as "gas concentration measurement", which has a single function. Although most equipment is also equipped with differential pressure sensors, its purpose is limited to monitoring whether the filter membrane is clogged in order to prompt the user to replace it. It does not deeply explore the rich information contained in the differential pressure data. For example, it cannot convert the differential pressure change into particulate matter mass concentration information, let alone conduct correlation analysis between gas concentration and particulate matter state. The data dimension is narrow and the value is limited.

[0007] 3. Low level of intelligence and lack of diagnostic capabilities: Traditional analyzers only provide simple alarms when the concentration exceeds the standard, and cannot perform in-depth diagnosis of data anomalies. When the measured value fluctuates, it is difficult for maintenance personnel to quickly distinguish whether it is a change in the emission source itself, a malfunction of the sampling system (such as filter membrane damage, pipeline leakage) or a problem with the instrument itself. They need to rely on manual experience to check one by one, which is time-consuming, labor-intensive and prone to misjudgment. Summary of the Invention

[0008] The purpose of this invention is to provide a portable multi-channel flue gas analyzer that is capable of intelligent adsorption compensation, has advanced diagnostic functions, and can adapt to various working conditions. This high-precision portable multi-channel flue gas analyzer aims to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention 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 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.

[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 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.

[0012] 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.

[0013] 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.

[0014] Preferably, 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 the 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.

[0015] Preferably, the specific method for the flue gas analysis scenario model to obtain the cumulative particulate matter volume data intercepted between two time points includes:

[0016] 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.

[0017] Preferably, the method for generating the corrected gas content data includes the following steps:

[0018] 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;

[0019] 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.

[0020] 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.

[0021] Preferably, the control system further includes a safety early warning module, which 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 ;

[0022] 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.

[0023] As a preferred method, the correlation analysis of curve features includes 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:

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Preferably, in the adsorption mapping table, for complex particulate matter systems containing multiple particulate matter with significant adsorption efficiency, the comprehensive adsorption efficiency factor ξ_total is calculated using a weighted average algorithm based on the mass percentage (ωi) of each component: ξ_total = Σ(ωi * ξi), where i is the particulate matter component number, and this ξ_total value is used to calculate the effective adsorption product Δm_eff.

[0028] Preferably, the flue gas analysis system further 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 values ​​in the data set with a new calibration baseline value C0 after calibration is completed, and then re-enables the data compensation function.

[0029] Preferably, when issuing an early warning report, the security early warning module simultaneously outputs a related data snapshot, the snapshot containing 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].

[0030] As a preferred embodiment, the suitcase is also included, wherein the interior of the suitcase has a layered structure, with a first panel, a second panel and a third panel arranged from top to bottom;

[0031] Several integrated modules consisting of the control system and the flue gas filtration system are installed side by side on the first layer panel.

[0032] The second layer panel is equipped with a flue gas analysis system corresponding to the number of modules in the first layer panel;

[0033] The third-layer panel is equipped with a battery, an integrated electrical supply system, and a battery and electrical control integrated system for powering the entire device.

[0034] The suitcase has ventilation openings on its side and is equipped with a cooling fan for heat dissipation.

[0035] In summary, the beneficial effects of this invention are:

[0036] This invention significantly improves the measurement accuracy and functional value of portable flue gas analyzers through integrated innovative hardware design and intelligent algorithms. Its core lies in the creation of a dynamic "flue gas analysis scenario model," achieving for the first time precise quantification and real-time compensation of the particulate matter adsorption gas effect, outputting corrected data that is infinitely close to the actual concentration. This solves the long-standing problem of systematic measurement bias that has plagued the industry. Furthermore, the device transcends single measurement functions, possessing intelligent diagnostic capabilities. By correlating the characteristics of changes in gas and particulate matter data, it can accurately predict potential problems such as filter membrane damage and dust collector malfunctions, achieving a leap from passive maintenance to predictive maintenance. The highly integrated multi-channel module design within the portable case supports simultaneous multi-point / multi-component monitoring, greatly improving operational efficiency. Simultaneously, the model-based design endows it with strong adaptive capabilities, allowing it to quickly adapt to different industrial emission environments by switching scenario models, ensuring consistency, accuracy, and reliability of measurements under various complex operating conditions. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the overall structure of a portable multi-channel flue gas analyzer according to the present invention;

[0039] Figure 2 This is a schematic diagram of the internal first layer panel structure of a portable multi-channel flue gas analyzer according to the present invention;

[0040] Figure 3 This is a schematic diagram of the internal second-layer panel structure of a portable multi-channel flue gas analyzer according to the present invention;

[0041] Figure 4 This is a schematic diagram of the flue gas analysis system of a portable multi-channel flue gas analyzer according to the present invention;

[0042] Figure 5 This is a schematic diagram of the internal third-layer panel structure of a portable multi-channel flue gas analyzer according to the present invention;

[0043] Figure 6 This is a schematic diagram of the internal panel layer of a portable multi-channel flue gas analyzer according to the present invention.

[0044] Figure label: 1. Cooling fan; 2. Handbag; 3. Control system; 4. Flue gas filtration system; 5. Switch; 7. Flue gas analysis system; 8. Electrical wiring; 9. Circuit integration system module; 10. Flue gas analysis module; 11. Flue gas pipeline; 12. Gas 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 Implementation

[0045] The present invention will now be described in further detail with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. These drawings are simplified schematic diagrams, which are only used to illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0046] 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.

[0047] All features disclosed in this specification, or all steps in all disclosed methods or processes, may be combined in any way, except for mutually exclusive features and / or steps.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] The control system 3 includes a data compensation module and a scene configuration module;

[0052] 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.

[0053] The flue gas composition data and particulate matter composition data from the analysis environment are obtained and input into the scenario configuration module to configure the corresponding flue gas analysis scenario model. First, flue gas data from the environment to be monitored is extracted from the factory or independently and analyzed to obtain the composition data of the flue gas and the corresponding particulate matter composition data (e.g., flue gas components: SO2, NOx, CO, CO2, etc.; particulate matter components: SiO2, Al2O3, CaO, Na2O, K2O, unburned carbon, etc.). An adsorption mapping table between particulate matter components and flue gas components is established, defining the adsorption affinity relationships between different particulate matter components and different flue gas components, for example:

[0054] CaO (calcium oxide) has a strong chemical adsorption on acidic gases such as SO2, HCl, and HF (forming calcium sulfate, calcium chloride, etc.).

[0055] Unburned carbon (C) has a strong physical adsorption capacity for VOCs (volatile organic compounds), Hg (mercury), etc. (through van der Waals forces).

[0056] SiO2 and Al2O3 (silicon dioxide and aluminum oxide) are chemically inert and have very weak adsorption capacity.

[0057] Based on the qualitative relationships described above, each pair of "particulate matter-gas" relationships is assigned a quantified adsorption efficiency factor ξ, where ξ is a dimensionless coefficient representing the multiple by which the particulate matter of that component can be adsorbed relative to a standard reference (such as standard activated carbon). For example:

[0058] ξ(CaO-SO2) = 3.5 (strong adsorption capacity);

[0059] ξ(C-Hg) = 18 (extremely strong adsorption capacity for mercury);

[0060] ξ(SiO2-SO2) = 0.1 (the adsorption capacity is very weak).

[0061] Simultaneously, the particulate matter component with the largest proportion in the particulate matter is extracted, and the corresponding pressure difference-volume relationship coefficient α is assigned according to the characteristics of the particulate matter component with the largest proportion, forming a flue gas analysis scenario model with adsorption efficiency factor and α.

[0062] Specifically, the system analyzes the input particulate matter composition data and identifies the components with the largest mass percentage and / or the highest adsorption efficiency factor, which are defined as "main active components".

[0063] For example, if the composition data is: CaO: 45%, SiO2: 15%, Al2O3: 15%, C: 10%, then CaO is identified as the main active ingredient. The module's built-in knowledge base stores the α values ​​of different types of particulate matter, and this coefficient is obtained through laboratory calibration.

[0064] α defines the physical mass (mg) corresponding to a unit pressure difference (Pa) generated by a specific type of particulate matter, i.e., Δm = α * Δ(ΔP). The value of α varies depending on the characteristics of the particulate matter. Porous, lightweight carbon black particles (small α value) and dense, heavy mineral particles (large α value) have completely different actual retained masses when generating the same pressure difference. The system retrieves the corresponding α value from the library based on the determined main active component (such as CaO) (e.g., α_CaO = 0.15 mg / Pa).

[0065] Ultimately, all parameters are integrated into a dynamic data structure, forming an instantaneous and computable flue gas analysis scenario model.

[0066] Model output example:

[0067] Scene name: "Municipal solid waste incineration - dry desulfurization - high calcium fly ash";

[0068] Adsorption mapping matrix (ξ_matrix): {CaO: {SO2: 3.5, HCl: 4.2}, C: {Hg: 18.0,...}, ...};

[0069] Pressure differential-volume factor (α): 0.15 mg / Pa;

[0070] Reference temperature and humidity: 150°C, 20%;

[0071] Timestamp: 2023-10-27 10:00:00.

[0072] The data compensation module constructs an adsorption compensation strategy based on the configured flue gas analysis scenario model to compensate for the adsorption of flue gas components by particulate matter.

[0073] The flue gas filtration system 4 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.

[0074] Specifically, it includes the following steps:

[0075] Step 1: Data Synchronization and Acquisition

[0076] High-frequency data acquisition: The differential pressure sensor samples in real time at a high frequency (e.g., 1 Hz) to measure the pressure values ​​upstream and downstream of the filter membrane and calculate the instantaneous pressure difference ΔP.

[0077] Timestamp marking: Each differential pressure data ΔP is marked with a high-precision timestamp T, forming a strictly corresponding differential pressure-time data pair (ΔP, T).

[0078] Data set construction: The system runs continuously, thus 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 reference value when the filter membrane is clean.

[0079] Step 2: Calculate the physical particulate matter volume (Δm)

[0080] Request calculation: When the system needs to know the time interval [T] n-i ,T n When the amount of particulate matter intercepted is reached, a request will be sent to the scene model.

[0081] Obtaining the pressure difference change: The scenario model queries the pressure difference value ΔP for the corresponding time point from the dataset. n-i and ΔP n And calculate the pressure difference change: Δ(ΔP) = ΔP n - ΔP n-i .

[0082] This change in pressure difference directly reflects the increase in flow resistance caused by the accumulation of new particulate matter.

[0083] The physical mass of particulate matter is calculated using the pressure difference-productivity conversion coefficient (α) pre-stored in the scene model: Δm = α * Δ(ΔP).

[0084] Step 3: Correct to effective adsorption product (Δm_eff)

[0085] Call the adsorption efficiency factor (ξ): The system reads the predefined adsorption efficiency factor ξ from the currently active flue gas analysis scenario model and calculates the effective adsorption volume: Δm_eff = Δm *ξ;

[0086] In the adsorption mapping table, for complex particulate matter systems containing multiple particulate matter with significant adsorption efficiency, the comprehensive adsorption efficiency factor ξ_total is calculated using a weighted average algorithm based on the mass percentage (ωi) of each component: ξ_total = Σ(ωi * ξi), where i is the particulate matter component number, and this ξ_total value is used to calculate the effective adsorption product Δm_eff.

[0087] Δm_eff is no longer the simple physical mass of the particulate matter, but is converted into an equivalent standard adsorption dose. It answers the question "how much of the material with standard adsorption capacity this pile of particulate matter is equivalent to", thus providing the only reliable basis for subsequent precise chemisorption compensation.

[0088] The flue gas analysis system includes a flue gas analysis module 10, a circuit integration system module 9, a gas extraction module 12, and a flue gas pipeline 11. The flue gas analysis module detects the content of gas components in the flue gas and outputs the gas content data detected at corresponding time points.

[0089] 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 an adsorption compensation value to supplement the detected gas content data to form a 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 flue gas analysis module performs zero-point or span automatic calibration, and after calibration is completed, updates the reference value in the data set with a new calibration benchmark value C0, and then re-enables the data compensation function.

[0090] Specifically, it includes the following steps:

[0091] Step 1: Establish a time-gas content data set

[0092] Data synchronization record:

[0093] The flue gas analysis module 10 outputs a concentration value C after each gas concentration measurement. n (e.g., SO2 = 80.5 ppm), and also for each concentration value C n Add a high-precision, strictly synchronized timestamp T n .

[0094] The system organizes this data into an ordered set: {(T0,C0), (T1,C1), (T2,C2)……(T…)} n C n )}.

[0095] The special meaning of C0: It usually refers to the first measurement value after the system is started and the filter membrane is clean, or the reference value set at a specific time (such as after calibration). Subsequent compensation calculations may refer to this value.

[0096] Step 2: Calculate the adsorption compensation value (ΔQ)

[0097] Obtain the effective adsorption product (Δm_eff):

[0098] The compensation algorithm needs to calculate the time interval [T] n-1 T n The adsorption compensation value within the [database] is used to send a request to the flue gas analysis scenario model: "Please provide T [database]." n-1 To T n "Effective adsorption accumulation within a time period".

[0099] After receiving the request, the scene model executes the following sub-steps:

[0100] Query historical differential pressure data to obtain ΔP n - ΔP n-1 .

[0101] Calculate the physical product: Δm = α * (ΔP) n - ΔP n-1 ).

[0102] Call the current model's adsorption efficiency factor (ξ) and make corrections: Δm_eff = Δm * ξ.

[0103] The scene model returns Δm_eff to the compensation algorithm.

[0104] Call the adsorption coefficient (β):

[0105] The compensation algorithm calls the scene model again to request the current particulate matter concentration relative to the target gas (i.e., the concentration value C). n The adsorption coefficient β of the corresponding gas (such as SO2).

[0106] The physical meaning of β: It represents "how many milligrams of gas can be adsorbed per milligram of effective adsorption volume of particulate matter". This coefficient is obtained through laboratory adsorption isotherm tests.

[0107] Calculate the adsorption compensation value (ΔQ):

[0108] Calculate the absolute mass of the adsorbed gas: ΔQ = β * Δm_eff.

[0109] ΔQ is measured in mg (milligrams), and it represents the concentration of amino acids and amino acids in the atmosphere at [T]. n-1 T nThe total mass of target gas adsorbed by the newly added particulate matter during this period.

[0110] Step 3: Data Formatting and Compensation

[0111] Unit conversion and formatting:

[0112] The concentration value C output by the flue gas analysis module n The unit is usually ppm (volume concentration) or mg / m³ (mass concentration).

[0113] 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.

[0114] 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 ;

[0115] 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.

[0116] Specifically, it includes the following steps:

[0117] Step 1: Exception Triggering and Data Extraction

[0118] Anomaly detection:

[0119] Continuously monitor the gas content data for each test C n .

[0120] Calculate the absolute difference between the value and the reference value C0: |C n - C0|.

[0121] When |C n- C0| > K (When the difference threshold is reached, the early warning analysis process is triggered.)

[0122] Time point locking:

[0123] The system records the current abnormal time point T. n .

[0124] The system traces back historical data to find the time point T at which the gas content data first began to show a sustained change. n-i This can be determined by calculating the slope or continuous difference using a sliding window, avoiding false triggering due to a single noise point.

[0125] Step 2: Feature extraction of particulate matter volume change curve

[0126] System obtains T n-i To T n High-resolution particulate matter accumulation data (typically effective adsorption accumulation Δm_eff) over a given time period, and extraction of the following key features:

[0127] Slope or rate of change: Calculates the increase in particulate matter volume per unit time, directly reflecting the growth rate of particulate matter load.

[0128] Acceleration: Calculates the rate of change of the rate of change itself. It is used to determine whether the problem occurs suddenly or gradually intensifies. If the acceleration is large, it indicates that there is a sudden event.

[0129] Curve shape:

[0130] Stable type: The accumulation increases at a uniform and stable rate, which is the normal operating condition.

[0131] Step type: At a certain point in time, the product suddenly increases significantly, and then remains stable at the new level.

[0132] Pulse type: The volume suddenly spikes and then quickly falls back.

[0133] Accelerated growth type: The rate of accumulation growth is getting faster and faster.

[0134] Step 3: Association Analysis and Root Cause Inference

[0135] By correlating the types of gas concentration anomalies with the characteristics of particulate matter accumulation changes, an analytical logic matrix is ​​constructed to infer the most likely root cause.

[0136] 1. Sudden drop in concentration (C) n (much smaller than C0), a step-like growth curve shape, triggering an emergency warning;

[0137] The sudden increase in particulate matter load leads to a surge in adsorption capacity, resulting in the adsorption of a large amount of gas.

[0138] Cause: The filter membrane is damaged, allowing large particles in the high-temperature flue gas to penetrate directly through the damaged area and impact the downstream pipeline or sensor, forming a new, unmeasured adsorption layer, leading to severely distorted measurements. This is one of the most dangerous faults, as illustrated by an emergency warning scenario as follows:

[0139] Urgent warning: Suspected filter membrane damage

[0140] Time: T n .

[0141] Phenomenon: SO2 concentration drops sharply, and the particulate matter accumulation curve shows a step jump.

[0142] Diagnosis: The filter membrane may be damaged, causing particulate matter to contaminate the gas path and severely distorting the measurement data.

[0143] Recommendation: Immediately perform system self-tests (such as zero-point and span checks) and prepare to shut down the system to replace the filter membrane.

[0144] 2. Sudden drop in concentration (C) n (significantly smaller than C0), exhibiting an accelerated growth curve pattern, triggering a high-level early warning;

[0145] The particulate matter load increased abnormally, resulting in the adsorption of excessive gas.

[0146] Dust collection equipment malfunctions, such as bag breakage in bag filters or power outages in electrostatic precipitators, can cause particulate matter concentrations in flue gas to far exceed design values, leading to filter membrane overload. For example, a high-level warning might be triggered as follows:

[0147] Advanced warning: Dust removal efficiency has decreased significantly.

[0148] Time: T n .

[0149] Phenomenon: Gas concentration continues to decrease, while the rate of increase in particulate matter accumulation continues to accelerate.

[0150] Diagnosis: The upstream dust removal facilities (bag filter / electrostatic precipitator) may be malfunctioning, leading to an increase in particulate matter emission concentration.

[0151] Recommendation: Check the dust collector's operating parameters (differential pressure, current, and cleaning program).

[0152] 3. Slow concentration drift (C) n Slow changes correspond to a curve shape that is stable but has an abnormal growth rate, triggering a general warning.

[0153] The particulate matter load growth rate is inconsistent with the historical period or the set value, but the morphology is normal.

[0154] Causes: Process fluctuations or fuel changes. For example, a coal-fired power plant burning coal from different sources (coal quality changes), or changes in the composition of waste at an incinerator, leading to alterations in fly ash characteristics (such as adsorption capacity ξ). A typical warning scenario is as follows:

[0155] General warning: Changes in particulate matter emission characteristics;

[0156] Time: T n .

[0157] Phenomenon: Gas concentration drifts, and the growth rate of particulate matter accumulation deviates from the baseline.

[0158] Diagnosis: Changes in the concentration or composition of particulate matter in the inlet flue gas may be related to fuel or process adjustments.

[0159] Recommendation: Monitor the process operation status and manually update the scenario model parameters when necessary.

[0160] 4. After a momentary concentration spike, the curve returns to its corresponding pulse-like shape, triggering an alert.

[0161] A brief, high-intensity particulate impact event.

[0162] Cause: Operational event. For example, during the back-flushing cleaning process of a dust collector, a large amount of particulate matter is briefly blown towards the sampling probe, forming a "plume" impact. One warning scenario is as follows:

[0163] Warning: Operational event interference;

[0164] Time: T n .

[0165] Phenomenon: Gas concentration changes instantaneously and recovers rapidly, and particulate matter accumulation shows synchronous pulse peaks.

[0166] Diagnosis: The data anomaly is highly correlated with upstream dust removal and other operational events; the system has automatically compensated for it.

[0167] Recommendation: No action is required; this is merely an event log.

[0168] Step 4: Early Warning Report Generation and Output

[0169] Based on the correlation analysis results, the system automatically generates a structured early warning report, including:

[0170] Warning levels: Emergency, High, Normal, Alert.

[0171] Trigger time: T n .

[0172] Anomaly Description: Summary of data anomalies related to gas and particulate matter.

[0173] Diagnostic conclusion: The root cause of the systemic inference.

[0174] Recommendations: Provide specific action guidelines for operators.

[0175] Related data snapshot: Attached T n-1 To T n Curves showing the changes in gas concentration and particulate matter accumulation over a period of time.

[0176] Through the above steps, the safety early warning module has achieved a leap from "phenomenon monitoring" to "root cause diagnosis". It no longer simply says "concentration exceeds the limit", but can analyze and say "the concentration exceeds the limit because the filter membrane is likely broken, which is evidence (step increase of particulate matter)", which greatly improves the intelligence level and operation and maintenance efficiency of the system and provides strong data support for preventive maintenance.

[0177] In summary, the portable multi-channel flue gas analyzer provided by this invention, through the combination of highly integrated hardware design and intelligent software algorithms, brings revolutionary technological progress and significant beneficial effects, mainly reflected in the following aspects:

[0178] 1. A revolutionary improvement in measurement accuracy and reliability:

[0179] Traditional portable flue gas analyzers treat particulate matter as simple interference, and its adsorption effect leads to unavoidable systematic negative biases in gas concentration measurements, resulting in low data reliability. This invention innovatively embeds an adsorption compensation model into a portable device. By dynamically quantifying the physical properties (α coefficient) and chemical adsorption properties (ξ, β coefficients) of particulate matter through a flue gas analysis scenario model, it can accurately calculate the amount of gas adsorbed by particulate matter and compensate for it in real time, ultimately outputting corrected gas content data that is infinitely close to the true concentration of flue gas. This fundamentally solves the accuracy problem that has long plagued the flue gas monitoring industry, enabling the data quality of portable analyzers to reach a level comparable to high-end fixed CEMS, providing a unique and accurate data foundation for environmental enforcement, pollution discharge accounting, and process diagnosis.

[0180] 2. A significant leap forward in equipment intelligence and diagnostic capabilities:

[0181] This equipment transcends the realm of a mere "measuring tool," evolving into an intelligent diagnostic system. Its unique safety warning module goes beyond simple "concentration exceeding limits" alarms; it performs in-depth root cause analysis by correlating abnormal gas concentrations with changes in particulate matter accumulation curves. For example, it can accurately distinguish between distinct but serious faults such as "filter membrane damage," "dust collector malfunction," and "process fluctuations," providing tiered warnings and specific handling suggestions. This is equivalent to equipping operators with an experienced expert, enabling a shift from reactive to predictive maintenance, significantly improving operational efficiency, and preventing decision-making errors caused by measurement distortion.

[0182] 3. The superior portability and versatility brought about by integrated and modular design:

[0183] By highly integrating six complete analysis modules (gas path, circuit, and analysis module) into a single carrying case, the device achieves multi-channel parallel measurement while maintaining portability. This design allows for simultaneous and efficient monitoring of multiple pollutants at different locations or at the same location, resulting in significantly higher operational efficiency than the traditional method of carrying multiple single-function devices.

[0184] The layered layout and modular design optimize heat dissipation (through a side-mounted cooling fan), power supply (a dedicated battery layer), and gas path isolation, ensuring the stability and reliability of the equipment. A single unit can monitor gas concentration, particulate matter concentration (converted via differential pressure), and multiple other parameters, offering powerful functionality and excellent cost-effectiveness.

[0185] 4. Strong adaptability and wide applicability to various scenarios:

[0186] Traditional analyzers often suffer from measurement inaccuracies when changing measurement scenarios (such as switching from a coal-fired power plant to a waste incineration plant) due to changes in particulate matter characteristics. The scenario configuration module of this invention allows users to select or input fuel and process information to load a pre-set scenario model containing specific α, ξ, and β parameters with a single click. This enables the system to instantly adapt to the new flue gas environment, ensuring consistent measurement accuracy in any scenario. This "flexible" design greatly expands the application range of the equipment, enabling it to easily cope with the complex and ever-changing industrial site monitoring needs.

[0187] In summary, this portable multi-channel flue gas analyzer, through collaborative innovation in both hardware and software, successfully integrates laboratory-level precision analysis capabilities and the continuous diagnostic functions of a fixed system into a portable platform. This not only represents a qualitative leap in measurement accuracy but also sets a new industry benchmark in terms of equipment intelligence, functional integration, and wide applicability, possessing significant market application value and promising prospects. The above description is merely a specific embodiment of the invention, but the scope of protection is not limited thereto. Any variations or substitutions conceived without inventive effort should be included within the scope of protection of the invention. Therefore, the scope of protection of the invention should be determined by the 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 2, characterized in that: In the adsorption mapping table, for complex particulate matter systems containing multiple particles with significant adsorption efficiencies, the comprehensive adsorption efficiency factor 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.

Images