Multi-unit coupled VOCs treatment evaluation method and system

Abstract

The invention discloses a multi-unit coupled VOCs treatment evaluation method and system, and solves the problems that VOCs treatment multi-unit combination scheme design and evaluation depend on an experience-driven method and a numerical simulation tool method and have many defects. The method comprises the following steps: constructing a database including VOCs emission scenes and VOCs types of each scene, randomly generating multiple different types of proportion simulation cases, scene air volumes and concentration gradient ranges in each scene, determining each VOCs treatment multi-unit combination and an effect evaluation function thereof, establishing a simulation emission working condition combination, and calculating the VOCs treatment effect of each multi-unit combination. And calculating effect values of all the multi-unit combinations of all the simulated emission working conditions, and selecting an optimal effect value to obtain a corresponding VOCs treatment scheme. And inputting a to-be-evaluated emission working condition, performing simulated emission working condition matching by the database, and obtaining a VOCs treatment scheme corresponding to the matched simulated emission working condition. According to the method, the design cost of small and medium-sized enterprises in the actual VOCs emission working condition is reduced, the process verification time is shortened, and the industrial VOCs treatment scheme standardization problem is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of VOCs treatment, in particular to a multi-unit coupled VOCs treatment evaluation method and system. BACKGROUND

[0002] Volatile organic compounds (VOCs) are an important class of pollutants, as key precursors of ozone (O3) and PM2.5, they are the core object of atmospheric pollution control. Their environmental hazards not only lie in the formation of photochemical smog and haze, but also directly threaten human health, such as benzene, formaldehyde and other components have been explicitly listed as strong carcinogens.

[0003] VOCs waste gas is complex in composition (such as benzene series, esters, halogenated hydrocarbons, etc.), with large fluctuations in concentration and diverse emission conditions, so single treatment technology cannot meet the actual needs, and multi-unit combined process becomes an inevitable choice. Taking the combination of "condensation + absorption + adsorption" as an example: condensation method can efficiently recover high-concentration and high-boiling-point VOCs components (such as oily solvents), reducing the load of subsequent treatment; absorption method is aimed at water-soluble substances (such as alcohols, ketones), which can be quickly separated by chemical reagents; the adsorption unit uses activated carbon or molecular sieve to adsorb low-concentration and large-volume waste gas, which is concentrated after desorption and sent to the combustion or recovery device, greatly improving the treatment efficiency and reducing energy consumption. This combination mode breaks through the limitations of single technology, for example, condensation method cannot handle low-concentration waste gas, and pure adsorption will produce waste activated carbon secondary pollution, the combined process realizes the effect of "1+1>2" through synergistic optimization. In addition, the combined technology takes into account the economy and environmental protection: the condensate recovered solvent can be directly reused, the absorption liquid can be regenerated and recycled, and the adsorption unit reduces carbon emissions through waste heat utilization, which meets the requirements of circular economy. With the increasingly stringent emission standards and the complex industry conditions, multi-unit combined technology has become an inevitable path for VOCs deep treatment due to its flexibility, high efficiency and resource potential.

[0004] The current design and evaluation of multi-unit combined VOCs treatment scheme mainly rely on two types of technical schemes: experience-driven method and numerical simulation tool. Traditional experience-driven method is based on industry manuals, design specifications and expert experience, which matches the characteristics of VOCs components (such as boiling point, polarity, explosion limit) with the applicability rules of treatment units (condensation, adsorption, RCO, etc.), combined with simple formula to estimate equipment parameters.

[0005] The experience-driven method has significant defects: first, the rules are rigid and difficult to adapt to complex working conditions (such as multi-component mixing and temperature and humidity fluctuations); second, the calculation is rough, and the energy consumption and cost estimation rely on static empirical coefficients (such as the fixed adsorption capacity of activated carbon being 0.3 g / g), ignoring dynamic factors such as equipment performance degradation (such as adsorbent degradation) and market fluctuations (such as time-of-use pricing); third, the synergy is poor, and when multiple units are combined, only the efficiency of each unit is simply added, without considering the interaction between units (such as the increase in humidity after condensation leading to a decrease in adsorbent performance). In addition, the manual scheme comparison and selection takes several days and is highly dependent on expert level, making it difficult for small and medium-sized enterprises to bear the cost of technical consultation.

[0006] Another is the design scheme evaluation method based on numerical simulation of simulation tools, such as Aspen Plus, COMSOL multi-physical field model, or CFD (Computational Fluid Dynamics) software. This type of tool can accurately predict removal efficiency, energy consumption, and equipment size by establishing mass-energy balance equations for VOCs treatment units and simulating internal flow and reaction processes using finite element analysis. Some commercial software integrates a cost database, which can correlate equipment parameters with procurement prices.

[0007] However, the numerical simulation scheme still has three major bottlenecks: first, the calculation complexity is high, and multi-unit coupled simulation requires several hours to several days, which cannot meet the real-time decision-making needs of engineering; second, the knowledge threshold is high, and modeling requires a good understanding of thermodynamics, fluid mechanics, and software operation, limiting the application of grassroots environmental protection personnel. In addition, existing tools rely on idealized assumptions (such as steady-state working conditions and uniform flow fields), making it difficult to handle non-steady-state load fluctuations commonly encountered in practice (such as intermittent emissions and concentration mutations), resulting in a deviation of more than 30% between simulation results and actual operation.

[0008] The current patent application (application number 202410280734.3), entitled "A Control Method and System for a VOC Waste Purification and Treatment Device," discloses a method that includes acquiring historical VOC waste gas purification control data and performing independent control parameter analysis based on control objectives for different waste gas types to form independent control parameter relationship data corresponding to different waste gas types; based on the historical VOC waste gas purification control data, performing constraint control relationship analysis based on control objectives for different constraint control parameters to form constraint control parameter relationship data corresponding to different constraint control parameters; acquiring real-time VOC waste gas information to be treated and real-time control objective information, and combining the independent control parameter relationship data and constraint control parameter relationship data to perform real-time control parameter analysis to determine real-time control parameter data. This patent uses historical data analysis combined with real-time control objectives to determine control parameter data. While analysis based on historical data can indeed compensate for accuracy deviations caused by simulation, the generalization ability of historical data is limited. Furthermore, the proposed patented method system only proposes adjustments and designs to optimize control data for a specific process combination. It cannot determine the use of a specific treatment unit or unit combination before the device is built. Summary of the Invention

[0009] This invention primarily addresses the shortcomings of relying on experience-driven methods and numerical simulation tools in the design and evaluation of multi-unit VOCs treatment schemes. It provides a multi-unit coupled VOCs treatment evaluation method and system. Based on different VOCs emission conditions, the system simulates and calculates the equipment value and energy consumption of multi-unit coupled schemes for different VOCs, constructing a simulation database of multi-unit coupled VOCs treatment schemes. Using a data matching algorithm, it ultimately selects multi-unit coupled VOCs treatment schemes that meet the equipment's energy consumption value and treatment effect based on the VOCs to be treated. The system uses a database of VOCs treatment cases or VOCs treatment simulation cases as its core, integrating typical VOCs emission characteristic parameters (components, concentration, air volume, humidity) to determine the feasibility of multi-unit combined schemes. Then, based on intelligent judgment logic such as equipment value assessment functions and energy consumption assessment functions, it constructs a hierarchical analysis database based on VOCs emission characteristics. Finally, by inputting the emission conditions of the target enterprise, the system automatically matches the case database, calculates the deviation of the target enterprise's VOCs emission conditions, and ultimately outputs an optimized scheme that combines economic efficiency and compliance.

[0010] The above-mentioned technical problems of the present invention are mainly solved by the following technical solution: a multi-unit coupled VOCs treatment assessment method, comprising the following steps: Construct a VOCs simulation case database, including: Define VOC emission scenarios and the types of VOCs in each scenario, and randomly generate multiple simulation cases with different proportions of each type in each scenario. Set the air volume and concentration gradient values ​​for the VOCs emission scenario. Obtain the combinations of multiple units for VOCs treatment, and define the effect evaluation function for each combination of multiple units. Establish simulated emission conditions, calculate the effect value of all multi-unit combinations under each simulated emission condition, and select the VOCs treatment scheme corresponding to the optimal effect value. Input the emission conditions to be evaluated, and the database will simulate the emission conditions to obtain the VOCs treatment schemes and effect values ​​corresponding to the simulated emission conditions. This is the best treatment scheme and effect estimate for the emission conditions to be evaluated.

[0011] This invention covers VOCs treatment scenarios across four major chemical industries. By constructing a dynamic simulation case database of VOCs component proportions, airflow gradients, concentration gradients, and random proportions of multiple components, it overcomes the limitations of traditional manual design. This invention establishes a three-dimensional mapping relationship between VOCs emission conditions, process parameters, and energy consumption value. It generates simulation conditions on the order of 10^5 using a Monte Carlo algorithm, covering 95% of the discrete scenarios in traditional experience-based case libraries. The method improves the adaptability of complex actual VOCs emission conditions to the simulation case database, enhancing equipment selection accuracy and the accuracy of equipment value and energy consumption assessment. The application of this invention reduces design costs for SMEs in actual VOCs emission conditions, shortens process verification cycles to within minutes, effectively solves the standardization problem of VOCs treatment solutions in the industry, and provides a quantifiable decision support tool.

[0012] This invention provides a system method for establishing a multi-unit coupled VOCs simulation case database for different VOCs emission conditions in actual production. The emission conditions to be evaluated are matched with the simulated emission conditions, and the VOCs cases with the smallest operating condition error are selected. At this time, the corresponding multi-unit coupled VOCs treatment process is the optimal process. The equipment value and energy consumption calculation of the corresponding simulation case are the effect evaluation of the emission conditions to be evaluated. This process is the system method for selecting and evaluating multi-unit coupled VOCs treatment schemes that meet the equipment energy consumption value and treatment effect.

[0013] This invention uses a database of VOCs treatment cases or simulated VOCs treatment cases as its core, integrating typical VOCs emission characteristic parameters (components, concentration, air volume). Based on intelligent discrimination logic using equipment value functions and equipment energy consumption functions, it performs hierarchical analysis according to VOCs emission characteristics to determine the feasibility of multi-unit combination schemes. Specifically, by inputting simulated VOCs emission conditions, a database matching algorithm is used to construct a database of simulated emission conditions, optimal schemes, and effect evaluation data. When the emission conditions to be evaluated are input, the algorithm automatically matches simulated emission conditions from the simulated case database based on the principle of minimum error, ultimately outputting an optimized scheme that combines economic efficiency and compliance.

[0014] As a preferred embodiment, the setting of VOCs emission scenarios and the types of VOCs in each scenario includes: Industry emission scenarios are classified according to industrial level, and VOCs emission scenarios under each category are determined. Based on the actual VOCs emission range, determine the types of VOCs in each VOCs emission scenario.

[0015] To confirm VOCs emission scenarios, based on the current development status of the chemical industry, emission scenarios are categorized by industrial level into four main categories: petrochemical industry emission scenarios, fine chemical industry emission scenarios, solvent-using industry emission scenarios, and industrial coating industry emission scenarios. The VOCs emission scenarios under each category are then determined. Specifically, the petrochemical industry emission scenarios include: petroleum refining and petrochemical derivative production VOCs emission scenarios. The fine chemical industry emission scenarios include: coal chemical industry, coking, pharmaceutical, pesticide manufacturing, and paint and ink manufacturing industry emission scenarios. The solvent-using industry emission scenarios include: packaging and printing, electronic product manufacturing, and dyeing VOCs emission scenarios. The industrial coating industry emission scenarios include: automobile manufacturing, furniture manufacturing, machinery manufacturing, steel structure manufacturing, metal shipbuilding, and container manufacturing VOCs emission scenarios. Based on the actual VOCs emission range and relevant data, the types of VOCs within each industry emission scenario are determined.

[0016] As a preferred embodiment, the method of randomly generating multiple simulation cases with different proportions in various scenarios includes: Based on the types of VOCs actually emitted in different VOCs emission scenarios, multiple simulation cases with different proportions of VOCs types are randomly generated in the same VOCs emission scenario. The proportions of all types of VOCs are generated randomly and added together to be 1.

[0017] This solution randomly generates multiple simulation cases with different VOCs percentages in each scenario based on the actual VOCs emitted. For example, 100 simulation cases with different VOCs percentages are randomly generated. In each simulation case, the percentage content of all VOCs is randomly generated, and the sum of the percentage contents of all VOCs is 1. Furthermore, a stratified sampling method is used to ensure that the various VOCs are evenly distributed in the randomized simulation cases within the percentage gradients of 0%~5%, 5%~10%, 10%~15%, 15%~20%, 20%~25%, ..., 85%~90%, 90%~95%, and 95%~100%.

[0018] As a preferred embodiment, the setting of the air volume and concentration gradient range for VOCs emission scenarios includes: The air volume range is determined based on the actual VOCs situation. The air volume range is divided into several gradient intervals, and the average value of each gradient interval is calculated as the gradient value of each air volume gradient interval. The concentration range is determined based on the actual situation of VOCs, and the concentration range is divided into several gradient intervals. The average value of each gradient interval is calculated as the gradient value of each concentration gradient interval.

[0019] Air volume is an important emission parameter in VOCs simulation case studies. Based on current real-world VOCs cases, the air volume range in current VOCs treatment cases is 500-8000 Nm³. 3 To better cover real-world operating conditions, the airflow range was set between 200-10000 Nm / hr. 3 The airflow range is divided into several gradients, preferably five gradients, ranging from 200 to 2160 Nm / hr. 3 / hr, 2160~4120Nm 3 / hr, 4120~6080 Nm 3 / hr, 6080~8040 Nm 3 / hr, 8040~10000 Nm 3 / hr, the above airflow gradient intervals are divided into an optimal scheme, which can be modified as needed. The mean value for each gradient interval is calculated to be 1180 Nm. 3 / hr、3140 Nm 3 / hr, 5100 Nm 3 / hr、7060 Nm 3 / hr and 9020Nm 3 / hr serves as the representative value for each airflow gradient range.

[0020] Similarly, VOC emission concentration is an important emission parameter in VOC simulation case studies. Based on current real-world VOC cases, the concentration range in current VOC treatment cases is between 300 and 50,000 ppmv. To better cover real-world case conditions, the concentration range is determined to be between 200 and 50,000 ppmv, and the air volume range is divided into several gradients, preferably five gradients: 200-700 ppmv, 700-1400 ppmv, 1400-4000 ppmv, 4000-10000 ppmv, and 10000-50000 ppmv. The above concentration gradient ranges are considered an optimal scheme, and the mean values ​​of each gradient range are calculated as 400 ppmv, 1000 ppmv, 3000 ppmv, 7500 ppmv, and 25000 ppmv, respectively, serving as representative values ​​for each concentration gradient range.

[0021] As a preferred embodiment, the acquisition of the VOCs treatment multi-unit combination includes: Based on all current multi-unit VOCs processing schemes, obtain the multi-unit combinations of each VOCs processing scheme.

[0022] This solution summarizes and analyzes all current multi-unit VOCs treatment solutions, and statistically identifies all existing multi-unit combinations for VOCs treatment. These multi-unit combinations include, but are not limited to: Adsorption-desorption + condensation Adsorption-desorption + absorption Adsorption-desorption + RCO Condensation + Adsorption-Desorption + Absorption Condensation + Adsorption-Desorption + Cryogenic Condensation + Adsorption-Desorption + RCO Absorption + Adsorption-Desorption + Absorption Absorption + Adsorption-Desorption + Condensation Absorption + condensation + adsorption-desorption + RCO.

[0023] This solution acquires all multi-unit combinations of VOCs treatment schemes, models them in chemical simulation software such as Aspenplus, calculates the energy flow of each unit's treated materials based on VOCs emission characteristic parameters, evaluates the treatment effect and energy loss, and achieves accurate calculation of process value and energy consumption under specific simulation disclosure.

[0024] As a preferred option, the performance evaluation function for multi-unit combination includes the value evaluation function for multi-unit combination equipment and the energy consumption evaluation function for multi-unit combination equipment; Among them, the multi-unit combined equipment valuation function calculates the value of each piece of equipment required under the current multi-unit combination. The energy consumption assessment function for multi-unit combined equipment calculates the energy consumption required for each piece of equipment under the current multi-unit combination.

[0025] As a preferred option, based on the simulation case, a combination of simulated emission conditions is established using combinations of air volume concentration gradient values; Based on simulated emission conditions, the total value of the equipment is calculated according to the multi-unit combined equipment value assessment function; The total energy consumption value of the equipment is obtained by summing the products of the energy consumption of each piece of equipment and the value of the unit energy consumption, and the operating time of the equipment within the life cycle of the device. The combined effect of multiple units is the sum of the total value of the equipment and the total value of the equipment energy consumption; The multi-unit combination corresponding to the minimum effect value was selected as the VOCs treatment scheme for the simulated emission conditions.

[0026] This scheme simulates emission conditions and calculates the value and energy consumption results of the combined emission conditions under the multi-unit combination by using the multi-unit combined equipment value assessment function and the equipment energy consumption assessment function, thus obtaining the final effect value.

[0027] The effect value of a multi-unit combination is the sum of the total equipment value of all required equipment and the total equipment energy consumption value under the multi-unit combination. The total equipment energy consumption value is the sum of the energy consumption values ​​of all required equipment. The specific calculation of equipment energy consumption value is the product of the sum of equipment energy consumption, the unit energy consumption value, and the operating time of the equipment within the device's life cycle.

[0028] Based on the above steps, the effect values ​​of all multi-unit combinations are calculated for all simulated emission conditions, and the optimal VOCs treatment scheme is obtained. The simulated emission conditions and the corresponding optimal VOCs treatment scheme are stored in the VOCs simulation case database.

[0029] As a preferred approach, simulated emission condition matching is performed using a database, including: Input the emission conditions to be evaluated, including the components and proportions of the emissions; To determine the corresponding VOCs emission scenarios for the emission scenarios to be assessed, calculate the similarity between the emission conditions to be assessed and various simulated cases of VOCs emission scenarios. The simulated case with the minimum similarity is selected as the matching simulated case.

[0030] This scheme includes emission conditions, including the types and proportions of emission components. Based on the emission scenario, a corresponding VOCs emission scenario is determined. Then, based on the types and proportions of emission components, the scheme compares the closest simulation case among various random proportions of VOCs emission cases under the VOCs emission scenario. Specifically, the similarity between the types and proportions of emission components and the simulation cases with various random proportions under the VOCs emission scenario is calculated. This similarity is calculated using the root mean square error (RMSE), and the simulation case with the smallest similarity value is selected as the matching simulation case. This intelligent matching method, employing the principle of minimizing the RMSE, improves the efficiency of filtering actual operating conditions and database simulation cases.

[0031] As a preferred approach, simulated emission condition matching is performed using a database, including: Input the emission conditions to be evaluated, including VOCs emission volume and concentration; The input air volume is compared with the air volume gradient interval to determine the gradient interval in which the input air volume is located, and the gradient value of the gradient interval is used as the matching air volume gradient value. The input concentration is compared with the concentration gradient interval to determine the gradient interval in which the input concentration is located, and the gradient value of the gradient interval is used as the matching concentration gradient value.

[0032] The method analyzes equipment and energy consumption value data to accurately match the optimal combination logic of units such as condensation, adsorption, absorption, and RCO, and calculates the return on investment and carbon emission compliance. This avoids the blindness of manual selection and significantly improves the scientific, economic, and compliant nature of the governance solution.

[0033] A multi-unit coupled VOCs governance assessment system, comprising: Database Unit: Define VOCs emission scenarios and VOCs types in each scenario, randomly generate multiple simulation cases with different proportions of different types in each scenario, set the air volume and concentration gradient range of VOCs emission scenarios, obtain the combination of multiple units for VOCs treatment, set the effect evaluation function of each combination of multiple units, establish a combination of simulated emission conditions, calculate the effect value of all combinations of multiple units under each simulated emission condition, select the optimal effect value to obtain the corresponding VOCs treatment scheme; Matching Unit: Input the emission conditions to be evaluated, and the database will perform simulated emission condition matching to obtain the VOCs treatment solution corresponding to the matched simulated emission conditions.

[0034] This invention's system integrates pollutant characteristic databases, technical parameter libraries, and equipment and energy consumption value analysis data. It can accurately match the optimal combination logic of units such as "condensation," "adsorption," "absorption," and "RCO," and calculate the return on investment and carbon emission compliance, avoiding the blindness of manual selection. This system significantly improves the scientific, economic, and compliant nature of treatment solutions, helping enterprises achieve emission reduction targets at minimal cost. It also provides standardized assessment tools for regulators, promoting the upgrading of VOCs treatment.

[0035] Therefore, the advantages of the present invention are: This invention covers VOCs treatment scenarios across four major chemical industries. By constructing a dynamic simulation case database of VOCs component proportions, airflow gradients, concentration gradients, and random proportions of multiple components, it overcomes the limitations of traditional manual design. A three-dimensional mapping relationship between VOCs emission conditions, process parameters, and energy consumption value is established. A Monte Carlo algorithm generates simulation conditions on the order of 10^5, covering 95% of the discrete scenarios in traditional experience case libraries. The method improves the adaptability of complex actual VOCs emission conditions to the simulated VOCs solution database, enhancing equipment selection accuracy and the accuracy of equipment value and energy consumption assessment. The application of this invention reduces design costs for SMEs in actual VOCs emission conditions, shortens process verification cycles to within minutes, and effectively solves the standardization problem of VOCs treatment solutions in the industry. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating one method of the present invention. Detailed Implementation

[0037] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0038] Example:

[0039] This embodiment presents a multi-unit coupled VOCs governance assessment method, such as... Figure 1 As shown, it includes the following steps: S1. Construct a VOCs simulation case database, including: S11. Define VOC emission scenarios and the types of VOCs in each scenario, and randomly generate multiple simulation cases with different proportions of each type in each scenario. S12. Set the air volume and concentration gradient range for VOCs emission scenarios. S13. Obtain the multi-unit combination of VOCs treatment and set the effect evaluation function of each multi-unit combination; S14. Establish simulated emission condition combinations, calculate the effect value of all multi-unit combinations under each simulated emission condition, and select the optimal effect value to obtain the corresponding VOCs treatment scheme.

[0040] S2. Input the emission conditions to be evaluated, and the database will perform simulated emission condition matching to obtain the VOCs treatment solution corresponding to the matched simulated emission conditions.

[0041] This invention covers VOCs treatment scenarios across four major chemical industries. By constructing a dynamic simulation case database of VOCs component proportions, airflow gradients, concentration gradients, and random proportions of multiple components, it overcomes the limitations of traditional manual design. This invention establishes a three-dimensional mapping relationship between VOCs emission conditions, process parameters, and energy consumption, and generates simulation conditions on the order of 10^5 using the Monte Carlo algorithm, covering 90% of the discrete scenarios in traditional experience case libraries.

[0042] This method improves the adaptability of complex actual VOCs emission conditions to simulated VOCs solution databases, enhancing the accuracy of equipment selection and the evaluation of equipment value and energy consumption. The application of this invention reduces design costs for SMEs in actual VOCs emission conditions, shortens process verification cycles to within minutes, effectively solves the standardization problem of VOCs treatment solutions in the industry, and provides a quantifiable decision support tool.

[0043] This invention provides a database of multi-unit coupled VOCs simulation cases for different VOCs emission conditions in actual production. It matches the emission conditions to be evaluated with the simulated emission conditions, selecting the VOCs case with the smallest operating error. The corresponding multi-unit coupled VOCs treatment process is then considered the optimal process. The equipment value and energy consumption calculation of the corresponding simulated case constitute the effect evaluation of the emission conditions to be evaluated. This process is a systematic method for selecting and evaluating multi-unit coupled VOCs treatment schemes that meet the requirements of equipment energy consumption value and treatment effect. This invention uses a database of VOCs treatment cases or VOCs treatment simulation cases as its core, integrating typical VOCs emission characteristic parameters (components, concentration, air volume) to determine the feasibility of multi-unit combination schemes. Based on intelligent discrimination logic using equipment value functions and equipment energy consumption functions, hierarchical analysis is performed according to VOCs emission characteristics. In other words, by inputting the VOCs emission conditions of the simulated components, a database of simulated emission conditions, optimal solutions, and effect evaluation data is constructed through a database matching algorithm. When the emission conditions to be evaluated are input, the simulated emission conditions are automatically matched to the simulated case database based on the principle of minimizing error, and finally, an optimized solution that is both economical and compliant is output.

[0044] As a preferred embodiment, VOCs emission scenarios and VOCs types in each scenario are defined, including: S111. Classify industry emission scenarios according to industrial level and determine the VOCs emission scenarios under each category.

[0045] To establish a VOCs simulation case database, the VOCs emission scenarios must first be determined. Based on the current development status of the chemical industry, industry emission scenarios are classified according to industrial level, and divided into four categories: petrochemical industry emission scenarios, fine chemical industry emission scenarios, solvent-using industry emission scenarios, and industrial coating industry emission scenarios.

[0046] The VOCs emission scenarios under each category are identified. Specifically, the petrochemical industry emission scenarios include: oil refining and petrochemical derivative production. The fine chemical industry emission scenarios include: coal chemical, coking, pharmaceutical, pesticide manufacturing, and paint and ink manufacturing. The solvent-using industry emission scenarios include: packaging and printing, electronic product manufacturing, and dyeing. The industrial coating industry emission scenarios include: automobile manufacturing, furniture manufacturing, machinery manufacturing, steel structure manufacturing, metal shipbuilding, and container manufacturing.

[0047] S112. Determine the types of VOCs in each VOCs emission scenario based on the actual VOCs emission range.

[0048] Based on the actual VOCs emission range and relevant data, determine the types of VOCs in each industry emission scenario.

[0049] As a preferred embodiment, multiple simulation cases with different proportions are randomly generated in each scenario, including: S113. Based on the actual VOCs emitted in different VOCs emission scenarios, multiple simulation cases with different VOCs types and their proportions are randomly generated within the same VOCs emission scenario. In this embodiment, 100 simulation cases with different VOCs types and their proportions are preferably generated randomly. The percentage content of all VOCs types is randomly generated, and the random percentage content of all VOCs types is added together to equal 1. A stratified sampling method is used to ensure that in the multiple simulation cases generated randomly, the various VOCs types are evenly distributed in the proportion gradient of 0%~5%, 5%~10%, 10%~15%, 15%~20%, 20%~25%, ..., 85%~90%, 90%~95%, 95%~100%.

[0050] As a preferred embodiment, the air volume and concentration gradient range for the VOCs emission scenario are set, including: S121. Determine the air volume range based on the actual VOCs situation, divide the air volume range into several gradients, and calculate the average value of each gradient interval as the representative value of each air volume gradient interval.

[0051] Air volume is an important emission parameter in VOCs simulation case studies. Based on current real-world VOCs cases, the air volume range in current VOCs treatment cases is 500-8000 Nm³. 3 To better cover real-world operating conditions, the airflow range was set between 200-10000 Nm / hr. 3 The airflow range is divided into several gradients, preferably five: 200~2160 Nm3 / hr, 2160~4120 Nm3 / hr, 4120~6080 Nm3 / hr, 6080~8040 Nm3 / hr, and 8040~10000 Nm3 / hr. This airflow gradient range is considered a preferred scheme and can be modified as needed. The mean values ​​for each gradient range are calculated as follows: 1180 Nm3 / hr, 3140 Nm3 / hr, 5100 Nm3 / hr, 7060 Nm3 / hr, and 9020 Nm3 / hr, serving as representative values ​​for each airflow gradient range.

[0052] S122. Determine the concentration range based on the actual situation of VOCs, divide the concentration range into several gradients, and calculate the average value of each gradient interval as the representative value of each concentration gradient interval.

[0053] Similarly, VOC emission concentration is an important emission parameter in VOC simulation case studies. Based on current real-world VOC cases, the concentration range in current VOC treatment cases is between 300 and 50,000 ppmv. To better cover real-world case conditions, the concentration range is determined to be between 200 and 50,000 ppmv, and the air volume range is divided into several gradients, preferably five gradients: 200-700 ppmv, 700-1400 ppmv, 1400-4000 ppmv, 4000-10000 ppmv, and 10000-50000 ppmv. The above concentration gradient ranges are considered an optimal scheme, and the mean values ​​of each gradient range are calculated as 400 ppmv, 1000 ppmv, 3000 ppmv, 7500 ppmv, and 25000 ppmv, respectively, serving as representative values ​​for each concentration gradient range.

[0054] As a preferred embodiment, obtaining the combination of multiple units for VOCs treatment includes: S131. Based on all current VOCs processing schemes with multi-unit processing, obtain the multi-unit combination of each VOCs processing scheme.

[0055] This paper summarizes and analyzes all current multi-unit VOCs treatment solutions, and statistically identifies all existing multi-unit combinations for VOCs treatment. These multi-unit combinations include, but are not limited to: Adsorption-desorption + condensation Adsorption-desorption + absorption Adsorption-desorption + RCO Condensation + Adsorption-Desorption + Absorption Condensation + Adsorption-Desorption + Cryogenic Condensation + Adsorption-Desorption + RCO Absorption + Adsorption-Desorption + Absorption Absorption + Adsorption-Desorption + Condensation Absorption + condensation + adsorption-desorption + RCO.

[0056] All multi-unit combinations of VOCs treatment schemes are obtained, and models are built in chemical simulation software such as Aspenplus. Based on the VOCs emission characteristic parameters, the energy flow of the treated materials of each unit is calculated, the treatment effect and energy loss are evaluated, and the process value and energy consumption under specific simulation are accurately calculated.

[0057] As a preferred embodiment, an effect evaluation function for each multi-unit combination is defined, including: S132. The effect evaluation function of multi-unit combination includes the value evaluation function of multi-unit combination equipment and the energy consumption evaluation function of multi-unit combination equipment.

[0058] Define a value assessment function for each piece of equipment in a multi-unit combination scenario. This function is used to calculate the value of each piece of equipment required under the current multi-unit combination.

[0059] (1) Adsorbent dosage, adsorption tank design parameters and value assessment: According to Aspen Plus chemical simulation, the amount of VOCs that the adsorbent needs to adsorb, q, can be determined. eff (kg / hr), VOCs gas flow rate is Q (Nm³). 3 / hr), gas concentration is C (kg / m³) 3 The bulk density of the adsorbent is ρ (kg / m³). 3 The adsorption ratio h (mg / g) of the adsorbent (generally taken as 40 mg / g), the gas flow rate V of the adsorption bed (range 0.1~0.6 m / s), τ0 is the bed breakthrough time, τ B τ0 is the time for complete bed penetration, the bed diameter is D, the adsorption saturation height is H0, the bed design height is H, E represents the ratio of the adsorption capacity still present in the transmission zone at the breakthrough point to the total adsorption capacity of the adsorbent, E is taken as 0.4~0.6 (generally 0.5), τ0 is taken as 35 min in this system, τ0 is... B / τ0 = 1.3~2.8 (generally taken as 2.2), where W is the amount of adsorbent used. The value assessment function for adsorbent usage is expressed as follows:

[0060] Among them, M Adsorbent The value of adsorbent dosage.

[0061] Assuming the number of devices connected in parallel is n, the number of adsorption tanks in series is m, the tank thickness is d (generally 10mm), and Φ is a price correction parameter (between 2 and 3), the adsorption tank value function is:

[0062] Among them, M Equipment Value of the adsorption tank.

[0063] (2) Calculation of heat exchanger heat exchange area and value assessment According to Aspen Plus simulation, the heat transfer power P (W) of the condenser module can be obtained. With U = 30 in Aspen Plus, the heat transfer area S given by Aspen Plus can be calculated. a According to the process flow, the heat exchange temperature difference t is known. Δ Given a heat transfer coefficient of K and a thermal conductivity of 0.060 kW / (m*K) for 304 stainless steel, the heat transfer area is S. C Φ is the price correction parameter (between 1 and 2), typically taken as 1.5. The valuation function is then expressed as follows:

[0064] Among them, M ex For the value of the heat exchanger, S() indicates that the larger of the two areas is taken as the calculated value.

[0065] (3) Refrigeration unit selection and value assessment According to Aspen Plus simulations, the required cooling capacity P for VOCs gaseous fluid to cool to a corresponding temperature can be obtained by passing through a heat exchanger. e The power required to cool the refrigerant to a temperature lower than that of VOCs gas is Pc (kJ / hr), where the cooling capacity utilization rate is h (typically taken as 0.9), and the compressor power is P. cold (kJ / hr), EER is the energy efficiency ratio (generally taken as 2.3), and the value assessment function is expressed as: Calculate compressor power:

[0066] This section establishes a correspondence between different refrigeration unit models and their corresponding values. Different models of refrigeration units correspond to different compressor powers and refrigeration temperatures, and thus different values. Refrigeration equipment is selected based on the compressor power and refrigeration temperature (corresponding temperature). In actual industrial projects, VOCs condensation is typically performed at -30°C and -115°C, but the cooling capacity at -115°C is generally provided by liquid nitrogen. The value of the refrigeration unit is determined based on the selected refrigerant type.

[0067] (4) Vacuum pump selection and value assessment Based on Aspen Plus simulations and calculation manuals, the vacuum pump can provide the theoretical pumping capacity Q under operating conditions. l (m 3 / s), d is the diameter of the vent pipe, usually taken as 15cm, the nitrogen purging velocity is u, usually taken as 15m / s, and the leakage gas volume is Q. S (m 3 / s), δP is the pressure difference between the inlet and outlet pumps (Pa), and the pump efficiency h (usually 50%~70%) is generally taken as 60%. Then the value assessment function is expressed as: Calculate the power of the vacuum pump as P gas As shown in the formula below:

[0068] Establish a correspondence between different models of vacuum pumps and their corresponding values. Different models of vacuum pumps correspond to different pumping capacities and pressure differences, and thus to different values. Select a model based on the pump's theoretical pumping capacity and ultimate pressure difference, and obtain the corresponding value based on the selected vacuum pump model.

[0069] (5) Absorption tower construction parameters and value assessment According to Aspen Plus simulations, the required number of theoretical plates N (calculated based on 1m of packing per theoretical plate) and the gas throughput V of the absorption tower can be determined. S (m 3 / s), the air pumping speed is u (m / s, ranging from 0.7 to 1 m / s, generally taken as 1 m / s), and the diameter of the gas inlet pipe is d. in (Generally DN15 is chosen), Q in Q out Inlet and outlet gas volume flow rate (m³) 3 / s), the residence time of the liquid in the bottom of the absorption tower is τ w (Value range 5-10 min, generally 10 min is used), the diameter of the absorption tower is D (m), and V is the total volume of liquid in the tower bottom (m³). 3 The liquid level in the bottom of the absorption tower is H. w (m), the total surface area of ​​the absorption tower shell is S at (m 3The absorption tower's value assessment function is expressed as: The surface area of ​​the absorption tower shell can be calculated using the following formula:

[0070] Where the thickness of the absorption tower body is d (generally 10mm), the density of stainless steel is ρs, and Φ is a price correction parameter (between 2 and 3), then the value of the absorption tower is:

[0071] (6) Construction parameters and value assessment of phase separator Phase separators are classified as vertical or horizontal; this example uses a vertical phase separator. Aspen Plus simulations show that the maximum gas volumetric flow rate V in the phase separator is... Gmax (m 3 / h), liquid volumetric flow rate V in the phase separator L (m 3 / h), gas flow rate is u e (m / s, generally taken as 0.7-1m / s), the phase separator diameter is D (m), the residence time of the liquid in the phase separator is τ (range 0.5-1h), and the liquid height is H. L (m), the total surface area of ​​the phase separator shell is S sp (m 3 The valuation function is expressed as follows: The total surface area of ​​the phase separator housing is calculated using the following formula:

[0072] The phase separator shell thickness is d (typically 10mm), and the density of the stainless steel is ρ. s If Φ is the price correction parameter (between 2 and 3), then the value of the phase separator is:

[0073] (7) Liquid pump selection and value assessment Based on Aspen Plus simulations and calculation manuals, vacuum pumps and liquid pumps are needed for applications such as refrigerant delivery and solvent transport. The flow rate of the liquid pump depends on the height H of the solvent absorption tower. tower N tower The theoretical plate number and the solvent flow rate Q of the absorber are given. tower and solvent density r sol The pump power for refrigerant transfer depends on the refrigerant flow rate Q calculated from the simulation. cold Heat exchanger height H e and refrigerant density r cold The pump efficiency h is expressed by the following value assessment function: The power of a liquid pump is calculated using the following formula:

[0074] Establish a correspondence between different models of liquid pumps and their corresponding values. Different models of liquid pumps correspond to different flow rates and ranges, and thus to different values. Select a model based on the flow rate and range, and obtain the corresponding value based on the selected liquid pump model.

[0075] (8) RCO unit selection and value assessment According to Aspen Plus simulations, the flow rate Q of the nitrogen-VOCs mixed gas removed in the adsorption tank can be determined. m (Nm 3 / hr), the space rate of the RCO catalyst W sp 5000~40000 (m) 3 / m 3 ×h -1 The unit price of the non-precious metal catalyst is 2.5w / m. 3 The space velocity of non-precious metal catalysts is 7500 m / s. 3 / m 3 ×h -1 The value assessment function is calculated as follows: Calculate the required volumetric amount of RCO catalyst:

[0076] Calculate the value of RCO catalysts:

[0077] Calculate the value of the RCO device:

[0078] (9) Valuation of Peripheral Equipment If the required VOCs volume to be treated is Q, then the value assessment function for peripheral equipment (pipelines, valves, instruments, mechanical instruments only, no remote control) is expressed as:

[0079] Define an energy consumption evaluation function for each device in a multi-unit combination scenario. This function is used to calculate the required energy consumption of each device in the current multi-unit combination scenario.

[0080] (1) Energy consumption assessment of refrigeration unit According to Aspen Plus simulations, the required cooling capacity P for VOCs gaseous fluid to cool to a corresponding temperature can be obtained by passing through a heat exchanger. e(kJ / hr), while the power required to cool the refrigerant to a temperature lower than that of the VOCs gas is P. C (kJ / hr), where the cooling capacity utilization rate is h (generally taken as 0.9), the compressor power is Pcold (kJ / hr), and EER is the energy efficiency ratio (generally taken as 2.3). The energy consumption evaluation function is expressed as follows:

[0081] (2) Energy consumption assessment of vacuum pump The pumping volume Q of the vacuum pump can be obtained from the design parameters of the adsorption tank. l (m 3 / s), d is the diameter of the vent pipe, usually taken as 15cm, the nitrogen purging velocity is u, usually taken as 15m / s, and the leakage gas volume is Q. S (m 3 / s), δP is the pressure difference between the inlet and outlet of the pump (Pa), and the pump efficiency h (usually 50%~70%) is generally taken as 60%. Then the vacuum pump power is P. gas The energy consumption assessment function is expressed as follows:

[0082] (3) Liquid pump energy consumption assessment Aspen Plus simulations show that liquid pumps are needed for applications such as refrigerant delivery and solvent absorption transport. The pump's flow rate depends on the height H of the solvent absorption tower. tower N tower The theoretical plate number and the solvent flow rate Q of the absorber are given. tower and solvent density r sol The pump power for refrigerant transfer depends on the refrigerant flow rate Q calculated from the simulation. cold Heat exchanger height H e and refrigerant density r cold If the pump efficiency is h, then the energy consumption evaluation function is as follows:

[0083] (4) RCO supplemental energy consumption assessment According to Aspen Plus simulations, the enthalpy of combustion of all substances and the energy W0 released after complete combustion of all VOCs can be obtained, and the catalytic combustion reaction temperature is t. r The typical values ​​are 300°C, gas flow rate Q, device operating time T, initial gas temperature t0, and specific heat of the gas C. gas Since each RCO start-up is for treating desorbed gas, the catalyst is heated to the appropriate temperature during startup. The temperature that the RCO catalyst can maintain is t. c The volume of catalyst used is V c The catalyst density is rc Energy replenishment is required. +1 If the gas flow rate is too high, the energy required to maintain the gas temperature is W. +2 The energy consumption assessment function is as follows:

[0084] As a preferred embodiment, a simulated emission condition combination is established, the effect value of all multi-unit combinations under each simulated emission condition is calculated, and the optimal effect value is selected to obtain the corresponding VOCs treatment scheme, including: S141. Combine the set simulation cases, air volume gradient values, and concentration gradient values ​​to establish simulated emission conditions. All simulated emission conditions constitute a simulated emission condition combination.

[0085] S142. For each simulated emission condition, calculate its effect value under all multi-unit combinations, including: Based on simulated emission conditions, the total value of the equipment is calculated according to the multi-unit combined equipment value assessment function; The total energy consumption value of the equipment is obtained by summing the products of the energy consumption of each piece of equipment and the value of the unit energy consumption, and the operating time of the equipment within the life cycle of the device. The combined effect of multiple units is the sum of the total value of the equipment and the total value of the equipment energy consumption; Specifically, the formula for calculating the effect value of multi-unit combinations is as follows:

[0086] Among them, M eqi (i=1,2,3,…n) represents the value of all devices, P i (i=1,2,3,…n) represents the energy consumption of all devices, t i (i=1,2,3,…n) represents the operating time of all devices within the device's lifecycle, C i (i=1,2,3,…n) represents the unit energy price.

[0087] S143. Select the multi-unit combination corresponding to the minimum effect value as the VOCs treatment scheme for the simulated emission conditions.

[0088] The minimum effective value is selected from all multi-unit combinations calculated for the simulated emission condition, and the multi-unit combination corresponding to the minimum effective value is taken as the optimal VOCs treatment scheme for the simulated emission condition.

[0089] As a preferred embodiment, step S2 specifically includes: S21. Input the emission conditions to be evaluated. The emission conditions include the components, component proportions, concentrations, and air volume data of VOC emissions.

[0090] S22. To compare and determine the VOCs emission scenarios in the corresponding simulation case database for the emission scenarios to be assessed, calculate the similarity between the emission conditions to be assessed and various simulation cases of VOCs emission scenarios.

[0091] Specifically, the emission scenarios to be evaluated are compared with the established VOCs emission scenarios to determine the matching VOCs emission scenarios.

[0092] S23. Input the components and proportions of VOC emissions. Based on the types and proportions of emission components, compare the data with the closest simulation case among 100 simulation cases with random proportions in the VOCs emission scenario. That is, calculate the similarity between the types and proportions of emission components and the simulation cases with multiple random proportions in the VOCs emission scenario. This similarity is calculated using the root mean square error. The similarity calculation formula is:

[0093] Where δ represents the similarity, i represents the type of substance, and x represents the similarity score. i For the proportion of VOCs types and components to be evaluated, y i To simulate the proportion of VOCs by type and component in the case, ∑y i =1.

[0094] The simulated case with the minimum similarity is selected as the matching simulated case.

[0095] S24. Input air volume and concentration data, compare the input air volume with the air volume gradient interval, determine the gradient interval in which the input air volume is located, and obtain the gradient value of the gradient interval as the matching air volume gradient value.

[0096] The input concentration is compared with the concentration gradient interval to determine the gradient interval in which the input concentration is located, and the gradient value of the gradient interval is used as the matching concentration gradient value.

[0097] S25. Determine the simulated emission conditions based on the matched simulation case, the matched air volume gradient value, and the matched concentration gradient value, and obtain the corresponding VOCs treatment solution based on the simulated emission conditions.

[0098] Ultimately, we obtain the equipment value and energy consumption calculation for specific components, concentrations, and air volumes under the emission scenarios to be evaluated, as well as the corresponding optimal VOCs treatment solutions.

[0099] This embodiment establishes a simulation database based on the multi-level VOCs emission scenario operating condition characteristics (emission scenario / component gradient / air volume gradient / concentration gradient). It generates 100 random operating conditions for typical scenarios in various industries through a random weighting algorithm. It evaluates various multi-unit combination schemes by combining equipment value function and energy consumption assessment function, and finally forms a data mapping relationship between VOCs multi-dimensional operating condition data and the best matching scheme.

[0100] By utilizing the principle of minimizing root mean square error to match the actual VOC operating conditions with the proportion of each component in the database, and by constructing a three-level screening system based on feature dimensions (component proportion / concentration gradient / airflow gradient), rapid retrieval of process solutions across operating conditions and joint evaluation of energy consumption value can be achieved.

[0101] By integrating multiple parameterized value calculation formulas and multiple dynamic energy consumption models of various equipment units, a process combination evaluation function model with nonlinear optimization capabilities is formed.

[0102] In another implementation of this embodiment, a VOCs neural network matching model is used to obtain a multi-unit coupled VOCs treatment scheme that meets the energy consumption value and treatment effect of the equipment by inputting the emission conditions to be evaluated. The specific process includes: collecting historical emission condition data, processing the collected data, establishing a mapping relationship between the historical emission condition data and the optimal treatment scheme and its energy consumption value to construct a training dataset, then constructing and training a neural network, obtaining a matching model after training, inputting the emission conditions to be evaluated into the model, and outputting the optimal treatment scheme and effect estimate for the emission conditions to be evaluated.

[0103] This embodiment also includes a multi-unit coupled VOCs governance assessment system for implementing the above method. The system includes: Database Unit: Define VOCs emission scenarios and VOCs types in each scenario, randomly generate multiple simulation cases with different proportions of different types in each scenario, set the air volume and concentration gradient range of VOCs emission scenarios, obtain the combination of multiple units for VOCs treatment, set the effect evaluation function of each combination of multiple units, establish a combination of simulated emission conditions, calculate the effect value of all combinations of multiple units under each simulated emission condition, select the optimal effect value to obtain the corresponding VOCs treatment scheme; Matching Unit: Input the emission conditions to be evaluated, and the database will perform simulated emission condition matching to obtain the VOCs treatment solution corresponding to the matched simulated emission conditions.

[0104] By integrating pollutant characteristic databases, technical parameter libraries, and equipment and energy consumption cost analysis data, the system can accurately match the optimal combination logic of units such as "condensation," "adsorption," "absorption," and "RCO," and calculate the return on investment and carbon emission compliance, avoiding the blindness of manual selection. This invention significantly improves the scientific, economic, and compliant nature of treatment solutions, helping enterprises achieve emission reduction targets at the lowest cost, and also providing standardized assessment tools for regulators, promoting the upgrading of VOCs treatment.

[0105] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for evaluating VOCs treatment of multi-cell coupling, characterized in that, The method comprises the following steps: The VOCs simulation case database is constructed, comprising: Set the VOCs emission scene and the VOCs species in each scene, and randomly generate a plurality of different species proportion simulation cases in each scene, Set the wind volume and concentration gradient value of the VOCs emission scene, Obtain the VOCs treatment multi-unit combination, and set the effect evaluation function of each multi-unit combination, Establish a simulation emission working condition combination, calculate the effect value of all multi-unit combinations under each simulation emission working condition, and select the optimal effect value to obtain the corresponding VOCs treatment scheme; Input the emission working condition to be evaluated, match the simulation emission working condition by the database, and obtain the VOCs treatment scheme and effect value corresponding to the matched simulation emission working condition.

2. The method of claim 1, wherein, The setting of the VOCs emission scene and the VOCs species in each scene comprises: Classify the industrial emission scene according to the industrial level, and determine the VOCs emission scene under each classification; According to the actual VOCs emission range, determine the VOCs species in each VOCs emission scene.

3. The method of claim 2, wherein: The random generation of a plurality of different species proportion simulation cases in each scene comprises: According to the actual VOCs emission in different VOCs emission scenes, a plurality of different VOCs species proportion simulation cases are randomly generated in the same VOCs emission scene, and the random proportion content of all species VOCs is generated and added to 1.

4. The method of claim 1 or 2 or 3, wherein: The setting of the wind volume and concentration gradient value of the VOCs emission scene comprises: According to the actual VOCs, determine the wind volume range, divide the wind volume range into several gradient intervals, and calculate the average value of each gradient interval as the gradient value of each wind volume gradient interval; According to the actual VOCs, determine the concentration range, divide the concentration range into several gradient intervals, and calculate the average value of each gradient interval as the gradient value of each concentration gradient interval.

5. The method of claim 1 or 2 or 3, wherein, The obtaining of the VOCs treatment multi-unit combination comprises: According to all the VOCs treatment schemes of the multi-unit, obtain the multi-unit combination of each VOCs treatment scheme.

6. The VOCs treatment evaluation method of claim 4, wherein: The effect evaluation function of the multi-unit combination comprises a multi-unit combination device value evaluation function and a multi-unit combination device energy consumption evaluation function; The multi-unit combination device value evaluation function calculates the value of each device required in the current multi-unit combination, The multi-unit combination device energy consumption evaluation function calculates the energy consumption of each device required in the current multi-unit combination.

7. The VOCs treatment evaluation method of claim 6, wherein: According to the simulation case, the wind volume and concentration gradient value combination establishes a simulation emission working condition combination; Based on the simulation emission working condition, the device value sum is calculated according to the multi-unit combination device value evaluation function; According to the product of the total sum of the energy consumption of each device and the value per unit energy consumption and the running time of the device in the life cycle of the device, the total sum of the energy consumption value of the device is obtained; The effect value of the multi-unit combination is the sum of the total sum of the device value and the total sum of the energy consumption value of the device; The multi-unit combination corresponding to the minimum effect value is selected as the VOCs treatment scheme of the simulation emission working condition.

8. The method of claim 7, wherein, The simulation emission working condition matching by the database comprises: Input the emission condition to be evaluated, including the components and proportions of the emissions; Determine the corresponding VOCs emission scenario for the emission condition to be evaluated, and calculate the similarity between the emission condition to be evaluated and the various simulation cases of the VOCs emission scenario; Select the simulation case corresponding to the minimum similarity value as the matching simulation case.

9. The method of claim 7, wherein, Database simulation emission condition matching, including: Input the emission condition to be evaluated, including the VOCs emission air volume and concentration; Compare the input air volume with the air volume gradient interval to determine the gradient interval in which the input air volume is located, and obtain the gradient value of the gradient interval as the matching air volume gradient value; Compare the input concentration with the concentration gradient interval to determine the gradient interval in which the input concentration is located, and obtain the gradient value of the gradient interval as the matching concentration gradient value.

10. A multi-cell coupled VOCs management evaluation system, implementing the method as claimed in any one of claims 1-9, characterized in that, Including: Database unit: set the VOCs emission scenario and the VOCs species in each scenario, randomly generate various simulation cases with different species proportions in each scenario, set the VOCs emission scenario air volume and concentration gradient range, obtain the VOCs treatment multi-unit combination, set the effect evaluation function of each multi-unit combination, establish the simulation emission condition combination, calculate the effect value under all multi-unit combinations of each simulation emission condition, and select the optimal effect value to obtain the corresponding VOCs treatment scheme; Matching unit: input the emission condition to be evaluated, perform database simulation emission condition matching, and obtain the VOCs treatment scheme and effect value corresponding to the matching simulation emission condition.

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