A VOCs emission accounting system and method for industrial enterprises

Abstract

The present invention discloses a VOCs emission accounting system and method for industrial enterprises, which relates to the technical field of VOCs emission accounting. After completing the wind speed optimization adjustment, the method calculates the change in VOCs concentration before and after the optimization, generates an optimization effect percentage E, and performs a preliminary comparative evaluation with the control threshold Kz. This mechanism can help enterprises evaluate the effect of wind speed adjustment in real time. If the optimization effect does not meet the expected goal, it is optimized again through the iterative adjustment mechanism until the best effect is achieved. Furthermore, the enterprise can combine the VOC governance upgrade plan to conduct a comprehensive assessment of VOCs emissions based on the difference value Gap, and analyze whether equipment upgrades or process adjustments are needed based on the difference value. Through this continuous optimization mechanism, enterprises can continuously improve the VOCs emission control effect, ensure that emissions are controlled within the compliance standards, and ultimately achieve long-term and stable achievement of emission control targets.

Description

Technical Field

[0001] The present invention relates to the technical field of VOCs emission accounting, and in particular to a VOCs emission accounting system and method for industrial enterprises. Background Art

[0002] The emission of volatile organic compounds (VOCs) is an important issue in environmental protection and industrial emission management. With the increasing global attention to environmental pollution control and air quality management, VOCs emissions in the industrial sector, especially in the chemical, coatings, solvent and other industries, have become a key monitoring focus. VOCs are not only one of the main components of air pollution, but also a precursor of atmospheric ozone, which has a serious impact on the ecological environment and human health. Therefore, how to accurately calculate the VOCs emissions of industrial enterprises and take effective optimization measures has become a key issue in current industrial environmental protection work. Against this background, the monitoring and control technology for VOCs emissions has gradually developed into a comprehensive method including sensor technology, data processing technology, model prediction and optimization algorithms, forming a technical system for industrial VOCs emission accounting and control.

[0003] Although many industrial enterprises are currently equipped with VOCs emission monitoring systems, and some enterprises have taken certain wind speed adjustment or temperature and humidity control measures to reduce VOCs emissions, the existing VOCs emission accounting methods still have certain limitations. First, traditional emission accounting methods usually rely on simple emission factors and historical data, and lack real-time monitoring and adjustment mechanisms for the dynamic changes of VOCs emissions under different working conditions. Secondly, the impact of factors such as wind speed, temperature and humidity on VOCs emissions has a complex nonlinear relationship. Existing models often ignore the interaction between these factors, resulting in limited optimization effects. In addition, most of the existing optimization methods are based on experience or rough means, which makes it difficult to achieve fine adjustments for specific working conditions. Due to the lack of scientific algorithm support, it is difficult for enterprises to achieve the best emission control effect through precise adjustment in actual operations. Summary of the Invention

[0004] In response to the deficiencies in the prior art, the present invention provides a VOCs emission accounting system and method for industrial enterprises, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: comprising the following steps:

[0006] S1. Installing a sensor group at the exhaust gas emission channel to collect sensor data groups in real time and transmitting the sensor data groups to the accounting server via a wireless communication network;

[0007] S2. Receive the sensor data set in real time in the accounting server, pre-process the sensor data set, obtain a digital data set, establish a multivariate model, extract the digital data set and input it into the multivariate model to calculate and output the VOCs concentration value Ycnd;

[0008] S3. Based on the obtained VOCs concentration value Ycnd, combined with the optimization algorithm, calculate and output the objective function F(S) to optimize and adjust the wind speed of the exhaust gas emission channel;

[0009] S4. After re-optimization and adjustment, calculate the change in VOCs concentration before and after the wind speed adjustment, output the optimization effect percentage E, and set a control threshold Kz. Perform a preliminary comparative evaluation of the control threshold Kz and the optimization effect percentage E, analyze the optimization adjustment effect of the exhaust gas emission channel wind speed, and generate an iterative adjustment mechanism based on the evaluation results.

[0010] S5. After the VOC control is upgraded, S1 and S2 are executed for the second time to output the second VOCs predicted concentration Ycnd2, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl within a certain time range. The total VOCs emissions Zpfl are then difference calculated with the target value to obtain the difference value Gap, and the VOCs emissions are analyzed based on the data results of the difference value Gap.

[0011] Preferably, said S1 includes S11 and S12;

[0012] S21, collecting sensor data in real time by installing a sensor group inside the exhaust gas emission channel;

[0013] The sensor group includes a wind speed sensor, a temperature sensor and a humidity sensor;

[0014] The sensor data set includes wind speed S, temperature T and humidity H;

[0015] S12. Utilize the built-in communication modules of all sensors in the sensor group to establish wireless communication connection between the accounting server and the sensor group by setting up a 5G wireless communication network, and then transmit the collected sensor data group to the accounting server.

[0016] Preferably, said S2 includes S21 and S22;

[0017] S21. Receive a sensor data set in real time on the accounting server and preprocess the sensor data set, including denoising, data cleaning, and normalization. After the preprocessing, the sensor data set is timestamped according to the sensor acquisition time to obtain a digital data set.

[0018] The digital data set includes the wind speed S at time t t, temperature T at time t t and humidity H at time t t ;

[0019] S22, using regression analysis method to calculate the multivariate model of VOCs concentration and wind speed S, temperature T and humidity H, and extracting digital data group to input into the multivariate model, calculate and output VOCs concentration value Ycnd, and predict the wind speed S at the current time t t , temperature T at time t t and humidity H at time t t VOCs concentration under the conditions;

[0020] The VOCs concentration value Ycnd is calculated and output by the following multivariable model;

[0021] ;

[0022] Where, Ycnd t Indicates the VOCs concentration value Ycnd at time t,

[0023] represents the quadratic term of wind speed S at time t, represents the quadratic term of the temperature T at time t, represents the quadratic term of humidity H at time t, S t T t Indicates the cross-influence value of wind speed and temperature; S t H t Indicates the cross-influence value of wind speed and humidity; T t H t Indicates the cross-influence value of temperature and humidity;

[0024] It represents the baseline regression coefficient of VOCs concentration in exhaust gas when the independent variable is zero, 、 and represents the wind speed S at time t t , temperature T at time t t and humidity H at time t t The regression coefficient of 、 and represents the wind speed at time t , temperature T at time t t and humidity H at time t t The regression coefficient of the quadratic term, 、 and They represent the regression coefficients of the cross-influence value of wind speed and temperature, the cross-influence value of wind speed and humidity, and the cross-influence value of temperature and humidity, respectively.

[0025] Preferably, said S3 includes S31;

[0026] S31. Based on the obtained VOCs concentration value Ycnd, an optimization algorithm is used to calculate and output an objective function F(S). The obtained objective function F(S) is sent to the exhaust gas emission channel control system through the calculation server to optimize and adjust the exhaust gas emission channel wind speed S.

[0027] The VOCs concentration value Ycnd is calculated and output by the following optimization algorithm;

[0028] ;

[0029] Where, Ycnd t (S t ) represents the predicted VOCs concentration value under wind speed S at time t, n represents the number of samples, Ycnd target Indicates the target VOCs concentration, represents the trade-off factor, S min Indicates the lower limit of wind speed.

[0030] Preferably, said S4 includes S41 and S42;

[0031] S41. After optimizing and adjusting the wind speed S of the exhaust gas emission channel, re-execute S1 and S2 to output the optimized VOCs concentration value Ycnd after optimization and adjustment. after , when the optimized VOCs concentration value Ycnd after Combined with the VOCs concentration value Ycnd before optimization and adjustment, the optimization effect percentage E is calculated and output to analyze the effect of wind speed S optimization;

[0032] The optimization effect percentage E is calculated and outputted by the following algorithm formula:

[0033] ;

[0034] Where, E(S t ) represents the optimization effect percentage under wind speed S at time t, Ycnd after (S t ) represents the optimized VOCs concentration value under wind speed S at time t.

[0035] Preferably, S42, according to the control target of VOCs emission, the control threshold Kz is set, and the optimization effect percentage E (S t ) and the control pre-threshold Kz for preliminary comparative evaluation, and analyze the VOCs concentration emission optimization after the exhaust gas emission channel wind speed S is optimized. The specific evaluation contents are as follows;

[0036] When the optimization effect percentage E(S t )>control threshold Kz, it indicates that the optimization is successful and the optimized wind speed S is maintained;

[0037] When the optimization effect percentage E(S t ) ≤ the control threshold Kz, it indicates that the optimization fails to achieve the goal. At this time, an iterative adjustment mechanism is executed, which iteratively executes steps S2 to S4 based on the wind speed S after each optimization adjustment until the wind speed optimization adjustment is successful and stops the iteration.

[0038] Preferably, said S5 includes S51, S52 and S53;

[0039] S51. After the wind speed S is optimized and adjusted, S1 and S2 are re-executed to output the second VOCs predicted concentration Ycnd2 for a second time, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl at a specific wind speed within a certain time range;

[0040] The total VOCs emission Zpfl is calculated and output by the following algorithm formula;

[0041] ;

[0042] Where Zpfl (S t ) represents the total VOCs emissions obtained from the optimized wind speed S at time t, Ycnd2 t (S t ) represents the second VOCs predicted concentration obtained by optimizing the wind speed S at time t, and △t represents the time interval.

[0043] Preferably, S52, set the target VOCs total emission Zpfl target , and the total VOCs emission Zpfl (S t ), perform difference calculation and output the difference value Gap;

[0044] The difference value Gap is calculated and outputted by the following algorithm formula;

[0045] .

[0046] Preferably, S53, a secondary comparative evaluation is performed based on the output result of the difference value Gap to analyze the total emission of VOCs in the exhaust gas emission channel after the wind speed optimization adjustment. The specific evaluation content is as follows;

[0047] When the difference value Gap≤0, it means that the total emission is normal after the wind speed S is optimized and adjusted, and the current plan continues to be implemented;

[0048] When the difference value Gap>0, it means that the total emission is abnormal after the wind speed S is optimized and adjusted. At this time, the accounting server prompts to upgrade the equipment and process adjustments to upgrade the VOCs control.

[0049] An industrial enterprise VOCs emission accounting system includes a sensor acquisition module, a data modeling and relationship analysis module, a wind speed optimization module, an effect evaluation and iteration module, and a comprehensive analysis module;

[0050] The sensor acquisition module collects sensor data in real time by installing a sensor group at the exhaust gas emission channel, and transmits the sensor data group to the accounting server through a wireless communication network;

[0051] The data modeling and relationship analysis module receives the sensor data group in real time in the accounting server, pre-processes the sensor data group, obtains the digital data group, extracts the digital data group and inputs it into the multivariate model by establishing a multivariate model, and calculates and outputs the VOCs concentration value Ycnd;

[0052] The wind speed optimization module calculates and outputs the objective function F(S) based on the obtained VOCs concentration value Ycnd and combines it with the optimization algorithm to optimize and adjust the wind speed of the exhaust gas emission channel;

[0053] After the re-optimization adjustment, the effect evaluation and iteration module calculates the change in VOCs concentration before and after the wind speed adjustment, outputs the optimization effect percentage E, and sets a control threshold Kz. After a preliminary comparison and evaluation of the control threshold Kz and the optimization effect percentage E, the optimization adjustment effect of the exhaust gas emission channel wind speed is analyzed, and an iterative adjustment mechanism is generated based on the evaluation results;

[0054] After the comprehensive analysis module is upgraded through VOC governance, S1 and S2 are executed for the second time to output the second VOCs predicted concentration Ycnd2, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl within a certain time range. The difference between the total VOCs emissions Zpfl and the target value is calculated to obtain the difference value Gap, and the VOCs emissions are analyzed based on the data results of the difference value Gap.

[0055] The present invention provides a system and method for calculating VOCs emissions from industrial enterprises. It has the following beneficial effects:

[0056] (1) This method installs a sensor group in the exhaust gas emission channel to collect data such as wind speed, temperature and humidity in real time, and transmits this data to the accounting server through a wireless communication network, so that enterprises can achieve real-time monitoring of exhaust gas emissions. This method can ensure high-frequency and real-time acquisition of data, which greatly improves the accuracy and timeliness of data compared to traditional manual sampling and data recording methods. Through effective wireless communication means, these important parameters are transmitted to the accounting server in real time, so that subsequent VOCs emission prediction and adjustment plans can be started in a timely manner, and the emission situation can be accurately evaluated and managed, thereby improving the company's emission management level and environmental protection capabilities.

[0057] (2) This method can effectively predict VOCs concentration by preprocessing the data and establishing a multivariate model in combination with regression analysis. This model comprehensively considers wind speed, temperature, humidity and the cross-influence between them, providing a more accurate VOCs concentration prediction value, thereby providing data support for subsequent wind speed optimization and adjustment. By combining the optimization algorithm to fine-tune the wind speed of the exhaust gas emission channel and calculating the objective function F(S), the company can adjust the wind speed in real time according to different emission targets to achieve precise control. Compared with traditional control methods, this method fully considers complex environmental factors, ensures the effective control of VOCs concentration, and can respond to changing working conditions through real-time adjustment.

[0058] (3) After completing the wind speed optimization adjustment, this method calculates the change in VOCs concentration before and after optimization, generates the optimization effect percentage E, and performs a preliminary comparative evaluation with the control threshold Kz. This mechanism can help enterprises evaluate the effect of wind speed adjustment in real time. If the optimization effect does not meet the expected goal, it will be optimized again through the iterative adjustment mechanism until the best effect is achieved. Furthermore, enterprises can combine the VOC governance upgrade plan to conduct a comprehensive assessment of VOCs emissions based on the difference value Gap, and analyze whether equipment upgrades or process adjustments are needed based on the difference value. Through this continuous optimization mechanism, enterprises can continuously improve the VOCs emission control effect, ensure that emissions are controlled within the compliance standards, and ultimately achieve long-term and stable emission control goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic diagram of the steps of a method for calculating VOCs emissions from industrial enterprises according to the present invention;

[0060] Figure 2 This is a flow chart of a VOCs emission accounting system for industrial enterprises according to the present invention. DETAILED DESCRIPTION

[0061] 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.

[0062] Example 1

[0063] See also Figure 1 The present invention provides a method for calculating VOCs emissions from industrial enterprises. To achieve the above objectives, the present invention is implemented through the following technical solutions: comprising the following steps:

[0064] S1. Installing a sensor group at the exhaust gas emission channel to collect sensor data groups in real time and transmitting the sensor data groups to the accounting server via a wireless communication network;

[0065] S2. Receive the sensor data set in real time in the accounting server, pre-process the sensor data set, obtain a digital data set, establish a multivariate model, extract the digital data set and input it into the multivariate model to calculate and output the VOCs concentration value Ycnd;

[0066] S3. Based on the obtained VOCs concentration value Ycnd, combined with the optimization algorithm, calculate and output the objective function F(S) to optimize and adjust the wind speed of the exhaust gas emission channel;

[0067] S4. After the optimization adjustment, calculate the change in VOCs concentration before and after the wind speed adjustment, output the optimization effect percentage E, and set the control threshold Kz. Then, conduct a preliminary comparative evaluation of the control threshold Kz and the optimization effect percentage E, analyze the optimization adjustment effect of the exhaust gas emission channel wind speed, and generate an iterative adjustment mechanism based on the evaluation results;

[0068] S5. After the VOC control is upgraded, S1 and S2 are executed for the second time to output the second VOCs predicted concentration Ycnd2, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl within a certain time range. The total VOCs emissions Zpfl are then difference calculated with the target value to obtain the difference value Gap, and the VOCs emissions are analyzed based on the data results of the difference value Gap.

[0069] In this embodiment, the method installs a sensor array in the exhaust gas emission channel to collect sensor data in real time and transmit the data to a calculation server via a wireless communication network. The calculation server then preprocesses the real-time data and calculates the VOC concentration value Ycnd using a multivariate regression model. This process enables accurate prediction of VOC concentrations based on environmental factors, providing scientific data support. Based on the calculated VOC concentration value Ycnd, an optimization algorithm is further used to calculate the objective function, thereby optimizing the wind speed in the exhaust gas emission channel to ensure optimal emission control. After adjusting the wind speed, the method compares the VOC concentrations before and after optimization, outputting the optimization effect percentage E, and comparing it with the preset control threshold Kz to determine whether further wind speed adjustment is necessary. If the optimization does not achieve the expected effect, the system automatically initiates an iterative adjustment mechanism to further refine the wind speed adjustment until the optimal effect is achieved. Finally, after the VOC control upgrade, the system performs data collection and analysis again, combines the new second predicted VOC concentration Ycnd2, calculates the total VOC emissions Zpfl, and calculates the difference between this and the target emissions to obtain the difference value Gap. This difference provides companies with key information on whether their VOC emissions are compliant. If the difference exceeds the standard, the company is prompted to upgrade equipment or adjust processes. This method enables real-time monitoring and precise control of VOC emissions. It not only dynamically adjusts wind speed to optimize exhaust emissions based on environmental changes, but also ensures that VOC emissions remain within compliance through continuous iterative optimization and equipment upgrades, thus avoiding environmental pollution and potential legal risks. Furthermore, the system optimization process makes corporate emissions control more precise and efficient, effectively reducing emission costs and improving environmental benefits.

[0070] Example 2

[0071] Specifically: S1 includes S11 and S12;

[0072] S21, collecting sensor data in real time by installing a sensor group inside the exhaust gas emission channel;

[0073] The sensor group includes wind speed sensor, temperature sensor and humidity sensor;

[0074] The sensor data set includes wind speed S, temperature T and humidity H;

[0075] S12. Utilize the built-in communication modules of all sensors in the sensor group to establish wireless communication connection between the accounting server and the sensor group by setting up a 5G wireless communication network, and then transmit the collected sensor data group to the accounting server.

[0076] In this embodiment, the method uses a sensor group to accurately measure environmental parameters such as wind speed S, temperature T and humidity H, ensuring the comprehensiveness and accuracy of data collection. These sensor data are connected to the accounting server through a built-in communication module, ensuring real-time and data transmission stability. The use of 5G networks not only improves the rate of data transmission, but also enhances the stability and coverage of the network, ensuring the continuity of remote real-time monitoring and data updates. The core purpose of this implementation method is to provide accurate and real-time environmental parameter data support for subsequent VOCs concentration analysis and emission optimization. Through efficient data collection and transmission, the system can obtain the environmental conditions of the exhaust gas emission channel in real time, providing a basis for the accuracy of the VOCs concentration prediction model, thereby achieving more accurate emission control. In addition, the application of 5G networks has greatly improved the real-time and reliability of data transmission, providing a guarantee for subsequent real-time adjustment and optimization.

[0077] Example 3

[0078] Specifically: S2 includes S21 and S22;

[0079] S21. Receive the sensor data set in real time on the accounting server and preprocess the sensor data set, including denoising, data cleaning, and normalization. After preprocessing, mark the sensor data set with a timestamp based on the sensor acquisition time to obtain a digital data set.

[0080] The digital data set includes the wind speed S at time t t , temperature T at time t t and humidity H at time t t ;

[0081] S22, using regression analysis method to calculate the multivariate model of VOCs concentration and wind speed S, temperature T and humidity H, and extracting digital data group to input into the multivariate model, calculate and output VOCs concentration value Ycnd, and predict the wind speed S at the current time t t , temperature T at time t t and humidity H at time t t VOCs concentration under the conditions;

[0082] The VOCs concentration value Ycnd is calculated and output by the following multivariable model;

[0083] ;

[0084] Where, Ycnd t Indicates the VOCs concentration value Ycnd at time t,

[0085] represents the quadratic term of wind speed S at time t, represents the quadratic term of the temperature T at time t, represents the quadratic term of humidity H at time t, S t T t It represents the cross-effect value of wind speed and temperature, reflecting the synergistic effect of the simultaneous changes of wind speed and temperature on VOCs concentration. For example, high temperature under high wind speed may lead to higher volatilization amount. t H t It represents the cross-influence value of wind speed and humidity, describing the effect of wind speed change on VOCs diffusion under different humidity conditions; T t H t Represents the cross-effect value of temperature and humidity, describing the combined impact of high temperature and high humidity on VOCs concentration;

[0086] It represents the baseline regression coefficient of VOCs concentration in exhaust gas when the independent variable is zero, 、 and represents the wind speed S at time t t , temperature T at time t t and humidity H at time t t The regression coefficient of 、 and represents the wind speed at time t , temperature T at time t t and humidity H at time t t The regression coefficient of the quadratic term, 、 and The regression coefficients representing the cross-influence values ​​of wind speed and temperature, wind speed and humidity, and temperature and humidity, respectively, are calculated using the training of a fitted machine learning model of a historical digital data set to minimize the error between the predicted value and the actual value, thereby determining the optimal values ​​of these coefficients.

[0087] In this embodiment, the method improves the accuracy and reliability of the data by receiving the sensor data in real time and preprocessing it in the accounting server. The preprocessing steps include denoising, data cleaning and standardization, which ensure the validity of the sensor data and mark the collection time of each data by time stamp, thereby forming a high-quality digital data set. It provides accurate input for subsequent VOCs concentration prediction. Subsequently, based on the preprocessed data, a multivariate model between VOCs concentration and environmental factors was established using regression analysis. This model can accurately capture the changing pattern of VOCs concentration under different environmental conditions by considering the quadratic terms and cross terms of each environmental factor. For example, under high wind speed and high temperature conditions, the volatilization of VOCs may increase, and the impact of wind speed changes on VOCs diffusion under different humidity conditions is also different. The regression model optimizes the coefficient fitting process by minimizing the error between the predicted value and the actual value, thereby obtaining the optimal regression coefficient. The beneficial effects of this implementation are reflected in the following aspects: First, the data preprocessing step effectively eliminates noise and inconsistencies, making subsequent analysis results more reliable; second, the multivariate model established through regression analysis can accurately predict VOCs concentration, fully considering the complex relationship between wind speed, temperature and humidity, thereby improving the accuracy of VOCs emission monitoring; finally, the use of historical data for model training makes the model more adaptable and accurate, providing strong support for real-time emission prediction and subsequent optimization and adjustment.

[0088] Example 4

[0089] Specifically: S3 includes S31;

[0090] S31. Based on the obtained VOCs concentration value Ycnd, an optimization algorithm is used to calculate and output an objective function F(S). The obtained objective function F(S) is sent to the exhaust gas emission channel control system through the calculation server to optimize and adjust the exhaust gas emission channel wind speed S.

[0091] The VOCs concentration value Ycnd is calculated and output by the following optimization algorithm;

[0092] ;

[0093] Where, Ycnd t (S t ) represents the predicted VOCs concentration value under wind speed S at time t, n represents the number of samples, that is, the number of data points considered in one optimization cycle, Ycnd target Indicates the target VOCs concentration, which is the target emission value set according to the enterprise or environmental protection standards. represents the trade-off factor used to balance the penalty cost of VOCs concentration error and wind speed adjustment, S minIndicates the lower limit of wind speed, which is used to prevent the wind speed from being too low, causing the exhaust gas to accumulate in the channel.

[0094] In this embodiment, the method implements the core link of wind speed optimization adjustment based on the VOCs concentration value Ycnd. Specifically, the obtained VOCs concentration value is first used to calculate the output objective function F(S) through the optimization algorithm. This objective function F(S) comprehensively considers the target value Ycnd of the VOCs concentration. target The objective function includes a trade-off factor to balance the VOCs concentration error and the penalty cost of wind speed adjustment, thereby avoiding the problem of exhaust gas accumulation in the channel due to low wind speed. min A lower limit for wind speed is set to ensure it doesn't drop too low, impacting exhaust gas flow and emission efficiency. After the objective function F(S) is calculated and output, it's transmitted to the exhaust gas emission channel control system via the accounting server for wind speed optimization and adjustment. This process achieves a dynamic balance between wind speed and VOC concentration, allowing the exhaust gas emission channel to adjust wind speed in the most economical and efficient manner while meeting environmental standards, reducing unnecessary energy consumption and emissions. The optimization algorithm provides a scientific basis for wind speed adjustment, ensuring that while achieving VOC concentration targets, wind speed adjustment does not cause additional energy waste or environmental burden. Secondly, setting a lower limit for wind speed prevents excessively low wind speeds, ensuring that exhaust gas does not accumulate in the emission channel, thereby improving its efficiency and safety. Finally, through dynamic adjustment of the objective function, wind speed can be optimized according to different environmental and production conditions during actual operation, making overall VOC emission control more flexible and precise, effectively supporting the company's dual goals of environmental protection and cost control.

[0095] Example 5

[0096] Specifically: S4 includes S41 and S42;

[0097] S41. After optimizing and adjusting the wind speed S of the exhaust gas emission channel, re-execute S1 and S2 to output the optimized VOCs concentration value Ycnd after optimization and adjustment. after , in the optimization of VOCs concentration value Ycnd after Combined with the VOCs concentration value Ycnd before optimization and adjustment, the optimization effect percentage E is calculated and output to analyze the effect of wind speed S optimization;

[0098] The optimization effect percentage E is calculated and output by the following algorithm formula;

[0099] ;

[0100] Where, E(St ) represents the optimization effect percentage under wind speed S at time t, Ycnd after (S t ) represents the optimized VOCs concentration value under wind speed S at time t.

[0101] S42, according to the control target of VOCs emission, set the control threshold Kz, and set the optimization effect percentage E (S) at the wind speed S at time t. t ) and the control pre-threshold Kz for preliminary comparative evaluation, and analyze the VOCs concentration emission optimization after the exhaust gas emission channel wind speed S is optimized. The specific evaluation contents are as follows;

[0102] When the optimization effect percentage E(S t )>control threshold Kz, it indicates that the optimization is successful and the optimized wind speed S is maintained;

[0103] When the optimization effect percentage E(S t ) ≤ the control threshold Kz, it indicates that the optimization fails to achieve the goal. At this time, the iterative adjustment mechanism is executed. The iterative adjustment mechanism iteratively executes steps S2 to S4 based on the wind speed S after each optimization adjustment until the wind speed optimization adjustment is successful and the iteration is stopped.

[0104] In this embodiment, the method performs a comprehensive evaluation and feedback mechanism of the optimization effect after the wind speed is optimized and adjusted. First, based on the optimized and adjusted exhaust gas discharge channel wind speed S, S1 and S2 are re-executed to obtain a new VOCs concentration value Ycnd after Then, the optimized VOCs concentration value Ycnd after Compare with the concentration value Ycnd before adjustment and calculate the optimization effect percentage E. This optimization effect percentage E reflects the degree of improvement in VOCs concentration after wind speed adjustment, and can quantify the actual effect of wind speed optimization on VOCs emissions. The calculation formula of the optimization effect percentage E provides an accurate evaluation of the wind speed adjustment effect. By comparing with the preset control threshold Kz, it is determined whether the optimization has achieved the expected goal. If the optimization effect percentage E at a certain moment of wind speed S is greater than the control threshold Kz, it means that the optimization is successful and the optimized wind speed can continue to be maintained. If the optimization effect percentage E is less than or equal to the control threshold Kz, it means that the optimization has not achieved the expected goal and the iterative adjustment mechanism needs to be started. The iterative adjustment mechanism will cyclically execute steps S2 to S4 based on the wind speed S after each optimization until the optimization achieves the expected effect. Through this implementation method, this stage realizes dynamic feedback and optimization iteration of wind speed optimization adjustment, ensuring that the VOCs emission control system can be flexibly adjusted and optimized according to the effect of wind speed adjustment during actual operation.

[0105] Example 6

[0106] Specifically: S5 includes S51, S52 and S53;

[0107] S51. After the wind speed S is optimized and adjusted, S1 and S2 are re-executed to output the second VOCs predicted concentration Ycnd2 for a second time, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl at a specific wind speed within a certain time range;

[0108] The total VOCs emission Zpfl is calculated and output by the following algorithm formula;

[0109] ;

[0110] Where Zpfl (S t ) represents the total VOCs emissions obtained from the optimized wind speed S at time t, Ycnd2 t (S t ) represents the second VOCs predicted concentration obtained by optimizing the wind speed S at time t, and △t represents the time interval.

[0111] S52. Set the target total VOCs emission Zpfl target , and the total VOCs emission Zpfl (S t ), perform difference calculation and output the difference value Gap;

[0112] The difference value Gap is calculated and output by the following algorithm formula;

[0113] .

[0114] S53. Perform a secondary comparative evaluation based on the output result of the difference value Gap to analyze the total VOCs emission of the exhaust gas emission channel after the wind speed optimization adjustment. The specific evaluation contents are as follows;

[0115] When the difference value Gap≤0, it means that the total emission is normal after the wind speed S is optimized and adjusted, and the current plan continues to be implemented;

[0116] When the difference value Gap>0, it means that the total emission is abnormal after the wind speed S is optimized and adjusted. At this time, the accounting server prompts to upgrade the equipment and adjust the processing technology to upgrade the VOCs treatment. For example, more efficient catalytic combustion devices such as RTO, activated carbon adsorption system or washing tower can be introduced to further reduce the VOCs concentration.

[0117] In this embodiment, the method re-executes steps S1 and S2 after optimizing and adjusting the wind speed S to obtain the secondary VOCs predicted concentration Ycnd2, and calculates the total VOCs emissions Zpfl at a specific wind speed by combining this concentration value with the set time range. The total VOCs emissions Zpfl is an important indicator for evaluating the effect of VOCs emission control, and can fully reflect the actual emission level of exhaust gas emissions after optimizing the wind speed. The application of the calculation formula can clearly show the emission dynamics of VOCs at different wind speeds. Next, by setting the target total VOCs emissions Zpfl target , calculate the difference between the actual calculated total VOCs emissions Zpfl and the target emissions to obtain the difference value Gap. As a key evaluation parameter, the difference value Gap can intuitively reflect the gap between the optimization measures and the target emissions, and provide a basis for further decision-making. A secondary comparative evaluation is conducted based on the difference value Gap to further analyze the total VOCs emissions. If the difference value Gap is less than or equal to zero, it means that the optimized VOCs emissions have met or are lower than the target value, indicating that the optimization is successful and the current emission control plan can continue to be implemented. If the difference value Gap is greater than zero, it means that the optimized VOCs emissions have failed to reach the target emission level. At this time, the system will automatically prompt for VOCs governance upgrades, and it is recommended to introduce more efficient equipment or adjust the production process, such as introducing RTO, activated carbon adsorption system or washing tower and other technical means to further reduce VOCs concentrations and ensure that emissions meet standards.

[0118] Example 7

[0119] See also Figure 1 and Figure 2 ,A VOCs emission accounting system for industrial enterprises, including a sensor acquisition module, a data modeling and relationship analysis module, a wind speed optimization module, an effect evaluation and iteration module, and a comprehensive analysis module;

[0120] The sensor acquisition module collects sensor data in real time by installing a sensor group at the exhaust emission channel, and transmits the sensor data group to the accounting server through the wireless communication network;

[0121] The data modeling and relationship analysis module receives the sensor data set in real time from the accounting server, pre-processes the sensor data set, obtains the digital data set, establishes a multivariate model, extracts the digital data set and inputs it into the multivariate model to calculate and output the VOCs concentration value Ycnd;

[0122] The wind speed optimization module calculates and outputs the objective function F(S) based on the obtained VOCs concentration value Ycnd and combines it with the optimization algorithm to optimize and adjust the wind speed of the exhaust gas emission channel;

[0123] After re-optimization and adjustment, the effect evaluation and iteration module calculates the change in VOCs concentration before and after the wind speed adjustment, outputs the optimization effect percentage E, and sets the control threshold Kz. A preliminary comparative evaluation is then conducted between the control threshold Kz and the optimization effect percentage E to analyze the optimization adjustment effect of the exhaust gas emission channel wind speed. An iterative adjustment mechanism is then generated based on the evaluation results.

[0124] After the comprehensive analysis module is upgraded through VOC control, S1 and S2 are executed for the second time to output the second VOCs predicted concentration Ycnd2, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl within a certain time range. The total VOCs emissions Zpfl are then differentially calculated with the target value to obtain the difference value Gap, and the VOCs emissions are analyzed based on the data results of the difference value Gap.

[0125] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for calculating VOCs emissions from industrial enterprises, characterized by: The following steps are involved: S1. Installing a sensor group at the exhaust gas emission channel to collect sensor data groups in real time and transmitting the sensor data groups to the accounting server via a wireless communication network; Said S1 includes S11 and S12; S21, collecting sensor data in real time by installing a sensor group inside the exhaust gas emission channel; The sensor group includes a wind speed sensor, a temperature sensor and a humidity sensor; The sensor data set includes wind speed S, temperature T and humidity H; S12, using the built-in communication modules of all sensors in the sensor group, wirelessly connecting the accounting server to the sensor group by setting up a 5G wireless communication network, and then transmitting the collected sensor data group to the accounting server; S2. Receive the sensor data set in real time in the accounting server, pre-process the sensor data set, obtain a digital data set, establish a multivariate model, extract the digital data set and input it into the multivariate model to calculate and output the VOCs concentration value Ycnd; Said S2 includes S21 and S22; S21. Receive a sensor data set in real time on the accounting server and preprocess the sensor data set, including denoising, data cleaning, and normalization. After the preprocessing, the sensor data set is timestamped according to the sensor acquisition time to obtain a digital data set. The digital data set includes the wind speed S at time t t , temperature T at time t t and humidity H at time t t ; S22, using regression analysis method to calculate the multivariate model of VOCs concentration and wind speed S, temperature T and humidity H, and extracting digital data group to input into the multivariate model, calculate and output VOCs concentration value Ycnd, and predict the wind speed S at the current time t t , temperature T at time t t and humidity H at time t t VOCs concentration under the conditions; The VOCs concentration value Ycnd is calculated and output by the following multivariable model; Where, Ycnd t Indicates the VOCs concentration value Ycnd at time t; represents the quadratic term of wind speed S at time t, represents the quadratic term of the temperature T at time t, represents the quadratic term of humidity H at time t, S t T t Indicates the cross-influence value of wind speed and temperature; S t H t Indicates the cross-influence value of wind speed and humidity; T t H t Indicates the cross-influence value of temperature and humidity; α0 represents the baseline regression coefficient of VOCs concentration in exhaust gas when the independent variable is zero, α1, α2 and α3 represent the wind speed S at time t t , temperature T at time t t and humidity H at time t t The regression coefficients of α4, α5 and α6 represent the wind speed at time t. Temperature T at time t t and humidity H at time t t The regression coefficients of the quadratic terms, α7, α8, and α9 represent the regression coefficients of the cross-influence values ​​of wind speed and temperature, wind speed and humidity, and temperature and humidity, respectively; S3. Based on the obtained VOCs concentration value Ycnd, combined with the optimization algorithm, calculate and output the objective function F(S), and optimize and adjust the wind speed S of the exhaust gas emission channel; Said S3 includes S31; S31. Based on the obtained VOCs concentration value Ycnd, an optimization algorithm is used to calculate and output an objective function F(S). The obtained objective function F(S) is sent to the exhaust gas emission channel control system through the calculation server to optimize and adjust the wind speed S of the exhaust gas emission channel. The objective function F(S) is calculated and outputted by the following optimization algorithm: Where, Ycnd t (S t ) represents the predicted VOCs concentration value under wind speed S at time t, n represents the number of samples, Ycnd target represents the target VOCs concentration, β represents the trade-off factor, S min Indicates the lower limit of wind speed; S4. After re-optimization and adjustment, calculate the change in VOCs concentration before and after the wind speed adjustment, output the optimization effect percentage E, and set a control threshold Kz. Perform a preliminary comparative evaluation of the control threshold Kz and the optimization effect percentage E, analyze the optimization adjustment effect of the exhaust gas emission channel wind speed, and generate an iterative adjustment mechanism based on the evaluation results. Said S4 includes S41 and S42; S41. After optimizing and adjusting the wind speed S of the exhaust gas emission channel, re-execute S1 and S2 to output the optimized VOCs concentration value Ycnd after optimization and adjustment. after , when the optimized VOCs concentration value Ycnd after Combined with the VOCs concentration value Ycnd before optimization and adjustment, the optimization effect percentage E is calculated and output to analyze the effect of wind speed S optimization; The optimization effect percentage E is calculated and outputted by the following algorithm formula: Where, E(S t ) represents the optimization effect percentage under wind speed S at time t, Ycnd after (S t ) represents the optimized VOCs concentration value under wind speed S at time t; S42, according to the control target of VOCs emission, set the control threshold Kz, and set the optimization effect percentage E(S) at the wind speed S at time t. t ) and the control pre-threshold Kz for preliminary comparative evaluation, and analyze the VOCs concentration emission optimization after the wind speed S in the exhaust gas emission channel is optimized. The specific evaluation contents are as follows; When the optimization effect percentage E(S t )>control threshold Kz, it indicates that the optimization is successful and the optimized wind speed S is maintained; When the optimization effect percentage E(S t )≤control threshold Kz, it indicates that the optimization fails to achieve the goal, and an iterative adjustment mechanism is executed at this time. The iterative adjustment mechanism iteratively executes steps S2 to S4 based on the wind speed S after each optimization adjustment until the wind speed optimization adjustment is successful and stops the iteration; S5. After the VOC control is upgraded, S1 and S2 are executed again to output the second VOCs predicted concentration Ycnd2. A comprehensive calculation is performed to output the total VOCs emissions Zpfl within a certain time range. The total VOCs emissions Zpfl are then subtracted from the target value to obtain the difference value Gap. The VOCs emissions are analyzed based on the data results of the difference value Gap. Said S5 includes S51, S52 and S53; S51. After the wind speed S is optimized and adjusted, S1 and S2 are re-executed to output the second VOCs predicted concentration Ycnd2 for a second time, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl at a specific wind speed within a certain time range; The total VOCs emission Zpfl is calculated and output by the following algorithm formula; Where Zpfl(S t ) represents the total VOCs emissions obtained from the optimized wind speed S at time t, Ycnd2 t (S t ) represents the second VOCs predicted concentration obtained by optimizing the wind speed S at time t, and △t represents the time interval; S52. Set the target total VOCs emission Zpfl target , and the total VOCs emission Zpfl(S t ), perform difference calculation and output the difference value Gap; The difference value Gap is calculated and outputted by the following algorithm formula; Gap=Zpfl(S t )-Zpfl target ; S53. Perform a secondary comparative evaluation based on the output result of the difference value Gap to analyze the total VOCs emission of the exhaust gas emission channel after the wind speed optimization adjustment. The specific evaluation contents are as follows; When the difference value Gap≤0, it means that the total emission is normal after the wind speed S is optimized and adjusted, and the current plan continues to be implemented; When the difference value Gap>0, it means that the total emission is abnormal after the wind speed S is optimized and adjusted. At this time, the accounting server prompts to upgrade the equipment and process adjustments to upgrade the VOCs control.

2. A VOCs emission accounting system for industrial enterprises, applied to the VOCs emission accounting method for industrial enterprises according to any one of claim 1, characterized in that: It includes sensor acquisition module, data modeling and relationship analysis module, wind speed optimization module, effect evaluation and iteration module and comprehensive analysis module; The sensor acquisition module collects sensor data in real time by installing a sensor group at the exhaust gas emission channel, and transmits the sensor data group to the accounting server through a wireless communication network; The data modeling and relationship analysis module receives the sensor data group in real time in the accounting server, pre-processes the sensor data group, obtains the digital data group, extracts the digital data group and inputs it into the multivariate model by establishing a multivariate model, and calculates and outputs the VOCs concentration value Ycnd; The wind speed optimization module calculates and outputs the objective function F(S) based on the obtained VOCs concentration value Ycnd and combines it with the optimization algorithm to optimize and adjust the wind speed of the exhaust gas emission channel; After the re-optimization adjustment, the effect evaluation and iteration module calculates the change in VOCs concentration before and after the wind speed adjustment, outputs the optimization effect percentage E, and sets a control threshold Kz. After a preliminary comparison and evaluation of the control threshold Kz and the optimization effect percentage E, the optimization adjustment effect of the exhaust gas emission channel wind speed is analyzed, and an iterative adjustment mechanism is generated based on the evaluation results; After the comprehensive analysis module is upgraded through VOC governance, S1 and S2 are executed for the second time to output the second VOCs predicted concentration Ycnd2, and a comprehensive calculation is performed to output the total VOCs emissions Zpfl within a certain time range. The difference between the total VOCs emissions Zpfl and the target value is calculated to obtain the difference value Gap, and the VOCs emissions are analyzed based on the data results of the difference value Gap.

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