Multi-tower linkage control method and system in waste gas treatment and medium

Abstract

The invention provides a multi-tower linkage control method and system in waste gas treatment and a medium, and relates to the technical field of automatic control, and the method comprises the following steps: building a multi-tower linkage control architecture; collecting target waste gas composition data, performing working condition matching analysis on the composition data and a waste gas cooperative treatment tower group, determining process parameters of a matched treatment tower and a target treatment tower group for linkage treatment, and monitoring waste gas change parameters of the treatment towers; and when the waste gas change parameter reaches a waste gas switching threshold value, performing dynamic switching regulation and control by adopting a multi-tower linkage switching strategy, and determining a multi-tower linkage control strategy parameter to perform closed-loop treatment control on the target waste gas. The technical problems that in the prior art, a real-time sensing and dynamic response mechanism based on waste gas composition changes is lacked, so that the utilization rate of a treatment tower set is low, and complex and changeable waste gas treatment requirements cannot be efficiently and stably met are solved, and the technical effect of improving the waste gas treatment efficiency and stability is achieved.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to a multi-tower linkage control method, system and medium for waste gas treatment. Background Art

[0002] With the continuous acceleration of the industrialization process, the problem of harmful waste gas emissions generated in various industrial production processes is becoming increasingly serious. In the waste gas treatment process, the method of using multiple treatment towers for coordinated and collaborative treatment has been widely adopted because it can improve the efficiency of waste gas purification. The existing waste gas multi-tower linkage control method is usually based on a fixed treatment process and preset process parameters, and the target waste gas is sent to multiple treatment towers in sequence or in parallel for purification. Some systems switch tower groups through simple time rotation or based on load balancing rules. However, these waste gas treatment systems are mostly based on static control modes and lack the ability to respond in real time to the complexity of waste gas components and concentration fluctuations. As a result, in actual operation, it is impossible to match the optimal treatment process path in time. It is easy for some treatment towers to be overloaded while other towers are idle, resulting in uneven tower group utilization and reduced treatment efficiency. In addition, due to the lag in control strategy response, the waste gas treatment effect fluctuates significantly, making it difficult to meet emission standards in a continuous and stable manner. Summary of the Invention

[0003] The present application provides a multi-tower linkage control method, system and medium for waste gas treatment, which solves the technical problems in the prior art such as the fixed control mode of the waste gas treatment system and the lack of real-time perception and dynamic response mechanism based on changes in waste gas composition, resulting in low utilization rate of the treatment tower group and inability to efficiently and stably respond to complex and changeable waste gas treatment needs. It achieves the technical effect of dynamically switching and regulating the multi-tower linkage treatment process based on real-time monitoring of waste gas, thereby improving the efficiency and stability of waste gas treatment.

[0004] In view of the above problems, on the one hand, the present application provides a multi-tower linkage control method in waste gas treatment, the method comprising: building a multi-tower linkage control architecture, the multi-tower linkage control architecture comprising a waste gas collaborative treatment tower group and a linkage control module, and deploying an Internet of Things monitoring module in each treatment tower of the waste gas collaborative treatment tower group; starting the waste gas component analyzer to collect and obtain the component data of the target waste gas, activating the linkage control module to perform working condition matching analysis on the component data and the waste gas collaborative treatment tower group, and determining the process parameters of N matching treatment towers and the target treatment tower group; based on the process parameters of the target treatment tower group, the target waste gas is transported to the N matching treatment towers for linkage treatment, and at the same time, the waste gas change parameters of N treatment towers are obtained through the Internet of Things monitoring module; when the waste gas change parameters of the N treatment towers reach the waste gas switching threshold, a multi-tower linkage switching strategy is adopted to dynamically switch and regulate the waste gas change parameters of the N treatment towers, determine the multi-tower linkage control strategy parameters, and perform closed-loop treatment control on the target waste gas through the multi-tower linkage control strategy parameters.

[0005] On the other hand, the present application also provides a multi-tower linkage control system for waste gas treatment, the system including: a control architecture building module for building a multi-tower linkage control architecture, the multi-tower linkage control architecture including a waste gas collaborative treatment tower group and a linkage control module, and an Internet of Things monitoring module is arranged in each treatment tower of the waste gas collaborative treatment tower group; a working condition matching analysis module for starting the waste gas component analyzer to collect and obtain the component data of the target waste gas, activating the linkage control module to perform working condition matching analysis on the component data and the waste gas collaborative treatment tower group, and determining the process parameters of N matching treatment towers and the target treatment tower group; a linkage processing module for transporting the target waste gas to the N matching treatment towers for linkage processing based on the process parameters of the target treatment tower group, and at the same time obtaining the waste gas change parameters of the N treatment towers through the Internet of Things monitoring module; a switching control module for dynamically switching and controlling the waste gas change parameters of the N treatment towers using a multi-tower linkage switching strategy when the waste gas change parameters of the N treatment towers reach the waste gas switching threshold, determining the multi-tower linkage control strategy parameters, and performing closed-loop treatment control on the target waste gas through the multi-tower linkage control strategy parameters.

[0006] In a third aspect, the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the multi-tower linkage control method in the above-mentioned waste gas treatment are implemented.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects:

[0008] Establishing a multi-tower coordinated control architecture lays the foundation for intelligent and coordinated waste gas treatment. By installing an IoT monitoring module within each treatment tower, real-time monitoring and data transmission are achieved within each tower, providing the necessary hardware and data support for subsequent precise control and dynamic adjustments. The exhaust gas composition analyzer collects exhaust gas composition data, which is then matched to the operating conditions by the coordinated control module. This step accurately determines the most suitable treatment tower combination and process parameters based on the actual exhaust gas composition, avoiding the poor targeted treatment of traditional fixed models and improving the accuracy and efficiency of exhaust gas treatment. Based on the determined process parameters, the exhaust gas is transported to the matching treatment tower for coordinated treatment. Simultaneously, the IoT module monitors fluctuating exhaust gas parameters. This step enables multi-tower coordinated operation and provides real-time access to critical data during the treatment process, providing a basis for further dynamic control. When the fluctuating exhaust gas parameters reach the switching threshold, a multi-tower coordinated switching strategy is implemented for dynamic control, and the control strategy parameters are determined to achieve closed-loop control. This step allows for timely adjustment of the treatment method based on real-time changes in exhaust gas composition, ensuring stable and efficient treatment results.

[0009] In summary, this application realizes the real-time collection and intelligent analysis of the components of the waste gas by building a multi-tower linkage control architecture that includes a waste gas collaborative treatment tower group, a linkage control module and an Internet of Things monitoring module. It can dynamically match the treatment tower group and process parameters according to the waste gas characteristics, and continuously monitor the waste gas change parameters during the treatment process. When it is detected that the waste gas change reaches the set threshold, the system can adopt a multi-tower linkage switching strategy for dynamic regulation, thereby realizing real-time optimization of the waste gas treatment path and process. The overall solution effectively solves the problems of delayed response of the existing waste gas treatment system, uneven utilization of the treatment tower group, and unstable treatment effect, and achieves the technical effect of improving the waste gas treatment efficiency and ensuring stable and standard emissions.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic flow chart of a multi-tower linkage control method for waste gas treatment provided in an embodiment of the present application.

[0012] Figure 2 A schematic flow chart of determining process parameters of N matching treatment towers and a target treatment tower group in a multi-tower linkage control method for waste gas treatment provided in an embodiment of the present application.

[0013] Figure 3This is a schematic diagram of the structure of a multi-tower linkage control system for waste gas treatment provided in an embodiment of the present application.

[0014] Description of the accompanying drawings: control architecture building module 10, working condition matching and analysis module 20, linkage processing module 30, switching and control module 40. DETAILED DESCRIPTION

[0015] The embodiments of the present application provide a multi-tower linkage control method, system and medium for waste gas treatment, thereby solving the technical problems in the prior art of low utilization of the treatment tower group and inability to efficiently and stably respond to complex and changeable waste gas treatment needs due to the fixed control mode of the waste gas treatment system and the lack of real-time perception and dynamic response mechanism based on changes in waste gas composition. The technical effect of improving the efficiency and stability of waste gas treatment is achieved by dynamically switching and regulating the multi-tower linkage treatment process based on real-time monitoring of waste gas.

[0016] Example 1, as Figure 1 As shown, the embodiment of the present application provides a multi-tower linkage control method for exhaust gas treatment, the method comprising:

[0017] Step S100: constructing a multi-tower linkage control architecture, which includes a waste gas collaborative treatment tower group and a linkage control module, and deploying an Internet of Things monitoring module in each treatment tower of the waste gas collaborative treatment tower group.

[0018] Specifically, before carrying out multi-tower linkage control, it is necessary to build a complete multi-tower linkage control architecture as the basic framework for subsequent dynamic regulation and efficient linkage processing. First, several exhaust gas treatment towers are selected as exhaust gas collaborative treatment tower groups. The tower group includes a variety of different types of treatment towers for exhaust gas purification, such as adsorption-desorption towers, wet scrubbers, activated carbon towers, etc. At the same time, an Internet of Things monitoring module is installed inside each treatment tower. The Internet of Things monitoring module includes gas sensors, temperature and humidity sensors and other types of sensors and wireless communication modules. It is used to collect various environmental parameters such as exhaust gas composition, concentration, temperature, pressure, etc. in real time in the tower, and transmit these data back to the control module or system for subsequent decision-making. Then, a linkage control module is introduced (which can be based on an industrial control server or an industrial PC platform) to analyze the exhaust gas situation, assign processing tasks, and switch the tower group status to manage and coordinate the operations of each tower in real time.

[0019] Step S200: Start the exhaust gas composition analyzer to collect component data of the target exhaust gas, activate the linkage control module to perform working condition matching analysis on the component data and the exhaust gas collaborative treatment tower group, and determine the process parameters of N matching treatment towers and the target treatment tower group.

[0020] Specifically, the target exhaust gas refers to the exhaust gas that currently needs to be treated. Before starting the exhaust gas treatment process, enable the exhaust gas component analyzer (such as a gas chromatograph or infrared analyzer) to detect the various components and their concentrations in the current target exhaust gas to obtain the component data of the target exhaust gas. Then activate the linkage control module, which calls the built-in process database (which pre-stores various types of exhaust gas and tower group operating experience data), and matches the exhaust gas collaborative treatment tower group in the process database based on the component data, and selects N matching treatment towers for the target exhaust gas and the target treatment tower group process parameters corresponding to the N matching treatment towers. These target treatment tower group process parameters include the treatment process flow between each tower and the operating condition parameters of each treatment tower.

[0021] This step achieves accurate analysis of waste gas composition and intelligent matching of treatment tower groups, improves the pertinence and efficiency of waste gas treatment, and ensures the stability and reliability of the treatment effect.

[0022] Step S300: Based on the process parameters of the target treatment tower group, the target exhaust gas is transported to the N matching treatment towers for linkage treatment, and at the same time, the exhaust gas change parameters of the N treatment towers are obtained through the Internet of Things monitoring module.

[0023] Specifically, according to the target treatment tower group process parameters matched in the previous step, the target exhaust gas is transported to N matching treatment towers through valve control or flow distributor for linkage treatment. For example, the quantitative diversion of gas is achieved by controlling the multi-way electric valve through PLC. At the same time, the Internet of Things monitoring module deployed in each treatment tower begins to collect the treatment status data of each treatment tower (such as outlet exhaust gas concentration, tower temperature, etc.) in real time to form N treatment tower exhaust gas change parameters. These data are uploaded to the linkage control module to determine whether the treatment status is stable and whether the strategy needs to be adjusted. For example, in a waste gas treatment system containing volatile organic compounds, according to the target treatment tower group process parameters, the exhaust gas is sequentially treated through a catalytic oxidation tower and an activated carbon adsorption tower, and the Internet of Things monitoring module is used to monitor the concentration changes of volatile organic compounds in the exhaust gas in real time.

[0024] This step ensures the continuity and stability of the waste gas treatment process and prevents local overload or treatment failure through multi-tower collaborative processing and real-time monitoring of the operating status of each treatment unit.

[0025] Step S400: When the exhaust gas change parameters of the N treatment towers reach the exhaust gas switching threshold, a multi-tower linkage switching strategy is used to dynamically switch and regulate the exhaust gas change parameters of the N treatment towers, determine the multi-tower linkage control strategy parameters, and perform closed-loop treatment control on the target exhaust gas through the multi-tower linkage control strategy parameters.

[0026] Specifically, the exhaust gas switching threshold refers to the triggering criteria when exhaust gas parameter changes reach set limits, including concentration excursions, temperature limits, and humidity limits. When exhaust gas parameter changes reach or exceed these thresholds, it indicates that the current treatment tower combination or process parameters no longer meet treatment requirements and require switching. The linkage control module continuously monitors the exhaust gas parameters of N treatment towers. When it detects that a parameter reaches the set exhaust gas switching threshold, it immediately triggers a multi-tower linkage switching strategy to dynamically adjust the treatment tower combination or process parameters and determine new multi-tower linkage control strategy parameters. This multi-tower linkage switching strategy is a dynamic adjustment rule designed to address exhaust gas treatment anomalies, such as backup tower activation and flow redistribution. The strategy formulation process is based on rule base retrieval and executed in conjunction with a PID dynamic control algorithm. Based on real-time monitoring and strategy calculation results, new multi-tower linkage control strategy parameters are generated, such as "backup tower T5 activated, intake air flow adjusted to 60% of the original value." These multi-tower linkage control strategy parameters are then executed for exhaust gas treatment, forming a complete closed-loop control mechanism that enables adaptive adjustment of the treatment process and maintains consistent compliance with exhaust gas treatment standards.

[0027] Further, such as Figure 2 As shown, step S200 includes:

[0028] Step S210: loading the waste gas treatment process database of the waste gas collaborative treatment tower group through the linkage control module, the waste gas treatment process database including waste gas composition data, tower group process data and corresponding treatment effect data.

[0029] Step S220: performing component feature identification and tower group operating condition clustering on the exhaust gas treatment process database to obtain multi-tower operating condition operation processing rules.

[0030] Step S230: performing tower group operating condition matching on the component data according to the multi-tower operating condition operation processing rule, and determining N matching processing towers.

[0031] Step S240: performing tower group process analysis on the N matching treatment towers and the component data based on the waste gas treatment process database to obtain target treatment tower group process parameters.

[0032] Specifically, the linkage control module first connects to and loads the exhaust gas treatment process database. This exhaust gas treatment process database can be stored in a cloud server, edge server, or local data center, using SQL structured storage or NoSQL distributed storage to facilitate high-speed queries. The database content includes: different exhaust gas components and their concentration characteristics; corresponding treatment tower group configurations, such as activated carbon towers, washing towers, and catalytic tower combination solutions; related treatment process parameters, such as optimal treatment temperature, tower pressure, adsorbent replacement cycle, etc.; tower group process corresponding to the removal rate of each component in the exhaust gas, compliance status, etc. By loading the exhaust gas treatment process database, rich historical experience data can be obtained, providing a comprehensive and reliable information basis for subsequent component analysis and tower group matching.

[0033] First, the waste gas component data in the waste gas treatment process database is identified by component characteristics. For example, by setting key pollutants (such as benzene, organic amines, acidic gases, etc.) for classification, the concentration range characteristics are extracted at the same time. Then, based on the identification results and existing treatment records, the operating condition clustering algorithm (such as K-means) is used to divide the historical tower group operation data into several categories, each category representing a specific operating condition (such as high-concentration organic waste gas low-temperature adsorption treatment condition). Finally, a multi-tower operating condition operation processing rule is formed. The rule clarifies which treatment tower combinations and corresponding operating parameters can achieve the best treatment effect under what waste gas component characteristics and operating conditions, and is used to guide subsequent rapid matching and decision-making.

[0034] Based on the established multi-tower operating rules, the real-time collected component data is compared. This matching process can utilize a rule engine or intelligent matching methods based on similarity calculations (such as Euclidean distance or cosine similarity). This matching process quickly identifies the tower group most suitable for treating the waste gas composition and concentration, and N matching towers are determined for subsequent process analysis.

[0035] Based on the waste gas treatment process database, we conduct an in-depth analysis of the N selected matching treatment towers and their current composition data to refine the corresponding target treatment tower group process parameters. This process analysis includes selecting the adsorbent or reactant type, setting key parameters such as gas treatment flow rate, operating temperature, reaction time, and process flow. These detailed process parameters are integrated to form the target treatment tower group process parameters, guiding each treatment tower to operate according to the optimal process during the actual waste gas treatment process.

[0036] Through the above steps, the treatment plan suitable for the current target waste gas is accurately extracted from the historical waste gas treatment experience data, including the matching treatment tower combination and detailed process parameters, which lays the foundation for the subsequent efficient and stable treatment of waste gas, and significantly improves the intelligence level and treatment efficiency of the entire waste gas treatment process.

[0037] Furthermore, step S220 includes:

[0038] Step S221: extracting exhaust gas key features from the exhaust gas treatment process database to obtain exhaust gas component feature dimensions, where the exhaust gas component feature dimensions include component proportion, concentration gradient, component toxicity, and component polarity.

[0039] Step S222: classify and identify the waste gas treatment process database using the waste gas component characteristic dimension to determine a waste gas component characteristic parameter set.

[0040] Step S223: performing correlation mapping between the exhaust gas component characteristic parameter set and the tower group process data in the exhaust gas treatment process database to obtain an exhaust gas characteristic-tower group process data set.

[0041] Step S224: performing K-means clustering analysis and generating operating condition rules based on the exhaust gas characteristics-tower group process data set to obtain the multi-tower operating condition operation processing rules.

[0042] Specifically, feature engineering algorithms (such as PCA preprocessing followed by feature extraction) are used to statistically analyze a large amount of waste gas sample data in the waste gas treatment process database, extracting the following four types of characteristic information: component proportion, that is, the proportion of each pollutant in the waste gas; concentration gradient, that is, the rate of change of concentration of each component at different sampling points or time periods; component toxicity, which classifies the health hazard level of each pollutant according to industry standards; component polarity, that is, the polarity of the component is determined based on its molecular structure and physical properties, as component polarity affects the adsorption and dissolution behavior of the waste gas. The above four types of characteristics are combined to obtain the characteristic dimensions of the waste gas composition.

[0043] The exhaust gas treatment process database is classified and identified using the exhaust gas component characteristic dimension, generating a feature vector for each exhaust gas sample to form a set of exhaust gas component characteristic parameters. This classification and identification allows the exhaust gas data to be structured and standardized, facilitating subsequent cluster analysis.

[0044] According to the exhaust gas component characteristic parameter set, the treatment effect data corresponding to different tower group processes in the exhaust gas treatment process database are searched, and the optimal tower group process configuration (with the best treatment effect) that matches the characteristic parameters of each exhaust gas component is found. The corresponding relationship between the exhaust gas characteristics and the tower group process parameters is established, and the exhaust gas characteristic-tower group process data set is generated. The direct connection between the exhaust gas characteristics and the treatment process is opened up, providing data support for quickly matching the optimal tower group strategy.

[0045] K-means clustering is performed on the exhaust gas characteristics and tower group process data set to automatically classify similar exhaust gas samples and their corresponding tower group operating conditions. Multi-tower operating condition processing rules are then generated based on the data characteristics of each cluster. These rules can be solidified and dynamically called using the rule engine.

[0046] Through the above steps, a complete set of intelligent processing basis is provided for the entire exhaust gas treatment process, which facilitates the rapid and accurate selection of the optimal treatment solution according to different exhaust gas characteristics.

[0047] Furthermore, step S240 includes:

[0048] Step S241: extracting associated data from the waste gas treatment process database based on the N matching treatment towers to obtain a treatment tower group treatment process data set.

[0049] Step S242: obtaining a processing tower group process factor set, wherein the processing tower group process factor set includes a tower group connection mode, a tower group working sequence, and a tower group working parameter.

[0050] Step S243: using the process factor set of the treatment tower group to initialize particles of the treatment process data set of the treatment tower group, and constructing a process particle space of the treatment tower group.

[0051] Step S244: using the component data as constraint parameters, performing tower group process analysis in the process particle space of the processing tower group to obtain target processing tower group process parameters.

[0052] Specifically, based on the N matching treatment towers that have been determined, the linkage control module extracts relevant treatment process data from the exhaust gas treatment process database to generate a treatment tower group treatment process data set. This data set contains the treatment process parameters of each matching treatment tower, such as the operating temperature, flow rate, load range, etc. of the tower group, and ensures that these parameters can cover the treatment requirements of different exhaust gas components. Next, the process factor set of the treatment tower group is obtained, including the connection mode of the tower group (such as series or parallel connection), the working sequence (such as which tower is processed first and which tower is processed later), and the working parameters of the tower group (such as temperature setting, flow rate adjustment, etc.). These process factor sets can be summarized and defined through process design documents or expert experience.

[0053] Based on the treatment tower group treatment process data set and the treatment tower group process factor set, the particle swarm optimization algorithm is used to initialize the particles, converting each set of process factor combinations into a particle to construct the treatment tower group process particle space. Each particle represents a possible process solution and will be evaluated in subsequent optimization. Then, within the treatment tower group process particle space, the target exhaust gas composition data is used as a constraint condition to execute the tower group process analysis process, screen and optimize the particles, and select the process solution that best suits the target exhaust gas treatment. In this process, the particle swarm optimization algorithm is used to evaluate the pros and cons of each particle in terms of treatment effect, and the particles are iteratively optimized using the tower group exhaust gas treatment objectives (such as maximizing treatment efficiency, minimizing operating costs, etc.), and finally the treatment tower group process parameter combination with the highest particle fitness is found, which is the target treatment tower group process parameter. These parameters will be used for further exhaust gas treatment process execution and optimization control.

[0054] Through the above steps, it can be ensured that the process adjustment during the waste gas treatment process is more scientific and precise, thereby achieving the best waste gas treatment effect and being able to efficiently adapt to changes in different waste gas components.

[0055] Furthermore, step S244 includes:

[0056] Step S244 - 1 : Using the component data as constraint parameters, a tower group process search is performed in the processing tower group process particle space to obtain a processing tower group process particle retrieval library.

[0057] Step S244-2: Obtain the exhaust gas treatment target of the tower group, perform index evaluation and fitting on the process parameter space of the treatment tower group based on the exhaust gas treatment target of the tower group, and construct a tower group exhaust gas treatment effect function.

[0058] Step S244-3: using the tower group exhaust gas treatment effect function to perform iterative evaluation and optimization in the treatment tower group process particle retrieval library to determine the initial treatment tower group process parameters with the maximum particle fitness.

[0059] Step S244-4: Based on the characteristic information of the N matching treatment towers, the process parameters of the initial treatment tower group are modified to determine the process parameters of the target treatment tower group.

[0060] Specifically, the target exhaust gas composition data is first input into the treatment tower group process particle space as a constraint parameter. A tower group process search is performed, traversing all possible process parameter combinations (particles) and screening out process solutions that meet the composition requirements. This creates a treatment tower group process particle search library. This library records the relevant data of different treatment tower combinations and process solutions, providing a broad foundation for process solutions in the subsequent optimization process.

[0061] The exhaust gas treatment objectives of the tower group are determined, i.e., the goal of achieving specified exhaust gas emission standards after treatment. These objectives include control requirements for exhaust gas concentration, temperature, toxicity, and other factors. Based on these exhaust gas treatment objectives, the system evaluates and fits the process parameter space of the treatment tower group to construct an exhaust gas treatment performance function for the tower group. This performance function quantifies the exhaust gas treatment efficiency and stability of the treatment tower group under given parameters, thus providing guidance for the optimization process. An example of an exhaust gas treatment performance function for the tower group is as follows: F = ω1 × Er + ω2 × (1-Ec) + ω3 × (1-Ep). F represents the exhaust gas treatment performance of the tower group, or particle fitness. Er represents the removal rate of the main exhaust gas components, ranging from 0 to 1, with higher values indicating better treatment performance. Ec represents the energy consumption percentage of the treatment process, ranging from 0 to 1, with lower values indicating better energy efficiency. Ep represents the proportion of newly generated secondary pollutants during the exhaust gas treatment process, ranging from 0 to 1, with lower values indicating better byproduct control. ω1, ω2, and ω3 are the importance weight coefficients of removal rate, energy consumption, and by-product control, respectively, satisfying ω1+ω2+ω3=1, and can be set according to actual production needs.

[0062] Then, the tower group exhaust gas treatment effect function is used to perform iterative evaluation and optimization in the treatment tower group process particle retrieval library. Initially, the particles in the treatment tower group process particle retrieval library are randomly distributed, and each particle has its own position (process parameter combination) and speed. Calculate the fitness of each particle, that is, substitute the treatment effect function to calculate its fitness value. Then, sort the particles according to the fitness size to find the current optimal particle. In subsequent iterations, each particle adjusts its speed and position according to its own historical optimal position and the global optimal position, continuously updates its own process parameter combination, and recalculates its fitness. This process is repeated until the set number of iterations or the fitness convergence condition is reached. Finally, the initial treatment tower group process parameters with the largest particle fitness are determined.

[0063] Tower characteristic information refers to each tower's performance parameters, operating status, design parameters, and historical operating data, reflecting its processing capacity and aging. Based on the characteristic information of N matching towers, the initial tower group process parameters are modified to determine the most suitable target tower group process parameters, providing a precise control basis for subsequent waste gas treatment processes.

[0064] Furthermore, step S244-4 includes:

[0065] Step S244-41: Determine the processing capacities and aging degrees of the N processing towers based on the characteristic information of the N matching processing towers.

[0066] Step S244-42: The ratio of the aging degree of the N treatment towers to the treatment capacity of the N treatment towers is used as the redundancy coefficient of the N treatment towers.

[0067] Step S244-43: performing redundancy correction on the initial process parameters of the treatment tower group based on the N process tower redundancy coefficients to determine the target process parameters of the treatment tower group.

[0068] Specifically, first, based on the characteristic information such as the performance parameters, operating status, design parameters and historical operating data of N matching treatment towers, the relevant models or empirical formulas are used to determine the treatment capacity and aging degree of each treatment tower. Treatment capacity refers to the maximum treatment volume or efficiency of each treatment tower under specific operating conditions, and the aging degree reflects the performance degradation that may occur in the tower body during long-term use. For example, the treatment capacity of an activated carbon adsorption tower can be calculated based on its adsorption capacity and residual adsorption capacity, and the aging degree can be determined by the percentage of decrease in adsorption efficiency or the ratio of the amount of waste gas that has been treated to the amount of waste gas corresponding to the design service life. For catalytic oxidation towers, the treatment capacity can be evaluated by the activity and reaction efficiency of the catalyst, and the aging degree can be determined by the degree of decrease in catalyst activity or the ratio of the time that has been in operation to the design life.

[0069] Next, the ratio of the degree of aging of the N treatment towers to their processing capacity is calculated and used as the redundancy factor. The redundancy factor reflects the standby capacity and load capacity of each treatment tower in its current state. Towers with advanced aging may not operate effectively under high loads, so the redundancy factor serves as an important basis for adjusting load distribution and tower group configuration.

[0070] Based on the calculated redundancy coefficient, the initial treatment tower group process parameters are corrected for redundancy. During this process, the process parameters of the treatment tower group (such as working sequence, connection mode, treatment load, etc.) will be appropriately adjusted according to the redundancy correction results to determine the final target treatment tower group process parameters to ensure that the overall exhaust gas treatment efficiency is maximized while avoiding overload or waste of resources. For example, for the activated carbon adsorption tower, since the redundancy coefficient is large, it means that its remaining available capacity is small, and the adsorption time needs to be appropriately increased to ensure the adsorption effect; for the catalytic oxidation tower, if the redundancy coefficient is small, its treatment capacity is relatively sufficient, and the catalytic temperature can be appropriately lowered to save energy; for the wet scrubber, if the redundancy coefficient is moderate, the scrubbing liquid flow rate can be appropriately adjusted. This process can be achieved by establishing a correction model or using an empirical formula to adjust the process parameters according to the size of the redundancy coefficient. For example, using a linear correction method, the larger the redundancy coefficient, the greater the correction amplitude.

[0071] Furthermore, step S400 includes:

[0072] Step S410: performing exhaust gas exceeding standard pattern identification on the multi-tower linkage switching strategy to obtain an exhaust gas exceeding standard pattern set, and selecting a spare tower in the exhaust gas collaborative treatment tower group according to the exhaust gas exceeding standard pattern set to obtain a spare collaborative treatment tower group.

[0073] Step S420: Associating the exhaust gas exceeding standard mode set with the standby collaborative treatment tower group by switching mode to obtain an exceeding standard mode-standby tower group switching strategy.

[0074] Step S430: Dynamically switch and regulate the exhaust gas change parameters of the N treatment towers based on the over-standard mode-standby tower group switching strategy to determine the multi-tower linkage control strategy parameters.

[0075] Specifically, the exhaust gas change parameters of N matching treatment towers are continuously monitored through the linkage control module. When it is detected that the exhaust gas emission parameters of a certain treatment tower (such as the concentration of key pollutants, gas temperature, etc.) exceed the set standard threshold, the exhaust gas exceeding standard pattern recognition is started. Based on the characteristics such as the abnormal trend of exhaust gas components, the magnitude and duration of pollutant concentration exceeding the standard, cluster analysis and pattern matching methods are used to identify the exceeding standard pattern in the current exhaust gas treatment process and generate an exhaust gas exceeding standard pattern set. For example, the concentration of toxic gases exceeds the standard instantaneously, the gas temperature exceeds the standard continuously, and the acid gas concentration exceeds the standard frequently. Subsequently, according to the characteristics of each exceeding standard pattern (such as the category of exceeding standard components, the intensity of exceeding standard, the fluctuation characteristics, etc.), the spare treatment tower units with redundant processing capacity, no overload and adaptability to the exceeding standard components are selected from the exhaust gas collaborative treatment tower group to form a spare collaborative treatment tower group.

[0076] The identified exhaust gas exceeding standard patterns are mapped and associated with the corresponding backup co-treatment tower groups to generate an exceeding standard pattern-backup tower group switching strategy. The switching strategy includes key parameters such as switching conditions (such as exceeding standard trigger thresholds), backup tower activation sequence, flow distribution ratio, and switching delay settings. This ensures that when exhaust gas exceeds standard, it can quickly switch to the appropriate backup tower group for supplementary treatment or load sharing.

[0077] Based on the above-generated over-standard mode-backup tower switching strategy, the system monitors and dynamically analyzes changes in the exhaust gas parameters of N treatment towers in real time. When the trigger condition for the corresponding over-standard mode is detected, the system automatically switches to the backup tower, adjusts the exhaust gas flow distribution, and simultaneously optimizes the linkage treatment parameters, such as gas flow rate and reactant dosage, to form new multi-tower linkage control strategy parameters. The linkage control module applies the multi-tower linkage control strategy parameters to the exhaust gas treatment control process, completing closed-loop regulation of the target exhaust gas, ensuring continuous exhaust gas treatment and compliance with emission standards.

[0078] Furthermore, step S430 includes:

[0079] Step S431: Based on the exceeding standard mode-standby tower group switching strategy, the N treatment tower exhaust gas change parameters are matched with the standby tower group to obtain a standby switching tower group.

[0080] Step S432: Using a PID controller to perform regulation and analysis on the standby switching tower group based on the exhaust gas change parameters of the N treatment towers, and determine the multi-tower linkage control strategy parameters.

[0081] Specifically, based on the generated excess mode-backup tower group switching strategy, the exhaust gas variation parameters currently collected from the N treatment towers are analyzed in real time to extract key variation characteristics, including the rate of change of pollutant concentration, the amplitude of total load fluctuation, and single-tower anomaly indicators. Based on the spare tower selection rules corresponding to each excess mode, the spare tower units that best match the current exhaust gas variation parameters are retrieved and combined to form a spare switching tower group. The spare switching tower group prioritizes treatment tower units with high redundancy, low aging, and treatment processes adapted to the target exhaust gas composition characteristics to ensure rapid recovery of exhaust gas treatment performance after switching.

[0082] Next, a proportional-integral-differential (PID) controller is used to dynamically control and analyze the exhaust gas change parameters of N treatment towers and the standby switching tower group. The PID controller uses exhaust gas change parameters (such as concentration exceeding the standard value, abnormal flow deviation) as input, calculates the error amount, and outputs the control amount in real time according to the set proportional coefficient, integral time and differential time, and adjusts the key process parameters such as the intake flow rate, reactant injection rate and operating pressure of the standby switching tower group, so as to achieve a smooth transition and balanced processing load of the standby switching tower group. By continuously correcting the exhaust gas parameter feedback during the dynamic switching process, the multi-tower linkage control strategy parameters that can meet the exhaust gas emission standards and process stability are finally determined. The linkage control module applies the above strategy parameters to the control logic of the entire tower group to complete the seamless switching and intelligent linkage regulation of the exhaust gas treatment capacity between multiple towers.

[0083] Through the above steps, the spare switching tower group can be accurately matched according to the exhaust gas change parameters, and the PID controller can be used for fine-tuning to ensure the stability of the exhaust gas treatment process and the accuracy of the treatment effect, further improving the intelligence level and treatment effect of the exhaust gas treatment process.

[0084] In summary, the multi-tower linkage control method for waste gas treatment provided by the embodiments of the present application has the following beneficial effects:

[0085] The embodiment of the present application realizes the real-time collection and intelligent analysis of the components of the waste gas by building a multi-tower linkage control architecture including a waste gas collaborative treatment tower group, a linkage control module and an Internet of Things monitoring module. It can dynamically match the treatment tower group and process parameters according to the waste gas characteristics, and continuously monitor the waste gas change parameters during the treatment process. When it is detected that the waste gas change reaches the set threshold, the system can adopt a multi-tower linkage switching strategy for dynamic regulation, thereby realizing real-time optimization of the waste gas treatment path and process. The overall solution effectively solves the problems of delayed response of the existing waste gas treatment system, uneven utilization of the treatment tower group, and unstable treatment effect, and achieves the technical effect of improving the waste gas treatment efficiency and ensuring stable and standard emissions.

[0086] Example 2, as Figure 3 As shown, based on the same inventive concept as the aforementioned embodiment 1, the embodiment of the present application provides a multi-tower linkage control system for waste gas treatment, the system comprising:

[0087] The control architecture building module 10 is used to build a multi-tower linkage control architecture, which includes a waste gas collaborative treatment tower group and a linkage control module, and an Internet of Things monitoring module is arranged in each treatment tower of the waste gas collaborative treatment tower group.

[0088] The operating condition matching analysis module 20 is used to start the exhaust gas composition analyzer to collect and obtain the component data of the target exhaust gas, activate the linkage control module to perform operating condition matching analysis on the component data and the exhaust gas collaborative treatment tower group, and determine the process parameters of N matching treatment towers and the target treatment tower group.

[0089] The linkage processing module 30 is used to transport the target exhaust gas to the N matching treatment towers for linkage processing based on the process parameters of the target treatment tower group, and at the same time obtain the exhaust gas change parameters of the N treatment towers through the Internet of Things monitoring module.

[0090] The switching control module 40 is used to dynamically switch and control the N treatment tower exhaust gas change parameters using a multi-tower linkage switching strategy when the exhaust gas change parameters of the N treatment towers reach the exhaust gas switching threshold, determine the multi-tower linkage control strategy parameters, and perform closed-loop treatment control on the target exhaust gas through the multi-tower linkage control strategy parameters.

[0091] Furthermore, the operating condition matching analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0092] The exhaust gas treatment process database of the exhaust gas collaborative treatment tower group is loaded through the linkage control module, and the exhaust gas treatment process database includes exhaust gas composition data, tower group process data and corresponding treatment effect data; the exhaust gas treatment process database is subjected to component feature identification and tower group operating condition clustering to obtain multi-tower operating condition operation processing rules; the tower group operating condition is matched with the component data according to the multi-tower operating condition operation processing rules to determine N matching treatment towers; the tower group process is analyzed for the N matching treatment towers and the component data based on the exhaust gas treatment process database to obtain the target treatment tower group process parameters.

[0093] Furthermore, the operating condition matching analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0094] Key features of waste gas are extracted from the waste gas treatment process database to obtain waste gas component feature dimensions, where the waste gas component feature dimensions include component proportion, concentration gradient, component toxicity, and component polarity; the waste gas component feature dimensions are used to classify and identify the waste gas treatment process database to determine a waste gas component feature parameter set; the waste gas component feature parameter set and the tower group process data in the waste gas treatment process database are correlated and mapped to obtain a waste gas feature-tower group process data set; K-means clustering analysis and operating condition rule generation are performed based on the waste gas feature-tower group process data set to obtain the multi-tower operating condition operation processing rule.

[0095] Furthermore, the operating condition matching analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0096] Based on the N matching treatment towers, associated data is extracted from the waste gas treatment process database to obtain a treatment tower group treatment process data set; a treatment tower group process factor set is obtained, and the treatment tower group process factor set includes a tower group connection mode, a tower group working sequence, and a tower group working parameter; the treatment tower group process factor set is used to initialize particles of the treatment tower group treatment process data set to construct a treatment tower group process particle space; the component data is used as a constraint parameter, and a tower group process analysis is performed in the treatment tower group process particle space to obtain target treatment tower group process parameters.

[0097] Furthermore, the operating condition matching analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0098] The component data is used as a constraint parameter, and a tower group process search is performed in the process particle space of the treatment tower group to obtain a treatment tower group process particle retrieval library; the tower group exhaust gas treatment target is obtained, and the index evaluation and fitting of the process parameter space of the treatment tower group is performed based on the tower group exhaust gas treatment target to construct a tower group exhaust gas treatment effect function; the tower group exhaust gas treatment effect function is used to perform iterative evaluation and optimization in the process particle retrieval library of the treatment tower group to determine the initial treatment tower group process parameters with the maximum particle fitness; the initial treatment tower group process parameters are corrected based on the characteristic information of the N matching treatment towers to determine the target treatment tower group process parameters.

[0099] Furthermore, the operating condition matching analysis module 20 of the embodiment of the present application is further configured to perform the following steps:

[0100] Based on the characteristic information of the N matching treatment towers, the processing capacities of the N treatment towers and the aging degrees of the N treatment towers are determined; the ratios of the aging degrees of the N treatment towers to the processing capacities of the N treatment towers are used as N treatment tower redundancy coefficients; and based on the N treatment tower redundancy coefficients, the process parameters of the initial treatment tower group are redundancy corrected to determine the process parameters of the target treatment tower group.

[0101] Furthermore, the switching control module 40 in the embodiment of the present application is further configured to perform the following steps:

[0102] The multi-tower linkage switching strategy is used to identify the waste gas exceeding standard pattern to obtain a waste gas exceeding standard pattern set, and a spare tower is selected in the waste gas collaborative treatment tower group according to the waste gas exceeding standard pattern set to obtain a spare collaborative treatment tower group; the waste gas exceeding standard pattern set and the spare collaborative treatment tower group are associated with the switching mode to obtain the exceeding standard pattern-spare tower group switching strategy; based on the exceeding standard pattern-spare tower group switching strategy, the waste gas change parameters of the N treatment towers are dynamically switched and regulated to determine the multi-tower linkage control strategy parameters.

[0103] Furthermore, the switching control module 40 in the embodiment of the present application is further configured to perform the following steps:

[0104] Based on the exceeding standard mode-spare tower group switching strategy, the N treatment tower exhaust gas change parameters are matched with the spare tower group to obtain the spare switching tower group; the PID controller is used to regulate and analyze the spare switching tower group based on the N treatment tower exhaust gas change parameters to determine the multi-tower linkage control strategy parameters.

[0105] Through the detailed description of the multi-tower linkage control method in exhaust gas treatment in the foregoing specification, those skilled in the art can clearly understand the multi-tower linkage control system in exhaust gas treatment in this embodiment. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional modules and beneficial effects, the relevant details can be referred to the method section.

[0106] In addition, based on the same inventive concept as the aforementioned embodiment 1, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor, the various processes of the embodiment of the multi-tower linkage control method in the above-mentioned waste gas treatment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0107] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-tower linkage control method for waste gas treatment, characterized in that: The method comprises: Build a multi-tower linkage control architecture, which includes a waste gas collaborative treatment tower group and a linkage control module, and deploy an Internet of Things monitoring module in each treatment tower of the waste gas collaborative treatment tower group; Starting the exhaust gas composition analyzer to collect and obtain component data of the target exhaust gas, activating the linkage control module to perform working condition matching analysis on the component data and the exhaust gas collaborative treatment tower group, and determining process parameters of N matching treatment towers and the target treatment tower group; Based on the process parameters of the target treatment tower group, the target exhaust gas is transported to the N matching treatment towers for linkage treatment, and at the same time, the exhaust gas change parameters of the N treatment towers are obtained through the Internet of Things monitoring module; When the exhaust gas change parameters of the N treatment towers reach the exhaust gas switching threshold, a multi-tower linkage switching strategy is adopted to dynamically switch and regulate the exhaust gas change parameters of the N treatment towers, determine the multi-tower linkage control strategy parameters, and use the multi-tower linkage control strategy parameters to perform closed-loop treatment control on the target exhaust gas.

2. The multi-tower linkage control method for waste gas treatment according to claim 1, characterized in that: The determining of the process parameters of the N matching treatment towers and the target treatment tower group includes: Loading the waste gas treatment process database of the waste gas collaborative treatment tower group through the linkage control module, the waste gas treatment process database including waste gas composition data, tower group process data and corresponding treatment effect data; Performing component feature identification and tower group operating condition clustering on the exhaust gas treatment process database to obtain multi-tower operating condition processing rules; Matching the component data to tower group operating conditions according to the multi-tower operating condition processing rules to determine N matching processing towers; Based on the waste gas treatment process database, tower group process analysis is performed on the N matching treatment towers and the component data to obtain target treatment tower group process parameters.

3. The multi-tower linkage control method for waste gas treatment according to claim 2, characterized in that: The obtaining of multi-tower operating condition processing rules includes: Extracting key features of waste gas from the waste gas treatment process database to obtain waste gas component characteristic dimensions, wherein the waste gas component characteristic dimensions include component proportion, concentration gradient, component toxicity, and component polarity; Classifying and identifying the waste gas treatment process database using the waste gas component characteristic dimension to determine a waste gas component characteristic parameter set; Correlating and mapping the exhaust gas component characteristic parameter set with the tower group process data in the exhaust gas treatment process database to obtain an exhaust gas characteristic-tower group process data set; K-means cluster analysis and operating condition rule generation are performed based on the exhaust gas characteristics-tower group process data set to obtain the multi-tower operating condition operation processing rules.

4. The multi-tower linkage control method for waste gas treatment according to claim 2, characterized in that: The process parameters of the target treatment tower group are obtained, including: Extracting associated data from the waste gas treatment process database based on the N matching treatment towers to obtain a treatment tower group treatment process data set; Obtaining a process factor set of a treatment tower group, wherein the process factor set of the treatment tower group includes a tower group connection mode, a tower group operation sequence, and a tower group operation parameter; Initializing particles of the treatment tower group treatment process data set using the treatment tower group process factor set to construct a treatment tower group process particle space; The component data is used as a constraint parameter, and tower group process analysis is performed in the process particle space of the treatment tower group to obtain target treatment tower group process parameters.

5. The multi-tower linkage control method for waste gas treatment according to claim 4, characterized in that: The process parameters of the target treatment tower group are obtained, including: Using the component data as constraint parameters, a tower group process search is performed in the processing tower group process particle space to obtain a processing tower group process particle retrieval library; Obtaining a tower group exhaust gas treatment target, performing an index evaluation and fitting on the process parameter space of the treatment tower group based on the tower group exhaust gas treatment target, and constructing a tower group exhaust gas treatment effect function; Using the exhaust gas treatment effect function of the tower group, iterative evaluation and optimization are performed in the process particle retrieval library of the treatment tower group to determine the initial process parameters of the treatment tower group with the maximum particle fitness; The initial process parameters of the treatment tower group are modified based on the characteristic information of the N matching treatment towers to determine the target process parameters of the treatment tower group.

6. The multi-tower linkage control method for waste gas treatment according to claim 5, characterized in that: The step of determining the target processing tower group process parameters includes: Determining the processing capacity and aging degree of the N processing towers according to the characteristic information of the N matching processing towers; The ratio of the aging degree of the N treatment towers to the treatment capacity of the N treatment towers is used as the redundancy coefficient of the N treatment towers; Redundancy correction is performed on the initial process parameters of the treatment tower group based on the N process tower redundancy coefficients to determine the target process parameters of the treatment tower group.

7. The multi-tower linkage control method for waste gas treatment according to claim 1, characterized in that: Determining the multi-tower linkage control strategy parameters includes: Performing exhaust gas exceeding standard pattern identification on the multi-tower linkage switching strategy to obtain an exhaust gas exceeding standard pattern set, and selecting a spare tower in the exhaust gas collaborative treatment tower group according to the exhaust gas exceeding standard pattern set to obtain a spare collaborative treatment tower group; Associating the exhaust gas exceeding standard mode set with the standby collaborative treatment tower group by switching mode to obtain an exceeding standard mode-standby tower group switching strategy; Based on the over-standard mode-standby tower group switching strategy, the exhaust gas change parameters of the N treatment towers are dynamically switched and regulated to determine the multi-tower linkage control strategy parameters.

8. The multi-tower linkage control method for waste gas treatment according to claim 7, characterized in that: The method of dynamically switching and regulating the exhaust gas change parameters of the N treatment towers based on the over-standard mode-standby tower group switching strategy to determine the multi-tower linkage control strategy parameters includes: Based on the over-standard mode-standby tower group switching strategy, the N treatment tower exhaust gas change parameters are matched with the standby tower group to obtain a standby switching tower group; A PID controller is used to regulate and analyze the standby switching tower group based on the exhaust gas change parameters of the N treatment towers to determine the multi-tower linkage control strategy parameters.

9. The multi-tower linkage control system in waste gas treatment is characterized by: The system is used to execute the multi-tower linkage control method in exhaust gas treatment according to any one of claims 1 to 8, comprising: A control architecture building module is used to build a multi-tower linkage control architecture, which includes a waste gas collaborative treatment tower group and a linkage control module, and an Internet of Things monitoring module is deployed in each treatment tower of the waste gas collaborative treatment tower group; A working condition matching and analysis module is used to start the exhaust gas composition analyzer to collect and obtain the component data of the target exhaust gas, activate the linkage control module to perform working condition matching analysis on the component data and the exhaust gas collaborative treatment tower group, and determine the process parameters of N matching treatment towers and the target treatment tower group; A linkage processing module, configured to transport the target exhaust gas to the N matching treatment towers for linkage processing based on the process parameters of the target treatment tower group, and simultaneously obtain the exhaust gas change parameters of the N treatment towers through the Internet of Things monitoring module; The switching control module is used to dynamically switch and control the exhaust gas change parameters of the N treatment towers using a multi-tower linkage switching strategy when the exhaust gas change parameters of the N treatment towers reach the exhaust gas switching threshold, determine the multi-tower linkage control strategy parameters, and perform closed-loop treatment control on the target exhaust gas through the multi-tower linkage control strategy parameters.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-tower linkage control method for exhaust gas treatment as described in any one of claims 1 to 8 are implemented.

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