Beta-ray method-based emission smoke dust concentration on-line monitoring optimization method and system

Abstract

The invention relates to a beta-ray method-based emission smoke dust concentration online monitoring optimization method and system, and belongs to the field of smoke dust concentration monitoring. Aiming at the problem of measurement errors caused by beta ray attenuation and ray source fluctuation due to dampness of filter paper in a high-humidity environment, a two-stage dehumidification and dynamic process cooperative control technical scheme is provided: a dynamic heating system is adopted to heat the outer wall of an exhaust pipe to inhibit condensation, and a low-temperature constant-temperature drying device is combined to recover the tensile strength of the filter paper; the circulation process of automatic drying, sampling and detection of the filter paper is realized through a three-station integrated design; and establishing a smoke concentration calibration model based on the signal standard deviation, and correcting the fluctuation interference of the radiation source. According to the invention, humidity interference is effectively eliminated, the measurement precision and the system stability are improved, and continuous online monitoring in a high-humidity industrial scene is realized.

Description

Technical Field

[0001] The present invention belongs to the field of smoke concentration monitoring, and relates to an online monitoring optimization method and system for emission smoke concentration based on a beta ray method. Background Art

[0002] Beta-ray smoke concentration monitoring technology is widely used in industrial emissions monitoring. It calculates smoke mass concentration by measuring the change in beta-ray intensity before and after it penetrates a filter paper strip. However, existing technology has significant drawbacks in practical applications, particularly in high-humidity environments and during long-term continuous monitoring. This leads to reduced monitoring accuracy and insufficient system stability.

[0003] First, high flue gas humidity severely interferes with the monitoring system. When the relative humidity of flue gas reaches 70% to 95%, the filter paper strip becomes soaked due to moisture absorption, exponentially decreasing the penetration of beta rays, causing measurement signal distortion and even monitoring failure. Furthermore, the tensile strength of the filter paper strip decreases significantly in a damp state, from 32N in a dry state to below 10N, making it prone to breakage during operation, leading to monitoring interruptions.

[0004] Secondly, traditional methods fail to fully consider the impact of fluctuations in beta-ray source intensity. Existing techniques typically assume a constant number of particles released by the decay of the source. However, in practice, the intensity of the source fluctuates due to factors such as ambient temperature and equipment aging. This can lead to increased deviations in the intensity measurements before and after sampling, significantly reducing the accuracy of concentration calculations.

[0005] Furthermore, existing continuous monitoring technologies have limitations. Current research focuses on improving the direct-reading beta-ray method or integrating it with light scattering technology. However, these methods lack system optimization for key issues such as filter paper dehumidification and temperature control in high-humidity environments and compensating for radiation source fluctuations. This makes it difficult to meet the stringent long-term operational stability requirements of the HJ76-2017 standard for monitoring equipment. The wide temperature variations and frequent extreme humidity values in industrial flue gas ducts further exacerbate the risk of failure of existing technologies.

[0006] Therefore, there is an urgent need for an optimization method that can simultaneously overcome humidity interference, suppress radiation source fluctuations, and achieve high-precision continuous online monitoring, so as to improve the applicability and reliability of the β-ray method in complex industrial environments. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide an online monitoring optimization method and system for emission smoke concentration based on the β-ray method, which is used to solve the problems of measurement failure caused by moisture in the filter paper belt in a high humidity environment and decreased monitoring accuracy caused by fluctuations in the intensity of the β-ray source.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] A beta-ray-based online monitoring optimization method for exhaust smoke concentration includes the following steps:

[0010] S1: The outer wall of the exhaust pipe is heated to 65°C by the dynamic heating system 1 to suppress condensation of the flue gas. At the same time, the filter paper 4 is dried at a constant temperature of 40°C by the drying device 11, forming a two-stage dehumidification process;

[0011] S2: The filter paper belt is linked by slide 9 and the following process is cyclically performed in three stations:

[0012] Dry the filter paper tape in the drying position;

[0013] Smoke dust samples are collected at the sampling location and trapped on the filter paper strip;

[0014] At the detection position, a radioactive source 3 is used to emit β rays, and the receiver 2 measures the initial ray intensity I1 after drying and the ray intensity I2 after sampling;

[0015] S3: Based on the formula:

[0016]

[0017] A smoke concentration calibration model is established, where k is the correction coefficient, r is the effective sampling radius of the filter paper, μm is the smoke mass absorption coefficient, V is the sampling volume, and I 1j and I 2j is the ray intensity measured for the jth time, and is the average value of the ray intensity measured multiple times;

[0018] S4. Calculate and output a corrected smoke concentration value according to the calibration model.

[0019] Furthermore, in S2, the three-station cycle process specifically includes:

[0020] In the drying position, the semiconductor heating element is used for constant temperature drying at 40°C, and the drying time is controlled by the temperature feedback system;

[0021] At the sampling position, the pipe wall temperature is maintained at 65°C by the dynamic heating system 1, while the sampling flow is adjusted by the proportional valve 7 and the flow sensor 6;

[0022] At the detection position, the moving distance of the filter paper belt is recorded by the encoder 10, and the beta ray intensity measurement is triggered.

[0023] Furthermore, the correction coefficient k is obtained through experimental calibration, specifically, collecting multiple groups of ln(I1 / I2) signals under known smoke concentration, calculating the linear relationship between their standard deviation and concentration, and fitting to obtain the k value.

[0024] Furthermore, in the double-stage dehumidification treatment, the tensile strength of the filter paper tape is restored to 28-32N, and the attenuation of the beta-ray transmittance is controlled within the range of 0.58-1.36%.

[0025] Furthermore, in S2 , the movement accuracy of the slide 9 is ±0.1 mm, and the position of the filter paper belt is controlled by the paper winding system 5 and the encoder 10 in coordination.

[0026] An online monitoring system for exhaust smoke concentration based on the beta ray method, comprising:

[0027] The dynamic heating system 1 is installed on the outer wall of the exhaust pipe to heat the pipe wall to 65°C to prevent condensation, and is connected to the proportional valve 7 and the flow sensor 6 through wires;

[0028] The drying device 11 is located in the drying position and is mechanically linked to the paper winding system 5 through the slide 9. It has a built-in semiconductor heating element and a temperature sensor for drying the filter paper strip at a constant temperature of 40°C.

[0029] The three-station integrated structure includes a drying station, a sampling station, and a detection station arranged in sequence along the moving direction of the filter paper belt, wherein:

[0030] The sampling position is provided with an air extraction pipe connected to the dynamic heating system 1, and the sampling flow is adjusted by a proportional valve 7 and a flow sensor 6. The sampling pump 8 is electrically connected to the proportional valve 7 to control the air extraction rate;

[0031] The detection position is provided with a radiation source 3 and a receiver 2, which are arranged opposite to each other with an adjustable distance between them, and the receiver 2 communicates with the processor via a data line;

[0032] The slide 9 is mechanically connected to the encoder 10 and the paper winding system 5, and is used to drive the filter paper belt to circulate between the three stations. The encoder 10 is connected to the processor through a signal line to feedback the filter paper position;

[0033] The processor is electrically connected to the flow sensor 6, the proportional valve 7, the drying device 11, the receiver 2 and the encoder 10, and is used to execute the method according to any one of claims 1 to 5.

[0034] Furthermore, the dynamic heating system 1 adopts a proportional-integral-derivative (PID) temperature control algorithm, and its temperature control module interacts with the flow sensor 6 data to dynamically adjust the heating power according to the real-time flow rate.

[0035] Furthermore, the contact surface between the semiconductor heating element of the drying device 11 and the filter paper tape is provided with a thermal conductive silica gel layer, the contact area accounts for ≥80%, and the temperature sensor sampling frequency is 10 Hz.

[0036] Furthermore, the receiver 2 is a Geiger-Mueller counter, the detection sensitivity of which is 0.1 cps, and the distance between the receiver 2 and the radiation source 3 can be adjusted in the range of 5 to 20 mm.

[0037] Furthermore, the processor has a built-in smoke concentration calibration model, and the parameter μm in the model has a value range of 0.15 to 0.25 m 2 / kg, and the sampling volume V is calculated by multiplying the real-time flow data of the flow sensor 6 by the sampling time.

[0038] The beneficial effects of the present invention are as follows: the present invention significantly improves the reliability and accuracy of smoke concentration monitoring in high humidity environments through a two-stage dehumidification design and dynamic process control. Specifically, the technology of combining low-temperature drying with dynamic heating is used to effectively eliminate the abnormal beta-ray attenuation caused by moisture on the filter paper belt, while restoring the mechanical strength of the filter paper and avoiding the risk of operational interruption. Through the three-station cyclic monitoring process and precise positioning of the slide, the fully automated coordination of filter paper drying, sampling and testing is achieved, ensuring the stable operation of continuous online monitoring.

[0039] Furthermore, a smoke concentration calibration model based on signal standard deviation characteristics can adaptively correct for interference from fluctuations in beta-ray source intensity, significantly reducing system errors and improving monitoring consistency under different operating conditions. This solution not only addresses the failure of traditional methods in extreme humidity environments, but also enhances the long-term stability of the system through coordinated optimization of algorithms and hardware, meeting the high-standard monitoring requirements in complex industrial environments.

[0040] In summary, the present invention has achieved breakthroughs in moisture resistance, measurement accuracy, and continuous monitoring capabilities, providing an efficient and reliable online monitoring solution for smoke concentration in high-humidity industrial scenarios such as coal-fired power plants.

[0041] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0043] Figure 1 Schematic diagram of the dehumidification mechanism of the β-ray online monitoring equipment;

[0044] Figure 2 This is a schematic diagram of the structure of the β-ray online monitoring equipment.

[0045] Figure numerals: 1-dynamic heating system, 2-receiver, 3-radiation source, 4-filter paper, 5-paper roll system, 6-flow sensor, 7-proportional valve, 8-sampling pump, 9-slide, 10-encoder, 11-drying device. DETAILED DESCRIPTION

[0046] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0047] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0048] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0049] 1. Technical solution

[0050] See also Figure 1 and Figure 2 The present invention provides an optimized β-ray method smoke concentration online monitoring system, comprising:

[0051] 1.1 Dehumidification mechanism design

[0052] Filter paper belt drying device: uses constant temperature 40℃ low temperature to dry the filter paper belt, eliminating the influence of humidity on the penetration of β rays (the pulse value is only reduced by 0.58-1.36% after drying, and the tensile strength of the filter paper is restored to a nearly dry state;

[0053] Dynamic heating system: The outer wall of the exhaust pipe is heated to 65℃ to prevent the flue gas from condensing and wetting the filter paper belt and the inner wall of the pipe.

[0054] 1.2β-ray online monitoring equipment

[0055] Three-station integrated design: integrated drying station, sampling station, and detection station, and the filter paper belt is linked by a slide to realize the "filter paper drying → detection I1 → sampling → secondary drying → detection I2" cycle process.

[0056] 1.3 Improved monitoring algorithm

[0057] Core principle: Establish a smoke concentration calibration model based on the standard deviation characteristic value of the ln(I1 / I2) signal:

[0058]

[0059] Corrected the effect of beta-ray source fluctuations.

[0060] 2. Innovation

[0061] Two-stage dehumidification design: Dynamic heating (65°C) combined with low-temperature drying (40°C) solves the problems of decreased tensile strength and condensation of filter paper strips.

[0062] Fluctuation suppression algorithm: For the first time, the linear characteristics of the signal standard deviation are used to correct the coefficient k through formula (2) to reduce the interference of line source fluctuations on the measurement.

[0063] Circular monitoring process: Through automatic switching of three workstations, the slide accurately controls the movement of the filter paper belt, realizing unattended real-time continuous online monitoring, adapting to the needs of industrial scenarios.

[0064] 3. Technical Effect

[0065] Humidity resistance: After dehumidification, the filter paper tape remains dry, the beta ray transmittance is stable, and there is no risk of breakage during equipment operation.

[0066] Improved accuracy: After the algorithm was introduced, the measurement error was reduced from 12.78% to 8.87%, and the error volatility was reduced, meeting the requirements of the HJ76 standard.

[0067] Continuous monitoring capability: Through automatic switching of three workstations, unattended real-time continuous monitoring is achieved, suitable for high-humidity industrial scenarios such as coal-fired power plants (temperature -20 to 60°C, humidity 20 to 99% RH).

[0068] After enabling the improvements: the error dropped to 8.87% and system stability was significantly improved.

[0069] Example 1

[0070] This embodiment is aimed at a high-humidity flue gas environment, and the implementation steps are as follows:

[0071] (1) Dynamic dehumidification stage: Start the dynamic heating system 1 to stabilize the temperature of the outer wall of the exhaust pipe at 65°C to prevent smoke condensation; at the same time, the drying device 11 pre-dries the filter paper strip at a constant temperature of 40°C to restore the tensile strength of the filter paper to above 28N.

[0072] (2) Three-station cycle control:

[0073] The slide 9 drives the filter paper belt to move to the drying position, and the drying device 11 continues drying for 30 seconds;

[0074] The filter paper belt moves to the sampling position, and the proportional valve 7 adjusts the sampling flow rate to 16.7 L / min according to the feedback from the flow sensor 6. The smoke particles are trapped on the surface of the filter paper;

[0075] The filter paper strip moves to the detection position, the radiation source 3 emits beta rays, and the receiver 2 measures the initial intensity I1; the sampled filter paper strip returns to the detection position for a second measurement of the intensity I2.

[0076] (3) Automatic reset: After the detection is completed, the paper roll system 5 is positioned by the encoder 10, and the filter paper belt is driven to move to the next sampling point, and the above process is executed in a loop.

[0077] The filter paper strip remains dry in a high humidity environment, avoiding the risk of breakage and enabling unattended continuous monitoring.

[0078] Example 2

[0079] This embodiment addresses the problem of β-ray source fluctuations, and the implementation steps are as follows:

[0080] (1) Data acquisition: In a smoke-free environment, the monitoring system is continuously operated to collect 10 sets of radiation intensity signals I1j and I2j before and after sampling.

[0081] (2) Standard deviation calculation: The processor calculates the standard deviation σ of ln(I1 / I2) according to the formula and establishes a linear relationship model between σ and the preset smoke concentration.

[0082] (3) Correction coefficient calibration: At a known concentration of 5 mg / m 3 In the calibration environment, the correction coefficient k was adjusted to minimize the measurement error, and k was finally determined to be 1.12.

[0083] (4) Real-time correction: In actual monitoring, the processor dynamically corrects the concentration calculation results based on the current σ value and the calibration model.

[0084] Eliminate systematic deviations caused by radiation source fluctuations and improve measurement consistency.

[0085] Example 3

[0086] This embodiment is applied to flue monitoring of coal-fired power plants, and the implementation steps are as follows:

[0087] (1) Environmental adaptation: After the system is started, the dynamic heating system 1 raises the exhaust pipe temperature to 65°C, and the drying device 11 maintains the filter paper at 40°C to adapt to the flue humidity environment of 90% RH.

[0088] (2) Full process monitoring:

[0089] The filter paper belt completes a "drying-sampling-testing" cycle every 5 minutes, and the sliding table 9 moves with an accuracy of ±0.1mm;

[0090] Receiver 2 transmits I1 and I2 signals to the processor in real time, and calculates the smoke concentration through the calibration model;

[0091] The flow sensor 6 and the proportional valve 7 dynamically adjust the flow rate to ensure that the error of the sampling volume V is ≤2%.

[0092] (3) Abnormal handling: When the tensile strength of the filter paper is lower than 25N, the system triggers an alarm and suspends operation, and automatically resumes after drying is completed.

[0093] In extreme humidity and temperature fluctuation environments, stable online monitoring with an error of ≤9% is achieved, meeting the requirements of HJ76 standards.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for online monitoring and optimization of exhaust smoke concentration based on the β-ray method, characterized by: The following steps are involved: S1: The outer wall of the exhaust pipe is heated to 65°C by a dynamic heating system (1) to suppress condensation of the flue gas, and the filter paper (4) is dried at a constant temperature of 40°C by a drying device (11), thereby forming a two-stage dehumidification process; S2: The filter paper belt is linked by the slide (9) to cyclically execute the following process in three stations: Dry the filter paper tape in the drying position; Smoke dust samples are collected at the sampling location and trapped on the filter paper strip; At the detection position, a radioactive source (3) is used to emit beta rays, and the initial ray intensity I1 after drying and the ray intensity I2 after sampling are measured by a receiver (2); S3: Based on the formula: A smoke concentration calibration model is established, where k is the correction coefficient, r is the effective sampling radius of the filter paper, μm is the smoke mass absorption coefficient, V is the sampling volume, and I 1j and I 2j is the ray intensity measured for the jth time, and is the average value of the ray intensity measured multiple times; S4. Calculate and output a corrected smoke concentration value according to the calibration model.

2. The online monitoring optimization method for exhaust smoke concentration based on the β-ray method according to claim 1 is characterized in that: In S2, the three-station cycle process specifically includes: In the drying position, the semiconductor heating element is used for constant temperature drying at 40°C, and the drying time is controlled by the temperature feedback system; At the sampling position, the pipe wall temperature is maintained at 65°C by a dynamic heating system (1), while the sampling flow rate is adjusted by a proportional valve (7) and a flow sensor (6); At the detection position, the moving distance of the filter paper belt is recorded by an encoder (10), and the beta ray intensity measurement is triggered.

3. The online monitoring and optimization method for exhaust smoke concentration based on the β-ray method according to claim 1 is characterized in that: The correction coefficient k is obtained through experimental calibration, specifically, collecting multiple groups of ln(I1 / I2) signals under known smoke concentration, calculating the linear relationship between their standard deviation and concentration, and fitting to obtain the k value.

4. The online monitoring optimization method for exhaust smoke concentration based on the β-ray method according to claim 1 is characterized in that: In the double-stage dehumidification treatment, the tensile strength of the filter paper tape is restored to 28-32N, and the attenuation of the beta-ray transmittance is controlled within the range of 0.58-1.36%.

5. The online monitoring optimization method for exhaust smoke concentration based on the β-ray method according to claim 1 is characterized in that: In the above-mentioned S2, the movement accuracy of the slide (9) is ±0.1 mm, and the position of the filter paper belt is controlled in coordination with the paper winding system (5) and the encoder (10).

6. An online monitoring system for exhaust smoke concentration based on the β-ray method, characterized by: include: A dynamic heating system (1) is provided on the outer wall of the exhaust pipe, for heating the pipe wall to 65° C. to prevent condensation, and is connected to a proportional valve (7) and a flow sensor (6) via a wire; The drying device (11) is located at the drying position and is mechanically linked to the paper winding system (5) through the slide (9). The drying device (11) has a built-in semiconductor heating element and a temperature sensor for drying the filter paper strip at a constant temperature of 40°C. The three-station integrated structure includes a drying station, a sampling station, and a detection station arranged in sequence along the moving direction of the filter paper belt, wherein: The sampling position is provided with an air extraction pipe connected to the dynamic heating system (1), and the sampling flow rate is adjusted by a proportional valve (7) and a flow sensor (6), and a sampling pump (8) is electrically connected to the proportional valve (7) to control the air extraction rate; The detection position is provided with a radiation source (3) and a receiver (2), which are arranged relative to each other with an adjustable distance between them, and the receiver (2) communicates with the processor via a data line; The slide (9) is mechanically connected to the encoder (10) and the paper winding system (5) and is used to drive the filter paper belt to circulate between the three stations. The encoder (10) is connected to the processor through a signal line to feedback the filter paper position; A processor is electrically connected to the flow sensor (6), the proportional valve (7), the drying device (11), the receiver (2) and the encoder (10), and is used to execute the method according to any one of claims 1 to 5.

7. The online monitoring system for exhaust smoke concentration based on the β-ray method is characterized by: The dynamic heating system (1) adopts a proportional-integral-derivative (PID) temperature control algorithm, and its temperature control module interacts with the flow sensor (6) data to dynamically adjust the heating power according to the real-time flow.

8. The online monitoring system for exhaust smoke concentration based on the β-ray method is characterized by: The contact surface between the semiconductor heating element of the drying device (11) and the filter paper tape is provided with a heat-conducting silica gel layer, the contact area accounts for ≥80%, and the temperature sensor sampling frequency is 10 Hz.

9. The online monitoring system for exhaust smoke concentration based on the β-ray method is characterized by: The receiver (2) is a Geiger-Mueller counter with a detection sensitivity of 0.1 cps and an adjustable distance between the receiver and the radiation source (3) within a range of 5 to 20 mm.

10. The online monitoring system for exhaust smoke concentration based on the β-ray method is characterized by: The processor has a built-in smoke concentration calibration model, and the parameter μm in the model has a value range of 0.15 to 0.25m 2 / kg, and the sampling volume V is calculated by multiplying the real-time flow data of the flow sensor (6) by the sampling time.

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