A restaurant oil fume online monitoring system and method

Abstract

The present invention provides an online monitoring system and method for restaurant oil fume, which belongs to the technical field of environmental monitoring. The system comprises an equipment selection module, a preprocessing module, an emission inventory module and an early warning module. VOCs data in restaurant oil fume are collected in real time through online monitoring equipment, and high-activity components are determined in combination with existing research. The collected oil fume is roughly extracted and pre-processed, a VOCs emission inventory is constructed, emission characteristics of different components are analyzed, and activity characteristics are analyzed in combination with environmental data to achieve early warning, improve the timeliness and accuracy of monitoring, increase the updating frequency of VOCs emission data, timely reflect the pollution status, reduce the influence of environmental factors on monitoring results, and improve the reliability of data.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an online monitoring system and method for restaurant oil smoke. Background Art

[0002] With the acceleration of urbanization and the development of the catering industry, catering fumes have become one of the important sources of urban air pollution. Fumes contain a variety of volatile organic compounds (VOCs), which not only have an impact on the environment, but traditional monitoring methods usually rely on offline sampling, which leads to low data update frequency and cannot reflect the emission of VOCs in real time. In addition, they may be affected by environmental factors, resulting in inaccurate monitoring data.

[0003] Therefore, the present invention provides a restaurant oil fume online monitoring system and method. Summary of the invention

[0004] The present invention provides an online monitoring system and method for restaurant oil fumes. The system collects VOCs data in restaurant oil fumes in real time through online monitoring equipment, determines highly active components in combination with existing research, performs rough extraction and pre-processing on the collected oil fumes, constructs a VOCs emission inventory, analyzes the emission characteristics of different components, performs activity characteristic analysis in combination with environmental data, realizes early warning, improves the timeliness and accuracy of monitoring, increases the updating frequency of VOCs emission data, reflects pollution conditions in a timely manner, reduces the influence of environmental factors on monitoring results, and improves data reliability.

[0005] The present invention provides a restaurant oil fume online monitoring system, comprising:

[0006] Equipment selection module: Based on existing literature, determine the common VOCs components in restaurant fumes, and clearly identify the VOCs high-activity components that need to be monitored, and select online monitoring equipment based on the VOCs components and VOCs high-activity components;

[0007] Pre-processing module: clarify the sampling location, use online monitoring equipment to perform crude extraction of oil smoke from the sampling location, and perform pre-processing on the crude extraction results;

[0008] Emission inventory module: construct a first emission inventory of VOCs from catering sources based on the pre-processing results, analyze the emission characteristics of different VOCs components according to the first emission inventory, and form a second emission inventory;

[0009] Early warning module: Determine the activity characteristic indicators of VOCs emissions based on the second emission inventory combined with the environmental data of the sampling location, and issue an early warning.

[0010] The present invention provides a restaurant fume online monitoring system, an equipment selection module, comprising:

[0011] Extraction component unit: extract VOCs components from restaurant fume according to existing literature, and organize the extracted VOCs components by category;

[0012] Highly active component unit: Count the frequency of VOCs and corresponding synonyms appearing together with specific words in existing literature, and filter the category sorting results according to the statistical results to obtain VOCs highly active components;

[0013] Equipment determination unit: selects the first online monitoring equipment according to the VOCs high-activity component, selects the second online monitoring equipment according to the remaining components of the VOCs component, and selects the third online monitoring equipment based on the VOCs component.

[0014] The present invention provides a restaurant fume online monitoring system, a pre-processing module, comprising:

[0015] Rough extraction unit: determine user needs, clarify sampling locations based on user needs, use a first online monitoring device to perform a first rough extraction, use a second online monitoring device to perform a second rough extraction, and use a third online monitoring device to perform a third rough extraction;

[0016] The first pre-processing unit: performs concentration judgment on the first rough extraction result, and if the concentration of the identified VOCs high-activity component is lower than the first preset concentration, performs the first pre-processing on the first online monitoring device;

[0017] The second pre-processing unit: performs concentration judgment on the second rough extraction result, and if the concentration of the identified remaining component is lower than the second preset concentration, performs second pre-processing on the second online monitoring device;

[0018] The third pre-processing unit is configured to match the first pre-processing result and the second pre-processing result with the third rough extraction result, respectively, and then perform the third pre-processing on the third online monitoring device.

[0019] The present invention provides a restaurant oil fume online monitoring system, the third pre-processing unit comprising:

[0020] The matching block: matching the first concentration extraction result after the first pre-processing with the third rough extraction result to obtain a first matching degree, and matching the second concentration extraction result with the third rough extraction result to obtain a second matching degree;

[0021] Missing block: derive missing components based on the first degree of fit and the second degree of fit, and perform third pre-processing on the third online monitoring device according to the missing components.

[0022] The present invention provides an online monitoring system for restaurant fume, an emission inventory module, comprising:

[0023] Characteristic determination unit: based on the first pre-processing result, the second pre-processing result and the third pre-processing result, a first emission inventory of VOCs from the catering source is determined and constructed; based on the first emission inventory, a first emission characteristic of the VOCs high-activity component is analyzed, and a second emission characteristic of the remaining components is analyzed;

[0024] Proportional connection unit: comparing the first emission characteristic with the second emission characteristic based on the first emission inventory, determining the emission ratio of the VOCs high-activity component and the remaining components in the VOCs component, and judging the emission ratio connection between the VOCs high-activity component and the remaining components based on the emission ratio;

[0025] Time connection unit: according to the first emission inventory, the emission peak time of the VOCs high-activity components and the remaining components is obtained, and the time connection analysis of the first emission characteristic and the second emission characteristic is performed to obtain the emission time connection between the first emission characteristic and the second emission characteristic;

[0026] Inventory unit: a second emission inventory is obtained based on the emission ratio relationship and the emission time relationship.

[0027] The present invention provides an online monitoring system for restaurant fume, a time contact unit, which is used for:

[0028] ,in, represents the emission of highly active components at time t; represents the emission of the remaining components at time t; represents the initial emission of the highly active component at time t = 0; represents the initial emission of the remaining components at time t = 0; represents the decay constant of the highly active component over time; represents the decay constant of the remaining components over time; The gain constant of the remaining components affected by the highly active component; The gain constant of the high-activity component affected by the remaining components; It represents the decay characteristics of the highly active component over time t; represents the decay characteristics of the remaining components over time t; k represents right The influence intensity constant; m represents right The influence intensity constant of

[0029] According to the comparison and A temporal relationship of emissions between the first emissions characteristic and the second emissions characteristic is determined.

[0030] The present invention provides a restaurant oil fume online monitoring system and an early warning module, comprising:

[0031] Index determination unit: collects environmental data of the sampling location, determines the chemical properties of the VOCs components based on the second emission inventory, evaluates the photochemical reaction activity of the VOCs components in combination with the chemical properties and environmental data, and extracts the activity characteristic index of the VOCs emission based on the evaluation results;

[0032] First warning threshold unit: sets the first warning threshold according to the historical event table and the activity characteristic index;

[0033] Simulation unit: Use chemical transport models and activity characteristic indicators to simulate VOCs emissions and obtain simulation results on the temporal and spatial distribution of VOCs, ozone and secondary organic aerosols;

[0034] Generation contribution unit: Identify key VOCs species in VOCs emissions based on simulation results and historical monitoring data, analyze the reaction paths of the key VOCs species, determine the contribution of key VOCs species to the generation of ozone and secondary organic aerosols, and then determine the contribution of VOCs emissions to the generation of ozone and secondary organic aerosols;

[0035] Second warning threshold unit: determine the second warning value based on the monitoring result of the first warning threshold and the generated contribution, determine the second warning threshold based on the first warning value and the second warning value for online warning of restaurant fumes, and perform online warning of restaurant fumes based on the first warning threshold and the second warning threshold.

[0036] The present invention provides a method for online monitoring of restaurant fume, comprising:

[0037] Step 1: Based on existing literature, determine the common VOCs components in restaurant fumes, and identify the VOCs high-activity components that need to be monitored, and select online monitoring equipment based on the VOCs components and VOCs high-activity components;

[0038] Step 2: Identify the sampling location, use online monitoring equipment to perform crude extraction of oil smoke from the sampling location, and perform pre-processing on the crude extraction results;

[0039] Step 3: construct a first emission inventory of VOCs from catering sources based on the pre-processing results, analyze the emission characteristics of different VOCs components according to the first emission inventory, and form a second emission inventory;

[0040] Step 4: Determine the activity characteristic indicators of VOCs emissions based on the second emission inventory combined with the environmental data of the sampling location and issue an early warning.

[0041] Compared with the prior art, the beneficial effects of the present application are as follows: real-time VOCs data in restaurant fumes are collected through online monitoring equipment, highly active components are determined in combination with existing research, the collected fumes are roughly extracted and pre-processed, a VOCs emission inventory is constructed, the emission characteristics of different components are analyzed, and activity characteristics are analyzed in combination with environmental data to achieve early warning, improve the timeliness and accuracy of monitoring, increase the updating frequency of VOCs emission data, timely reflect the pollution status, reduce the impact of environmental factors on monitoring results, and improve the reliability of data.

[0042] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a structural schematic diagram of an online monitoring system for restaurant oil smoke provided by an embodiment of the present invention;

[0046] Figure 2 It is a flow chart of a method for online monitoring of restaurant oil smoke provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0048] Embodiment 1:

[0049] The embodiment of the present invention provides a restaurant oil fume online monitoring system. Figure 1 As shown, including:

[0050] Equipment selection module: Based on existing literature, determine the common VOCs components in restaurant fumes, and clearly identify the VOCs high-activity components that need to be monitored, and select online monitoring equipment based on the VOCs components and VOCs high-activity components;

[0051] Pre-processing module: clarify the sampling location, use online monitoring equipment to perform crude extraction of oil smoke from the sampling location, and perform pre-processing on the crude extraction results;

[0052] Emission inventory module: construct a first emission inventory of VOCs from catering sources based on the pre-processing results, analyze the emission characteristics of different VOCs components according to the first emission inventory, and form a second emission inventory;

[0053] Early warning module: Determine the activity characteristic indicators of VOCs emissions based on the second emission inventory combined with the environmental data of the sampling location, and issue an early warning.

[0054] In this embodiment, VOCs components in restaurant fumes include benzene, toluene, xylene, acetone, formaldehyde, acetaldehyde, ethyl acetate, etc.; VOCs high-activity components refer to volatile organic compounds in restaurant fumes that have strong photochemical reaction activity or significant impact on air quality, and are usually easy to participate in reactions under light conditions to generate ozone or other secondary pollutants, such as benzene: it will rapidly undergo photochemical reactions under sunlight to generate ozone; the remaining components refer to VOCs that exist in restaurant fumes but have relatively low activity and do not contribute much to or have little impact on photochemical reactions. These components may exist stably in the environment but are not easy to participate in photochemical reactions, such as toluene: although it has certain volatility, its photochemical activity is relatively low.

[0055] In this embodiment, the online monitoring equipment includes a first online monitoring equipment, a second online monitoring equipment and a third online monitoring equipment. The first online monitoring equipment is usually a device for highly active VOCs components, such as a gas chromatography-mass spectrometry (GC-MS) device; the second online monitoring equipment is a device for the remaining components, such as a photoionization monitor (PID) or an electrochemical sensor; the third online monitoring equipment may be a more comprehensive device, such as a gas chromatography-spectrometry analyzer (GC-FID) or a tunable laser absorption spectrometer (TDLAS).

[0056] In this embodiment, the sampling location is a VOCs sampling location selected according to user needs, such as the smoke exhaust vent of a restaurant kitchen, an indoor air quality monitoring point, and a surrounding environment monitoring point.

[0057] In this embodiment, crude extraction refers to the preliminary step of extracting volatile organic compounds (VOCs) from restaurant fumes. Physical or chemical methods are usually used to separate and concentrate the VOCs components in the fumes to facilitate subsequent analysis. For example, gas chromatography-mass spectrometry (GC-MS) technology is used to extract fume samples, and solvent extraction or solid phase microextraction (SPME) and other methods are used to obtain VOCs in the fumes.

[0058] In this embodiment, the crude extraction result refers to the sample obtained after the crude extraction step, which contains the preliminary concentrate of various VOCs components extracted from restaurant fume. These results usually need further analysis to determine the types and concentrations of the components. For example, the crude extraction result may show a series of VOCs component concentration data, such as formaldehyde, benzene, ethylene, etc., and their relative abundance in the sample.

[0059] In this embodiment, pre-treatment refers to a step of further processing the crude extraction results to remove interfering substances, concentrate the target components, or improve the accuracy and sensitivity of the analysis. The purpose of pre-treatment is to provide a purer and more accurate sample for subsequent analysis, for example, filtering, concentrating, separating, etc. the crude extraction results, such as using solid phase extraction (SPE) to remove impurities or concentrate the target VOCs components for gas chromatography analysis.

[0060] In this embodiment, the first emission inventory is the emission components and concentrations of volatile organic compounds (VOCs) from the catering source determined based on the first, second and third pre-processing results.

[0061] In this embodiment, the second emission inventory is an updated emission inventory based on the emission ratio connection and the emission time connection. For example, the updated second emission inventory may include highly active components: formaldehyde, benzene (concentration adjustment), and remaining components: toluene, xylene, ethyl acetate (concentration adjustment). The specific concentrations may be adjusted according to actual monitoring data.

[0062] In this embodiment, emission characteristics refer to the emission pattern and behavior of volatile organic compounds (VOCs) in a specific source (such as restaurant fumes), which usually include component composition, types and concentration distribution of different VOCs. For example, some catering places may emit more formaldehyde and benzene, while other places may mainly emit alcohols and ketones. Emission intensity, the emission of different VOCs, is usually expressed in mass concentration (such as micrograms / cubic meter) or volume concentration (such as ppm). Emission intensity can reflect the pollution level in a specific operation or time period. Emission source characteristics, the impact of different catering activities (such as frying, grilling, soup, etc.) on VOCs emissions. Different cooking methods will produce different types and concentrations of VOCs.

[0063] In this embodiment, the activity characteristic index refers to an index for describing the emission characteristics of VOCs and their activity in photochemical reactions, which is extracted by evaluating the chemical properties of VOCs components and environmental data.

[0064] The working principle and beneficial effects of the above technical solution are: real-time collection of VOCs data in restaurant fumes through online monitoring equipment, determination of highly active components in combination with existing research, rough extraction and pre-processing of the collected fumes, construction of a VOCs emission inventory, analysis of emission characteristics of different components, and analysis of activity characteristics in combination with environmental data to achieve early warning, improve the timeliness and accuracy of monitoring, increase the updating frequency of VOCs emission data, timely reflect pollution conditions, reduce the impact of environmental factors on monitoring results, and improve data reliability.

[0065] Embodiment 2:

[0066] The embodiment of the present invention provides a restaurant oil fume online monitoring system, a device selection module, including:

[0067] Extraction component unit: extract VOCs components from restaurant fume according to existing literature, and organize the extracted VOCs components by category;

[0068] Highly active component unit: Count the frequency of VOCs and corresponding synonyms appearing together with specific words in existing literature, and filter the category sorting results according to the statistical results to obtain VOCs highly active components;

[0069] Equipment determination unit: selects the first online monitoring equipment according to the VOCs high-activity component, selects the second online monitoring equipment according to the remaining components of the VOCs component, and selects the third online monitoring equipment based on the VOCs component.

[0070] In this embodiment, the categories include aromatic hydrocarbons: benzene, toluene, xylene, alcohols: ethanol, isopropanol, ketones: acetone, cyclohexanone, aldehydes: formaldehyde, acetaldehyde.

[0071] In this embodiment, synonyms refer to words with the same meaning as the VOCs components, for example, "toluene" can also be called "methylbenzene", and "xylene" can refer to "p-xylene" or "o-xylene".

[0072] In this embodiment, the specific vocabulary refers to characteristic vocabulary corresponding to the highly active components, including reactivity, volatility and toxicity.

[0073] The working principle and beneficial effects of the above technical solution are: extract VOCs components in restaurant fumes through literature research, organize them by category, count the frequency of occurrence of VOCs and their synonyms, screen out highly active components, and select appropriate online monitoring equipment based on these highly active components to ensure comprehensive monitoring of VOCs emissions and improve the accuracy and timeliness of monitoring.

[0074] Embodiment 3:

[0075] The embodiment of the present invention provides a restaurant fume online monitoring system, a pre-processing module, comprising:

[0076] Rough extraction unit: determine user needs, clarify sampling locations based on user needs, use a first online monitoring device to perform a first rough extraction, use a second online monitoring device to perform a second rough extraction, and use a third online monitoring device to perform a third rough extraction;

[0077] The first pre-processing unit: performs concentration judgment on the first rough extraction result, and if the concentration of the identified VOCs high-activity component is lower than the first preset concentration, performs the first pre-processing on the first online monitoring device;

[0078] The second pre-processing unit: performs concentration judgment on the second rough extraction result, and if the concentration of the identified remaining component is lower than the second preset concentration, performs second pre-processing on the second online monitoring device;

[0079] The third pre-processing unit is configured to match the first pre-processing result and the second pre-processing result with the third rough extraction result, respectively, and then perform the third pre-processing on the third online monitoring device.

[0080] In this embodiment, user demand is the user's specific requirements for VOCs monitoring, such as monitoring frequency, target pollutant types, etc. For example, a catering company hopes to monitor benzene and formaldehyde in oil smoke at a frequency of once per hour.

[0081] In this embodiment, the first rough extraction is to extract the concentration of VOCs highly active components using a first online monitoring device, for example, using a gas chromatography-mass spectrometry (GC-MS) device to perform real-time monitoring of the concentration of VOCs highly active components at the smoke exhaust port.

[0082] In this embodiment, the second rough extraction is a residual component concentration extraction performed using a second online monitoring device, for example, a photoionization monitor is used inside a restaurant to monitor the residual component concentration.

[0083] In this embodiment, the third crude extraction is a further concentration extraction of VOCs using a third online monitoring device, which is usually performed after the first two crude extractions. It aims to obtain more comprehensive VOCs component information and help confirm and supplement components that were not fully captured in the first two extractions. For example, after the first two extractions use gas chromatography and spectral analysis, the third crude extraction can use mass spectrometry technology to detect more complex VOCs mixtures, especially those components that are at low concentrations or difficult to separate in the first two extractions.

[0084] In this embodiment, the concentration judgment is to analyze the extraction results to determine whether the VOCs concentration meets the preset standard. For example, by analyzing the first crude extraction result, it is found that the benzene concentration is 50 ppb, which is lower than the first preset concentration of 100 ppb.

[0085] In this embodiment, the first preset concentration is a concentration threshold set for highly active components. For example, the first preset concentration of benzene is set to 100 ppb.

[0086] In this embodiment, the first pre-processing is a subsequent processing performed on the first online monitoring device to improve the accuracy of the monitoring result, for example, adjusting the sensitivity of the device and performing calibration to improve the detection capability of low concentration benzene.

[0087] In this embodiment, the second preset concentration is a concentration threshold set for the remaining components. For example, the second preset concentration of formaldehyde is set to 200 ppb.

[0088] In this embodiment, the second pre-processing is performed on the second online monitoring device, such as replacing a sensor or increasing a sampling time to improve the detection capability of low-concentration formaldehyde.

[0089] In this embodiment, the third pre-processing is further processing based on the pre-processing results. For example, the results of the first and second pre-processing are combined to optimize the operating parameters of the third online monitoring device to ensure accurate monitoring of all VOCs components.

[0090] In this embodiment, the matching process is to analyze the concentration extraction results after the first and second pre-processing, evaluate the degree of matching with the third rough extraction result, calculate the first and second degrees of matching, determine the missing components based on the degree of matching, and optimize the pre-processing of the third online monitoring equipment accordingly.

[0091] The working principle and beneficial effects of the above technical solution are: by determining user needs and sampling locations, three types of online monitoring equipment are used for graded rough extraction to ensure comprehensive monitoring of VOCs in restaurant fumes, and through concentration judgment and pre-processing, equipment performance is optimized to improve the recognition rate of highly active components, achieve real-time and accurate VOCs monitoring, and provide reliable data support for environmental management.

[0092] Embodiment 4:

[0093] The embodiment of the present invention provides a restaurant oil fume online monitoring system, the third pre-processing unit comprising:

[0094] The matching block: matching the first concentration extraction result after the first pre-processing with the third rough extraction result to obtain a first matching degree, and matching the second concentration extraction result with the third rough extraction result to obtain a second matching degree;

[0095] Missing block: derive missing components based on the first degree of fit and the second degree of fit, and perform third pre-processing on the third online monitoring device according to the missing components.

[0096] In this embodiment, the first concentration extraction result is the concentration data of VOCs high-activity components obtained after the first pre-treatment, for example, benzene: 150 ppb, toluene: 100 ppb, xylene: 80 ppb. After the first pre-treatment (such as gas adsorption, condensation, etc.) to remove interfering substances and concentrate high-activity components, the obtained high-activity component concentration data may be, the first concentration extraction result is benzene: 80 ppb, toluene: 50 ppb, xylene: 30 ppb.

[0097] In this embodiment, the second concentration extraction result is the concentration data of the remaining components obtained after the second pre-treatment. For example, after the second pre-treatment of ethyl acetate (45 ppb), the second concentration extraction result obtained is ethyl acetate (48 ppb).

[0098] In this example, the agreement is the degree of consistency between the concentration data obtained at different extraction or processing stages.

[0099] In this embodiment, the first degree of agreement is the degree of agreement between the concentrations of the same components in the first concentration extraction result and the third crude extraction result, usually expressed as a numerical value. For example, the benzene in the first concentration extraction result is 80 ppb, and the benzene in the third crude extraction result is 70 ppb, then the corresponding first degree of agreement is 87.5%.

[0100] In this embodiment, the second degree of agreement is the degree of agreement between the second concentration extraction result and the third crude extraction result. For example, the second concentration extraction result is ethyl acetate (48 ppb) and the third crude extraction result is ethyl acetate (38.4 ppb). The second degree of agreement is calculated to be 80%.

[0101] In this embodiment, the missing components are VOCs components that are found to be insufficiently extracted or monitored after the first and second fit analyses. For example, if it is found in the comparison that certain components (such as formaldehyde) are not detected in the first and second concentration extraction results, formaldehyde is considered to be a missing component.

[0102] In this embodiment, the third pre-processing is an additional processing performed on the third online monitoring device for the missing components so that these components can be accurately extracted and analyzed. For example, after formaldehyde is found as a missing component, the pre-processing parameters (such as temperature, adsorbent type) of the third online monitoring device are adjusted according to the characteristics of formaldehyde to ensure that formaldehyde can be effectively captured.

[0103] The working principle and beneficial effects of the above technical solution are: by analyzing the concentration extraction results after the first and second pre-processing, evaluating the consistency with the third rough extraction result, calculating the first and second consistency, judging the missing components according to the consistency, and optimizing the pre-processing of the third online monitoring equipment accordingly, so as to improve the detection accuracy and completeness of VOCs and enhance the reliability of the monitoring system.

[0104] Embodiment 5:

[0105] The embodiment of the present invention provides a restaurant fume online monitoring system, an emission inventory module, including:

[0106] Characteristic determination unit: based on the first pre-processing result, the second pre-processing result and the third pre-processing result, a first emission inventory of VOCs from the catering source is determined and constructed; based on the first emission inventory, a first emission characteristic of the VOCs high-activity component is analyzed, and a second emission characteristic of the remaining components is analyzed;

[0107] Proportional connection unit: comparing the first emission characteristic with the second emission characteristic based on the first emission inventory, determining the emission ratio of the VOCs high-activity component and the remaining components in the VOCs component, and judging the emission ratio connection between the VOCs high-activity component and the remaining components based on the emission ratio;

[0108] Time connection unit: according to the first emission inventory, the emission peak time of the VOCs high-activity components and the remaining components is obtained, and the time connection analysis of the first emission characteristic and the second emission characteristic is performed to obtain the emission time connection between the first emission characteristic and the second emission characteristic;

[0109] Inventory unit: a second emission inventory is obtained based on the emission ratio relationship and the emission time relationship.

[0110] In this embodiment, the first emission characteristic refers to the characteristic of the highly active VOCs component extracted from the first emission inventory, including its concentration, chemical properties and possible sources, for example, the highly active component: benzene, characteristic, benzene: concentration 80 ppb, mainly comes from oil smoke and spice volatilization.

[0111] In this embodiment, the second emission characteristic refers to the emission characteristic of the remaining components, including its concentration and possible sources, for example, the remaining components: ethyl acetate, characteristic, ethyl acetate: concentration 50 ppb, mainly comes from the volatilization of food ingredients.

[0112] In this embodiment, the emission ratio relationship refers to the proportional relationship between the high-activity components and the remaining components in the overall VOCs emissions. For example, the total emissions of high-activity components are: 80 ppb (benzene) + 30 ppb (formaldehyde) = 110 ppb, and the total emissions of the remaining components are: 60 ppb (toluene) + 40 ppb (xylene) + 50 ppb (ethyl acetate) = 150 ppb. The emission ratio is: the proportion of high-activity components in the total emissions is 110 / 260≈42.3%.

[0113] In this embodiment, the specific emission ratio of the high-activity component to the remaining components is, for example, high-activity component: 42.3%, remaining component: 57.7%

[0114] In this embodiment, the peak emission time is the time when the emissions of the highly active components and the remaining components of VOCs determined according to the emission inventory reach the highest concentration. For example, the peak emission time of the highly active components (such as formaldehyde and benzene) is 12:00 noon, and the peak emission time of the remaining components (such as toluene and xylene) is 3:00 pm.

[0115] In this embodiment, the temporal connection analysis is to analyze the relationship between the peak emission times of the highly active components and the remaining components. For example, it is found that the peak emission time of the highly active components is at 12:00 noon, while the peak emission time of the remaining components is at 3:00 pm, indicating that the emission of the highly active components is more directly correlated with cooking activities.

[0116] In this embodiment, the emission time connection is the relationship between the emission time of the high-activity component and the remaining components. For example, the emission time of the high-activity component is concentrated in the peak meal time (such as 12:00 noon), while the emission time of the remaining components extends into the afternoon, showing different temporal characteristics of the two.

[0117] The working principle and beneficial effects of the above technical solution are: by analyzing the first, second and third pre-processing results, constructing the first emission inventory of VOCs from catering sources, and analyzing the emission characteristics of high-activity components and remaining components, by comparing the emission characteristics and emission ratios, judging the relationship between the two, and analyzing the peak emission time, and finally forming the second emission inventory, improving the accuracy and reliability of the data, so as to achieve comprehensive monitoring and analysis of VOCs emissions.

[0118] Embodiment 6:

[0119] The embodiment of the present invention provides a restaurant oil fume online monitoring system, a time connection unit, which is used to:

[0120] ,in, represents the emission of highly active components at time t; represents the emission of the remaining components at time t; represents the initial emission of the highly active component at time t = 0; represents the initial emission of the remaining components at time t = 0; represents the decay constant of the highly active component over time; represents the decay constant of the remaining components over time; The gain constant of the remaining components affected by the highly active component; The gain constant of the high-activity component affected by the remaining components; It represents the decay characteristics of the highly active component over time t; represents the decay characteristics of the remaining components over time t; k represents right The influence intensity constant; m represents right The influence intensity constant of

[0121] According to the comparison and A temporal relationship of emissions between the first emissions characteristic and the second emissions characteristic is determined.

[0122] The working principle and beneficial effects of the above technical solution are: by establishing a mathematical model to calculate the emissions of highly active components and remaining components at any time t, taking into account factors such as initial emissions, attenuation constants and gain constants, analyzing the mutual influence between highly active components and remaining components, and by comparing the first and second emission characteristics, determining their emission time connection, thereby achieving an in-depth understanding of the VOCs emission dynamics.

[0123] Embodiment 7:

[0124] The embodiment of the present invention provides a restaurant oil fume online monitoring system, an early warning module, including:

[0125] Index determination unit: collects environmental data of the sampling location, determines the chemical properties of the VOCs components based on the second emission inventory, evaluates the photochemical reaction activity of the VOCs components in combination with the chemical properties and environmental data, and extracts the activity characteristic index of the VOCs emission based on the evaluation results;

[0126] First warning threshold unit: sets the first warning threshold according to the historical event table and the activity characteristic index;

[0127] Simulation unit: Use chemical transport models and activity characteristic indicators to simulate VOCs emissions and obtain simulation results on the temporal and spatial distribution of VOCs, ozone and secondary organic aerosols;

[0128] Generation contribution unit: Identify key VOCs species in VOCs emissions based on simulation results and historical monitoring data, analyze the reaction paths of the key VOCs species, determine the contribution of key VOCs species to the generation of ozone and secondary organic aerosols, and then determine the contribution of VOCs emissions to the generation of ozone and secondary organic aerosols;

[0129] Second warning threshold unit: determine the second warning value based on the monitoring result of the first warning threshold and the generated contribution, determine the second warning threshold based on the first warning value and the second warning value for online warning of restaurant fumes, and perform online warning of restaurant fumes based on the first warning threshold and the second warning threshold.

[0130] In this embodiment, the historical event table is an event database that records past VOCs emission events and their impacts on the environment, including concentration, time, location and related meteorological conditions. For example, Event 1: On June 15, 2023, the VOCs concentration in a certain catering area reached 150 ppb, accompanied by an increase in ozone concentration to 100 ppb. Event 2: On July 10, 2023, the VOCs concentration was 200 ppb, resulting in an increase in PM2.5 concentration.

[0131] In this embodiment, the chemical properties are the chemical characteristics of the VOCs components, including molecular structure, reactivity, volatility, etc. For example, benzene: molecular formula C6H6, has high volatility and photochemical reactivity.

[0132] In this embodiment, the photochemical reactivity is the ability of the VOCs component to participate in photochemical reactions under specific environmental conditions, which is usually related to its reaction rate and potential for generating ozone. For example, the photochemical reactivity index of benzene is 0.8 (high), which means that it is easy to generate ozone under sunlight. The photochemical reactivity index of formaldehyde is 0.9 (very high), and it reacts actively under light conditions.

[0133] In this embodiment, the evaluation process is a step of evaluating the photochemical reaction activity of VOCs components by combining environmental data and chemical properties. For example, environmental data of a specific area (such as temperature and humidity) is collected, the chemical properties of VOCs components (such as reactivity) are evaluated, the photochemical reaction activity index is calculated, and the activity characteristic index is obtained by combining the environmental data.

[0134] In this embodiment, the first warning threshold is a warning value of VOCs concentration and photochemical reaction activity set based on the historical event table and activity characteristic indicators. For example, the first warning threshold is set to a VOCs concentration of 150 ppb and a photochemical reaction activity index of 0.75. When the monitoring value exceeds this threshold, a warning is issued.

[0135] In this embodiment, the chemical transport model is a mathematical model used to simulate the transmission, reaction and transformation of VOCs in the atmosphere. For example, the CALPUFF model is used to simulate the diffusion and reaction of VOCs, taking into account factors such as wind speed, temperature, and humidity.

[0136] In this embodiment, the simulation results are the concentration distribution results of VOCs, ozone and secondary organic aerosol output by the chemical transport model. For example, the simulation results show that the VOCs concentration in a certain area reaches a maximum value of 200 ppb within 12 hours, and the ozone concentration reaches 120 ppb within the same time period.

[0137] In this embodiment, the temporal distribution and spatial distribution of VOCs, ozone and secondary organic aerosols are the concentration changes of each pollutant in time and space in the simulation results. For example, in temporal distribution, VOCs reach a peak at 12:00 noon and ozone reaches a peak at 3:00 pm. In spatial distribution, the concentration of VOCs is higher in the city center and lower in the surrounding areas.

[0138] In this embodiment, the key VOCs species are the VOCs components that contribute most to the generation of ozone and secondary organic aerosols, for example, the key VOCs species: benzene, formaldehyde, and ethylene.

[0139] In this embodiment, the generation contribution refers to the contribution of key VOCs species to the generation of ozone and secondary organic aerosols. For example, benzene contributes 30% to ozone generation and formaldehyde contributes 50%.

[0140] In this embodiment, reaction pathway analysis is to analyze the reaction pathways of key VOCs species in photochemical reactions and the secondary pollutants they generate. For example, the reaction pathway of benzene to generate ozone under light is: benzene → hydrogen peroxide → ozone, and the pathway of formaldehyde to generate secondary organic aerosols through photochemical reactions.

[0141] In this embodiment, the monitoring results are VOCs concentration and photochemical reaction activity data obtained in actual monitoring. For example, the monitoring results show that the VOCs concentration in a certain area reaches 160 ppb and the photochemical reaction activity index is 0.8.

[0142] In this embodiment, the second warning value warning threshold is an updated warning value warning threshold determined based on the monitoring result and generation contribution of the first warning threshold. For example, the second warning value warning threshold is set to a VOCs concentration of 180 ppb and a photochemical reaction activity index of 0.85. When the monitoring value exceeds this threshold, a higher alert is issued.

[0143] The working principle and beneficial effects of the above technical solution are: by setting the first warning threshold, combining historical events with activity characteristic indicators, monitoring the VOCs concentration and photochemical reaction activity of the second emission inventory, and using the chemical transport model to simulate the spatial and temporal distribution of VOCs, identifying key VOCs species and their contribution to ozone and secondary organic aerosols, and determining the second warning threshold based on the monitoring results, to achieve online warning of restaurant fumes and respond to potential air quality problems in a timely manner.

[0144] Embodiment 8:

[0145] The embodiment of the present invention provides a method for online monitoring of restaurant oil smoke. Figure 2 As shown, including:

[0146] Step 1: Based on existing literature, determine the common VOCs components in restaurant fumes, and identify the VOCs high-activity components that need to be monitored, and select online monitoring equipment based on the VOCs components and VOCs high-activity components;

[0147] Step 2: Identify the sampling location, use online monitoring equipment to perform crude extraction of oil smoke from the sampling location, and perform pre-processing on the crude extraction results;

[0148] Step 3: construct a first emission inventory of VOCs from catering sources based on the pre-processing results, analyze the emission characteristics of different VOCs components according to the first emission inventory, and form a second emission inventory;

[0149] Step 4: Determine the activity characteristic indicators of VOCs emissions based on the second emission inventory combined with the environmental data of the sampling location and issue an early warning.

[0150] The working principle and beneficial effects of the above technical solution are: real-time collection of VOCs data in restaurant fumes through online monitoring equipment, determination of highly active components in combination with existing research, rough extraction and pre-processing of the collected fumes, construction of a VOCs emission inventory, analysis of emission characteristics of different components, and analysis of activity characteristics in combination with environmental data to achieve early warning, improve the timeliness and accuracy of monitoring, increase the updating frequency of VOCs emission data, timely reflect pollution conditions, reduce the impact of environmental factors on monitoring results, and improve data reliability.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An online monitoring system for restaurant fume, characterized in that: include: Equipment selection module: Based on existing literature, determine the common VOCs components in restaurant fumes, and clearly identify the VOCs high-activity components that need to be monitored, and select online monitoring equipment based on the VOCs components and VOCs high-activity components; Pre-processing module: clarify the sampling location, use online monitoring equipment to perform crude extraction of oil smoke from the sampling location, and perform pre-processing on the crude extraction results; Emission inventory module: construct a first emission inventory of VOCs from catering sources based on the pre-processing results, analyze the emission characteristics of different VOCs components according to the first emission inventory, and form a second emission inventory; Early warning module: Determine the activity characteristic index of VOCs emission according to the second emission inventory combined with the environmental data of the sampling location, and issue an early warning; The device selection module includes: Extraction component unit: extract VOCs components from restaurant fume according to existing literature, and organize the extracted VOCs components by category; Highly active component unit: Count the frequency of VOCs and corresponding synonyms appearing together with specific words in existing literature, and filter the category sorting results according to the statistical results to obtain VOCs highly active components; An equipment determination unit: selecting a first online monitoring equipment according to the VOCs high-activity component, selecting a second online monitoring equipment according to the remaining components of the VOCs component, and selecting a third online monitoring equipment based on the VOCs component; The preprocessing module comprises: Rough extraction unit: determine user needs, specify sampling locations based on user needs, use a first online monitoring device to perform a first rough extraction, use a second online monitoring device to perform a second rough extraction, and use a third online monitoring device to perform a third rough extraction, wherein the rough extraction refers to the step of extracting volatile organic compounds (VOCs) from restaurant fumes, separating and concentrating the VOCs components in the fumes for subsequent analysis; The first pre-processing unit: performs concentration judgment on the first rough extraction result, and if the concentration of the identified VOCs high-activity component is lower than the first preset concentration, performs the first pre-processing on the first online monitoring device; The second pre-processing unit: performs concentration judgment on the second rough extraction result, and if the concentration of the identified remaining component is lower than the second preset concentration, performs second pre-processing on the second online monitoring device; The third pre-processing unit is configured to match the first pre-processing result and the second pre-processing result with the third rough extraction result, respectively, and then perform the third pre-processing on the third online monitoring device.

2. The restaurant fume online monitoring system according to claim 1, characterized in that: The third pre-processing unit includes: The matching block: matching the first concentration extraction result after the first pre-processing with the third rough extraction result to obtain a first matching degree, and matching the second concentration extraction result with the third rough extraction result to obtain a second matching degree; Missing block: derive missing components based on the first degree of fit and the second degree of fit, and perform third pre-processing on the third online monitoring device according to the missing components.

3. The restaurant fume online monitoring system according to claim 1, characterized in that: Emissions Inventory Module, including: Characteristic determination unit: based on the first pre-processing result, the second pre-processing result and the third pre-processing result, a first emission inventory of VOCs from the catering source is determined and constructed; based on the first emission inventory, a first emission characteristic of the VOCs high-activity component is analyzed, and a second emission characteristic of the remaining components is analyzed; Proportional connection unit: comparing the first emission characteristic with the second emission characteristic based on the first emission inventory, determining the emission ratio of the VOCs high-activity component and the remaining components in the VOCs component, and judging the emission ratio connection between the VOCs high-activity component and the remaining components based on the emission ratio; Time connection unit: according to the first emission inventory, the emission peak time of the VOCs high-activity components and the remaining components is obtained, and the time connection analysis of the first emission characteristic and the second emission characteristic is performed to obtain the emission time connection between the first emission characteristic and the second emission characteristic; Inventory unit: a second emission inventory is obtained based on the emission ratio relationship and the emission time relationship.

4. The restaurant fume online monitoring system according to claim 1, characterized in that: Time contact unit, used for: ,in, represents the emission of highly active components at time t; represents the emission of the remaining components at time t; represents the initial emission of the highly active component at time t = 0; represents the initial emission of the remaining components at time t = 0; represents the decay constant of the highly active component over time; represents the decay constant of the remaining components over time; The gain constant of the remaining components affected by the highly active component; The gain constant of the high-activity component affected by the remaining components; It represents the decay characteristics of the highly active component over time t; represents the decay characteristics of the remaining components over time t; k represents right The influence intensity constant; m represents right The influence intensity constant of According to the comparison and A temporal relationship of emissions between the first emissions characteristic and the second emissions characteristic is determined.

5. The restaurant fume online monitoring system according to claim 1, characterized in that: Early warning module, including: Index determination unit: collects environmental data of the sampling location, determines the chemical properties of the VOCs components based on the second emission inventory, evaluates the photochemical reaction activity of the VOCs components in combination with the chemical properties and environmental data, and extracts the activity characteristic index of the VOCs emission based on the evaluation results; First warning threshold unit: sets the first warning threshold according to the historical event table and the activity characteristic index; Simulation unit: Use chemical transport models and activity characteristic indicators to simulate VOCs emissions and obtain simulation results on the temporal and spatial distribution of VOCs, ozone and secondary organic aerosols; Generation contribution unit: Identify key VOCs species in VOCs emissions based on simulation results and historical monitoring data, analyze the reaction paths of the key VOCs species, determine the contribution of key VOCs species to the generation of ozone and secondary organic aerosols, and then determine the contribution of VOCs emissions to the generation of ozone and secondary organic aerosols; The second warning threshold unit determines the second warning value based on the monitoring result of the first warning threshold and the generated contribution, and performs online warning for restaurant oil smoke according to the first warning value and the second warning value.

6. A method for online monitoring of restaurant fume, characterized in that: include: Step 1: Based on existing literature, determine the common VOCs components in restaurant fumes, and identify the VOCs high-activity components that need to be monitored, and select online monitoring equipment based on the VOCs components and VOCs high-activity components; Step 2: Identify the sampling location, use online monitoring equipment to perform crude extraction of oil smoke from the sampling location, and perform pre-processing on the crude extraction results; Step 3: construct a first emission inventory of VOCs from catering sources based on the pre-processing results, analyze the emission characteristics of different VOCs components according to the first emission inventory, and form a second emission inventory; Step 4: Determine the activity characteristic index of VOCs emissions based on the second emission inventory combined with the environmental data of the sampling location, and issue an early warning; The step 2 comprises: Step 21: Extract VOCs components in restaurant fume according to existing literature, and organize the extracted VOCs components by category; Step 22: Count the frequencies of VOCs and corresponding synonyms appearing together with specific words in existing literature, and filter the classification results according to the statistical results to obtain VOCs high-activity components; Step 23: selecting a first online monitoring device according to the VOCs high-activity component, selecting a second online monitoring device according to the remaining components of the VOCs component, and selecting a third online monitoring device based on the VOCs component; The step 3 comprises: Step 31: Determine user needs, clarify sampling locations based on user needs, use a first online monitoring device to perform a first rough extraction, use a second online monitoring device to perform a second rough extraction, and use a third online monitoring device to perform a third rough extraction, wherein the rough extraction refers to the step of extracting volatile organic compounds (VOCs) from restaurant fume, separating and concentrating the VOCs components in the fume for subsequent analysis; Step 32: Performing concentration judgment on the first crude extraction result, if the concentration of the identified VOCs high-activity component is lower than the first preset concentration, performing a first pre-processing on the first online monitoring device; Step 33: performing concentration judgment on the second rough extraction result, and if the concentration of the identified remaining component is lower than the second preset concentration, performing a second pre-processing on the second online monitoring device; Step 34: The first pre-processing result and the second pre-processing result are respectively matched with the third rough extraction result, and then the third online monitoring device is subjected to the third pre-processing.

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