An industrial waste gas digital monitoring and intelligent diagnosis method

Abstract

The application discloses an industrial waste gas digital monitoring and intelligent diagnosis method, aiming at solving the problems of existing monitoring methods, such as lag, inaccurate diagnosis and lack of closed-loop management. The method comprises the following steps: building a digital monitoring network, arranging monitoring terminals at key nodes of emission sources to collect multi-dimensional waste gas data; preprocessing the original data, eliminating abnormalities, supplementing missing data and standardizing; constructing an intelligent diagnosis model and training and optimizing, inputting the standardized data to identify emission abnormalities and potential faults, and outputting targeted rectification suggestions; the data processing center archives and manages the whole process data, supports query statistics, and regularly updates the model to improve the precision. The method realizes waste gas monitoring digitalization, diagnosis intelligentization and rectification precision, improves the monitoring accuracy and real-time performance, reduces the misjudgment probability and the treatment and supervision cost, forms a complete closed-loop management, and adapts to the waste gas treatment and supervision needs of various industrial scenes.

Description

Technical Field

[0001] This invention relates to the field of industrial waste gas monitoring technology, specifically to a digital monitoring and intelligent diagnostic method for industrial waste gas. Background Technology

[0002] Currently, industrial waste gas monitoring mostly adopts traditional manual sampling and offline detection methods, which suffers from low monitoring efficiency, data lag, and untimely anomaly identification. The diagnostic process is highly dependent on human experience, and differences in the professional level of operators can easily lead to misjudgments and missed diagnoses, making it difficult to meet the actual needs of accurate supervision and efficient treatment of industrial waste gas, and also unable to timely avoid environmental violation risks.

[0003] Existing digital monitoring methods mostly only collect and transmit waste gas data, lacking effective intelligent diagnostic modules and targeted rectification guidance capabilities, thus failing to form a complete closed-loop management system of "monitoring-diagnosis-rectification-archiving." This results in a lack of clear direction for waste gas treatment, poor treatment effects, and increased environmental supervision and operation and maintenance costs for enterprises, making it difficult to adapt to the waste gas treatment needs of various industrial scenarios. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a digital monitoring and intelligent diagnosis method for industrial waste gas, which enables digital monitoring, intelligent diagnosis, and precise rectification, thereby reducing regulatory and treatment costs and improving waste gas treatment efficiency.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is: a method for digital monitoring and intelligent diagnosis of industrial waste gas, comprising the following steps: Step 1: Establish a digital monitoring network for industrial waste gas, deploy monitoring terminals at key nodes of the emission source, covering waste gas emission outlets, transmission pipelines and surrounding sensitive areas, and ensure that each terminal communicates bidirectionally with the data processing center to achieve real-time data transmission; Step 2: Collect multi-dimensional data such as exhaust gas component content, emission flow rate, temperature, pressure and humidity through monitoring terminals, and simultaneously record information such as collection time and location to form raw monitoring data; Step 3: Preprocess the raw data, remove anomalies and supplement missing data, and then perform standardized transformation to obtain digital monitoring data in a unified format; Step 4: Build an intelligent diagnostic model, which is trained and optimized based on historical monitoring data, treatment process parameters and pollutant standards, and has the functions of data analysis, anomaly identification and fault diagnosis; Step 5: Input standardized data into the model, analyze and compare the standard and normal parameter thresholds in real time to identify abnormal emissions and potential faults; Step Six: The model outputs the anomaly type, severity, cause, and targeted rectification suggestions; Step 7: The data processing center centrally stores and manages monitoring data, diagnostic results, and rectification records, forming a complete archive that supports querying, statistics, and trend prediction.

[0006] Preferably, the monitoring terminal includes gas, flow, temperature, pressure and humidity sensors and a data acquisition module. The gas sensors detect common pollutants such as sulfur dioxide and nitrogen oxides, and the detection accuracy meets industry standards. The acquisition module adopts a combination of timed and triggered acquisition, with adjustable intervals, and automatically accelerates acquisition when data changes abruptly.

[0007] Preferably, the data preprocessing specifically includes: filtering out abnormal data that exceeds a reasonable range, supplementing missing data through interpolation between adjacent time periods; standardizing and transforming all data to eliminate differences in units, ensure data comparability, and avoid affecting diagnostic results.

[0008] Preferably, the intelligent diagnostic model construction process is as follows: collect historical monitoring, equipment operation, fault records and emission standard data, construct a training dataset and classify and label it; use machine learning algorithms to train the model, iteratively optimize parameters to improve recognition accuracy and diagnostic efficiency, and put it into use after verification.

[0009] Preferably, the model anomaly identification process is as follows: the input data is compared with the pollutant standard and the normal process threshold; if it exceeds the range or deviates from the threshold, it is judged as an anomaly; according to the degree of deviation, it is divided into three anomaly levels: light, medium and heavy, corresponding to different early warning mechanisms.

[0010] Preferably, the rectification suggestions include suggestions for process adjustment, equipment maintenance and emergency handling, which clearly define the direction of process adjustment, the parts and cycles of equipment maintenance, the emergency response procedures and the division of responsibilities, so as to ensure that the rectification can be implemented.

[0011] Preferably, the data processing center has data encryption storage function to prevent data leakage and tampering; it supports access from multiple terminals such as computers and mobile phones, making it convenient for managers to view data and receive early warnings in real time, and realize remote supervision.

[0012] Preferably, it also includes a model update step, which involves regularly collecting new monitoring, fault and rectification feedback data, iteratively updating model parameters and diagnostic logic, and improving model adaptability and diagnostic accuracy.

[0013] Preferably, the digital monitoring and diagnostic archive includes monitoring points, terminal parameters, various data, diagnostic results, and rectification status, and is stored in a time-based category. It supports multi-condition query and statistics, providing data support for waste gas treatment and environmental supervision.

[0014] The advantages of this invention compared to the prior art are: To improve the accuracy and real-time performance of monitoring, monitoring terminals are deployed at key nodes of emission sources to achieve real-time collection and transmission of multi-dimensional data. Combined with data preprocessing to eliminate the impact of abnormal and missing data, the monitoring data is ensured to be true and reliable, solving the problems of lagging and large errors in traditional monitoring data.

[0015] It enables intelligent diagnosis and precise rectification. Through training and optimization of the intelligent diagnostic model, it can quickly identify abnormal exhaust gas emissions and potential faults, output targeted rectification suggestions, replace manual experience diagnosis, reduce the probability of misjudgment, improve the efficiency of exhaust gas treatment, and reduce the risk of environmental violations.

[0016] A closed-loop management system is constructed, which archives and manages data throughout the entire process of monitoring, diagnosis, and rectification through a data processing center. This system supports querying, statistics, and trend prediction, providing data support for environmental supervision and process optimization, and reducing supervision and governance costs. Detailed Implementation

[0017] The present invention will now be described in further detail.

[0018] Example 1 This embodiment provides a digital monitoring and intelligent diagnostic method for industrial waste gas, applicable to waste gas monitoring in small and medium-sized chemical enterprises. The specific steps are as follows: Step 1: Establish a digital monitoring network for industrial waste gas. Based on the characteristics of small and medium-sized chemical enterprises, deploy one small integrated monitoring terminal at each of three key nodes: the waste gas emission outlet, the waste gas transmission pipeline interface of the reactor, and the sensitive area at the plant boundary. The terminals are bidirectionally connected to the data processing center via 4G wireless communication, with transmission latency controlled within 10 seconds, enabling real-time uploading of monitoring data. Management personnel can view the data in real time through the terminals.

[0019] Step two, multi-dimensional data acquisition. The monitoring terminal has built-in gas, flow, temperature, pressure, and humidity sensors and acquisition modules. The gas sensors focus on detecting sulfur dioxide and nitrogen oxides, with detection accuracy meeting relevant industry standards and an error of no more than ±5%. The acquisition module uses a combination of timed and triggered acquisition. The timed interval is adjustable in 5 minutes, and automatic expedited acquisition is triggered when data changes exceed 10%, with an expedited acquisition interval of 1 minute. The acquisition time, location, production conditions, and other information are recorded simultaneously to form complete raw monitoring data.

[0020] Step 3: Data Preprocessing. The data processing center first uses the 3σ criterion to remove outlier data and then supplements missing data through linear interpolation between adjacent time periods. Next, all data is standardized to eliminate differences in units, ensure comparability of indicators, and provide data in a unified format for model analysis.

[0021] Step four: Intelligent diagnostic model construction. Collect one year of historical monitoring data, treatment equipment parameters, fault records, and local pollutant standards from the enterprise to construct a training set containing normal, slightly abnormal, and severely abnormal data, and classify and label them. Use the random forest algorithm to train the model, optimize the parameters, and then verify it. The anomaly identification accuracy rate reaches over 98%, and the response time is no more than 5 seconds. After passing the verification, it is put into use.

[0022] Step 5: Anomaly Identification and Diagnosis. Standardized data is input into the model, and anomalies are determined by comparing them with pollutant standards and normal equipment thresholds. Anomalies are categorized into three levels—minor, moderate, and severe—based on the degree of deviation from the threshold, with corresponding early warning mechanisms to ensure timely detection.

[0023] Step Six: Output Rectification Suggestions. The model identifies the type, severity, cause, and rectification suggestions for abnormal outputs. For example, if sulfur dioxide exceeds the standard by 30%, it determines that desulfurization is incomplete and suggests adjusting the circulating water volume of the spray tower, cleaning the spray heads, specifying the rectification timeframe, and ensuring that the rectification can be implemented.

[0024] Step 7: Data Archiving and Management. The data processing center uses AES encryption to store monitoring, diagnostic, and rectification-related data, supports multi-terminal access for remote monitoring, archives data by time category, and supports multi-condition query and statistics, providing support for waste gas treatment and supervision.

[0025] In addition, new data is collected monthly to iterate and update the model, optimize parameters and diagnostic logic, improve model adaptability and accuracy, and adapt to changes in enterprise operating conditions and processes.

[0026] Example 2 This embodiment provides a digital monitoring and intelligent diagnostic method for industrial waste gas, applicable to waste gas monitoring in large coal-fired power plants. The specific steps are as follows: Step one: Establish a digital monitoring network for industrial waste gas. Based on the characteristics of power plant waste gas emissions, 10 high-precision industrial-grade monitoring terminals are deployed at each boiler exhaust outlet, the inlet and outlet of desulfurization and denitrification equipment, the main exhaust outlet, and sensitive areas around the plant. These terminals are suitable for high-temperature and high-dust operating conditions. The terminals are connected to the data processing center via wired Ethernet, with a transmission latency of no more than 5 seconds. A backup wireless module is also provided to ensure continuous and stable network operation.

[0027] Step two, multi-dimensional data acquisition. The monitoring terminal has a built-in high-precision sensor that focuses on detecting sulfur dioxide, nitrogen oxides, and particulate matter. The detection accuracy meets relevant industry standards, with an error of no more than ±3%. The acquisition module is timed at 2-minute intervals, and expedited acquisition is performed when data changes by more than 15% at 30-second intervals. The acquisition time, location, boiler load, and other information are recorded simultaneously to comprehensively reflect exhaust emissions and production conditions.

[0028] Step 3, Data Preprocessing. Outlier data is filtered using the Grubbs criterion, and a secondary check is added to prevent accidental deletion. Missing data is supplemented using adjacent interpolation combined with historical data from the same period to ensure accuracy. Subsequently, the data is standardized to eliminate dimensional differences, providing a reliable data foundation for the model.

[0029] Step four: Intelligent diagnostic model construction. Collect three years of historical monitoring data from the power plant, parameters of desulfurization and denitrification equipment, fault records, and relevant pollutant standards to construct a large-scale training set and classify and label it; use the gradient boosting tree algorithm to train the model, optimize the parameters, and then verify it. The anomaly identification accuracy rate reaches over 99%, and the response time is no more than 3 seconds, which can accurately identify various anomalies.

[0030] Step 5: Anomaly Identification and Diagnosis. The model compares exhaust gas indicators with standards and normal equipment thresholds to determine anomalies. Anomalies are categorized into three levels based on the degree of deviation, corresponding to different early warning and response measures. Severe anomalies trigger audible and visual alarms, boiler shutdown, and reporting to regulatory authorities, thus mitigating major regulatory risks.

[0031] Step Six: Output Rectification Recommendations. For the specific causes of the abnormal output and corresponding rectification recommendations, such as when nitrogen oxides exceed the standard by 40%, indicating a decrease in denitrification efficiency, it is recommended to adjust the ammonia injection grid, test and replace the catalyst, and clearly define the rectification timeline and emergency measures to ensure that the rectification is scientifically feasible.

[0032] Step 7: Data Archiving and Management. Data is stored using RSA encryption, with multi-level access controls to prevent leakage and tampering. Multi-terminal access enables remote and on-site collaborative monitoring. Data is archived by time, location, and boiler number, supporting multi-condition queries and statistics, generating reports and trend charts to provide comprehensive support for governance, operation, and supervision.

[0033] In addition, new data is collected quarterly and combined with standard updates to iterate and optimize the model to adapt to power plant load changes, process optimization, and standard upgrade requirements.

[0034] Example 3 This embodiment provides a digital monitoring and intelligent diagnostic method for industrial waste gas, applicable to waste gas monitoring in pharmaceutical and chemical enterprises. The specific steps are as follows: Step 1: Establish a digital monitoring network for industrial waste gas. Considering the highly corrosive and complex composition of waste gas from pharmaceutical and chemical enterprises, a total of 8 corrosion-resistant industrial-grade monitoring terminals were deployed at the emission outlets of production workshops, the inlets and outlets of waste gas treatment equipment, sensitive areas within the factory, and the emission outlets of laboratories. The terminals connect to the data processing center via a wireless LAN, with a transmission latency of no more than 8 seconds. They are equipped with a data caching function, capable of caching 24 hours of data during network interruptions and automatically uploading upon network recovery.

[0035] Step two, multi-dimensional data acquisition. The monitoring terminal has built-in sensors that focus on detecting sulfur dioxide, nitrogen oxides, and volatile organic compounds. The detection accuracy meets relevant industry standards, with an error of no more than ±4%. The acquisition module has an adjustable time interval of 3 minutes. When the data change exceeds 12%, expedited acquisition is performed at 45-second intervals. Information such as acquisition time, location, and production process stage is recorded simultaneously to accurately reflect the dynamics of exhaust gas emissions.

[0036] Step 3, Data Preprocessing. The Dixon criterion is used to filter out outlier data, and filtering is added to eliminate interference and ensure data stability. Missing data is supplemented through adjacent interpolation combined with process parameters to match actual operating conditions. The data is standardized to eliminate dimensional differences, providing high-quality data for model analysis.

[0037] Step four: Intelligent diagnostic model construction. Collect two years of historical monitoring data from the enterprise, parameters of waste gas treatment equipment, fault records, and relevant pollutant standards to construct a training set and classify and label it; use the support vector machine algorithm to train the model, optimize the parameters, and then verify it. The anomaly identification accuracy rate reaches over 98.5%, and the response time is no more than 4 seconds. It can accurately identify anomalies such as excessive volatile organic compounds and equipment blockage.

[0038] Step 5: Anomaly Identification and Diagnosis. The model compares exhaust gas indicators with standards and normal equipment thresholds to determine anomalies. Anomalies are categorized into three levels based on the degree of deviation, corresponding to different early warning and response measures. Severe anomalies trigger audible and visual alarms, production shutdowns, and emergency measures to mitigate environmental and safety risks.

[0039] Step Six: Output Rectification Recommendations. For the specific causes of abnormal emissions and corresponding rectification recommendations, such as when volatile organic compounds exceed the standard by 35%, indicating activated carbon adsorption saturation, it is recommended to replace the activated carbon promptly, optimize equipment parameters, clarify rectification deadlines and emergency measures, and ensure emissions meet standards.

[0040] Step 7: Data Archiving and Management. Data is stored using DES encryption, with a backup mechanism to prevent loss or tampering. Multi-terminal access is supported for seamless monitoring. Data is archived by time, location, and workshop number, supporting multi-condition queries and statistics, generating monitoring reports and trend charts to provide reliable support for governance, recovery, and supervision.

[0041] In addition, new data is collected every two months and combined with process adjustments to iterate and optimize the model to adapt to the enterprise's process, exhaust gas composition and standard upgrade needs.

[0042] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An industrial waste gas digital monitoring and intelligent diagnosis method, characterized in that, Includes the following steps: Step 1: Establish a digital monitoring network for industrial waste gas, deploy monitoring terminals at key nodes of the emission source, covering waste gas emission outlets, transmission pipelines and surrounding sensitive areas, and ensure that each terminal communicates bidirectionally with the data processing center to achieve real-time data transmission; Step 2: Collect multi-dimensional data such as exhaust gas component content, emission flow rate, temperature, pressure and humidity through monitoring terminals, and simultaneously record information such as collection time and location to form raw monitoring data; Step 3: Preprocess the raw data, remove anomalies and supplement missing data, and then perform standardized transformation to obtain digital monitoring data in a unified format; Step 4: Build an intelligent diagnostic model, which is trained and optimized based on historical monitoring data, treatment process parameters and pollutant standards, and has the functions of data analysis, anomaly identification and fault diagnosis; Step 5: Input standardized data into the model, analyze and compare the standard and normal parameter thresholds in real time to identify abnormal emissions and potential faults; Step Six: The model outputs the anomaly type, severity, cause, and targeted rectification suggestions; Step 7: The data processing center centrally stores and manages monitoring data, diagnostic results, and rectification records, forming a complete archive that supports querying, statistics, and trend prediction.

2. The industrial waste gas digital monitoring and intelligent diagnosis method according to claim 1, characterized in that: The monitoring terminal includes gas, flow, temperature, pressure and humidity sensors and a data acquisition module. The gas sensor detects common pollutants such as sulfur dioxide and nitrogen oxides, and the detection accuracy meets industry standards. The acquisition module adopts a combination of timed and triggered acquisition, with adjustable intervals, and automatically accelerates acquisition when data changes abruptly.

3. The industrial waste gas digital monitoring and intelligent diagnosis method according to claim 1, characterized in that: The data preprocessing specifically involves: filtering out abnormal data that exceeds a reasonable range, supplementing missing data through interpolation between adjacent time periods; and standardizing all data to eliminate differences in units, ensure data comparability, and avoid affecting diagnostic results.

4. The industrial waste gas digitized monitoring and intelligent diagnosis method according to claim 1, characterized in that: The intelligent diagnostic model construction process is as follows: collect historical monitoring, equipment operation, fault records and emission standard data, construct training dataset and classify and label it; use machine learning algorithms to train the model, iteratively optimize parameters to improve recognition accuracy and diagnostic efficiency, and put it into use after verification.

5. The industrial waste gas digital monitoring and intelligent diagnosis method according to claim 1, characterized in that: The model's anomaly identification process is as follows: the input data is compared with pollutant standards and normal process thresholds; if it exceeds the range or deviates from the threshold, it is judged as an anomaly; based on the degree of deviation, it is divided into three anomaly levels: light, medium, and severe, corresponding to different early warning mechanisms.

6. The industrial waste gas digital monitoring and intelligent diagnosis method according to claim 1, characterized in that: The proposed rectification measures include suggestions for process adjustment, equipment maintenance, and emergency response. These measures clarify the direction of process adjustment, the parts and cycles of equipment maintenance, emergency response procedures, and division of responsibilities to ensure that the rectification measures are feasible.

7. The industrial waste gas digital monitoring and intelligent diagnosis method according to claim 1, characterized in that: The data processing center has encrypted data storage capabilities to prevent data leakage and tampering; it supports access from multiple terminals such as computers and mobile phones, making it convenient for managers to view data and receive alerts in real time, thus enabling remote supervision.

8. The industrial waste gas digital monitoring and intelligent diagnosis method according to claim 1, characterized in that: It also includes a model update step, which involves regularly collecting new monitoring, fault, and rectification feedback data, iteratively updating model parameters and diagnostic logic, and improving model adaptability and diagnostic accuracy.

9. The method for digital monitoring and intelligent diagnosis of industrial waste gas according to claim 1, characterized in that: The digital monitoring and diagnostic archive includes monitoring points, terminal parameters, various data, diagnostic results, and rectification status. It is stored in categories according to time, supports multi-condition query and statistics, and provides data support for waste gas treatment and environmental protection supervision.