A real-time monitoring system for particulate matter concentration in a dedusting process

Abstract

The present application relates to particulate matter concentration monitoring technical field, specifically to a kind of particulate matter concentration real-time monitoring system in dust removal process, solve the technical problem of poor measurement stability of prior art.The system includes: data acquisition unit, for collecting particulate matter concentration monitoring value and ambient temperature value, particulate matter concentration monitoring value is characterized by real-time measurement obtained by micro-oscillating balance method particulate matter concentration;Correction factor determination unit is used for determining temperature deviation reference value and sequence stability index according to ambient temperature value and historical particulate matter concentration monitoring value sequence;Temperature deviation reference value is used to characterize the measurement deviation caused by temperature variation, and sequence stability index is used to characterize the change of particulate matter concentration monitoring value in the process of particulate matter concentration real-time monitoring;Concentration correction unit is used to obtain the corrected particulate matter concentration by weighted calculation according to temperature deviation reference value, sequence stability index and current particulate matter concentration monitoring value.

Description

Technical Field

[0001] This invention relates to the field of particulate matter concentration monitoring technology, specifically to a real-time particulate matter concentration monitoring system in a dust removal process. Background Technology

[0002] In modern industrial production (such as thermal power generation, metallurgical manufacturing, building materials production, chemical industry, and waste treatment), various processes generate a large amount of particulate matter pollution. High-efficiency dust removal systems have become crucial facilities for ensuring environmental quality, protecting personnel health, and achieving sustainable development. With increasingly stringent environmental regulations and rising demands for refined enterprise management, the industry has placed higher requirements on real-time, high-precision monitoring of particulate matter concentration during dust removal processes. Reliable monitoring data is needed to assess the operational performance of dust removal equipment (such as bag filters and electrostatic precipitators) and promptly detect efficiency declines or sudden failures.

[0003] Existing methods for monitoring particulate matter concentration typically employ sensors based on optical principles. However, this approach is susceptible to interference from particulate matter composition, particle size, color, and flue gas humidity, resulting in poor measurement stability and failing to meet the demand for high-precision monitoring of particulate matter concentration during dust removal processes. Summary of the Invention

[0004] To address the problem of poor measurement stability in existing technologies, the present invention aims to provide a real-time particulate matter concentration monitoring system during dust removal processes. The specific technical solution adopted is as follows:

[0005] This application provides a real-time particulate matter concentration monitoring system for a dust removal process, including:

[0006] The data acquisition unit is used to collect particulate matter concentration monitoring values ​​and ambient temperature values. The particulate matter concentration monitoring values ​​represent the particulate matter concentration measured in real time using the micro-oscillation balance method.

[0007] The correction factor determination unit is used to determine the temperature deviation reference value and the sequence stability index based on the ambient temperature value and the historical particulate matter concentration monitoring value sequence, respectively. The temperature deviation reference value is used to characterize the measurement deviation caused by temperature changes, and the sequence stability index is used to characterize the changes in particulate matter concentration monitoring values ​​during real-time monitoring.

[0008] The concentration correction unit is used to calculate the corrected particulate matter concentration by weighting the temperature deviation reference value, the sequence stability index and the current particulate matter concentration monitoring value.

[0009] In one possible implementation, the data acquisition unit includes a micro-oscillating balance particulate matter analyzer and a temperature sensor;

[0010] The micro-oscillation balance particulate matter analyzer is used to monitor particulate matter concentration values ​​in real time at preset time intervals.

[0011] Temperature sensors are used to monitor ambient temperature values ​​in real time;

[0012] The data acquisition unit is used to record particulate matter concentration monitoring values ​​and ambient temperature values ​​at each monitoring time, as well as the average value of ambient temperature changes; the average value of ambient temperature changes is the average of the absolute values ​​of the differences between the ambient temperature values ​​and the initial temperature values ​​within the monitoring period.

[0013] In one possible implementation, the correction factor determination unit includes a temperature deviation reference value calculation module and a sequence stability index calculation module;

[0014] The temperature deviation reference value calculation module is used to determine the temperature deviation reference value based on the ambient temperature value and temperature deviation experimental data. The temperature deviation experimental data is used to characterize the deviation between the monitored particulate matter concentration value obtained by the micro-oscillation balance method and the actual particulate matter concentration value under different ambient temperature ranges and different temperature change ranges.

[0015] The sequence stability index calculation module is used to determine the sequence stability index based on the changes in particulate matter concentration monitoring values ​​of subsequence segments in the historical particulate matter concentration monitoring value sequence.

[0016] In one possible implementation, the temperature deviation experimental data is obtained in the following way:

[0017] In multiple ambient temperature ranges and multiple temperature variation ranges, particulate matter concentrations were measured using both the micro-oscillation balance method and the reference measurement method. The deviation between the monitored particulate matter concentration values ​​obtained by the micro-oscillation balance method and the actual particulate matter concentration values ​​was calculated to generate temperature deviation experimental data. The ambient temperature ranges and temperature variation ranges were set in a tiered manner.

[0018] In one possible implementation, the temperature deviation reference value calculation module is specifically used for:

[0019] Match the current ambient temperature value at the monitoring time to the corresponding ambient temperature range in the temperature deviation experimental data;

[0020] Match the average ambient temperature change during the current monitoring period to the corresponding temperature change range in the temperature deviation experimental data;

[0021] Based on the matched ambient temperature range and temperature change range, the corresponding temperature deviation reference value is retrieved from the temperature deviation experimental data.

[0022] In one possible implementation, the sequence stability index calculation module includes a subsequence partitioning submodule, a variance calculation submodule, and a stability calculation submodule.

[0023] The subsequence division submodule is used to divide the historical particulate matter concentration monitoring value sequence into multiple subsequence segments, each subsequence segment containing a preset number of consecutive particulate matter concentration monitoring values;

[0024] The variance calculation submodule is used to calculate the variance of concentration change based on the difference between the particulate matter concentration monitoring values ​​at adjacent times in each subsequence segment;

[0025] The stability calculation submodule is used to determine the sequence stability index by normalizing the deviation between the concentration change variance of the previous subsequence segment and the average concentration change variance of all subsequence segments at the current time.

[0026] In one possible implementation, the concentration correction unit includes a first reference value calculation module and a second reference value calculation module;

[0027] The first reference value calculation module is used to calculate the first reference value of particulate matter concentration based on the temperature deviation reference value and the current particulate matter concentration monitoring value.

[0028] The second reference value calculation module is used to calculate the second reference value of particulate matter concentration based on the historical particulate matter concentration monitoring value sequence; wherein, the second reference value of particulate matter concentration is determined based on the average concentration change of the previous subsequence segment at the current time and the particulate matter concentration monitoring value at the previous time.

[0029] The concentration correction unit is used to determine the weighting factors corresponding to the first reference value of particulate matter concentration, the second reference value of particulate matter concentration, and the current monitoring value of particulate matter concentration, and to perform weighted calculation on the first reference value of particulate matter concentration, the second reference value of particulate matter concentration, and the current monitoring value of particulate matter concentration through the weighting factors to obtain the corrected particulate matter concentration.

[0030] In one possible implementation, the weighting factor corresponding to the first reference value of particulate matter concentration is determined based on the proportion of the temperature deviation reference value among the temperature deviation reference value, the sequence stability index, and the preset constant; the weighting factor corresponding to the second reference value of particulate matter concentration is determined based on the proportion of the sequence stability index among the temperature deviation reference value, the sequence stability index, and the preset constant; and the weighting factor corresponding to the current monitored value of particulate matter concentration is determined based on the proportion of the preset constant among the temperature deviation reference value, the sequence stability index, and the preset constant.

[0031] In one possible implementation, the real-time particulate matter concentration monitoring system during the dust removal process also includes an alarm unit;

[0032] The alarm unit is used to trigger an alarm signal when the corrected particulate matter concentration is greater than a preset concentration threshold.

[0033] In one possible implementation, the real-time particulate matter concentration monitoring system during the dust removal process also includes a storage unit and a display unit;

[0034] The storage unit is used to store temperature deviation experimental data, historical particulate matter concentration monitoring value sequences, ambient temperature values, and corrected particulate matter concentrations.

[0035] The display unit is used to display the corrected particulate matter concentration and ambient temperature value in real time.

[0036] The present invention has the following beneficial effects:

[0037] Based on the above technical solutions, the data acquisition unit in this embodiment uses a micro-oscillation balance method to ensure the basic accuracy of the monitoring data. Simultaneously, it collects temperature data to provide a basis for eliminating environmental interference. The correction factor determination unit extracts key parameters from two dimensions: temperature deviation and sequence stability. The concentration correction unit integrates multi-dimensional information through weighted calculation, effectively reducing measurement deviations caused by temperature interference and data anomalies. Compared with existing particulate matter concentration detection schemes, this application can significantly improve the accuracy of particulate matter concentration monitoring during dust removal, providing more reliable data support for judging the operating status of dust removal equipment. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present 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 present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a system architecture diagram of a real-time particulate matter concentration monitoring system in a dust removal process, provided as an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the structure of a data acquisition unit provided in one embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of a correction factor determination unit provided in one embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of a sequence stability index calculation module provided in one embodiment of the present invention;

[0043] Figure 5 This is a schematic diagram of the structure of a concentration correction unit provided in one embodiment of the present invention;

[0044] Figure 6This is a system architecture diagram of another real-time particulate matter concentration monitoring system in a dust removal process, provided as an embodiment of the present invention. Detailed Implementation

[0045] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a real-time particulate matter concentration monitoring system in a dust removal process proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] The specific solution of a real-time particulate matter concentration monitoring system in a dust removal process provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Please see Figure 1 This document illustrates a system architecture diagram of a real-time particulate matter concentration monitoring system during a dust removal process, according to an embodiment of the present invention. The real-time particulate matter concentration monitoring system 10 includes: a data acquisition unit 11, a correction factor determination unit 12, and a concentration correction unit 13. The functions and collaborative logic of each unit are as follows:

[0049] Data acquisition unit 11 is used to collect particulate matter concentration monitoring values ​​and ambient temperature values.

[0050] Among them, the particulate matter concentration monitoring value represents the particulate matter concentration measured in real time by the tapered element oscillating microbalance (TEOM) method.

[0051] For example, in this embodiment, the monitoring object is fine particulate matter (PM2.5, i.e. particulate matter in ambient air with an aerodynamic equivalent diameter of less than or equal to 2.5 micrometers). In practice, it can be extended to other types of particulate matter such as total suspended particulate matter (TSP) and inhalable particulate matter (PM10).

[0052] In some embodiments, the data acquisition unit 11 synchronously records the correspondence between the particulate matter concentration monitoring value and the ambient temperature value at each monitoring moment to ensure the temporal consistency of the data and provide a matching basis for subsequent corrections. For example, if the monitoring cycle is set to 6 minutes, a set of PM2.5 concentration values ​​and ambient temperature values ​​are collected every 6 minutes, and the collection timestamp of the set of data is stored.

[0053] The correction factor determination unit 12 is used to determine the temperature deviation reference value and the sequence stability index based on the ambient temperature value and the historical particulate matter concentration monitoring value sequence, respectively.

[0054] It should be noted that the micro-oscillation balance method can directly measure particulate matter mass and is unaffected by optical properties, meeting the requirements for high-precision real-time monitoring. However, existing monitoring systems based on the micro-oscillation balance method still have some limitations: Firstly, drastic changes in ambient temperature or failure of the heating system can cause thermal expansion and contraction of the oscillation element, affecting frequency stability. Furthermore, the micro-oscillation balance method calculates volumetric flow rate by measuring ambient temperature and air pressure in real time; temperature errors can lead to deviations in flow rate calculations, thus affecting the conversion formula for mass concentration and reducing the accuracy of mass measurement. Secondly, the system does not incorporate the stability of the particulate matter concentration monitoring data itself to verify the reliability of the correction results, making it difficult to completely eliminate deviations caused by single-factor corrections and failing to meet the requirements for high-precision monitoring of particulate matter concentration during dust removal. Therefore, this application can evaluate the impact of the monitoring data based on the above two dimensions, thereby achieving dynamic correction of the monitoring data and improving data accuracy.

[0055] The temperature deviation reference value is used to characterize the measurement deviation caused by temperature changes. Since the oscillation element of the micro-oscillation balance method is susceptible to thermal expansion and contraction due to temperature, which leads to frequency shift, this application can quantify the degree of deviation between the measured value and the true value under the current temperature conditions through the temperature deviation reference value.

[0056] Sequence stability indices are used to characterize the changes in particulate matter concentration monitoring values ​​during real-time monitoring. Particulate matter concentration changes during dust removal exhibit regularity (e.g., a rapid initial decrease followed by a slow decrease). By analyzing the stability of historical monitoring values, it can be determined whether the current monitoring value conforms to this regularity, thereby verifying the reliability of the correction results.

[0057] The concentration correction unit 13 is used to calculate the corrected particulate matter concentration by weighting the temperature deviation reference value, the sequence stability index and the current particulate matter concentration monitoring value.

[0058] This application uses a weighted approach to balance the impact of temperature interference compensation and data stability verification on the correction results. For example, if the temperature deviation reference value shows that the current temperature interference is large, the weight of the temperature deviation reference value is increased; if the sequence stability index shows that the historical data has small fluctuations and high reliability, the weight of the reference value corresponding to the sequence stability index is increased.

[0059] Based on the above technical solutions, in this embodiment, the data acquisition unit 11 uses a micro-oscillation balance method to ensure the basic accuracy of the monitoring data, while simultaneously acquiring temperature data to provide a basis for eliminating environmental interference. The correction factor determination unit 12 extracts key parameters from two dimensions: temperature deviation and sequence stability. The concentration correction unit 13 integrates multi-dimensional information through weighted calculation, effectively reducing measurement deviations caused by temperature interference and data anomalies. Compared with existing particulate matter concentration detection schemes, this application can significantly improve the accuracy of particulate matter concentration monitoring during dust removal, providing more reliable data support for judging the operating status of dust removal equipment.

[0060] As one possible embodiment of this application, combined with Figure 1 ,like Figure 2 As shown, in order to further clarify the specific implementation method of data acquisition and improve the reliability of data source, the above-mentioned data acquisition unit 11 includes a micro-oscillation balance particulate matter analyzer 111 and a temperature sensor 112.

[0061] Among them, the micro-oscillation balance particulate matter analyzer 111 is used to monitor particulate matter concentration values ​​in real time at preset time intervals.

[0062] For example, the preset time interval can be set to 6 minutes (or can be adjusted to 10 minutes, 30 minutes, etc. according to the needs of the dust removal scenario). The micro-oscillation balance particulate matter analyzer 111 directly outputs the particulate matter mass concentration by detecting the oscillation frequency shift caused by the accumulation of particulate matter on the filter membrane. Its measurement range is usually 0-1000μg / m³ or 0-10000μg / m³, and the lowest detection limit can reach 0.1μg / m³, which meets the monitoring accuracy requirements of industrial dust removal scenarios.

[0063] Temperature sensor 112 is used to monitor ambient temperature values ​​in real time.

[0064] For example, the temperature sensor 112 can be deployed in the monitoring environment where the micro-oscillation balance particulate matter analyzer 111 is located. The measurement accuracy needs to match the requirements of the micro-oscillation balance particulate matter analyzer (e.g., error ≤ ±0.5℃) to ensure that the temperature data can accurately reflect the impact on the oscillation element.

[0065] In some embodiments, the data acquisition unit 11 is used to record particulate matter concentration monitoring values ​​and ambient temperature values ​​at each monitoring time, as well as the average value of ambient temperature changes.

[0066] Among them, the average value of the change in ambient temperature is the average value of the absolute difference between the ambient temperature value and the initial temperature value within the monitoring period.

[0067] For example, the average value of the ambient temperature change is the average of the absolute values ​​of the differences between the ambient temperature values ​​at each time point within the monitoring period (e.g., 6 minutes) and the initial temperature value of the period. For instance, if the temperature is collected every minute within 6 minutes (in sequence: 25℃, 25.2℃, 25.1℃, 24.9℃, 25℃, 25.2℃), and the initial temperature is 25℃, then the absolute values ​​of the temperature differences are 0℃, 0.2℃, 0.1℃, 0.1℃, 0℃, 0.2℃, and the average value of the ambient temperature change is (0+0.2+0.1+0.1+0+0.2) / 6=0.1℃. This value can more comprehensively reflect the temperature fluctuations within the monitoring period and provide a more accurate basis for subsequent temperature deviation analysis.

[0068] As one possible embodiment of this application, combined with Figure 1 ,like Figure 3 As shown, in order to further clarify the detailed logic of the correction factor calculation and improve the accuracy of the correction factor, the correction factor determination unit 12 includes a temperature deviation reference value calculation module 121 and a sequence stability index calculation module 122.

[0069] The temperature deviation reference value calculation module 121 is used to determine the temperature deviation reference value based on the ambient temperature value and temperature deviation experimental data.

[0070] Among them, the temperature deviation experimental data is used to characterize the deviation between the monitored particulate matter concentration values ​​obtained by the micro-oscillation balance method and the actual particulate matter concentration values ​​under different ambient temperature ranges and different temperature change ranges.

[0071] For example, temperature deviation experimental data can be obtained in advance through controlled variable experiments to characterize the deviation between the monitored values ​​obtained by the micro-oscillation balance method and the actual values ​​of particulate matter concentration under different ambient temperature ranges and different temperature change ranges. This temperature deviation reference value calculation module 121 can match real-time temperature conditions with experimental data, thereby quickly determining the degree of deviation corresponding to the current temperature.

[0072] The sequence stability index calculation module 122 is used to determine the sequence stability index based on the changes in the particulate matter concentration monitoring values ​​of subsequence segments in the historical particulate matter concentration monitoring value sequence.

[0073] For example, this application can divide a long-term series such as historical particulate matter concentration monitoring value sequence into multiple short subsequence segments. By analyzing the concentration change patterns within the subsequence segments, the stability of historical data can be determined. For instance, if the concentration change amplitude within a certain subsequence segment is small and there are no sudden changes, it indicates that the data segment has high stability and can be used as a reliable reference for current correction.

[0074] Based on the above technical solution, this application embodiment further improves the practicality and accuracy of the system by refining the data acquisition unit 11 and the correction factor determination unit 12. By configuring the hardware structure of the micro-oscillation balance particulate matter analyzer and the temperature sensor 112 for data acquisition and setting key parameters such as preset time interval and average ambient temperature change, it is ensured that the acquired data is not only real-time but also fully reflects the impact of temperature fluctuations on the measurement. In addition, the temperature deviation reference value calculation module 121 and the sequence stability index calculation module 122 independently evaluate the two dimensions of temperature deviation and sequence stability, respectively, which significantly improves the controllability of data acquisition and the calculation accuracy of the correction factor, providing more reliable input parameters for subsequent particulate matter concentration correction.

[0075] As one possible embodiment of this application, the temperature deviation experimental data is obtained in the following manner:

[0076] In multiple ambient temperature ranges and multiple temperature variation ranges, particulate matter concentrations were measured using both the micro-oscillation balance method and the reference measurement method. The deviation between the monitored particulate matter concentration values ​​obtained by the micro-oscillation balance method and the actual particulate matter concentration values ​​was calculated, and temperature deviation experimental data were generated.

[0077] The ambient temperature range and temperature change range are set in a tiered manner.

[0078] For example, this application can set multiple ambient temperature ranges and multiple temperature variation ranges in a tiered manner. For instance, the ambient temperature ranges can be set to 10 groups (such as 25-26℃, 26-27℃, ..., 34-35℃) to cover the common ambient temperature ranges in industrial dust removal scenarios, and the temperature variation ranges can also be set to 10 groups (such as 0-0.1℃, 0.1-0.2℃, ..., 0.9-1.0℃) to simulate different degrees of temperature fluctuations.

[0079] Subsequently, under each combination of "ambient temperature range - temperature change range", the particulate matter concentration was simultaneously measured using the micro-oscillation balance method (consistent with the instrument used in the system) and a reference measurement method (such as the β-ray absorption method, which can more accurately detect particulate matter concentration; however, it has problems such as the inability to achieve continuous real-time monitoring, complex equipment, and high maintenance costs, and is mainly used for experimental measurements and cannot be applied to industrial production; its measurement results can be used as the "actual value of particulate matter concentration").

[0080] After the measurement is completed, the deviation (such as deviation ratio, absolute deviation, etc.) between the monitoring value obtained by the micro-oscillation balance method and the actual value obtained by the β-ray absorption method can be calculated for the multiple measurement results under each combination of conditions (for example, 30 measurements per combination to reduce random errors), and finally the temperature deviation experimental data can be generated.

[0081] For example, the deviation between the monitored value obtained by the micro-oscillation balance method and the actual value obtained by the β-ray absorption method satisfies the following formula:

[0082]

[0083] in, For the first Group Ambient Temperature Range, Number When considering the temperature variation range, the deviation between the monitored values ​​obtained by the micro-oscillation balance method and the actual values ​​obtained by the β-ray absorption method is calculated. For the first Group Ambient Temperature Range, Number The number of experimental measurements within the temperature variation range. For the first The actual value obtained by the β-ray absorption method in this experiment. For the first The monitoring values ​​obtained by the micro-oscillation balance method during this experiment were... The minimum value function is used to truncate the calculated deviation. When the calculated result is greater than 0.99, it indicates that the average deviation of the monitored value from the actual value is too large. This should be considered a fault or abnormal state, and the experimental data needs to be re-examined or the experiment needs to be repeated. Under a certain combination of conditions, the larger the difference between the particulate matter concentration values ​​obtained by the two methods, the larger the deviation ratio between the temperature change of the combined conditions and the micro-oscillation balance method measurement result and the actual result.

[0084] The following sections explain the calculation logic for the temperature deviation reference value and the sequence stability index.

[0085] As one possible embodiment of this application, the temperature deviation reference value calculation module 121 is specifically used to: match the ambient temperature value at the current monitoring time to the corresponding ambient temperature range in the temperature deviation experimental data, match the average ambient temperature change value of the current monitoring period to the corresponding temperature change range in the temperature deviation experimental data, and query the corresponding temperature deviation reference value from the temperature deviation experimental data based on the matched ambient temperature range and temperature change range.

[0086] For example, the ambient temperature at the current monitoring time is 25.3℃, which can be matched with the corresponding ambient temperature range of 25-26℃ in the temperature deviation experimental data. The average ambient temperature change during the current monitoring period is 0.15℃, which can be matched with the corresponding temperature change range of 0.1-0.2℃ in the experimental data. Then, based on the matched "ambient temperature range of 25-26℃" and "temperature change range of 0.1-0.2℃", the deviation value corresponding to this combination (such as a deviation ratio of 5%) is queried from the temperature deviation experimental data. This deviation value is the current temperature deviation reference value.

[0087] It should be noted that assessing the impact of temperature changes on particulate matter concentration alone may not be absolutely reliable. This application can also analyze monitoring data from adjacent time points. During the dust removal process, the particulate matter concentration should show a significant decrease in the early stage of dust removal. However, in the later stage of dust removal, as the particulate matter concentration decreases, the difficulty of dust removal increases, and the rate of decrease in particulate matter concentration may decrease significantly. Therefore, this application can also correct the real-time monitoring of particulate matter concentration by analyzing the characteristics of particulate matter concentration changes during the dust removal process in history.

[0088] As one possible embodiment of this application, combined with Figure 3 ,like Figure 4 As shown, the sequence stability index calculation module 122 includes a subsequence partitioning submodule 1221, a variance calculation submodule 1222, and a stability calculation submodule 1223.

[0089] The subsequence division submodule 1221 is used to divide the historical particulate matter concentration monitoring value sequence into multiple subsequence segments.

[0090] Each subsequence segment contains a preset number of particulate matter concentration monitoring values.

[0091] For example, the historical particulate matter concentration monitoring value sequence can be composed of the particulate matter concentration monitoring values ​​obtained from the start of dust removal to the current time, in the order of collection. Particulate matter concentration changes with dust removal time; the particulate matter concentration or concentration change values ​​corresponding to adjacent dust removal times may be similar. This application can divide the particulate matter concentration values ​​to facilitate analysis based on adjacent particulate matter concentration values. The division method can be: dividing the particulate matter concentration sequence from the current time to the historical monitoring time. The division step size and the length of the divided sequence can be set according to requirements, for example, it can be set to 5. That is, if the current time is t, then its adjacent sequence segments are composed of the particulate matter concentrations corresponding to t-1, t-2, ..., t-5.

[0092] Furthermore, in order to better analyze the subsequence segments and determine whether they can be used as references for particulate matter concentration analysis, a stability analysis of changes in adjacent subsequence segments is performed. Based on the obtained stability analysis, the difference between the current particulate matter concentration and the particulate matter concentration at adjacent time points is determined. If the monitoring sequence segment preceding the current monitoring time point is a relatively stable sequence, then the current particulate matter concentration is also in a relatively stable state, and the corresponding particulate matter condition may be similar to that at the five adjacent time points. This indicates that if the particulate matter condition is similar to that of adjacent particulate matter concentrations, it is also similar to that affected by external interference.

[0093] The variance calculation submodule 1222 is used to calculate the variance of concentration change based on the difference between the particulate matter concentration monitoring values ​​at adjacent times in each subsequence segment.

[0094] For example, the particulate matter concentration difference at time t can be represented by the difference between the particulate matter concentration at the current time t and the particulate matter concentration at the previous time (i.e., time t-1). At the same time, the particulate matter concentration change from the start of dust removal to the current time is divided into sub-sequence segments with a step size of 5. The variance of these sub-sequence segments is calculated. The smaller the variance of the particulate matter concentration obtained from adjacent sub-sequence segments, and the smaller its variance is among the variances corresponding to all historical sub-sequence segments, the more stable the adjacent particulate matter sequence is.

[0095] The stability calculation submodule 1223 is used to determine the sequence stability index by normalization based on the deviation between the concentration change variance of the previous subsequence segment and the average concentration change variance of all subsequence segments at the current time.

[0096] For example, the sequence stability index satisfies the following formula:

[0097]

[0098] in, As a sequence stability index, This represents the variance of the concentration change in the previous subsequence segment at the current time. The variance of the average concentration change across all subsequence segments. For normalization functions (e.g., maximum and minimum value normalization). It is a very small positive number (e.g.) To avoid the denominator being zero, the smaller the variance of the preceding subsequence segment at the current moment compared to the variance of particulate matter concentrations of all subsequence segments obtained from the start of dust removal to the current moment, the more stable the current period in the entire monitoring process is, and the more stable the current particulate matter concentration change is. The larger the sequence stability index (the value range is normalized to between 0 and 1 using a normalization function).

[0099] Based on the above technical solutions, the temperature deviation experimental data in this application can be compared using tiered intervals and dual methods, ensuring the objectivity and coverage of the deviation pattern and avoiding deviations caused by subjective estimation. The temperature deviation reference value can be matched between the ambient temperature range and the temperature change range, ensuring the accurate correspondence between real-time temperature conditions and experimental data and avoiding correction errors caused by temperature mismatch. In addition, the sequence stability index is characterized by quantitative indicators through subsequence division, deviation variance calculation, and normalization, improving the consistency of the analysis results and providing a key guarantee for the high accuracy of the final concentration correction.

[0100] The following is a further explanation of the correction process of the concentration correction unit 13 in the embodiments of this application.

[0101] As one possible embodiment of this application, combined with Figure 1 ,like Figure 5 As shown, the concentration correction unit 13 includes a first reference value calculation module 131 and a second reference value calculation module 132.

[0102] The first reference value calculation module 131 is used to calculate the first reference value of particulate matter concentration based on the temperature deviation reference value and the current particulate matter concentration monitoring value.

[0103] This first reference value for particulate matter concentration is the temperature-corrected reference value. For example, the first reference value for particulate matter concentration satisfies the following formula:

[0104]

[0105] in, This is the first reference value for particulate matter concentration. The current temperature deviation reference value to be matched (in the case of the first...) Group Ambient Temperature Range, Number (Taking the deviation between the monitored value obtained by the micro-oscillation balance method and the actual value obtained by the β-ray absorption method when the temperature changes within a range as an example). This represents the current particulate matter concentration monitoring value.

[0106] The second reference value calculation module 132 is used to calculate the second reference value of particulate matter concentration based on the historical particulate matter concentration monitoring value sequence.

[0107] The second reference value for particulate matter concentration is determined based on the average concentration change of the previous sub-sequence segment at the current time and the monitored particulate matter concentration value at the previous time. Taking a previous sub-sequence segment including the five adjacent time points as an example, this application can obtain the average value of the particulate matter concentration change corresponding to the five adjacent time points, and add the monitored particulate matter concentration value corresponding to the previous time point at the current time to the obtained average value of the particulate matter concentration change to obtain the second reference value for particulate matter concentration, denoted as . .

[0108] The concentration correction unit 13 is used to determine the weighting factors corresponding to the first reference value of particulate matter concentration, the second reference value of particulate matter concentration, and the current monitoring value of particulate matter concentration, and to perform weighted calculation on the first reference value of particulate matter concentration, the second reference value of particulate matter concentration, and the current monitoring value of particulate matter concentration through the weighting factors to obtain the corrected particulate matter concentration.

[0109] In some embodiments, the weighting factor corresponding to the first reference value of particulate matter concentration is determined based on the proportion of the temperature deviation reference value among the temperature deviation reference value, the sequence stability index, and the preset constant; the weighting factor corresponding to the second reference value of particulate matter concentration is determined based on the proportion of the sequence stability index among the temperature deviation reference value, the sequence stability index, and the preset constant; and the weighting factor corresponding to the current monitored value of particulate matter concentration is determined based on the proportion of the preset constant among the temperature deviation reference value, the sequence stability index, and the preset constant.

[0110] For example, the corrected particulate matter concentration satisfies the following formula:

[0111]

[0112] in, This is the corrected particulate matter concentration. The current temperature deviation reference value is matched. This is a sequence stability index, with a preset constant of 1. This is the first reference value for particulate matter concentration. This is the second reference value for particulate matter concentration. This represents the current particulate matter concentration monitoring value. This is the weighting factor corresponding to the first reference value of particulate matter concentration. The weighting factor corresponding to the second reference value of particulate matter concentration. This is the weighting factor corresponding to the current particulate matter concentration monitoring value.

[0113] Based on the above technical solutions, this application eliminates temperature interference by using a first reference value for particulate matter concentration, thus solving the measurement deviation caused by environmental factors. The second reference value for particulate matter concentration provides trend verification based on the stability of historical sequences, avoiding the overcompensation problem that may exist in temperature correction. The weighted calculation uses dynamic weighting factors to enable the correction results to be adaptively adjusted according to real-time operating conditions (degree of temperature interference, sequence stability), thereby achieving high-precision monitoring of particulate matter concentration.

[0114] As one possible embodiment of this application, combined with Figure 1 ,like Figure 6 As shown, to enhance the practicality and engineering value of the system, the real-time particulate matter concentration monitoring system 10 in the dust removal process of this application embodiment may further include an alarm unit 14, a storage unit 15, and a display unit 16.

[0115] Alarm unit 14 is used to trigger an alarm signal when the corrected particulate matter concentration is greater than a preset concentration threshold.

[0116] The preset concentration threshold can be set according to the design specifications or environmental protection standards of the dust removal equipment. For example, if the design outlet particulate matter concentration threshold of the dust removal equipment is 50 μg / m³, then when the corrected particulate matter concentration (such as 55 μg / m³) exceeds this threshold, the alarm unit 14 will immediately trigger an alarm (such as an audible and visual alarm, a remote SMS alarm, or a system pop-up alarm) to remind staff to check the dust removal equipment in a timely manner (such as whether there are problems such as damaged filter media or untimely dust removal) to avoid excessive particulate matter emissions or the expansion of equipment failure.

[0117] Storage unit 15 is used to store temperature deviation experimental data, historical particulate matter concentration monitoring value sequence, ambient temperature value and corrected particulate matter concentration.

[0118] For example, the stored data may include corresponding timestamps so that the monitoring situation of a specific time period can be traced later (such as querying the correlation between temperature fluctuations and concentration changes at a certain time 3 days ago). At the same time, the storage unit 15 must have a certain capacity and data backup function to avoid data loss.

[0119] Display unit 16 is used to display the corrected particulate matter concentration and ambient temperature value in real time.

[0120] For example, the display unit 16 can be an industrial touch screen to display data in both digital and curve formats. The digital format displays the current corrected concentration (e.g., 101.58 μg / m³) and ambient temperature (e.g., 25.3℃) in real time, while the curve format displays the concentration and temperature change trends over the past 24 hours, making it convenient for staff to intuitively grasp the concentration dynamics and environmental interference during the dust removal process.

[0121] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A real-time monitoring system for particulate matter concentration during a dust removal process, characterized in that, include: The data acquisition unit is used to acquire particulate matter concentration monitoring values ​​and ambient temperature values, wherein the particulate matter concentration monitoring values ​​represent the particulate matter concentration measured in real time by the micro-oscillation balance method; The correction factor determination unit is used to determine a temperature deviation reference value and a sequence stability index based on the ambient temperature value and the historical particulate matter concentration monitoring value sequence, respectively; wherein, the temperature deviation reference value is used to characterize the measurement deviation caused by temperature changes, and the sequence stability index is used to characterize the changes in particulate matter concentration monitoring values ​​during real-time particulate matter concentration monitoring. The concentration correction unit is used to calculate the corrected particulate matter concentration by weighting the temperature deviation reference value, the sequence stability index and the current particulate matter concentration monitoring value. The correction factor determination unit includes a temperature deviation reference value calculation module and a sequence stability index calculation module. The temperature deviation reference value calculation module is used to determine the temperature deviation reference value based on the ambient temperature value and the temperature deviation experimental data; wherein, the temperature deviation experimental data is used to characterize the deviation between the particulate matter concentration monitoring value obtained by the micro-oscillation balance method and the actual particulate matter concentration value under different ambient temperature ranges and different temperature change ranges. The sequence stability index calculation module is used to determine the sequence stability index based on the changes in particulate matter concentration monitoring values ​​of subsequence segments in the historical particulate matter concentration monitoring value sequence.

2. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 1, characterized in that, The data acquisition unit includes a micro-oscillating balance particulate matter analyzer and a temperature sensor; The micro-oscillating balance particulate matter analyzer is used to monitor particulate matter concentration values ​​in real time at preset time intervals. The temperature sensor is used to monitor the ambient temperature value in real time. The data acquisition unit is used to record the particulate matter concentration monitoring value and the ambient temperature value at each monitoring time, as well as the average value of the ambient temperature change; the average value of the ambient temperature change is the average of the absolute values ​​of the difference between the ambient temperature value and the initial temperature value within the monitoring period.

3. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 1, characterized in that, The temperature deviation experimental data were obtained through the following methods: In multiple ambient temperature ranges and multiple temperature variation ranges, particulate matter concentrations were measured using a micro-oscillation balance method and a reference measurement method, respectively. The deviation between the monitored particulate matter concentration values ​​obtained by the micro-oscillation balance method and the actual particulate matter concentration values ​​was calculated to generate the temperature deviation experimental data. The ambient temperature ranges and temperature variation ranges were set in a tiered manner.

4. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 1, characterized in that, The temperature deviation reference value calculation module is specifically used for: Match the ambient temperature value at the current monitoring time to the corresponding ambient temperature range in the temperature deviation experimental data; Match the average ambient temperature change during the current monitoring period to the corresponding temperature change range in the temperature deviation experimental data; Based on the matched ambient temperature range and temperature change range, the corresponding temperature deviation reference value is retrieved from the temperature deviation experimental data.

5. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 1, characterized in that, The sequence stability index calculation module includes a subsequence partitioning submodule, a variance calculation submodule, and a stability calculation submodule; The subsequence division submodule is used to divide the historical particulate matter concentration monitoring value sequence into multiple subsequence segments, each subsequence segment containing a preset number of consecutive particulate matter concentration monitoring values. The variance calculation submodule is used to calculate the concentration change variance based on the difference between the particulate matter concentration monitoring values ​​at adjacent times of each subsequence segment. The stability calculation submodule is used to determine the sequence stability index by normalization based on the deviation between the concentration change variance of the previous subsequence segment and the average concentration change variance of all subsequence segments at the current time.

6. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 1, characterized in that, The concentration correction unit includes a first reference value calculation module and a second reference value calculation module; The first reference value calculation module is used to calculate a first reference value for particulate matter concentration based on the temperature deviation reference value and the current particulate matter concentration monitoring value; The second reference value calculation module is used to calculate a second reference value of particulate matter concentration based on the historical particulate matter concentration monitoring value sequence; wherein, the second reference value of particulate matter concentration is determined based on the average concentration change of the previous sub-sequence segment at the current time and the particulate matter concentration monitoring value at the previous time. The concentration correction unit is used to determine the weighting factors corresponding to the first reference value of particulate matter concentration, the second reference value of particulate matter concentration, and the current monitoring value of particulate matter concentration, and to perform weighted calculation on the first reference value of particulate matter concentration, the second reference value of particulate matter concentration, and the current monitoring value of particulate matter concentration using the weighting factors to obtain the corrected particulate matter concentration.

7. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 6, characterized in that, The weighting factor corresponding to the first reference value of particulate matter concentration is determined based on the proportion of the temperature deviation reference value among the temperature deviation reference value, the sequence stability index, and the preset constant. The weighting factor corresponding to the second reference value of particulate matter concentration is determined based on the proportion of the sequence stability index among the temperature deviation reference value, the sequence stability index, and the preset constant. The weighting factor corresponding to the current monitored value of particulate matter concentration is determined based on the proportion of the preset constant among the temperature deviation reference value, the sequence stability index, and the preset constant.

8. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 1, characterized in that, The system also includes an alarm unit; The alarm unit is used to trigger an alarm signal when the corrected particulate matter concentration is greater than a preset concentration threshold.

9. The real-time particulate matter concentration monitoring system in the dust removal process according to claim 1, characterized in that, It also includes storage units and display units; The storage unit is used to store temperature deviation experimental data, historical particulate matter concentration monitoring value sequences, ambient temperature values, and corrected particulate matter concentrations. The display unit is used to display the corrected particulate matter concentration and ambient temperature value in real time.

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