A VOCs source tracing method for industrial parks based on fingerprint mapping and data-driven

Abstract

This invention discloses a fingerprint-based and data-driven VOC source tracing method for industrial parks. The method includes establishing a VOC source spectrum library for park enterprises, an emission scenario library, a meteorological scenario library, a meteorological-emission pollution source tracing library, a meteorological-emission pollution source tracing model, and pollution source tracing. By combining traditional fingerprint comparison methods with a data-driven source tracing model, the method can quickly and accurately identify suspicious outlets when pollutant levels exceed standards, enabling precise and rapid source tracing of small and medium-scale VOCs in the park. This method meets the requirements of industrial parks for highly accurate and timely VOC source tracing results, significantly enhancing industrial parks' pollution control capabilities.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric environmental protection technology, and in particular to a VOCs tracing method for industrial parks based on fingerprint and data-driven methods. Background Art

[0002] Chemical parks are modern, integrated industrial parks that integrate chemical production, scientific research, and daily life, and are a vital component of the chemical industry. Due to the high density of enterprises within chemical parks and the integration of numerous chemical production facilities and equipment, the spatiotemporal distribution of pollution emission sources and pollutant compositions is complex, making it difficult to trace the source of pollution after it occurs. This poses challenges to air pollution control and risk management in chemical parks. Furthermore, when pollutant levels exceed standards or complaints of odor or foul odor are reported, companies often buck-pass and shirk responsibility, complicating law enforcement inspections and regulatory controls.

[0003] Currently, cruise monitoring, receptor modeling, and source modeling are the primary methods used to track air pollution sources at smaller scales, such as industrial parks or enterprises. Cruise monitoring primarily identifies high-value points, but the distance between these points and the production areas of the enterprises makes it difficult to accurately locate pollution sources. Receptor modeling methods include chemical mass balance (CMB), positive matrix factorization (PMF), and principal component analysis (PCA). While these methods can effectively identify the contribution of known pollutant emission sources, they cannot accurately determine the spatial location of pollution sources. Furthermore, the limited number of receptor points makes it difficult to determine the actual distribution of pollutant concentrations within a region. Furthermore, the completeness of source components significantly affects the accuracy of receptor modeling, significantly limiting its application in small-scale source tracking. Source modeling methods include FLEXPART, AERMOD, and CALPUFF. These models can predict pollutant diffusion trends and the emission contributions of pollution sources. However, source modeling methods suffer from long run times, resulting in limited timeliness of source tracking results and an inability to quickly locate pollution sources. As industrial park management requirements continue to increase, the requirements for accuracy and speed in tracing pollution sources in industrial parks are also increasing. However, the current model results are lagging and cannot meet the needs of rapid pollution source tracing in industrial parks. Therefore, this application proposes a fingerprint-based and data-driven VOCs source tracing method for industrial parks to solve this problem. Summary of the Invention

[0004] The purpose of this application is to provide a VOCs source tracing method for industrial parks based on fingerprint maps and data-driven, which is used to solve the problems of low accuracy and slow speed in existing industrial park pollution source tracing.

[0005] The present invention establishes grouped emission scenarios based on the VOCs emission inventory of enterprises in the industrial park and the VOCs source spectrum of the enterprises, and establishes a meteorological scenario library through equal interval division based on the meteorological data of the industrial park over the years. The diffusion model is used to simulate the diffusion trend and emission impact of enterprise pollutants under different scenarios, and the meteorological scenarios and emission impacts are matched one by one by group to obtain a meteorological-emission pollution source tracing library. The data in the pollution source tracing library is used as a training sample and trained using an ANN artificial neural network to obtain a meteorological-emission pollution source tracing model. Based on the source tracing model, potential polluting enterprises can be obtained. The comparison of the VOCs spectra of the polluting enterprises and the receptor points can accurately locate the organized outlets and unorganized surface sources that cause excessive VOCs, thereby improving the timeliness and accuracy of VOCs source tracing in industrial parks.

[0006] A fingerprint-based and data-driven VOCs source tracing method for industrial parks includes the following steps:

[0007] S1, Establishment of a VOC source spectrum library for enterprises in the industrial park: By collecting and analyzing the composition of VOCs emitted from organized / unorganized outlets of enterprises, the test results are normalized and analyzed to form a VOC source spectrum for enterprises in the industrial park;

[0008] S2, establishment of emission scenario library: collect VOCs emission inventories of enterprises in the park, distribute the emission inventories in time and space based on the location of enterprises in the park and the production situation throughout the year, and build an emission scenario library for different VOCs components;

[0009] The VOCs emission inventory must be accurate to the enterprise, and the emission data must be accurate to the component; the source spectrum includes 115 VOCs components, which are divided into 29 alkanes, 11 alkenes, 17 aromatic hydrocarbons, 21 OVOCs, 35 halogenated hydrocarbons, carbon disulfide, and acetylene.

[0010] The emission inventory should include organized and unorganized emissions. Organized emissions from enterprises shall be treated as point sources, and unorganized emissions shall be treated as surface sources.

[0011] S3. Establishment of a meteorological scenario library: Based on the existing meteorological station monitoring data and WRF simulated meteorological field data in the park, the range of the park's meteorological parameters is determined. The meteorological parameters include: wind speed, wind direction, temperature, cloud cover, precipitation, and air pressure. The wind direction is set to 0-360°, where 0° and 360° are both due north. The cloud cover is a ten-point system with a range of 0-10. The intervals between each parameter are divided according to the range of the park's meteorological parameters over the years. The divided meteorological parameters are arranged and combined to obtain different meteorological scenarios.

[0012] S4, Establishment of a Meteorological-Emission Pollution Source Traceability Database: Combine emission scenarios and meteorological scenarios to form multiple meteorological-emission scenarios. Use a diffusion model (existing technology) to simulate the contribution of park enterprise emissions to the concentration of each VOC component at the receptor site under different meteorological-emission scenarios. Summarize the simulation results under each meteorological-emission scenario, and at the same time, match the meteorological scenarios with the model simulation results to obtain meteorological-emission pollution source traceability database samples, and compile them into a meteorological-emission pollution source traceability database;

[0013] The meteorological-emission scenario refers to multiple component emission scenarios corresponding to multiple meteorological scenarios. Therefore, each meteorological scenario also corresponds to multiple model simulation results. The models include but are not limited to AERMOD and CALPUFF models. When the model is applied, model parameters such as terrain and land use type need to be localized.

[0014] S5, Construction of a Weather-Emission Pollution Source Tracing Model: The dataset in the Weather-Emission Pollution Source Tracing Library is used as training samples, with meteorological conditions and pollution source names as input and pollution source contribution percentages as output. The Weather-Emission Pollution Source Tracing Model is trained through machine learning. The learning method is an artificial neural network (ANN). The accuracy of the Weather-Emission Pollution Source Tracing Model is improved by learning and adjusting parameters.

[0015] The name of the pollution source is the name of the enterprise.

[0016] The ANN neural network structure consists of three parts: input layer, hidden layer and output layer. Each layer is composed of multiple neurons, each neuron is interconnected with all neurons in the previous layer, and each connection corresponds to a weight. In the three layers of the neural network, the input layer is used to receive and input data, the hidden layer processes the data, and the output layer generates and outputs the generated results.

[0017] When learning and adjusting the parameters, the parameters are adjusted by minimizing the mean square error RMSE.

[0018]

[0019] in, is the true value of the i-th sample point, is the predicted value, and n is the number of data.

[0020] S6, Pollution Source Tracing: When the VOCs concentration at a receptor site exceeds the standard, real-time meteorological data and the component names of the VOCs at the time of the exceedance are input into the meteorological-emission pollution source tracing model. This can obtain the concentration contribution of various source emissions to the component names at the receptor site, allowing potential polluting enterprises to be quickly identified. The VOCs source spectrum of the potential polluting enterprise is retrieved and cosine similarity analysis is performed with the VOCs spectrum of the receptor site at the time of the exceedance. Ultimately, the emission outlet of the suspicious enterprise is identified, achieving rapid and precise VOC tracing in the park.

[0021] The real-time meteorological data includes wind direction, wind speed, and temperature, and the component names are the components that account for more than 10% of the VOCs at the receptor point.

[0022] The advantages of the present invention are: combining the traditional fingerprint comparison method with the data-driven tracing model, it can quickly and accurately find suspicious outlets when pollutant exceeds the standard, and realize the precise and rapid tracing of small and medium-scale VOCs in the industrial park. It can meet the industrial park's requirements for high accuracy and high timeliness of VOCs tracing results, and greatly improve the pollution control capabilities of the industrial park. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a flow chart of the technical solution of the present invention.

[0025] Figure 2 This is a schematic diagram of the enterprise source spectrum of the present invention.

[0026] Figure 3 Schematic diagram of the emission scenario library of the present invention.

[0027] Figure 4 This is a schematic diagram of the meteorological-emission pollution source tracing library of the present invention.

[0028] Figure 5 Schematic diagram of the traceability results of this embodiment. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Example, taking an industrial park as an example, a VOCs tracing method based on fingerprint and data driven in the industrial park, such as Figure 1 As shown, the following steps are included:

[0031] S1. Establishing a VOC Source Spectrum Library for Park Enterprises: A survey of the production processes of park enterprises was conducted, collecting both organized and unorganized emissions from these processes. A total of 115 VOC components were detected, including 29 alkanes, 11 alkenes, 17 aromatic hydrocarbons, 21 OVOCs, 35 halogenated hydrocarbons, carbon disulfide, and acetylene. Sampling was performed using Suma tanks and analyzed using GC-MS. The detected VOC component data were normalized to obtain the percentage of each component, thereby generating a VOC emission source spectrum for each enterprise's outlets.

[0032] S2, Establishing an Emission Scenario Library: Based on the company's location, emission inventory, and monthly production data, the inventory is temporally and spatially distributed, and multiple emission scenarios are created by grouping. Each emission scenario includes the company's location, component name, and component emission source intensity.

[0033] Furthermore, the number of enterprises in step S2 is 218.

[0034] Furthermore, the emission inventory in step S2 is specific to VOCs components. The enterprise VOCs emission inventory in the embodiment includes a total of 47 components.

[0035] Furthermore, the temporal variation of the source intensity of each component in step S2 is related to the monthly production situation of the enterprise, and the basis for time allocation includes but is not limited to the monthly production situation of the enterprise.

[0036] Furthermore, the emission scenarios in step S2 are constructed on a monthly basis, and the number of emission scenarios is 47 × 12, for a total of 564.

[0037] S3, establishment of meteorological scenario database: collect historical meteorological data of the park, including wind direction, wind speed, cloud cover, and temperature, count the range of meteorological parameters and divide them into equal intervals, among which cloud cover is divided into total cloud cover and low cloud cover.

[0038] Furthermore, in step S3, wind direction, wind speed, total cloud cover, low cloud cover, and temperature are defined as W, V, TC, LC, and T, respectively, with ranges of 0–365°, 0–12 m / s, 0–10, 0–10, and −8–40°C, and intervals of 45°, 2 m / s, 2, 2, and 4°C, respectively.

[0039] Furthermore, in step S3, different meteorological parameters are arranged and combined to obtain 8×6×5×5×12 meteorological scenarios, for a total of 14,400.

[0040] S4. Establish a weather-emission pollution source database: The 564 emission scenarios contained in the emission scenario database established in step S2 and the 14,400 meteorological scenarios contained in the meteorological scenario database established in step S3 are permuted and combined to generate weather-emission scenarios, totaling 564 x 14,400. These different weather-emission scenarios are input into the diffusion model to determine the contribution of each enterprise's emissions to the pollutant concentration at the receptor site under each weather-emission scenario.

[0041] Furthermore, the diffusion models in step S4 include AERMOD and CALPUFF, but are not limited to these two diffusion models. CALPUFF is mainly used to simulate the diffusion process of pollutants under calm and light wind conditions.

[0042] Furthermore, the diffusion model in step S4 needs to be localized. For AERMOD, the terrain data and land use data need to be localized. For the CALPUFF model, the terrain data, land use data, and ozone background concentration need to be localized.

[0043] Furthermore, the receptor point in step S4 is an environmentally sensitive point, which may be located at the factory boundary or near a residential area.

[0044] Furthermore, a set of model results obtained in step S4 is the contribution of 218 enterprises to the mass concentration of a certain component of VOCs at the receptor site.

[0045] Furthermore, in step S4, there are 564×14400 groups of model results.

[0046] Furthermore, the model results in step S4 need to correspond to the meteorological scenario and emission scenario.

[0047] S5, construction of meteorological-emission pollution source tracing model: the meteorological-emission pollution source tracing library results obtained in step S4 are used as training samples, and the training samples are transferred to the artificial neural network ANN. The meteorological conditions and emission scenarios are used as the input layer of the training, and the contribution value of enterprise emissions to the mass concentration of pollutants at the receiving point is used as the output layer of the training. The ratio of training set, validation set and test set is 8:1:1 respectively.

[0048] Furthermore, the training set in step S5 is used to train the model and fit the data distribution law, that is, to determine the parameters such as the weight and bias of the model. The validation set is used to verify the data set of the model performance. During the model training process, the validation set is used to adjust the model parameters to optimize the model performance. The test set is used to evaluate the data set of the model performance. After the model training is completed, the test set is used to evaluate the generalization ability of the model.

[0049] Furthermore, the ANN artificial neural network in step S5 has two hidden layers.

[0050] S6, pollution source tracing: When the VOCs concentration at the receptor point exceeds the standard, the VCOs component name information and real-time meteorological parameters are input into the meteorological-emission pollution source tracing model. The model is executed to obtain the contribution ratio of 218 enterprises' emissions to the concentration of the VOCs component name at the receptor point, and a list of potential polluting enterprises is obtained. The VOCs source spectrum of organized / unorganized emissions of potential polluting enterprises in the enterprise source spectrum library is retrieved, and a cosine similarity analysis is performed on it and the VOCs spectrum at the time when the receptor point exceeds the standard. Finally, the name of the pollution source that caused the VOCs concentration at the receptor point to exceed the standard is locked.

[0051] Furthermore, in step S6, the real-time weather conditions in the embodiment are: wind direction of 45°, wind speed of 5 m / s, and temperature of 30°C, and the constructed weather scenario is (45, 5, 30).

[0052] Furthermore, in step S6, the VOCs spectrum of the receptor point is sampled for detection and analysis only when the VOCs concentration exceeds the standard.

[0053] Furthermore, the determination of the number of receptor points in step S6 needs to be based on the traceability area and the surrounding building facilities. The contribution value of enterprise emissions to the pollutant concentration at the receptor point in the result includes and only includes the contribution of enterprise emissions to the pollutant concentration at the receptor point where VOCs exceed the standard.

[0054] Furthermore, the VCOs component name in step S6 refers to the component that accounts for more than 10% in the VOCs spectrum at the receptor site.

[0055] Furthermore, the name of the pollution source that causes the VOCs concentration at the receptor point to exceed the standard in step S6 can be an organized emission outlet or an unorganized emission surface source.

[0056] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fingerprint-based and data-driven VOCs source tracing method for industrial parks, characterized by: The specific steps include: S1, Establishment of a VOC source spectrum library for enterprises in the industrial park: By collecting and analyzing the composition of VOCs emitted from organized / unorganized outlets of enterprises, the test results are normalized and analyzed to form a VOC source spectrum for enterprises in the industrial park; S2, Establishment of emission scenario library: Collect VOCs emission inventories of enterprises in the park, distribute the emission inventories in time and space based on the location of enterprises in the park and their production situation throughout the year, and build an emission scenario library for different VOCs components; S3, Establishment of a meteorological scenario library: Based on the existing meteorological station monitoring data and WRF simulated meteorological field data of the park, the range of the park's meteorological parameters is determined. The intervals between each parameter are divided according to the range of the park's meteorological parameters over the years. The divided meteorological parameters are arranged and combined to obtain different meteorological scenarios; S4, Establishment of a Meteorological-Emission Pollution Source Traceability Database: Combine emission scenarios and meteorological scenarios to form multiple meteorological-emission scenarios. Use diffusion models to simulate the contribution of emissions from industrial park enterprises to the concentrations of various VOC components at receptor sites under different meteorological-emission scenarios. Summarize the simulation results under each meteorological-emission scenario, and simultaneously match the meteorological scenarios with the model simulation results to obtain meteorological-emission source traceability samples, which are then compiled into a meteorological-emission pollution source traceability database. S5, Construction of a Weather-Emission Pollution Source Tracing Model: The dataset in the Weather-Emission Pollution Source Tracing Library is used as training samples, with meteorological conditions and pollution source names as input and pollution source contribution percentages as output. The Weather-Emission Pollution Source Tracing Model is trained through machine learning. The learning method is an artificial neural network (ANN). The accuracy of the Weather-Emission Pollution Source Tracing Model is improved by learning and adjusting parameters. S6, pollution source tracing: When the VOCs concentration at the receptor point exceeds the standard, the real-time meteorological data and the component names of the VOCs at the time of exceeding the standard are input into the meteorological-emission pollution source tracing model to obtain the concentration contribution value of each enterprise's emissions to the component name of the receptor point, quickly find potential polluting enterprises, retrieve the VOCs source spectrum of the potential polluting enterprises, and perform cosine similarity analysis with the VOCs spectrum of the receptor point at the time of exceeding the standard. Finally, the discharge outlet of the suspicious enterprise is locked, realizing rapid and precise tracing of VOCs in the park.

2. The method for tracing VOCs in industrial parks based on fingerprint and data-driven methods according to claim 1, characterized in that: The emission inventory includes organized and unorganized emissions. Organized emissions from enterprises are treated as point sources, and unorganized emissions are treated as surface sources.

3. The method for tracing VOCs in industrial parks based on fingerprint and data-driven methods according to claim 1, characterized in that: The meteorological-emission scenario refers to multiple component emission scenarios corresponding to multiple meteorological scenarios. Therefore, each meteorological scenario also corresponds to multiple model simulation results. The models include AERMOD and CALPUFF models. When the model is applied, localization processing of terrain and land use type model parameters is required.

4. The method for tracing VOCs in industrial parks based on fingerprint and data-driven methods according to claim 1, characterized in that: The name of the pollution source is the name of the enterprise.

5. The method for tracing VOCs in industrial parks based on fingerprint and data-driven methods according to claim 1, characterized in that: The ANN neural network structure consists of three parts: input layer, hidden layer and output layer. Each layer is composed of multiple neurons, each neuron is interconnected with all neurons in the previous layer, and each connection corresponds to a weight. In the three layers of the neural network, the input layer is used to receive and input data, the hidden layer processes the data, and the output layer generates and outputs the generated results.

6. The method for tracing VOCs in industrial parks based on fingerprint and data-driven methods according to claim 1, characterized in that: When learning and adjusting parameters, the parameters are adjusted by minimizing the root mean square error RMSE. in, is the true value of the i-th sample point, is the predicted value, and n is the number of data.

7. The method for tracing VOCs in industrial parks based on fingerprint and data-driven methods according to claim 1, characterized in that: The real-time meteorological data includes wind direction, wind speed, and temperature, and the component names are the components that account for more than 10% of the VOCs at the receptor point.

8. The method for tracing VOCs in industrial parks based on fingerprint and data-driven methods according to claim 1, characterized in that: The meteorological parameters include wind speed, wind direction, temperature, cloud cover, precipitation, and air pressure. The wind direction is set to 0-360°, where 0° and 360° are both due north. The cloud cover is set to a scale of 10, ranging from 0 to 10.

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