Multi-tower linkage control methods, systems and media in waste gas treatment

Abstract

This application provides a multi-tower linkage control method, system, and medium for waste gas treatment, relating to the field of automatic control technology. The method includes: establishing a multi-tower linkage control architecture; collecting target waste gas composition data; performing operational condition matching analysis on the composition data and the waste gas co-treatment tower group to determine the process parameters of the matching treatment tower and the target treatment tower group for linkage processing; simultaneously monitoring the waste gas change parameters of the treatment towers; when the waste gas change parameters reach the waste gas switching threshold, employing a multi-tower linkage switching strategy for dynamic switching and regulation; and determining the multi-tower linkage control strategy parameters for closed-loop treatment control of the target waste gas. This application solves the technical problem in existing technologies where the lack of a real-time sensing and dynamic response mechanism based on waste gas composition changes leads to low utilization of the treatment tower group and an inability to efficiently and stably cope with complex and variable waste gas treatment needs, thus achieving the technical effect of improving waste gas treatment efficiency and stability.

Description

Technical Field

[0001] This application relates to the field of automatic control technology, specifically to a multi-tower linkage control method, system, and medium in waste gas treatment. Background Technology

[0002] With the accelerating pace of industrialization, the emission of harmful waste gases from various industrial production processes is becoming increasingly serious. In waste gas treatment, the method of using multiple treatment towers in a coordinated manner is widely adopted due to its ability to improve waste gas purification efficiency. Existing multi-tower coordinated control methods for waste gas typically rely on fixed treatment flows and preset process parameters, feeding the target waste gas sequentially or in parallel into multiple treatment towers for purification. Some systems switch tower groups through simple time rotation or load balancing rules. However, these waste gas treatment systems are mostly based on static control modes, lacking real-time response capabilities to the complexity of waste gas composition and concentration fluctuations. This leads to the inability to promptly match the optimal treatment process path during actual operation, easily resulting in some treatment towers operating under overload while others are idle, causing uneven tower group utilization and decreased treatment efficiency. Furthermore, due to the lag in control strategy response, the waste gas treatment effect fluctuates significantly, making it difficult to consistently and stably meet emission standards. Summary of the Invention

[0003] This application provides a multi-tower linkage control method, system, and medium for waste gas treatment. It solves the technical problem in the prior art that the waste gas treatment system has a fixed control mode and lacks a real-time sensing and dynamic response mechanism based on changes in waste gas composition, resulting in low utilization of treatment tower groups and an inability to efficiently and stably cope with complex and variable waste gas treatment needs. It achieves the technical effect of improving the efficiency and stability of waste gas treatment by dynamically switching and regulating the multi-tower linkage treatment process based on real-time monitoring of waste gas.

[0004] In view of the above problems, this application provides a multi-tower linkage control method for waste gas treatment. The method includes: constructing a multi-tower linkage control architecture, which includes a waste gas co-processing tower group and a linkage control module, and deploying an Internet of Things (IoT) monitoring module in each processing tower of the waste gas co-processing tower group; activating a waste gas composition analyzer to collect and acquire the composition data of the target waste gas, activating the linkage control module to perform operating condition matching analysis on the composition data and the waste gas co-processing tower group, and determining the process parameters of N matching processing towers and the target processing tower group; based on the process parameters of the target processing tower group, transporting the target waste gas to the N matching processing towers for linkage treatment, and simultaneously obtaining the waste gas change parameters of the N processing towers through the IoT monitoring module; when the waste gas change parameters of the N processing towers reach the waste gas switching threshold, using a multi-tower linkage switching strategy to dynamically switch and regulate the waste gas change parameters of the N processing towers, determining the multi-tower linkage control strategy parameters, and performing closed-loop treatment control of the target waste gas through the multi-tower linkage control strategy parameters.

[0005] On the other hand, this application also provides a multi-tower linkage control system for waste gas treatment. The system includes: a control architecture building module for building a multi-tower linkage control architecture, which includes a waste gas co-processing tower group and a linkage control module, and an Internet of Things (IoT) monitoring module is deployed in each processing tower of the waste gas co-processing tower group; a working condition matching and analysis module for activating a waste gas composition analyzer to collect the composition data of the target waste gas, activating the linkage control module to perform working condition matching and analysis on the composition data and the waste gas co-processing tower group, and determining the process parameters of N matching processing towers and the target processing tower group; a linkage processing module for transporting the target waste gas to the N matching processing towers for linkage processing based on the process parameters of the target processing tower group, and simultaneously obtaining the waste gas change parameters of the N processing towers through the IoT monitoring module; and a switching and control module for dynamically switching and controlling the waste gas change parameters of the N processing towers using a multi-tower linkage switching strategy when the waste gas change parameters of the N processing towers reach the waste gas switching threshold, determining the multi-tower linkage control strategy parameters, and performing closed-loop treatment control of the target waste gas through the multi-tower linkage control strategy parameters.

[0006] Thirdly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the multi-tower linkage control method for the above-mentioned waste gas treatment.

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

[0008] Building a multi-tower linkage control architecture lays the foundation for the intelligence and collaboration of the entire waste gas treatment solution. By setting up IoT monitoring modules in each treatment tower, real-time monitoring and data transmission of the internal conditions of each tower are achieved, providing the necessary hardware and data support for subsequent precise control and dynamic adjustment. Start the waste gas composition analyzer to collect waste gas composition data, and the linkage control module performs working condition matching analysis. This step can accurately determine the most suitable treatment tower combination and process parameters according to the actual composition of the waste gas, avoiding the problem of poor targeting in the traditional fixed mode and improving the accuracy and efficiency of waste gas treatment. Based on the determined process parameters, the waste gas is transported to the matching treatment tower for linkage treatment, and at the same time, the IoT module is used to monitor the waste gas change parameters. This step realizes multi-tower collaborative operation and can obtain key data during the treatment process in real time, providing a basis for further dynamic regulation. When the waste gas change parameters reach the switching threshold, a multi-tower linkage switching strategy is adopted for dynamic regulation, and the control strategy parameters are determined to achieve closed-loop control. This step can adjust the treatment method in a timely manner according to the real-time change of waste gas components, ensuring the stability and high efficiency of the treatment effect.

[0009] In summary, this application realizes the real-time collection and intelligent analysis of waste gas composition by building a multi-tower linkage control architecture including waste gas collaborative treatment tower groups, linkage control modules and IoT monitoring modules. It can dynamically match the treatment tower groups and process parameters according to the characteristics of the waste gas, and continuously monitor based on 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 the real-time optimization of the waste gas treatment path and process. The overall solution effectively solves the problems of lagging response, uneven utilization rate of treatment tower groups and unstable treatment effect in the existing waste gas treatment system, and achieves the technical effect of improving the waste gas treatment efficiency and ensuring stable and up-to-standard discharge.

[0010] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0011] Figure 1 It is a schematic flowchart of the multi-tower linkage control method in waste gas treatment provided by an embodiment of this application.

[0012] Figure 2 It is a schematic flowchart of determining N matching treatment towers and the process parameters of the target treatment tower group in the multi-tower linkage control method in waste gas treatment provided by an embodiment of this application.

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

[0014] Figure labeling: Control architecture construction module 10, working condition matching and analysis module 20, linkage processing module 30, switching and control module 40. Detailed Implementation

[0015] This application provides a multi-tower linkage control method, system, and medium for waste gas treatment, which solves the technical problem in the prior art where the waste gas treatment system has a fixed control mode and lacks a real-time sensing and dynamic response mechanism based on changes in waste gas composition, resulting in low utilization of treatment tower groups and an inability to efficiently and stably cope with complex and variable waste gas treatment needs. It achieves the technical effect of improving the efficiency and stability of waste gas treatment 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 in the embodiment of this application, a multi-tower linkage control method for waste gas treatment is provided, the method comprising:

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

[0018] Specifically, before implementing multi-tower coordinated control, a complete multi-tower coordinated control architecture needs to be established as the foundation for subsequent dynamic regulation and efficient coordinated processing. First, several waste gas treatment towers are selected as a waste gas co-treatment tower group. This group includes various types of treatment towers for waste gas purification, such as adsorption-desorption towers, wet scrubbing towers, and activated carbon towers. Simultaneously, an IoT monitoring module is installed inside each treatment tower. This module includes various types of sensors such as gas sensors, temperature and humidity sensors, and a wireless communication module. It is used to collect various environmental parameters such as waste gas composition, concentration, temperature, and pressure in real time, and transmit this data back to the control module or system for subsequent decision-making. Then, a coordinated control module (which can be based on an industrial control server or industrial PC platform) is introduced to analyze waste gas conditions, allocate treatment tasks, and switch tower group states to manage and coordinate the operation of each tower in real time.

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

[0020] Specifically, the target waste gas refers to the waste gas that needs to be treated. Before starting the waste gas treatment process, a waste gas composition analyzer (such as a gas chromatograph or infrared analyzer) is used to detect the various components and their concentrations in the target waste gas, obtaining composition data. Then, the linkage control module is activated. This module calls the built-in process database (which pre-stores operational experience data for various waste gases and tower groups). Based on the composition data, it matches the waste gas co-treatment tower groups in the process database, selecting N matching treatment towers for the target waste gas and the corresponding target treatment tower group process parameters. These target treatment tower group process parameters include the treatment process flow between each tower and the operating conditions parameters of each tower.

[0021] This step enables precise analysis of the waste gas composition and intelligent matching of the treatment tower group, improving the targeting and efficiency of waste gas treatment and ensuring the stability and reliability of the treatment effect.

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

[0023] Specifically, based on the process parameters of the target treatment tower group matched in the previous step, the target waste gas is delivered to N matching treatment towers for coordinated treatment via valve control or flow distributors. For example, a PLC-controlled multi-way electric valve enables quantitative gas diversion. Simultaneously, an IoT monitoring module deployed in each treatment tower begins to collect real-time processing status data (such as outlet waste gas concentration, tower temperature, etc.), forming N waste gas change parameters for each treatment tower. This data is uploaded to the coordinated control module to determine whether the processing status is stable and whether strategy adjustments are needed. For example, in a waste gas treatment system containing volatile organic compounds (VOCs), based on the process parameters of the target treatment tower group, the waste gas is sequentially treated through a catalytic oxidation tower and an activated carbon adsorption tower, while the IoT monitoring module monitors the concentration changes of VOCs in the waste gas in real time.

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

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

[0026] Specifically, the exhaust gas switching threshold refers to the triggering standard when exhaust gas parameters reach set limit values, including concentration exceeding the standard, temperature upper limit, humidity upper limit, etc. When the exhaust gas parameters reach or exceed this threshold, it indicates that the current treatment tower combination or process parameters can no longer meet the treatment requirements and switching is necessary. The linkage control module continuously monitors the exhaust gas parameters of N treatment towers. When it detects that the parameters have reached the set exhaust gas switching threshold, it immediately triggers the multi-tower linkage switching strategy to dynamically switch and regulate the treatment tower combination or process parameters, determining new multi-tower linkage control strategy parameters. This multi-tower linkage switching strategy is a dynamic adjustment rule designed to cope with exhaust gas treatment anomalies, such as the activation of the backup tower and flow redistribution. The strategy formulation process can be based on rule base retrieval combined with PID dynamic control algorithm execution. Based on real-time monitoring and strategy calculation results, new multi-tower linkage control strategy parameters are formed, such as "backup tower T5 is activated, and the inlet flow rate is adjusted to 60% of the original." Executing these multi-tower linkage control strategy parameters for exhaust gas treatment forms a complete closed-loop treatment control mechanism, realizing adaptive adjustment of the treatment process and maintaining continuous compliance with exhaust gas treatment standards.

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

[0028] Step S210: Load the waste gas treatment process database of the waste gas co-treatment tower group through the linkage control module. The waste gas treatment process database includes waste gas composition data, tower group process data and corresponding treatment effect data.

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

[0030] Step S230: Perform tower group operation condition matching on the component data according to the multi-tower operating condition processing rules to determine N matching processing towers.

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

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

[0033] First, the waste gas composition data in the waste gas treatment process database is characterized by its composition features. For example, key pollutants (such as benzene, organic amines, and acidic gases) are categorized, and concentration range characteristics are extracted. Next, based on the identification results and existing treatment records, a clustering algorithm (such as K-means) is used to divide the historical tower group operation data into several classes, each representing a specific operating condition (such as the low-temperature adsorption treatment condition for high-concentration organic waste gas). Finally, multi-tower operating condition rules are formed. These rules clarify which combinations of treatment towers and their corresponding operating parameters can achieve the best treatment effect under what waste gas composition characteristics and operating conditions, guiding subsequent rapid matching and decision-making.

[0034] Based on the established multi-tower operating rules, the real-time collected component data is compared. The matching process can employ a rule engine or an intelligent matching method based on similarity calculations (such as Euclidean distance or cosine similarity). Through matching, the most suitable tower group for treating this type of waste gas component and concentration is quickly identified, determining N matching treatment towers, ready for subsequent process analysis.

[0035] Based on the waste gas treatment process database, an in-depth analysis is conducted on the selected N matching treatment towers and their current component data to refine the corresponding target treatment tower group process parameters. Process analysis includes selecting the type of adsorbent or reactant, setting the gas treatment flow rate, operating temperature, reaction time, and process flow, among other key parameters. These detailed process parameters are then integrated to form the target treatment tower group process parameters, guiding each treatment tower to operate optimally during actual waste gas treatment.

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

[0037] Furthermore, step S220 includes:

[0038] Step S221: Extract key features of the waste gas from the waste gas treatment process database to obtain the waste gas component feature dimensions, which 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 composition feature dimensions to determine the waste gas composition feature parameter set.

[0040] Step S223: Associate and map the set of exhaust gas component characteristic parameters with the tower group process data in the exhaust gas treatment process database to obtain the exhaust gas characteristic-tower group process dataset.

[0041] Step S224: Perform K-means clustering analysis and generate operating condition rules based on the exhaust gas characteristics-tower group process dataset to obtain the multi-tower operating condition processing rules.

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

[0043] The waste gas treatment process database is classified and labeled using the waste gas composition feature dimension, generating a feature vector for each waste gas sample, forming a waste gas composition feature parameter set. This classification and labeling achieves the structuring and standardization of waste gas data, facilitating subsequent cluster analysis.

[0044] Based on the set of exhaust gas component characteristic parameters, the treatment effect data corresponding to different tower group processes in the exhaust gas treatment process database are searched. The optimal tower group process configuration (with the best treatment effect) that matches each exhaust gas component characteristic parameter is found. The correspondence between exhaust gas characteristics and tower group process parameters is established, and an exhaust gas characteristic-tower group process dataset is generated. This establishes a direct link between exhaust gas characteristics and treatment processes, providing data support for quickly matching the optimal tower group strategy.

[0045] Based on the exhaust gas characteristics-tower group process dataset, K-means clustering is performed to automatically classify similar exhaust gas samples and their corresponding tower group operating conditions. Then, multi-tower operating condition processing rules are generated according to the data characteristics of each cluster. These rules can be solidified and dynamically invoked using a rule engine.

[0046] The above steps provide a complete set of intelligent treatment guidelines for the entire waste gas treatment process, making it easy to quickly and accurately select the optimal treatment solution based on different waste gas characteristics.

[0047] Furthermore, step S240 includes:

[0048] Step S241: Based on the N matching treatment towers, perform correlation data extraction on the waste gas treatment process database to obtain the treatment tower group treatment process dataset.

[0049] Step S242: Obtain the process factor set of the processing tower group, which includes the tower group connection mode, tower group working sequence, and tower group working parameters.

[0050] Step S243: Initialize the processing process dataset of the processing tower group using the process factor set of the processing tower group to construct the process particle space of the processing tower group.

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

[0052] Specifically, based on the identified N matching treatment towers, the linkage control module extracts relevant treatment process data from the waste gas treatment process database to generate a treatment tower group treatment process dataset. This dataset contains the treatment process parameters for each matching treatment tower, such as the tower group's operating temperature, flow rate, and load range, ensuring that these parameters cover the treatment requirements for different waste gas components. Next, the process factor set for the treatment tower group is obtained, including the tower group's connection mode (e.g., series or parallel connection), operating sequence (e.g., which tower processes first, which tower processes last), and the tower group's operating parameters (e.g., temperature setting, flow rate adjustment). These process factor sets can be summarized and defined through process design documents or expert experience.

[0053] Based on the processing tower group's process dataset and process factor set, a particle swarm optimization (PSO) algorithm is used for particle initialization, transforming each combination of process factors into a single particle, thus constructing the processing tower group's process particle space. Each particle represents a possible process scheme, which will be evaluated in subsequent optimization. Then, within the processing tower group's process particle space, using the target waste gas composition data as constraints, a tower group process analysis process is executed to screen and optimize the particles, selecting the most suitable process scheme for treating the target waste gas. During this process, the PSO algorithm is used to evaluate the merits of each particle in terms of treatment effect. The particles are iteratively optimized using the tower group's waste gas treatment objectives (such as maximizing treatment efficiency and minimizing operating costs) to ultimately find the processing tower group's process parameter combination with the highest particle fitness, which is the target processing tower group's process parameter. These parameters will be used for further waste gas treatment process execution and optimization control.

[0054] By following the above steps, we can ensure that the process adjustments during the waste gas treatment process are more scientific and precise, thereby achieving the best waste gas treatment effect and efficiently adapting to changes in different waste gas components.

[0055] Furthermore, step S244 includes:

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

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

[0058] Step S244-3: Use the exhaust gas treatment effect function of the tower group 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 highest particle fitness.

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

[0060] Specifically, firstly, the composition data of the target waste gas is used as constraint parameters and input into the process particle space of the treatment tower group. A tower group process search is then performed, traversing all possible combinations of process parameters (particles). Process schemes that meet the composition requirements are selected, forming a treatment tower group process particle retrieval library. This library records relevant data on different treatment tower combinations and process schemes, providing a broad foundation for subsequent optimization processes.

[0061] The system aims to obtain the treatment objectives for the exhaust gas from the treatment tower group, namely, to ensure that the treated exhaust gas emissions meet the prescribed standards. These objectives include control requirements for exhaust gas concentration, temperature, toxicity, etc. Based on these treatment objectives, the system performs index evaluation and fitting on the process parameter space of the treatment tower group to construct the exhaust gas treatment effect function. The effect function is used to quantify the exhaust gas treatment efficiency and stability of the treatment tower group under given parameters, thereby providing guidance for the optimization process. An example of the exhaust gas treatment effect function for the treatment tower group is as follows: F=ω1×Er+ω2×(1-Ec)+ω3×(1-Ep). Where, F represents the exhaust gas treatment effect of the treatment tower group, i.e., particle fitness. Er represents the removal rate of the main components of the exhaust gas, with a value ranging from 0 to 1, and a higher value indicates a better treatment effect; Ec represents the energy consumption ratio in the treatment process, with a value ranging from 0 to 1, and a lower value indicates better energy efficiency; Ep represents the proportion of newly generated secondary pollutants in the exhaust gas treatment process, with a value ranging from 0 to 1, and a lower value indicates a better by-product control effect. ω1, ω2, and ω3 are the importance weight coefficients for 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, using the treatment effect function of the tower group's exhaust gas, iterative evaluation and optimization are performed in the process particle retrieval library of the treatment tower group. Initially, the particles in the process particle retrieval library of the treatment tower group are randomly distributed, and each particle has its own position (process parameter combination) and velocity. The fitness of each particle is calculated, that is, its fitness value is calculated by substituting it into the treatment effect function. Then, the particles are sorted according to their fitness values ​​to find the current optimal particle. In subsequent iterations, each particle adjusts its velocity and position based on its own historical optimal position and global optimal position, continuously updates its process parameter combination, and recalculates its fitness. This process is repeated until the set number of iterations or fitness convergence condition is reached. Finally, the initial treatment tower group process parameters with the highest particle fitness are determined.

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

[0064] Furthermore, step S244-4 includes:

[0065] Step S244-41: Determine the processing capacity and aging degree 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 processing towers to the processing capacity of the N processing towers is used as the redundancy coefficient of the N processing towers.

[0067] Steps S244-43: Based on the redundancy coefficients of the N processing towers, perform redundancy correction on the process parameters of the initial processing tower group to determine the process parameters of the target processing tower group.

[0068] Specifically, firstly, based on the performance parameters, operating status, design parameters, and historical operating data of N matched treatment towers, the processing capacity and aging degree of each tower are determined using relevant models or empirical formulas. Processing capacity refers to the maximum throughput or efficiency of each tower under specific operating conditions, while aging degree reflects the potential performance degradation of the tower during long-term use. For example, the processing capacity of an activated carbon adsorption tower can be calculated based on its adsorption capacity and remaining adsorption capacity, and the aging degree can be determined by the percentage decrease in adsorption efficiency or the ratio of the amount of waste gas already treated to the amount of waste gas corresponding to the design lifespan. For catalytic oxidation towers, the processing capacity can be evaluated by the catalyst activity and reaction efficiency, and the aging degree can be determined by the degree of decrease in catalyst activity or the ratio of the operating time to the design lifespan.

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

[0070] Based on the calculated redundancy coefficient, the process parameters of the initial treatment tower group are redundantly corrected. During this process, the process parameters of the treatment tower group (such as operating sequence, connection mode, and treatment load) are adjusted appropriately according to the redundancy correction results, thereby determining the final target treatment tower group process parameters to ensure maximum overall waste gas treatment efficiency while avoiding overload or resource waste. For example, for activated carbon adsorption towers, a large redundancy coefficient indicates limited remaining usable capacity, requiring an appropriate increase in adsorption time to ensure adsorption effectiveness. For catalytic oxidation towers, a small redundancy coefficient indicates relatively sufficient treatment capacity, allowing for a reduction in catalytic temperature to save energy. For wet scrubbing towers, a moderate redundancy coefficient allows for appropriate adjustment of the scrubbing liquid flow rate. This process can be achieved by establishing a correction model or using empirical formulas to adjust process parameters based on the magnitude of the redundancy coefficient. For instance, using a linear correction method, a larger redundancy coefficient results in a larger correction magnitude.

[0071] Furthermore, step S400 includes:

[0072] Step S410: Identify the exhaust gas exceeding the standard mode of the multi-tower linkage switching strategy, obtain the exhaust gas exceeding the standard mode set, and select a backup tower in the exhaust gas co-treatment tower group according to the exhaust gas exceeding the standard mode set to obtain a backup co-treatment tower group.

[0073] Step S420: Associate the set of exhaust gas exceeding the standard mode with the backup co-processing tower group to obtain the exceeding mode-backup tower group switching strategy.

[0074] Step S430: Based on the above-mentioned over-standard mode-standby tower group switching strategy, dynamically switch and regulate the waste gas change parameters of the N treatment towers to determine the multi-tower linkage control strategy parameters.

[0075] Specifically, the linkage control module continuously monitors the fluctuating parameters of the exhaust gas from N matched treatment towers. When the exhaust gas emission parameters (such as the concentration of key pollutants, gas temperature, etc.) of a certain treatment tower exceed the set standard threshold, the exhaust gas exceedance mode identification is initiated. Based on the abnormal trends of exhaust gas composition, the magnitude and duration of pollutant concentration exceedances, cluster analysis and pattern matching methods are used to identify the exceedance modes in the current exhaust gas treatment process and generate an exhaust gas exceedance mode set. For example, instantaneous exceedance of toxic gas concentration, continuous exceedance of gas temperature, and high-frequency exceedance of acidic gas concentration. Subsequently, based on the characteristics of each exceedance mode (such as the type of exceedance component, exceedance intensity, fluctuation characteristics, etc.), backup treatment tower units with redundant processing capacity, no overload, and suitable for exceedance components are selected from the exhaust gas co-treatment tower group to form a backup co-treatment tower group.

[0076] The identified sets of exhaust gas exceeding standards are mapped and associated with corresponding backup co-processing tower groups to generate an exceeding standard mode-backup tower group switching strategy. The switching strategy includes key parameters such as switching conditions (e.g., exceeding standard trigger threshold), backup tower activation order, flow allocation ratio, and switching delay setting to ensure that when exhaust gas exceeds standards, it can quickly switch to a suitable backup tower group for supplementary treatment or load sharing.

[0077] Based on the aforementioned excess mode-backup tower switching strategy, the changes in exhaust gas parameters of N treatment towers are monitored and dynamically analyzed in real time. When the corresponding excess mode trigger condition is detected, the backup tower is automatically switched, the exhaust gas flow distribution is adjusted, and the linkage treatment parameters, such as gas velocity and reactant dosage, are simultaneously optimized 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 the closed-loop control of the target exhaust gas and ensuring the continuity of exhaust gas treatment and compliance with emission standards.

[0078] Furthermore, step S430 includes:

[0079] Step S431: Based on the above-mentioned excess mode-backup tower group switching strategy, perform backup tower group matching on the exhaust gas change parameters of the N treatment towers to obtain backup switching tower groups.

[0080] Step S432: Use a PID controller to analyze and regulate the standby switching tower group based on the changing parameters of the exhaust gas from the N treatment towers, and determine the parameters of the multi-tower linkage control strategy.

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

[0082] Next, a proportional-integral-derivative (PID) controller is used to dynamically control and analyze the changing parameters of the exhaust gas from the N treatment towers and the standby switching tower group. The PID controller takes the changing exhaust gas parameters (such as concentration exceeding standards or abnormal flow rate deviations) as input, calculates the error, and outputs control quantities in real time according to the set proportional coefficient, integral time, and derivative time. This adjusts key process parameters such as the inlet flow rate, reactant dosing rate, and operating pressure of the standby switching tower group, thereby achieving a smooth transition and balanced treatment load for the standby switching tower group. Through continuous correction based on the feedback of exhaust gas parameters during the switching dynamic process, the multi-tower linkage control strategy parameters that meet exhaust gas emission standards and process stability are finally determined. The linkage control module applies these strategy parameters to the control logic of the entire tower group, completing seamless switching and intelligent linkage control of exhaust gas treatment capacity among multiple towers.

[0083] Through the above steps, the backup switching tower group can be accurately matched according to the changing parameters of the exhaust gas, and the PID controller can be used for fine control to ensure the stability of the exhaust gas treatment process and the accuracy of the treatment effect, thereby 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 in this application has the following beneficial effects:

[0085] This application embodiment establishes a multi-tower linkage control architecture comprising a waste gas co-treatment tower group, a linkage control module, and an IoT monitoring module. This architecture enables real-time acquisition and intelligent analysis of waste gas composition, dynamically matching treatment tower groups and process parameters based on waste gas characteristics, and continuously monitoring waste gas parameters during treatment. When a waste gas change reaches a set threshold, the system employs a multi-tower linkage switching strategy for dynamic adjustment, thereby achieving real-time optimization of the waste gas treatment path and process. The overall solution effectively solves the problems of sluggish response, uneven utilization of treatment tower groups, and unstable treatment effects in existing waste gas treatment systems, achieving the technical effect of improving waste gas treatment efficiency and ensuring stable emission compliance.

[0086] Example 2, as Figure 3 As shown, based on the same inventive concept as in Embodiment 1 above, this 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 co-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 co-treatment tower group.

[0088] The operating condition matching and analysis module 20 is used to start the exhaust gas composition analyzer to collect the composition data of the target exhaust gas, activate the linkage control module to perform operating condition matching and analysis on the composition data and the exhaust gas co-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 waste gas to the N matching processing towers for linkage processing based on the process parameters of the target processing tower group, and at the same time obtain the waste gas change parameters of the N processing towers through the Internet of Things monitoring module.

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

[0091] Furthermore, in this embodiment of the application, the working condition matching and parsing module 20 is also used to perform the following steps:

[0092] The linkage control module loads the waste gas treatment process database of the waste gas co-treatment tower group. The waste gas treatment process database includes waste gas composition data, tower group process data, and corresponding treatment effect data. The waste gas treatment process database is subjected to component feature identification and tower group operating condition clustering to obtain multi-tower operating condition processing rules. The component data is matched with tower group operating conditions according to the multi-tower operating condition processing rules to determine N matching treatment towers. Based on the waste gas treatment process database, the N matching treatment towers and the component data are analyzed to obtain the target treatment tower group process parameters.

[0093] Furthermore, in this embodiment of the application, the working condition matching and parsing module 20 is also used to perform the following steps:

[0094] Key features of the waste gas treatment process database are extracted to obtain waste gas component feature dimensions, including component proportion, concentration gradient, component toxicity, and component polarity. The waste gas treatment process database is then classified and labeled using these waste gas component feature dimensions to determine a set of waste gas component feature parameters. These parameters are then mapped to the tower group process data in the waste gas treatment process database to obtain a waste gas feature-tower group process dataset. Based on this dataset, K-means clustering analysis and operating condition rule generation are performed to obtain the multi-tower operating condition processing rules.

[0095] Furthermore, in this embodiment of the application, the working condition matching and parsing module 20 is also used to perform the following steps:

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

[0097] Furthermore, in this embodiment of the application, the working condition matching and parsing module 20 is also used to perform the following steps:

[0098] Using the component data as constraint parameters, a tower group process search is performed within 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 process parameter space of the treatment tower group is evaluated and fitted 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 within the process particle retrieval library of the treatment tower group to determine the initial treatment tower group process parameters with the highest 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, in this embodiment of the application, the working condition matching and parsing module 20 is also used to perform the following steps:

[0100] Based on the characteristic information of the N matching processing towers, the processing capacity and aging degree of the N processing towers are determined; the ratio of the aging degree of the N processing towers to the processing capacity of the N processing towers is used as the redundancy coefficient of the N processing towers; based on the redundancy coefficient of the N processing towers, the process parameters of the initial processing tower group are redundantly corrected to determine the process parameters of the target processing tower group.

[0101] Furthermore, in this embodiment of the application, the switching control module 40 is also used to perform the following steps:

[0102] The multi-tower linkage switching strategy identifies exhaust gas exceeding patterns to obtain an exhaust gas exceeding pattern set. Based on the exhaust gas exceeding pattern set, a backup tower is selected within the exhaust gas co-treatment tower group to obtain a backup co-treatment tower group. The exhaust gas exceeding pattern set and the backup co-treatment tower group are associated with switching modes to obtain an exceeding pattern-backup tower group switching strategy. Based on the exceeding pattern-backup tower group switching strategy, the exhaust gas change parameters of the N treatment towers are dynamically switched and controlled to determine the multi-tower linkage control strategy parameters.

[0103] Furthermore, in this embodiment of the application, the switching control module 40 is also used to perform the following steps:

[0104] Based on the aforementioned exceedance mode-backup tower group switching strategy, the exhaust gas variation parameters of the N treatment towers are matched with backup tower groups to obtain backup switching tower groups; a PID controller is used to regulate and analyze the backup switching tower groups based on the exhaust gas variation parameters of the N treatment towers to determine the parameters of the multi-tower linkage control strategy.

[0105] Through the foregoing detailed description of the multi-tower linkage control method in waste gas treatment, those skilled in the art can clearly understand that the multi-tower linkage control system in waste gas treatment in this embodiment corresponds to the system disclosed in Embodiment 2, and has corresponding functional modules and beneficial effects as it corresponds to the method disclosed in Embodiment 1. For relevant details, please refer to the method section.

[0106] Furthermore, based on the same inventive concept as the aforementioned Embodiment 1, this application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the various processes of the above-described multi-tower linkage control method embodiment in waste gas treatment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0107] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded 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 includes: A multi-tower linkage control architecture is established, which includes a waste gas co-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 co-treatment tower group. The exhaust gas composition analyzer is started to collect the composition data of the target exhaust gas. The linkage control module is activated to perform operating condition matching analysis on the composition data and the exhaust gas co-treatment tower group to determine 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 coordinated treatment, and the waste gas change parameters of the N treatment towers are obtained through the Internet of Things monitoring module. When the flue gas change parameters of the N treatment towers reach the flue gas switching threshold, a multi-tower linkage switching strategy is adopted to dynamically switch and regulate the flue gas change parameters of the N treatment towers, determine the multi-tower linkage control strategy parameters, and perform closed-loop treatment control of the target flue gas through the multi-tower linkage control strategy parameters.

2. The multi-tower linkage control method for waste gas treatment as described in claim 1, characterized in that, The determination of process parameters for N matching processing towers and the target processing tower group includes: The linkage control module loads the waste gas treatment process database of the waste gas co-treatment tower group, which includes waste gas composition data, tower group process data and corresponding treatment effect data. The waste gas treatment process database is subjected to component feature identification and tower group operating condition clustering to obtain multi-tower operating condition processing rules; According to the multi-tower operating condition processing rules, the component data is matched with tower group operating conditions to determine N matching processing towers; Based on the waste gas treatment process database, the process parameters of the target treatment tower group are obtained by analyzing the N matching treatment towers and the component data.

3. The multi-tower linkage control method for waste gas treatment as described in claim 2, characterized in that, The rules for obtaining multi-tower operating conditions include: Key features of waste gas are extracted from the waste gas treatment process database to obtain the waste gas component feature dimensions, which include component proportion, concentration gradient, component toxicity and component polarity. The waste gas treatment process database is classified and identified using the waste gas composition feature dimensions to determine the waste gas composition feature parameter set. The exhaust gas component feature parameter set and the tower group process data in the exhaust gas treatment process database are correlated and mapped to obtain the exhaust gas feature-tower group process dataset. Based on the exhaust gas characteristics-tower group process dataset, K-means clustering analysis and operating condition rule generation are performed to obtain the multi-tower operating condition processing rules.

4. The multi-tower linkage control method for waste gas treatment as described in claim 2, characterized in that, The process parameters for obtaining the target processing tower group include: Based on the N matching treatment towers, the associated data of the waste gas treatment process database is extracted to obtain the treatment tower group treatment process dataset. Obtain the process factor set of the processing tower group, which includes the tower group connection mode, tower group working sequence, and tower group working parameters. The processing tower group process factor set is used to initialize the processing tower group processing process dataset with particles, and the processing tower group process particle space is constructed. Using the component data as constraint parameters, process analysis of the processing tower group is performed within the process particle space of the processing tower group to obtain the process parameters of the target processing tower group.

5. The multi-tower linkage control method for waste gas treatment as described in claim 4, characterized in that, The process parameters for obtaining the target processing tower group include: Using the component data as constraint parameters, a tower group process search is performed in the process particle space of the processing tower group to obtain the process particle retrieval library of the processing tower group. Obtain the exhaust gas treatment target of the tower group, and perform index evaluation and fitting on the process parameter space of the treatment tower group based on the exhaust gas treatment target to construct the exhaust gas treatment effect function of the tower group. The exhaust gas treatment effect function of the tower group is used to perform iterative evaluation and optimization in the process particle retrieval library of the treatment tower group to determine the initial process parameters of the treatment tower group with the highest particle fitness. Based on the characteristic information of the N matching processing towers, the process parameters of the initial processing tower group are corrected to determine the process parameters of the target processing tower group.

6. The multi-tower linkage control method for waste gas treatment as described in claim 5, characterized in that, The determination of the process parameters for the target processing tower group includes: Based on the characteristic information of the N matching processing towers, determine the processing capacity and aging degree of the N processing towers; The ratio of the aging degree of the N processing towers to the processing capacity of the N processing towers is used as the redundancy coefficient of the N processing towers. Based on the redundancy coefficients of the N processing towers, the process parameters of the initial processing tower group are redundantly corrected to determine the process parameters of the target processing tower group.

7. The multi-tower linkage control method for waste gas treatment as described in claim 1, characterized in that, The determination of the multi-tower linkage control strategy parameters includes: The multi-tower linkage switching strategy is used to identify the exhaust gas exceeding the standard mode to obtain the exhaust gas exceeding the standard mode set, and the backup tower is selected in the exhaust gas co-treatment tower group according to the exhaust gas exceeding the standard mode set to obtain the backup co-treatment tower group. The set of exhaust gas exceeding the standard mode and the backup co-processing tower group are associated with the switching mode to obtain the exceeding mode-backup tower group switching strategy. Based on the aforementioned over-standard mode-backup tower group switching strategy, the waste gas change parameters of the N treatment towers are dynamically switched and controlled to determine the multi-tower linkage control strategy parameters.

8. The multi-tower linkage control method for waste gas treatment as described in claim 7, characterized in that, The dynamic switching and regulation of the exhaust gas change parameters of the N treatment towers based on the over-standard mode-standby tower group switching strategy, and the determination of multi-tower linkage control strategy parameters, include: Based on the above-mentioned excess mode-backup tower group switching strategy, the spare tower group is matched with the spare tower group for the N treatment tower exhaust gas change parameters to obtain the spare switching tower group. A PID controller is used to analyze and regulate the standby switching tower group based on the changing parameters of the exhaust gas from the N treatment towers, thereby determining the parameters of the multi-tower linkage control strategy.

9. A multi-tower linkage control system for waste gas treatment, characterized in that, The system is used to execute the multi-tower linkage control method for waste gas treatment according to any one of claims 1-8, including: A control architecture building module is used to build a multi-tower linkage control architecture, which includes a waste gas co-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 co-treatment tower group. The operating condition matching and analysis module is used to start the exhaust gas composition analyzer to collect the composition data of the target exhaust gas, and activate the linkage control module to perform operating condition matching and analysis on the composition data and the exhaust gas co-treatment tower group to determine the process parameters of N matching treatment towers and the target treatment tower group. The linkage processing module is used to transport the target waste gas to the N matching processing towers for linkage processing based on the process parameters of the target processing tower group, and at the same time obtain the waste gas change parameters of the N processing towers through the Internet of Things monitoring module. The switching and control module is used to dynamically switch and control the flue gas change parameters of the N treatment towers when the flue gas change parameters of the N treatment towers reach the flue gas switching threshold, determine the multi-tower linkage control strategy parameters, and perform closed-loop treatment control of the target flue 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 the processor, it implements the steps in the multi-tower linkage control method for waste gas treatment as described in any one of claims 1-8.

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