Graded purification method and system for organic waste gas in chemical workshop and computer equipment
By constructing a graded purification model for organic waste gas in chemical workshops and adjusting the waste gas distribution of purification units in real time, the problem of low efficiency in waste gas treatment in chemical workshops was solved, and a highly efficient and stable waste gas purification effect was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-10
- Publication Date
- 2026-03-13
AI Technical Summary
Existing organic waste gas treatment technologies in chemical workshops are insufficient to handle complex waste gas compositions and large concentration fluctuations. Multi-stage purification processes lack intelligence and load balancing, resulting in low purification efficiency.
A waste gas collection and purification model is constructed to generate a dynamic purification capacity surface. Data from the purification units is collected in real time, and the waste gas distribution ratio is dynamically adjusted to achieve adaptive purification.
It improves the efficiency and stability of waste gas purification, forms a self-optimizing cycle, adapts to changes in waste gas characteristics, and reduces energy waste.
Smart Images
Figure CN121648733A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of industrial waste gas treatment technology, and in particular to a method, system and computer equipment for graded purification of organic waste gas in a chemical workshop. Background Technology
[0002] Chemical plant waste gas is characterized by complex composition and large concentration fluctuations, posing limitations to existing treatment technologies. Single technologies such as adsorption, catalytic oxidation, or biological methods are insufficient to address all situations, while simple multi-technology cascading lacks intelligence and cannot adaptively adjust to dynamic changes in waste gas characteristics. Existing multi-stage purification processes focus only on specific equipment and parameters, failing to address load balancing and global optimization among the purification units. Although intelligent models are introduced, they are often not tightly integrated with specific waste gas purification processes, resulting in low purification efficiency. Summary of the Invention
[0003] This application provides a method for graded purification of organic waste gas in a chemical workshop, which aims to solve the aforementioned technical problems.
[0004] This application provides a method for graded purification of organic waste gas in a chemical workshop, including: Obtain the exhaust gas collection information of each purification unit to construct an exhaust gas collection and purification model; The waste gas collection and purification model is mapped to obtain a dynamic purification capacity surface, wherein the dynamic purification capacity surface is used to virtually characterize the real-time processing capacity and load status of each purification unit. A purification path is generated based on the waste gas collection information, and the purification path is mapped to the dynamic purification capability surface as a standard purification feature. The pretreated exhaust gas is sequentially passed through each of the purification units for graded purification, and exhaust gas data at the inlet and outlet of each purification unit are collected in real time. Based on the exhaust gas data, the exhaust gas collection and purification model is constructed in real time and mapped to the dynamic purification capacity surface to generate real-time purification features. A dynamic purification efficiency index is obtained by comparing the real-time purification characteristics with the standard purification characteristics. Based on the dynamic purification efficiency index, the distribution ratio of exhaust gas among the purification units is dynamically adjusted.
[0005] In an optional embodiment, the step of sequentially passing the pretreated exhaust gas through each of the purification units for graded purification, and collecting exhaust gas data at the inlet and outlet of each purification unit in real time, includes: Obtain the attribute information of each purification unit, and classify each purification unit into a primary adsorption purification module, a secondary catalytic oxidation module, or a tertiary biological purification module according to the attribute information; The pretreated waste gas is sequentially purified through a primary adsorption purification module, a secondary catalytic oxidation module, and a tertiary biological purification module, with real-time collection of waste gas data at the inlet and outlet of each module.
[0006] In an optional embodiment, the step of obtaining the exhaust gas collection information of each of the purification units to construct an exhaust gas collection and purification model, and mapping the exhaust gas collection and purification model to obtain a dynamic purification capability surface, includes: The location information of each waste gas emission point in the chemical workshop, the physical location of the purification unit, and the purification capacity are collected to construct a three-dimensional model of the workshop; The 3D model of the workshop is used as the waste gas purification model; Obtain the geometric center of the exhaust gas purification model, and construct a virtual sphere that can completely enclose all the purification units, using the purification unit farthest from the geometric center as the boundary. The exhaust gas purification model is mapped onto the surface of the virtual sphere to obtain the dynamic purification capacity surface, wherein the purification unit forms a capacity projection point on the dynamic purification capacity surface, and the capacity projection point is virtually associated with the purification processing capacity of each purification unit.
[0007] In an optional embodiment, the primary adsorption purification module uses an activated carbon fiber filter and monitors the adsorption saturation state information through a pressure sensor at a preset temperature. The secondary catalytic oxidation module uses a noble metal catalyst and calculates the real-time temperature adjustment under catalysis. The three-stage biological purification module uses a biological filter with composite microbial agents. When the hydrogen sulfide concentration in the exhaust gas exceeds the preset detection value, the enhanced aeration function is automatically activated.
[0008] In an optional embodiment, the step of obtaining the dynamic purification efficiency index by comparing the real-time purification characteristics and the standard purification characteristics includes: On the dynamic purification capability surface, the purification path is defined as a standard connection line; The actual path of the exhaust gas is obtained based on the real-time purification characteristics, and the actual path of the exhaust gas is defined as a real-time connection. Obtain the angle and distance difference data between the real-time connection and the standard connection; The dynamic purification efficiency index is calculated based on the angle and distance difference data.
[0009] In an optional embodiment, the step of dynamically adjusting the distribution ratio of exhaust gas among the purification units based on the dynamic purification efficiency index includes: Determine whether the dynamic purification efficiency index is within a preset range; If the dynamic purification efficiency index is not within the preset range and is higher than the maximum value of the preset range, the amount of waste gas inflow between each purification unit is adjusted. If the dynamic purification efficiency index is not within the preset range and is lower than the minimum value of the preset range, the efficiency deviation of each purification unit is obtained, the failed unit is located based on the efficiency deviation, and the cleaning or replacement command corresponding to the failed unit is triggered.
[0010] In an optional embodiment, the step of adjusting the amount of exhaust gas flowing into each of the purification units includes: Real-time status data of the capacity projection points of each purification unit on the dynamic purification capacity surface are obtained to calculate the capacity margin and efficiency weight of each purification unit. Based on the capacity margin and efficiency weight of each purification unit, and with the optimization objective of making the real-time purification characteristics converge to the standard purification characteristics, a flow allocation function is constructed. The flow distribution function is solved to obtain the waste gas inflow adjustment amount for each purification unit.
[0011] In an optional embodiment, the step of obtaining the efficiency deviation of each of the purification units and locating the failed unit based on the efficiency deviation includes: Real-time monitoring of pollutant concentrations at the inlet and outlet of each purification unit, and calculation of the actual purification efficiency of each purification unit; The actual purification efficiency of each purification unit is compared with the expected purification efficiency stored in the standard purification features, and the efficiency deviation is calculated. According to the direction of exhaust gas flow, determine in turn whether the efficiency deviation of each purification unit exceeds its allowable threshold and whether its inlet concentration is within the preset range. If the efficiency deviation of at least one of the purification units exceeds the threshold and its inlet concentration is within the preset range, then the unit is determined to be faulty. If the efficiency deviation of at least one of the purification units exceeds the threshold, but its inlet concentration is not within the preset range, then the preceding purification unit is traced back and determined to be a failed unit.
[0012] This application also provides a graded purification system for organic waste gas in a chemical workshop, including: A model building module is used to obtain the waste gas collection information of each purification unit to build a waste gas collection and purification model, wherein the waste gas collection information includes waste gas source, composition information and concentration information; The mapping module is used to map the exhaust gas collection and purification model to obtain a dynamic purification capability surface, wherein the dynamic purification capability surface is used to virtually characterize the real-time processing capacity and load status of each purification unit. The generation module is used to generate a purification path based on the source, composition and concentration information of the exhaust gas, and to map the purification path as a standard purification feature to the dynamic purification capability surface. The graded purification module is used to sequentially pass the pretreated waste gas through each of the purification units for graded purification, and to collect waste gas data at the inlet and outlet of each of the purification units in real time. A real-time construction module is used to construct the waste gas collection and purification model in real time based on the waste gas data, and map it to the dynamic purification capability surface to generate real-time purification features. The comparison module is used to compare the real-time purification features with the standard purification features to obtain a dynamic purification efficiency index; The dynamic adjustment module is used to dynamically adjust the distribution ratio of exhaust gas among the purification units based on the dynamic purification efficiency index.
[0013] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.
[0014] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.
[0015] The beneficial effects of this application are as follows: A waste gas collection and purification model is constructed based on the collected waste gas collection information, thereby integrating all waste gas-related information, virtually mapping the waste gas collection and purification model to obtain a dynamic purification capacity surface, and then planning the optimal purification path for waste gases of different sources and properties based on the waste gas collection information; after acquiring the actual waste gas, purification units are arranged sequentially according to the purification order to purify the waste gas; when the waste gas purification begins, waste gas data at the inlet and outlet of each purification unit is collected in real time, and the waste gas collection and purification model is constructed in real time based on this waste gas data, so as to deeply couple the waste gas purification process and the waste gas collection and purification model, that is, to collect waste gas data in real time. Mapping to the dynamic purification capability surface generates real-time purification characteristics. Comparing these characteristics with standard purification characteristics generates a dynamic purification efficiency index, revealing the gap between the actual purification efficiency and the standard purification efficiency. Based on this index, the distribution ratio of exhaust gas among the purification units is dynamically adjusted, making it independent of fixed operating parameters. Regardless of changes in exhaust gas characteristics, the real-time purification characteristics always converge towards the standard purification characteristics, resulting in a progressively smaller gap between the actual and standard purification efficiencies, eventually achieving consistency. This allows the system to approach ideal exhaust gas purification, forming a self-optimizing cycle that improves the efficiency and stability of exhaust gas purification. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.
[0017] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.
[0018] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.
[0019] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0020] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0021] like Figure 1-3 As shown, this application provides a method for graded purification of organic waste gas in a chemical workshop, comprising: S1. Obtain the exhaust gas collection information of each purification unit to construct an exhaust gas collection and purification model; S2. Map the exhaust gas collection and purification model to obtain a dynamic purification capability surface, wherein the dynamic purification capability surface is used to virtually characterize the real-time processing capacity and load status of each purification unit. S3. Generate a purification path based on the waste gas collection information, and map the purification path as a standard purification feature to the dynamic purification capability surface; S4. The pretreated exhaust gas is sequentially passed through each of the purification units for graded purification, and the exhaust gas data at the inlet and outlet of each purification unit is collected in real time. S5. Based on the exhaust gas data, the exhaust gas collection and purification model is constructed in real time and mapped to the dynamic purification capability surface to generate real-time purification features. S6. Based on the comparison between the real-time purification characteristics and the standard purification characteristics, a dynamic purification efficiency index is obtained; S7. Based on the dynamic purification efficiency index, dynamically adjust the distribution ratio of exhaust gas among the purification units.
[0022] As described in steps S1-S7 above, waste gas collection information can be collected by the gas concentration sensor, flow meter, pressure sensor, and other acquisition components of the purification unit. Based on the collected waste gas collection information, a waste gas collection and purification model is constructed, thereby integrating all waste gas-related information. The waste gas collection and purification model is then virtually mapped to obtain a dynamic purification capability surface. This dynamic purification capability surface includes the real-time processing capacity and load status of each purification unit. The real-time processing capacity refers to the effective purification efficiency that can be provided at a specific moment, considering both its own state and external conditions. This effective purification efficiency changes according to changes in its own state and external conditions. Real-time processing capacity is dynamic, encompassing both processing capacity and purification efficiency. Different purification units have different real-time processing capacities. In this application, purification units include adsorption units, catalytic oxidation units, and biological purification units. When a purification unit is an adsorption unit, the real-time processing capacity is the real-time processing capacity, representing the remaining capacity capable of adsorbing waste gas. When a purification unit is a catalytic oxidation unit, the real-time processing capacity is the remaining active sites of the catalyst. Although these active sites cannot be directly measured, their activity attenuation can be indirectly assessed through accumulated operating time, the total amount of high-concentration waste gas treated, and historical regeneration counts, thereby estimating their effective capacity. When a purification unit is a biological purification unit, the real-time processing capacity refers to the activity and degradation potential of the microbial population, which can be comprehensively judged by monitoring the pressure drop of the filter bed, the inlet and outlet concentration difference, and periodically detecting biomass. Purification efficiency can be calculated based on the concentration values at the inlet and outlet of each purification unit. The difference between the inlet and outlet concentrations, and the ratio of this difference to the inlet concentration, represents the purification efficiency. Therefore, the generated dynamic purification capacity surface can reflect the current purification status and remaining processing space of each purification unit. Based on the waste gas collection information, optimal purification paths are planned for waste gases of different sources and properties. For example, the purification path for high-concentration waste gas is preferentially a catalytic oxidation unit. The optimal purification path is used as a standard purification feature and mapped onto the dynamic purification capacity surface, thereby locking the standard purification feature onto the dynamic purification capacity surface. After acquiring the actual waste gas, it is pre-treated and then passed through purification units arranged in the purification order to purify the waste gas. In the prior art, when purifying waste gas, the equipment generally operates according to fixed operating parameters, thus failing to cope with changes in the purification process. In order to enable each purification unit to adaptively purify according to changes, this application collects waste gas data at the inlet and outlet of each purification unit in real time after the waste gas purification begins, and constructs the waste gas collection and purification model in real time based on the waste gas data to... The waste gas purification process and the waste gas collection and purification model are deeply coupled, that is, the waste gas data is mapped to the dynamic purification capability surface in real time to generate real-time purification characteristics. Therefore, the dynamic purification capability surface includes standard purification characteristics and real-time purification characteristics. By comparing the two, a dynamic purification efficiency index is generated, which shows the gap between the actual purification efficiency and the standard purification efficiency. Based on the dynamic purification efficiency index, the distribution ratio of waste gas among the purification units is dynamically adjusted, no longer relying on fixed operating parameters. No matter how the waste gas characteristics change, the real-time purification characteristics are always brought closer to the standard purification characteristics, so that the gap between the actual purification efficiency and the standard purification efficiency of the subsequent waste gas becomes smaller and smaller, eventually reaching consistency. This allows the waste gas purification to approach the ideal state, forming a self-optimizing cycle and improving the efficiency and stability of waste gas purification.
[0023] In one embodiment, step S4, which involves sequentially passing the pretreated exhaust gas through each of the purification units for graded purification and collecting exhaust gas data at the inlet and outlet of each purification unit in real time, includes: S41. Obtain the attribute information of each purification unit, so as to classify each purification unit into a primary adsorption purification module, a secondary catalytic oxidation module or a tertiary biological purification module according to the attribute information. S42. The pretreated waste gas is sequentially purified through a primary adsorption purification module, a secondary catalytic oxidation module, and a tertiary biological purification module, and the waste gas data at the inlet and outlet of each module are collected in real time.
[0024] As described in steps S41-S42 above, the number of purification units can be set according to actual needs. The attribute information includes adsorption attributes, catalytic oxidation attributes, and biological purification attributes. Therefore, they can be divided into a primary adsorption purification module, a secondary catalytic oxidation module, or a tertiary biological purification module according to their attributes. After the waste gas is pretreated, it is first physically adsorbed by the primary adsorption purification module, which is generally used to purify most medium- and high-concentration VOCs (Volatile Organic Compounds). The adsorbed waste gas includes medium- and low-concentration gases, and may also include recalcitrant / high-concentration gases. For recalcitrant / high-concentration gases, catalytic combustion can be carried out through the secondary catalytic oxidation module to generate harmless carbon dioxide and water. For medium- and low-concentration gases after adsorption, biodegradation can be carried out through the tertiary biological purification module. It is also used to remove trace amounts of easily biodegradable VOCs and odorous substances (such as hydrogen sulfide, mercaptans, etc.) that remain after the first two stages (primary adsorption purification module and secondary catalytic oxidation module) treatment, ensuring that the waste gas is odorless and ultimately meets emission standards. This application enables the understanding of the purification status of each module by real-time collection of inlet and outlet waste gas data for each module. The number of purification units in each module can be the same or different. For example, a primary adsorption purification module may include two adsorption purification units, using two parallel activated carbon adsorption tanks, and may also include a backup adsorption purification unit in case other adsorption purification units fail or have insufficient treatment capacity. A secondary catalytic oxidation module may include one or more catalytic oxidation purification units, such as one main catalytic reactor and one backup catalytic reactor. A tertiary biological purification module may include one or more biological purification units, such as two parallel biological filters. Furthermore, the number of primary adsorption purification modules, secondary catalytic oxidation modules, or tertiary biological purification modules can be customized according to requirements. For example, the purification system may include primary adsorption purification module A, primary adsorption purification module B, secondary catalytic oxidation module C, secondary catalytic oxidation module D, and tertiary biological purification module E. When pretreated waste gas is sequentially purified through the primary adsorption purification module, secondary catalytic oxidation module, and tertiary biological purification module, the number and order of purification units and modules can be set according to the characteristics of the waste gas, thereby maximizing purification efficiency.
[0025] In one embodiment, step S3, which involves acquiring the exhaust gas collection information of each purification unit to construct an exhaust gas collection and purification model, and mapping the exhaust gas collection and purification model to obtain a dynamic purification capability surface, includes: S31. Collect the location information of each waste gas emission point in the chemical workshop, the physical location of the purification unit and the purification capacity to construct a three-dimensional model of the workshop; S32. Use the workshop 3D model as the waste gas purification model; S33. Obtain the geometric center of the exhaust gas purification model, and construct a virtual sphere that can completely enclose all the purification units, using the purification unit farthest from the geometric center as the boundary. S34. Map the exhaust gas purification model onto the surface of the virtual sphere to obtain the dynamic purification capacity surface, wherein the purification unit forms a capacity projection point on the dynamic purification capacity surface, and the capacity projection point is virtually associated with the purification processing capacity.
[0026] As described in steps S31-S34 above, the waste gas collection information includes, but is not limited to: location information of each waste gas emission point, physical location of the purification unit, pipeline connection information, purification capacity, waste gas source, composition information, and concentration information; waste gas emission points include reactor exhaust ports and storage tank breather valves, and each purification unit includes adsorption tanks, catalytic oxidation furnaces, biological filters, etc. By acquiring the waste gas collection information, these data are preprocessed, and a 3D model of the workshop is constructed using 3D modeling software or a custom program. In the 3D model of the workshop, independent nodes are created for each waste gas emission point and purification unit, and their attribute information is associated. The attribute information of the waste gas emission point includes, but is not limited to: emission point number, waste gas source, main pollutant components, design emission concentration, emission temperature, and emission air volume; the attribute information of the purification unit includes, but is not limited to: unit number, type (adsorption, catalytic, biological), ideal treatment air volume, etc. The attributes include ideal removal efficiency, maximum allowable inlet concentration, operating temperature, pressure drop, catalyst / adsorbent type, and replacement cycle. The attribute information also includes connectivity relationships, i.e., the path of exhaust gas from the emission point through pipelines to the purification unit. A 3D workshop model is used as the exhaust gas purification model, with the geometric center of the model as the midpoint and the purification unit furthest from the geometric center as the boundary, to construct a virtual sphere that completely encloses all purification units. The exhaust gas purification model is then mapped onto the surface of the virtual sphere to obtain a dynamic purification capacity surface. This dynamic purification capacity surface can convert abstract exhaust gas collection information into concrete, visualized 3D model data, thereby facilitating the accurate reproduction of the spatial relationships of the purification system in virtual space. The purification units form capacity projection points on the dynamic purification capacity surface, and these projection points are virtually associated with the purification capacity of each purification unit. Capacity projection points refer to the multi-dimensional real-time status information of the purification unit, including remaining processing capacity, instantaneous purification efficiency, current operating load, operating parameters, and other information. More preferably, it also includes the path decision points of the exhaust gas flow. These path decision points are key nodes in the pipeline connection information, such as the mapping of three-way valves and diverter valves in the virtual model. When the optimal flow path of the exhaust gas is generated, virtual adjustment commands can be sent to these path decision points. These virtual adjustment commands will be immediately converted into physical signals to drive the actual electric valves or dampers on site to perform actions, thereby changing the actual flow direction of the exhaust gas.
[0027] In one embodiment, the primary adsorption purification module uses an activated carbon fiber filter and monitors the adsorption saturation state information through a pressure sensor at a preset temperature. The secondary catalytic oxidation module uses a noble metal catalyst, and the real-time temperature adjustment under catalysis is calculated. The calculation formula is as follows: T=T0+k ln( + 1); Where T represents the real-time adjusted temperature, T0 represents the base set temperature, k represents the correction coefficient related to catalyst activity, and C represents the real-time VOCs concentration monitoring value; C ref This indicates the reference concentration set for VOCs; The three-stage biological purification module uses a biological filter with composite microbial agents. When the hydrogen sulfide concentration in the exhaust gas exceeds the preset detection value, the enhanced aeration function is automatically activated.
[0028] In one embodiment, step S6, which compares the real-time purification characteristics with the standard purification characteristics to obtain the dynamic purification efficiency index, includes: S61. On the dynamic purification capability surface, the purification path is defined as a standard connection line; S62. Obtain the actual path of the exhaust gas based on the real-time purification characteristics, and define the actual path of the exhaust gas as a real-time connection. S63. Obtain the angle and distance difference data between the real-time connection and the standard connection; S64. Calculate the dynamic purification efficiency index based on the angle and distance difference data, wherein the calculation formula is: ; Among them, the This represents the dynamic purification efficiency index. This represents the baseline efficiency constant for purification in a chemical workshop. This represents the sum of the distance differences between all real-time connections and the standard connection. This represents the sum of the angle differences between all real-time connections and the standard connection. This indicates the elimination of the zero constant.
[0029] As described in steps S61-S64 above, it should be noted that when generating a purification path based on the waste gas collection information, it can be obtained by matching according to a matching rule library. This matching rule library specifies the adaptation relationship between different types of pollutants and purification unit types. The matching rule library includes at least the following: high-concentration, macromolecular, non-polar VOCs are preferentially matched with primary adsorption purification units; recalcitrant, highly toxic, small-molecule VOCs are preferentially matched with secondary catalytic oxidation units; and easily biodegradable, water-soluble toxic substances (such as hydrogen sulfide and ammonia) are preferentially matched with tertiary biological purification units. Based on the currently collected waste gas collection information and the matching rule library, a suitable purification unit type is assigned to each major pollutant component. Based on the real-time processing capacity and real-time load status of each purification unit, a theoretically optimal purification path that maximizes pollutant purification efficiency is generated as a standard path. This path is represented on the dynamic purification capacity surface as a standard line connecting the projection points of the relevant purification unit capacity. This standard line and its spatial relationship with each projection point together constitute the standard purification feature. The actual purification characteristics are constructed in real time based on the exhaust gas data in the exhaust gas collection and purification model and mapped onto the dynamic purification capacity surface. Therefore, the actual purification characteristics include the actual connection lines and their spatial relationship with each projection point. In this way, the dynamic purification efficiency index can be calculated based on the angle and distance difference data between the real-time connection lines and the standard connection lines. The dynamic purification efficiency index can be used to determine whether the purification efficiency of the purification unit has reached the ideal state, which facilitates the dynamic adjustment of the distribution ratio of exhaust gas among the purification units.
[0030] In one embodiment, step S7, which dynamically adjusts the distribution ratio of exhaust gas among the purification units based on the dynamic purification efficiency index, includes: S71. Determine whether the dynamic purification efficiency index is within a preset range; S72. If the dynamic purification efficiency index is not within the preset range and is higher than the maximum value of the preset range, adjust the amount of waste gas inflow between each purification unit. S73. If the dynamic purification efficiency index is not within the preset range and is lower than the minimum value of the preset range, then obtain the efficiency deviation of each purification unit, locate the failed unit based on the efficiency deviation, and trigger the cleaning or replacement command corresponding to the failed unit.
[0031] As described in steps S671-S73 above, in the prior art, once the purification efficiency reaches the ideal value, it is generally kept in that state of operation. This can easily lead to unbalanced load or energy waste. For example, this could be a high-energy-consuming catalytic oxidation unit treating waste gas that should be treated by a low-energy-consuming biological unit. Based on this, in this embodiment, when the dynamic purification efficiency index is higher than the maximum value of the preset range, the waste gas inflow is redistributed. This ensures high efficiency while optimizing energy consumption and balancing equipment load, avoiding overuse of some purification units, and saving energy and reducing consumption. When the purification efficiency is lower than the minimum value of the preset range, the specific failed unit is accurately located, which can quickly pinpoint the cause of the fault, avoid a complete shutdown of the purification system, and greatly shorten the time for the system to resume effective operation. Furthermore, the purification system can automatically trigger cleaning or replacement commands and switch in conjunction with backup purification units, ensuring the continuity and stability of the chemical production process.
[0032] In one embodiment, step S72, which adjusts the amount of exhaust gas inflow between each of the purification units, includes: S721. Obtain the real-time status data of the capacity projection points of each purification unit on the dynamic purification capacity surface to calculate the capacity margin and efficiency weight of each purification unit. S722. Based on the capacity margin and efficiency weight of each purification unit, and with the optimization objective of making the real-time purification characteristics converge to the standard purification characteristics, construct a flow allocation function; S723. Solve the flow distribution function to obtain the waste gas inflow adjustment amount for each purification unit.
[0033] As described in steps S721-S723 above, capacity margin refers to the remaining processing capacity (including remaining adsorption capacity, catalyst activity, microbial activity, etc.) and load of the purification unit in its current state; efficiency weight refers to the contribution weight of the purification unit to the overall purification efficiency in its current state; the formula for calculating capacity margin is: ; Among them, the Represents the i-th capability margin, the This represents the weighting coefficient corresponding to the load margin, where L represents the current load rate. The weighting coefficient represents the remaining processing capacity of the purification unit. An indicator representing the remaining processing capacity of a purification unit typically ranges from 0 to 1. The weighting coefficient corresponding to the purification energy consumption is described in the following text. This indicates the current unit purification energy consumption. This indicates the maximum permissible unit purification energy consumption.
[0034] The formula for calculating the efficiency weight is: ; Among them, the Represents the i-th efficiency weight, the This indicates the actual removal rate of pollutants by the purification unit at the current moment; the... This represents the average current purification efficiency of all comparable purification units of the same type. This represents the average current unit purification energy consumption of all comparable purification units of the same type.
[0035] The role of capacity margin is to measure how much spare capacity a purification unit has in its current state to handle new loads. The role of efficiency weight is to prioritize directing waste gas to purification units with high efficiency and low energy consumption during waste gas allocation, thereby optimizing the overall system performance. Therefore, waste gas can be preferentially allocated to purification units with high efficiency weights. Based on this principle, a flow allocation function can be constructed to make the real-time purification characteristics converge to the standard purification characteristics. Specifically, the current flow rate can be set as Qi, and the waste gas inflow adjustment amount as... Qi, then the adjusted current flow Qi = Qi + Qi, total exhaust gas flow rate is The ideal flow rate allocation for each purification unit is: ; in, This represents the ideal flow rate allocation for the i-th purification unit; The objective function to be optimized is: min[ ,in, Represents the system stability weighting coefficient, 0 < <1; This represents the sum of the squares of the flow adjustment amounts of all purification units. The larger the value, the more conservative the adjustment of the penalty traffic. This represents the sum of the deviations between the adjusted flow rate and the target flow rate, penalizing deviations from the ideal flow rate, and pursuing optimal performance. The constraints are: This means that the sum of the current flow rates of all purification units equals the total exhaust gas flow rate; Min(Qi)≤ ≤max(Qi) Based on the optimization objective function and constraints, sequential quadratic programming, genetic algorithm or particle swarm optimization algorithm are used for initialization and iteration, and finally convergence judgment is performed to find the optimal solution [ΔQ1, ΔQ2, ..., ΔQn], which is to generate the waste gas inflow adjustment amount for each purification unit.
[0036] In one embodiment, step S73, which involves obtaining the efficiency deviation of each purification unit and locating the failed unit based on the efficiency deviation, includes: S731. Real-time monitoring of the inlet and outlet pollutant concentrations of each purification unit, and calculation of the actual purification efficiency of each purification unit; S732. Compare the actual purification efficiency of each purification unit with the expected purification efficiency stored in the standard purification features, and calculate the efficiency deviation. S733. According to the direction of exhaust gas flow, determine in sequence whether the efficiency deviation of each purification unit exceeds its allowable threshold and whether its inlet concentration is within the preset range. S734. If the efficiency deviation of at least one of the purification units exceeds the threshold and its inlet concentration is within the preset range, then the unit is determined to be faulty. S735. If the efficiency deviation of at least one of the purification units exceeds the threshold, but its inlet concentration is not within the preset range, then trace back to the previous purification unit and determine that the previous purification unit is a failed unit.
[0037] As described in 731-735, by real-time monitoring of the inlet and outlet pollutant concentrations of each purification unit, calculating the actual purification efficiency of each purification unit and comparing it with the expected purification efficiency, it is possible to understand the efficiency deviation between each purification unit and the ideal state. By making judgments step by step, specific failed units can be accurately identified, significantly shortening the time for the system to resume effective operation.
[0038] This application also provides a graded purification system for organic waste gas in a chemical workshop, including: Model building module 1 is used to obtain the exhaust gas collection information of each purification unit to build an exhaust gas collection and purification model, wherein the exhaust gas collection information includes exhaust gas source, composition information and concentration information; The mapping module 2 is used to map the exhaust gas collection and purification model to obtain a dynamic purification capability surface, wherein the dynamic purification capability surface is used to virtually represent the real-time processing capacity and load status of each purification unit. The generation module 3 is used to generate a purification path based on the source, composition and concentration information of the exhaust gas, and to map the purification path as a standard purification feature to the dynamic purification capability surface. The graded purification module 4 is used to sequentially pass the pretreated waste gas through each of the purification units for graded purification, and to collect waste gas data at the inlet and outlet of each of the purification units in real time. The real-time construction module 5 is used to construct the waste gas collection and purification model in real time based on the waste gas data, and map it to the dynamic purification capability surface to generate real-time purification features. Comparison module 6 is used to compare the real-time purification features with the standard purification features to obtain a dynamic purification efficiency index; The dynamic adjustment module 7 is used to dynamically adjust the distribution ratio of exhaust gas among the purification units based on the dynamic purification efficiency index.
[0039] Each of the above modules, units, and sub-units is used to perform the respective steps in the above-mentioned graded purification method for organic waste gas in chemical workshops. The specific implementation methods are as described in the above-mentioned method embodiments, and will not be repeated here.
[0040] like Figure 3 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores all data required for the process of the graded purification method for organic waste gas in the chemical workshop. The network interface is used for communication with external terminals via a network connection. The computer program is executed by the processor to implement the graded purification method for organic waste gas in the chemical workshop.
[0041] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.
[0042] An embodiment of this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described methods for graded purification of organic waste gas in a chemical workshop.
[0043] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0044] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.
[0045] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for graded purification of organic waste gas in a chemical workshop, characterized in that, include: Obtain the exhaust gas collection information of each purification unit to construct an exhaust gas collection and purification model; The waste gas collection and purification model is mapped to obtain a dynamic purification capacity surface, wherein the dynamic purification capacity surface is used to virtually characterize the real-time processing capacity and load status of each purification unit. A purification path is generated based on the waste gas collection information, and the purification path is mapped to the dynamic purification capability surface as a standard purification feature. The pretreated exhaust gas is sequentially passed through each of the purification units for graded purification, and exhaust gas data at the inlet and outlet of each purification unit are collected in real time. Based on the exhaust gas data, the exhaust gas collection and purification model is constructed in real time and mapped to the dynamic purification capacity surface to generate real-time purification features. A dynamic purification efficiency index is obtained by comparing the real-time purification characteristics with the standard purification characteristics. Based on the dynamic purification efficiency index, the distribution ratio of exhaust gas among the purification units is dynamically adjusted.
2. The method for graded purification of organic waste gas in a chemical workshop according to claim 1, characterized in that, The step of sequentially passing the pretreated exhaust gas through each of the purification units for graded purification, and collecting exhaust gas data at the inlet and outlet of each purification unit in real time, includes: Obtain the attribute information of each purification unit, and classify each purification unit into a primary adsorption purification module, a secondary catalytic oxidation module, or a tertiary biological purification module according to the attribute information; The pretreated waste gas is sequentially purified through a primary adsorption purification module, a secondary catalytic oxidation module, and a tertiary biological purification module, with real-time collection of waste gas data at the inlet and outlet of each module.
3. The method for graded purification of organic waste gas in a chemical workshop according to claim 1, characterized in that, The steps of acquiring the exhaust gas collection information of each of the purification units to construct an exhaust gas collection and purification model, and mapping the exhaust gas collection and purification model to obtain a dynamic purification capacity surface include: The location information of each waste gas emission point in the chemical workshop, the physical location of the purification unit, and the purification capacity are collected to construct a three-dimensional model of the workshop; The 3D model of the workshop is used as the waste gas purification model; Obtain the geometric center of the exhaust gas purification model, and construct a virtual sphere that can completely enclose all the purification units, using the purification unit farthest from the geometric center as the boundary. The exhaust gas purification model is mapped onto the surface of the virtual sphere to obtain the dynamic purification capacity surface, wherein the purification unit forms a capacity projection point on the dynamic purification capacity surface, and the capacity projection point is virtually associated with the purification processing capacity of each purification unit.
4. The method for graded purification of organic waste gas in a chemical workshop according to claim 2, characterized in that, The primary adsorption and purification module uses an activated carbon fiber filter and monitors the adsorption saturation status information through a pressure sensor at a preset temperature. The secondary catalytic oxidation module uses a noble metal catalyst and calculates the real-time temperature adjustment under catalysis. The three-stage biological purification module uses a biological filter with composite microbial agents. When the hydrogen sulfide concentration in the exhaust gas exceeds the preset detection value, the enhanced aeration function is automatically activated.
5. The method for graded purification of organic waste gas in a chemical workshop according to claim 1, characterized in that, The step of obtaining the dynamic purification efficiency index by comparing the real-time purification characteristics and the standard purification characteristics includes: On the dynamic purification capability surface, the purification path is defined as a standard connection line; The actual path of the exhaust gas is obtained based on the real-time purification characteristics, and the actual path of the exhaust gas is defined as a real-time connection. Obtain the angle and distance difference data between the real-time connection and the standard connection; The dynamic purification efficiency index is calculated based on the angle and distance difference data.
6. The method for graded purification of organic waste gas in a chemical workshop according to claim 1, characterized in that, The step of dynamically adjusting the distribution ratio of exhaust gas among the purification units based on the dynamic purification efficiency index includes: Determine whether the dynamic purification efficiency index is within a preset range; If the dynamic purification efficiency index is not within the preset range and is higher than the maximum value of the preset range, the amount of waste gas inflow between each purification unit is adjusted. If the dynamic purification efficiency index is not within the preset range and is lower than the minimum value of the preset range, the efficiency deviation of each purification unit is obtained, the failed unit is located based on the efficiency deviation, and the cleaning or replacement command corresponding to the failed unit is triggered.
7. The method for graded purification of organic waste gas in a chemical workshop according to claim 6, characterized in that, The step of adjusting the amount of exhaust gas flowing into each of the purification units includes: Real-time status data of the capacity projection points of each purification unit on the dynamic purification capacity surface are obtained to calculate the capacity margin and efficiency weight of each purification unit. Based on the capacity margin and efficiency weight of each purification unit, and with the optimization objective of making the real-time purification characteristics converge to the standard purification characteristics, a flow allocation function is constructed. The flow distribution function is solved to obtain the waste gas inflow adjustment amount for each purification unit.
8. The method for graded purification of organic waste gas in a chemical workshop according to claim 6, characterized in that, The step of obtaining the efficiency deviation of each purification unit and locating the failed unit based on the efficiency deviation includes: Real-time monitoring of pollutant concentrations at the inlet and outlet of each purification unit, and calculation of the actual purification efficiency of each purification unit; The actual purification efficiency of each purification unit is compared with the expected purification efficiency stored in the standard purification features, and the efficiency deviation is calculated. According to the direction of exhaust gas flow, determine in turn whether the efficiency deviation of each purification unit exceeds its allowable threshold and whether its inlet concentration is within the preset range. If the efficiency deviation of at least one of the purification units exceeds the threshold and its inlet concentration is within the preset range, then the unit is determined to be faulty. If the efficiency deviation of at least one of the purification units exceeds the threshold, but its inlet concentration is not within the preset range, then the preceding purification unit is traced back and determined to be a failed unit.
9. A graded purification system for organic waste gas in a chemical workshop, characterized in that, include: A model building module is used to obtain the waste gas collection information of each purification unit to build a waste gas collection and purification model, wherein the waste gas collection information includes waste gas source, composition information and concentration information; The mapping module is used to map the exhaust gas collection and purification model to obtain a dynamic purification capability surface, wherein the dynamic purification capability surface is used to virtually characterize the real-time processing capacity and load status of each purification unit. The generation module is used to generate a purification path based on the source, composition and concentration information of the exhaust gas, and to map the purification path as a standard purification feature to the dynamic purification capability surface. The graded purification module is used to sequentially pass the pretreated waste gas through each of the purification units for graded purification, and to collect waste gas data at the inlet and outlet of each of the purification units in real time. A real-time construction module is used to construct the waste gas collection and purification model in real time based on the waste gas data, and map it to the dynamic purification capability surface to generate real-time purification features. The comparison module is used to compare the real-time purification features with the standard purification features to obtain a dynamic purification efficiency index; The dynamic adjustment module is used to dynamically adjust the distribution ratio of exhaust gas among the purification units based on the dynamic purification efficiency index.
10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.