Efficient air purification system for mixing photocatalysis and biological reaction

Abstract

The invention provides a photocatalysis and biological reaction mixed efficient air purification system, and relates to the technical field of biological air purification, the system comprises a photocatalysis reaction module, a biological reaction module, an intelligent control module, a sensor monitoring module, a data analysis and optimization module, and a modularization and self-maintenance module; the system is combined with a photocatalytic reaction module and a biological reaction module, a multi-stage degradation and efficient decomposition treatment mode is adopted, the photocatalytic reaction module firstly degrades pollutants in air into intermediate products, then the biological reaction module further degrades the intermediate products through microorganisms, and finally thorough purification is achieved. According to the multi-stage synergistic reaction, the air purification efficiency is remarkably improved, thorough removal of pollutants in the treatment process is ensured, the environmental risk is reduced, various indexes in the degradation process are dynamically adjusted through a multi-target optimization algorithm, it is ensured that each stage achieves the optimal effect, and the performance and stability of the whole system are further improved.

Description

Technical Field

[0001] The invention relates to the technical field of biological air purification, in particular to a high-efficiency air purification system combining photocatalysis with biological reaction. Background Art

[0002] According to the clean room equipped with a non-thermal plasma air disinfection purifier disclosed in China Publication No. "CN101922765B", the clean room includes an air purification unit, a reactor power supply, a fan, a clean room, a fresh air system, a controller, a return air ditch, an air filter, a static pressure box, a return air duct and a grid floor. The air purification unit is installed in the static pressure box at the upper part of the clean room to form an airtight environment. The air purification unit is a plasma reactor, which is provided with a positive electrode made of a plurality of nickel-chromium metal wires or metal strips arranged in parallel at equal distances in the same plane, and the two ends are fixed in corresponding grooves or protrusions on a conductive rail to prevent micro-discharge. The switch tube in the reactor power supply is a single-ended flyback inverter. The present invention provides a clean room with comprehensive air purification and disinfection effects, high efficiency, low energy consumption, low noise, simple structure, low maintenance cost and long service life. It is used in industrial clean rooms or biological clean rooms.

[0003] According to the photocatalytic air purifier control system disclosed in China's publication number "CN116447711A", the amount of polluted air sucked into the air inlet is adjusted by controlling the speed of the electronically controlled motor. After being sucked in, the polluted air first passes through the filter layer in the air evolution unit to adsorb suspended objects, and then is irradiated by the ultraviolet light source of the ultraviolet lamp tube under the photocatalyst to react chemically with harmful gases to achieve the purpose of purification. Finally, the clean air is discharged from the air outlet. The speed of the electronically controlled motor and the power of the ultraviolet lamp tube are specifically controlled as follows: the air quality detection unit detects the air quality signal, enters the classification pretreatment unit, and compares it with the air quality signals corresponding to the API level 5 and level 3 standards in turn, outputs a control signal +5V, a proportional factor value, and a charging voltage to the circuit control unit, and the single-chip microcomputer controller drives the electronically controlled motor / ultraviolet lamp tube to access the power supply power for full speed / full power, variable speed / variable power control, and intermittent work control, and can automatically adjust the fan air intake and air outlet, and the power of the ultraviolet light source of the ultraviolet lamp according to the control quality signal.

[0004] The above patent documents and prior art have the following technical problems when used:

[0005] Problem 1: Most existing air purification technologies rely on a single photocatalytic or biological reaction method, but a single treatment mode is often difficult to completely degrade complex organic pollutants. Although traditional photocatalytic technology can effectively degrade some pollutants, it is often limited by the type and concentration of pollutants, while biological reactions take a long time and have a weak ability to degrade intermediate products. Due to the limitations of these technologies, existing systems often have problems with low degradation efficiency and incomplete purification when facing diverse pollutants;

[0006] Problem 2: Most traditional air purification equipment is based on a fixed working mode and cannot automatically adjust working parameters according to changes in actual pollutant concentrations and environmental conditions. This makes the system often inefficient in different working environments. Even when the pollutant concentration is low, it still consumes a lot of energy, resulting in low energy efficiency and high operation and maintenance costs. It has poor adaptability to environmental conditions and cannot be flexibly adjusted under different pollutant types, concentrations and change rates, resulting in the system being unable to achieve the best treatment effect in certain specific environments.

[0007] Problem three: Existing air purification systems are often difficult to maintain and upgrade due to complex structures and redundant components. They also have high operation and maintenance costs. Since the various reaction modules and control modules usually lack independence and modular design, a lot of manual intervention is required for system expansion or maintenance, which affects the system's operability and maintenance efficiency. Summary of the invention

[0008] Technical issues solved

[0009] In view of the shortcomings of the prior art, the present invention provides a high-efficiency air purification system that combines photocatalysis and biological reaction, which solves the following problems:

[0010] 1. The single treatment of air pollution leads to low pollutant degradation efficiency and difficulty in achieving complete purification;

[0011] 2. The lack of intelligent adjustment mechanism leads to poor environmental adaptability and flexibility of the system;

[0012] 3. The Mirror Lake system has a complex structure and high operation and maintenance costs.

[0013] Technical Solution

[0014] To achieve the above objectives, the present invention is implemented through the following technical solutions: a high-efficiency air purification system that combines photocatalysis and biological reactions, the system comprising a photocatalytic reaction module, a biological reaction module, an intelligent control module, a sensor monitoring module, a data analysis and optimization module, a modularization and self-maintenance module, wherein:

[0015] The photocatalytic reaction module is used to perform photocatalytic degradation reaction on pollutants in the air, including nanostructured photocatalytic materials such as TiO2, ZnO and high-efficiency light source components, oxidizing and decomposing organic pollutants into small molecules or harmless gases through light, and adjusting the light source intensity and reaction time through fuzzy logic control algorithm to adapt to changes in pollutant concentration in the air;

[0016] The bioreactor module is used to process the intermediate products after photocatalytic degradation to achieve secondary degradation and thorough purification, including microbial communities (such as specific bacterial communities that degrade VOCs) and reactor components, further degrading pollutants through biological metabolism, dynamically optimizing the selection of microbial communities through genetic algorithms, and adjusting metabolic pathways in combination with reinforcement learning to improve the efficiency of bioreactions;

[0017] The intelligent control module is used to realize the coordination and scheduling of various modules of the system, including an intelligent coordination mechanism and a real-time controller, realizes the synchronous operation of photocatalysis and biological reaction through a rule base and a control strategy, and dynamically adjusts the working parameters of photocatalysis and biological reaction through a particle swarm optimization algorithm to ensure the maximization of the synergistic effect;

[0018] The sensor monitoring module is used to monitor environmental parameters such as air quality, pollutant concentration, temperature and humidity in real time, including multimodal sensors and monitoring networks, obtain multi-dimensional data through high-sensitivity sensing technology, analyze environmental parameter changes through multi-objective optimization algorithms, and provide real-time control basis for the system;

[0019] The data analysis and optimization module is used to analyze system operation data and pollutant degradation efficiency, provide intelligent optimization solutions, including data collectors and optimization models, predict failure risks and make maintenance suggestions through support vector machine (SVM) algorithms, learn historical data and environmental characteristics through reinforcement learning algorithms, optimize system operation strategies, and ensure long-term stable operation;

[0020] The modular and self-maintenance module is used to realize modular design and automatic maintenance of the system, including an independent photocatalytic module, a biological reaction module and a control module, and simplifies system expansion and upgrading through modular design.

[0021] Preferably, the light source component of the photocatalytic reaction module adopts a high-intensity ultraviolet light source with an adjustment function, which can intelligently adjust the light intensity under different pollutant concentrations, and adopts a high-surface-area nano-photocatalytic material. The photocatalytic performance of the material is optimized by surface chemical modification and nanostructure design. In combination with a fuzzy logic control algorithm, the light source intensity and reaction time are dynamically adjusted according to real-time sensor monitoring data, including pollutant concentration, temperature and humidity, to adapt to different changes in air quality. The free radicals and gaseous oxides generated in the photocatalytic degradation reaction can interact with the microorganisms in the biological reaction module to promote further degradation of pollutants.

[0022] Preferably, the bioreactor module uses intelligent control of the microbial population, optimizes the metabolic pathways and flora structure of the microorganisms through genetic algorithms and reinforcement learning algorithms, and specific species in the microbial population can efficiently degrade organic pollutants and their degradation products, and have strong environmental adaptability. The genetic algorithm optimizes the genome of the microorganism by simulating natural selection, so that it adapts to the characteristics of different pollutants and improves the degradation rate. The reinforcement learning algorithm further improves the efficiency of the bioreactor by dynamically adjusting the metabolic pathways of the microorganisms, learning the changes in pollutant characteristics and environmental conditions.

[0023] Preferably, the rule base of the intelligent control module controls the working mode of each module in real time by intelligently analyzing the pollutant type, concentration and environmental parameters (such as temperature and humidity). The particle swarm optimization algorithm optimizes the collaborative working time and intensity of photocatalysis and biological reaction according to the sensor monitoring data to achieve the best degradation efficiency, provide adaptability to different working conditions, and automatically adjust the working ratio of each module when the pollutant concentration changes to ensure efficient operation of the system.

[0024] Preferably, the multimodal sensor network of the sensor monitoring module integrates gas sensors, temperature and humidity sensors, and particulate matter sensors, and can monitor the concentrations of various pollutants in the air in real time. The multi-objective optimization algorithm performs real-time analysis on multi-source sensor data, predicts air quality change trends, and provides adjustment suggestions to the intelligent control module. The highly sensitive sensing technology ensures that pollutant changes can be accurately captured and responded to in a timely manner even in low pollutant concentration conditions.

[0025] Preferably, the data collector of the data analysis and optimization module collects data from sensors, reaction modules, and control modules in real time to ensure the timeliness and accuracy of the information. The SVM algorithm classifies the operating status of the equipment, predicts possible system failures and makes maintenance recommendations. The reinforcement learning algorithm automatically adjusts the system's operating strategy based on historical operating data and environmental characteristics to ensure long-term stable operation.

[0026] Preferably, the modularization and self-maintenance module makes each module of the system independent, and dynamically switches or increases or decreases modules according to different pollutant concentrations. Through automatic detection and self-maintenance technology, the system can realize self-cleaning, automatic repair and maintenance of modules to ensure long-term and efficient operation of the system. Each module adopts a standardized interface design to support rapid expansion and upgrading of the system. The self-maintenance mechanism includes automatic cleaning of photocatalytic materials, self-regulation of microbial populations and periodic maintenance of reactor components. The health status of the equipment is predicted and diagnosed in combination with the SVM algorithm, and potential faults are discovered and repaired in advance.

[0027] Preferably, the photocatalytic reaction module and the biological reaction module together constitute a pollutant multi-stage degradation and efficient decomposition module, which maximizes the degradation efficiency in multiple stages through the synergistic effect of photocatalysis and biological reaction. The pollutant multi-stage degradation and efficient decomposition module uses a multi-objective optimization algorithm (MOGA) to treat pollutants in stages to ensure that each stage can achieve the optimal effect, thereby achieving efficient synergy between each reaction stage. The photocatalytic reaction module first performs a preliminary degradation of the pollutants and converts them into intermediate products. The biological reaction module further decomposes the intermediate products through microorganisms to achieve a thorough purification effect. The MOGA algorithm optimizes multiple objectives in the multi-stage degradation process, including degradation rate, energy consumption, and system stability, and performs real-time regulation.

[0028] Beneficial Effects

[0029] The present invention provides a high-efficiency air purification system that combines photocatalysis and biological reaction. It has the following beneficial effects:

[0030] 1. The present invention adopts a system combining photocatalytic reaction and biological reaction modules, and adopts a multi-stage degradation and efficient decomposition treatment mode. The photocatalytic reaction module first degrades the pollutants in the air into intermediate products, and then the biological reaction module uses microorganisms to further degrade these intermediate products, and finally achieves thorough purification. This multi-stage synergistic reaction not only significantly improves the air purification efficiency, but also ensures the thorough removal of pollutants during the treatment process, reduces environmental risks, and dynamically adjusts various indicators in the degradation process through a multi-objective optimization algorithm (MOGA) to ensure that each stage achieves the optimal effect, further improving the performance and stability of the entire system.

[0031] 2. The present invention adopts a system integrating an intelligent control module and a sensor monitoring module, and monitors environmental parameters such as air quality, pollutant concentration, temperature and humidity in real time through multimodal sensors. The intelligent control module realizes dynamic adjustment of the photocatalytic and biological reaction modules through a rule base and a particle swarm optimization algorithm (PSO), ensuring that the system can automatically adjust the working status of each module according to the real-time pollutant concentration and environmental changes. The adaptive adjustment mechanism can not only improve the degradation efficiency, but also ensure the long-term stable operation of the system under different working conditions, thereby enhancing the intelligence and adaptability of the system and reducing the need for manual intervention.

[0032] 3. The present invention adopts a modular design, so that each functional module has independence, and can be dynamically switched or increased or decreased according to different pollutant concentrations. At the same time, through automatic detection and self-maintenance technology, each module can perform self-cleaning, automatic repair and maintenance, reducing the cost of manual maintenance. Combined with the SVM algorithm, the health status of the equipment is predicted and diagnosed, potential faults are discovered in advance and repaired, which greatly improves the reliability and long-term operation efficiency of the system. In addition, the modular design also supports the rapid expansion and upgrade of the system, and has strong flexibility and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The system module architecture diagram of the present invention is

[0034] Figure 2 A data transmission trend diagram of the system module of the present invention;

[0035] Figure 3 It is a diagram of the system operation steps of the present invention;

[0036] Figure 4 is the membership function curve of the fuzzy logic control of the present invention;

[0037] Figure 5 It is a diagram of the optimization process of optimizing bacterial colony configuration by the genetic algorithm of the present invention;

[0038] Figure 6 It is a schematic diagram of the change of pollutant concentration over time when the system of the present invention is running;

[0039] Figure 7 This is a schematic diagram of the dynamic collaborative degradation of pollutants by the system of the present invention;

[0040] Figure 8 is a spatial thermodynamic distribution diagram of the photocatalytic efficiency of the present invention;

[0041] Fig. 9 It is a schematic diagram of the dynamic distribution characteristics of the airflow of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0044] like Figures 1 to 9As shown, a high-efficiency air purification system that combines photocatalysis and biological reactions, the system includes a photocatalytic reaction module, a biological reaction module, an intelligent control module, a sensor monitoring module, a data analysis and optimization module, and a modular and self-maintenance module, wherein:

[0045] Photocatalytic reaction module: used for photocatalytic degradation of pollutants in the air, including nanostructured photocatalytic materials such as TiO2, ZnO and high-efficiency light source components. Organic pollutants are oxidized and decomposed into small molecules or harmless gases through light. The light source intensity and reaction time are adjusted through fuzzy logic control algorithm to adapt to changes in pollutant concentrations in the air. The light source component of the photocatalytic reaction module adopts a high-intensity ultraviolet light source with adjustment function, which can intelligently adjust the light intensity under different pollutant concentrations. It adopts nano-photocatalytic materials with high surface area, optimizes the photocatalytic performance of the material through surface chemical modification and nanostructure design, and combines fuzzy logic control algorithm to dynamically adjust the light source intensity and reaction time according to real-time sensor monitoring data, including pollutant concentration, temperature and humidity, to adapt to different air quality changes. The free radicals and gaseous oxides generated in the photocatalytic degradation reaction can interact with the microorganisms in the biological reaction module to promote further degradation of pollutants;

[0046] Bioreactor module: used to process the intermediate products after photocatalytic degradation to achieve secondary degradation and thorough purification, including microbial communities (such as specific bacterial communities that degrade VOCs) and reactor components. Pollutants are further degraded through biological metabolism. The selection of microbial communities is dynamically optimized through genetic algorithms. The metabolic pathways are adjusted in combination with reinforcement learning to improve the efficiency of biological reactions. The bioreactor module uses intelligent control of microbial communities, and optimizes the metabolic pathways and community structures of microorganisms through genetic algorithms and reinforcement learning algorithms. Specific strains in the microbial community can efficiently degrade organic pollutants and their degradation products, and have strong environmental adaptability. The genetic algorithm optimizes the genome of microorganisms by simulating natural selection, adapting them to different pollutant characteristics and improving the degradation rate. The reinforcement learning algorithm further improves the efficiency of biological reactions by dynamically adjusting the metabolic pathways of microorganisms, learning about changes in pollutant characteristics and environmental conditions.

[0047] Intelligent control module: used to coordinate and dispatch the modules of the system, including intelligent coordination mechanism and real-time controller. It realizes the synchronous operation of photocatalysis and biological reaction through rule base and control strategy. It dynamically adjusts the working parameters of photocatalysis and biological reaction through particle swarm optimization algorithm to ensure the maximization of synergistic effect. The rule base of intelligent control module controls the working mode of each module in real time by intelligently analyzing the pollutant type, concentration and environmental parameters (such as temperature and humidity). The particle swarm optimization algorithm optimizes the collaborative working time and intensity of photocatalysis and biological reaction according to the sensor monitoring data to achieve the best degradation efficiency, provide adaptability to different working states, and automatically adjust the working ratio of each module when the pollutant concentration changes to ensure the efficient operation of the system.

[0048] Sensor monitoring module: used to monitor air quality, pollutant concentration, temperature and humidity and other environmental parameters in real time, including multimodal sensors and monitoring networks, obtain multi-dimensional data through high-sensitivity sensing technology, analyze environmental parameter changes through multi-objective optimization algorithms, and provide real-time control basis for the system. The multimodal sensor network of the sensor monitoring module integrates gas sensors, temperature and humidity sensors and particulate matter sensors, and can monitor the concentration of various pollutants in the air in real time. The multi-objective optimization algorithm analyzes multi-source sensor data in real time, predicts the trend of air quality changes, and provides adjustment suggestions to the intelligent control module. High-sensitivity sensing technology ensures that even in the case of low-concentration pollutants, it can accurately capture pollutant changes and respond in time. The system integrates intelligent control modules and sensor monitoring modules, and monitors air quality, pollutant concentration, temperature and humidity and other environmental parameters in real time through multimodal sensors. The intelligent control module realizes dynamic adjustment of photocatalysis and biological reaction modules through rule base and particle swarm optimization algorithm (PSO), ensuring that the system can automatically adjust the working status of each module according to the real-time pollutant concentration and environmental changes. This adaptive adjustment mechanism can not only improve the degradation efficiency, but also ensure the long-term stable operation of the system under different working conditions, improve the intelligence and adaptive ability of the system, and reduce the need for manual intervention;

[0049] Data analysis and optimization module: used to analyze system operation data and pollutant degradation efficiency, and provide intelligent optimization solutions, including data collectors and optimization models. It predicts failure risks and makes maintenance suggestions through the support vector machine (SVM) algorithm, and optimizes system operation strategies through learning historical data and environmental characteristics through the reinforcement learning algorithm to ensure long-term stable operation. The data collector of the data analysis and optimization module collects data from sensors, reaction modules, and control modules in real time to ensure the timeliness and accuracy of information. The SVM algorithm predicts possible system failures and makes maintenance suggestions by classifying the equipment operation status. The reinforcement learning algorithm automatically adjusts the system's operation strategy based on historical operation data and environmental characteristics to ensure long-term stable operation.

[0050] Modular and self-maintenance module: used to realize modular design and automatic maintenance of the system, including independent photocatalytic module, bioreactor module and control module. The modular design simplifies system expansion and upgrade. The modular and self-maintenance module makes each module of the system independent, and dynamically switches or increases or decreases modules according to different pollutant concentrations. Through automatic detection and self-maintenance technology, the system can realize self-cleaning, automatic repair and maintenance of modules to ensure long-term and efficient operation of the system. Each module adopts a standardized interface design to support rapid expansion and upgrade of the system. The self-maintenance mechanism includes automatic cleaning of photocatalytic materials, self-regulation of microbial populations and weekly maintenance of reactor components. Periodic maintenance, combined with the SVM algorithm to predict and diagnose the health status of the equipment, detect potential faults in advance and repair them, and adopt modular design to make each functional module independent, which can be dynamically switched or increased or decreased according to different pollutant concentrations. At the same time, through automatic detection and self-maintenance technology, each module can perform self-cleaning, automatic repair and maintenance, reducing the cost of manual maintenance. Combined with the SVM algorithm to predict and diagnose the health status of the equipment, detect potential faults in advance and repair them, greatly improving the reliability and long-term operation efficiency of the system. In addition, the modular design also supports the rapid expansion and upgrade of the system, with strong flexibility and scalability.

[0051] The photocatalytic reaction module and the biological reaction module together constitute the pollutant multi-stage degradation and efficient decomposition module. The pollutant multi-stage degradation and efficient decomposition module maximizes the degradation efficiency in multiple stages through the synergistic effect of photocatalysis and biological reactions. The pollutant multi-stage degradation and efficient decomposition module uses a multi-objective optimization algorithm (MOGA) to treat pollutants in stages to ensure that each stage can achieve the best effect, thereby achieving efficient synergy between the reaction stages. The photocatalytic reaction module first performs a preliminary degradation of the pollutants and converts them into intermediate products. The biological reaction module further decomposes the intermediate products through microorganisms to achieve a thorough purification effect. The MOGA algorithm optimizes the multi-stage degradation process. The system combines photocatalytic reaction and biological reaction modules, and adopts a multi-stage degradation and efficient decomposition treatment mode. The photocatalytic reaction module first degrades the pollutants in the air into intermediate products, and then the biological reaction module uses microorganisms to further degrade these intermediate products, and finally achieves thorough purification. This multi-stage synergistic reaction not only significantly improves the air purification efficiency, but also ensures the thorough removal of pollutants during the treatment process, reducing environmental risks. The multi-objective optimization algorithm (MOGA) dynamically adjusts various indicators in the degradation process to ensure that each stage achieves the optimal effect, further improving the performance and stability of the entire system. Specific embodiment 2:

[0053] like Figures 1 to 9As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0054] According to the system structure content, the steps of each module in the entire system during operation are as follows:

[0055] Sp1: Sensor monitoring and data acquisition

[0056] The multimodal sensors in the sensor monitoring module are used to collect air quality parameters in real time, including multi-dimensional data such as pollutant concentration, temperature and humidity, and particulate matter. The collected data are transmitted to the data analysis and optimization module for preliminary processing. The data analysis and optimization module uses a multi-objective optimization algorithm to analyze the air quality change trend in real time, providing a control basis for the operation of subsequent modules;

[0057] Sp2: Intelligent control module scheduling working mode

[0058] The intelligent control module obtains the air quality analysis results from the data analysis and optimization module, determines the type and concentration characteristics of pollutants in combination with the rule base, and uses the particle swarm optimization algorithm (PSO) to dynamically adjust the working parameters of the photocatalytic reaction module and the biological reaction module, including light source intensity, reaction time, biological metabolic rate, etc. According to changes in environmental conditions, the operation ratio of each module is intelligently coordinated to ensure the maximization of the synergistic effect of photocatalysis and biological reaction;

[0059] Sp3: Photocatalytic reaction module to treat primary pollutants

[0060] The photocatalytic reaction module uses nanostructured photocatalytic materials (such as TiO2, ZnO) under the action of high-efficiency ultraviolet light sources to oxidize and decompose organic pollutants in the air into intermediate products or harmless gases. The fuzzy logic control algorithm dynamically adjusts the light source intensity and reaction time according to sensor feedback to adapt to changes in air quality. The air after primary treatment is transmitted to the bioreactor module for secondary purification;

[0061] Sp4: Bioreactor module deeply degrades intermediates

[0062] The bioreactor module uses specific microbial flora (such as bacteria that degrade VOCs) to perform secondary degradation on the intermediate products after the photocatalytic reaction to achieve thorough purification. The genetic algorithm optimizes the population structure of microorganisms to adapt them to the characteristics of different pollutants. The reinforcement learning algorithm adjusts the metabolic pathway of microorganisms in real time and dynamically optimizes the biodegradation efficiency according to environmental changes.

[0063] Sp5: Multi-stage degradation and coordinated treatment of pollutants

[0064] The photocatalytic reaction module and the biological reaction module work together to form a multi-stage degradation and efficient decomposition module for pollutants. The multi-objective optimization algorithm (MOGA) dynamically optimizes the reaction efficiency at each degradation stage, including degradation rate, energy consumption and system stability, to ensure the gradual and complete decomposition of pollutants.

[0065] Sp6: System operation status monitoring and optimization

[0066] The data analysis and optimization module analyzes system operation data in real time, including pollutant degradation efficiency, module working parameters, equipment status, etc. The support vector machine (SVM) algorithm predicts possible system failures, generates maintenance suggestions and feeds them back to the modularization and self-maintenance module. The reinforcement learning algorithm optimizes the system operation strategy based on historical operation data to improve long-term stability and adaptability.

[0067] Sp7: Modular operation and self-maintenance mechanism start

[0068] The modular and self-maintenance module dynamically switches the working ratio of the photocatalytic and biological reaction modules according to the operating status, or increases or decreases the modules to cope with the changes in pollutant concentration. The system starts the automatic cleaning, repair and maintenance functions, including the automatic cleaning of photocatalytic materials, the self-regulation of microbial populations and the periodic maintenance of reactor components. The system can be quickly expanded and upgraded through standardized interfaces to ensure long-term and efficient operation.

[0069] Sp8: System intelligent feedback and closed-loop optimization

[0070] The system compares the operating results with the sensor monitoring data to evaluate the air purification effect. Based on the feedback results, it adjusts the parameters of the photocatalytic reaction module and the biological reaction module in real time to form a closed-loop optimization mechanism to ensure that the pollutant treatment efficiency is always at the best level, while saving energy and reducing operating costs.

[0071] The entire system achieves efficient air purification under different environmental conditions through intelligent sensor monitoring, dynamic adjustment and modular design. The synergistic effect of photocatalysis and biological reaction modules ensures multi-stage degradation and thorough purification of pollutants, while optimizing energy efficiency and system stability. The intelligent control module and self-maintenance mechanism continuously optimize operating parameters and maintenance strategies based on real-time data, achieving adaptive adjustment of the system and long-term efficient operation. The combination of these steps not only improves the air purification effect, but also effectively reduces energy consumption and operation and maintenance costs, ensuring the flexibility and scalability of the system. Specific embodiment three:

[0073] like Figures 1 to 9 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0074] The algorithm content of each module in the whole system further includes the following contents:

[0075] The following are the relevant mathematical formulas, explanations and implementation steps, operation logic and beneficial effects of the algorithms involved in each module:

[0076] Photocatalytic reaction module - fuzzy logic control algorithm:

[0077] The fuzzy logic control system is based on the fuzzy rule base. Its core calculation process involves steps such as fuzzification, fuzzy reasoning and defuzzification. Taking the adjustment of light intensity as an example, the pollutant concentration is set as the input variable x (unit: mg / m 3 ), the light source intensity is the output variable y, (unit: W) and the fuzzy rule is as follows:

[0078] IfxisA1thenyisB1

[0079] IfxisA2thenyisB2 ...

[0081] Among them, A i is a fuzzy subset description of pollutant concentration (such as "low concentration", "medium concentration", "high concentration", etc.), B i It is a fuzzy subset description of the corresponding light source intensity (such as "low intensity", "medium intensity", "high intensity", etc.), x represents the real-time monitored concentration of pollutants in the air, which is the input of the algorithm and determines the adjustment direction of the subsequent light source intensity, and y represents the light source intensity to be adjusted, which is the output of the algorithm and directly affects the efficiency of the photocatalytic reaction;

[0082] The fuzzification process converts the actual pollutant concentration value into the membership of each fuzzy subset through the membership function. For example, for the Gaussian membership function:

[0083]

[0084] in, is a fuzzy subset A i The central value of is its standard deviation, which means that the current pollutant concentration x belongs to the fuzzy subset A i degree, Indicates that the current pollutant concentration x belongs to the fuzzy subset A i The membership degree of Determine the center position of the fuzzy subset, that is, the typical pollutant concentration value, Control the "width" of the membership function to reflect the sensitivity to the fuzzy division of concentration values;

[0085] Fuzzy reasoning is performed based on fuzzy rules. Commonly used reasoning methods include Mamdani reasoning, etc. By calculating the activation strength of each rule (i.e., the product of the prerequisite membership or taking the smaller operation), the fuzzy set about the output variable is obtained.

[0086] Finally, defuzzification converts the fuzzy output into an exact value. Common methods include the centroid method:

[0087]

[0088] Among them, y i is the fuzzy subset B i The corresponding typical values ​​(such as "low intensity" corresponds to 10W, "medium intensity" corresponds to 30W, etc.), Output the membership degree of fuzzy subset;

[0089] As the pollutant concentration changes, the input membership is continuously re-evaluated through the fuzzy logic algorithm, and the light source intensity is dynamically adjusted. For example, when the pollutant concentration increases, the membership shifts to the "high concentration" subset. After reasoning and defuzzification, the light source intensity increases to accelerate the photocatalytic reaction rate and ensure that air with different pollution levels can be effectively treated. It can handle complex and nonlinear input-output relationships without the need for precise mathematical models, and can flexibly respond to fluctuations in pollutant concentrations under different environments, thereby improving the adaptability of the photocatalytic reaction module to variable air quality. The redundancy and ambiguity of fuzzy rules enable the system to operate stably when there are certain errors in the sensor data or environmental interference, thereby ensuring the rationality of the light source intensity adjustment and maintaining the purification effect.

[0090] Bioreaction Module-Genetic Algorithm:

[0091] In the scenario of optimizing the structure of the microbial flora, the genome of the microbial flora is represented by binary code (each bit represents the presence or absence of a certain gene feature), the population size is N, the gene length is;, and the gene of individual i is represented by g i =[g i1 , g i2 , …, g iL ], fitness function F(g i ) measures the effectiveness of an individual in degrading pollutants, for example, the degradation rate of the target pollutant per unit time:

[0092]

[0093] Where m0 is the initial pollutant mass, m t is the mass of pollutant remaining after time t.

[0094] The main operations of genetic algorithms include selection, crossover and mutation:

[0095] Selection: Roulette selection based on fitness, the probability P of individual i being selected i for:

[0096]

[0097] Crossover: Randomly select two individuals g a and g b , perform a crossover operation at a random position k (1≤k<L) to generate two offspring individuals:

[0098]

[0099] Mutation: with a certain probability p m Perform mutation operation on each bit of individual gene, that is, g ij Negate (if g ij =0 becomes 1 and vice versa);

[0100] Starting from a random initial bacterial community structure, by continuously screening individuals with high fitness for reproduction, genetic recombination and mutation, the bacterial community gradually optimizes its ability to degrade pollutants in the process of genetic evolution and adapts to the characteristics of different pollutants. Each generation of the population develops towards a higher degradation efficiency, and can search in a complex bacterial community structure combination space to avoid falling into local optimality, find the best microbial community configuration that adapts to different pollutants, and improve the bioreactor module's ability to degrade a variety of pollutants. As the characteristics of pollutants change (such as composition and concentration fluctuations), the genetic algorithm continuously optimizes the bacterial community, without the need for manual and precise pre-setting of the bacterial community structure, so that the bioreactor module has the ability to dynamically adapt to the environment and maintain high-efficiency purification for a long time.

[0101] Biological Response Module-Reinforcement Learning Algorithm:

[0102] In the scenario of adjusting the metabolic pathway of microorganisms, taking the Q-learning algorithm as an example, the set of environmental states in which the microorganisms are located is S (including factors such as pollutant types, concentrations, ambient temperature and humidity), and the set of actions that the microorganisms can take is A (such as selecting different metabolic pathways, regulating the activity of metabolic enzymes, etc.). The Q-value function Q(s, a) is defined to represent the expected cumulative reward of taking action a∈A in state s∈S, and is updated through continuous iteration:

[0103]

[0104] Among them, α is the learning rate (0<α<1), which determines the degree of update of the old Q value by the newly acquired experience; γ is the discount factor (0<γ<1), which is used to weigh the importance of future rewards and current rewards; r is the immediate reward obtained after performing action a, which is usually related to the degradation effect of microorganisms on pollutants under this action, such as the increase in degradation amount; s' is the new environmental state transferred to after performing action a.

[0105] Implementation steps:

[0106] Initialization: For all s∈S and a∈A, initialize Q(s, a) values ​​to random or empirically preset values;

[0107] Environmental perception: Microorganisms perceive the current environmental status in real time, including information such as pollutant concentration, temperature and humidity obtained from the sensor monitoring module;

[0108] Action selection: According to the current Q(s, a) value, select action a from A through a certain strategy (such as ∈-greedy strategy, that is, randomly select actions with probability ∈ to explore new strategies, and select the action with the largest current Q value with probability 1-∈ to use existing experience);

[0109] Execute action: The microorganism executes the selected action a, changes the metabolic pathway, performs biological reactions, and the environmental state changes to s', and obtains an immediate reward r;

[0110] Q value update: According to the formula Q(s, a)←Q(s, a)+α[r+γmax a' Q(s', a')-Q(s, a)] updates the value of Q(s, a);

[0111] Iteration: Repeat the steps of environment perception, action selection, action execution, and Q-value update to continuously learn and optimize metabolic strategies.

[0112] Microorganisms constantly try different metabolic actions in the changing environment, and adjust their strategies based on the immediate rewards obtained and the expectations of future rewards (reflected by Q values). As the learning process progresses, they gradually focus on the metabolic pathways that can bring the best degradation effects under different environments, achieve dynamic optimization, and can respond to environmental changes in real time (such as sudden changes in pollutant concentrations, temperature and humidity fluctuations), autonomously learn to adjust microbial metabolic pathways, and ensure that the bioreactor module maintains high degradation efficiency under various working conditions. Through continuous learning, microorganisms "remember" past successful and failed strategies, and gradually form the optimal response mode for different environmental conditions, reducing dependence on human intervention and reducing the complexity of operation and maintenance.

[0113] Intelligent control module-particle swarm optimization algorithm:

[0114] Assume that the working parameters of the photocatalytic reaction module and the biological reaction module constitute the particle position vector X i =[x i1 , x i2 , …, x in ], where n is the number of parameters (such as photocatalytic light source intensity, reaction time, biological reaction metabolic rate, etc.), and the particle velocity vector is V i =[v i1 , v i2 , …, vin , and each particle also has its individual optimal position P i (the best parameter combination found by itself during the historical search process), the global optimal position G (the best parameter combination found by the entire particle swarm). The particle position and velocity update formulas are as follows:

[0115] v ij (t + 1) = w·v ij (t) + c1·r1·(p ij - x ij (t)) + c2·r2·(g j - x ij (t))

[0116] x ij (t + 1) = x ij (t) + v ij (t + 1)

[0117] Among them, t represents the number of iterations, w is the inertia weight (0 < w < 1), which balances the global exploration and local exploitation capabilities of the particles; c1 and c2 are learning factors (usually c1, c2 > 0), which respectively adjust the step sizes for the particles to approach their own optimal and global optimal positions; r1 and r2 are random numbers uniformly distributed in the interval [0, 1].

[0118] Implementation steps:

[0119] Initialization: Randomly initialize the positions X i and velocities V i of a group of particles within the parameter value range, and set the initial individual optimal P i = X i for each particle. By evaluating the system performance indicators corresponding to the initial particle positions (such as pollutant degradation efficiency, energy consumption, etc.), find the global optimal position G;

[0120] Iterative update:

[0121] For each particle i, calculate and update the velocity Y i (t + 1) according to the formula. The velocity update consists of three parts: the first term w·v ij (t) is based on the current velocity of the particle to maintain the motion inertia; the second term c1·r1·(p ij - x ij (t)) makes the particle approach its own historical optimal position, reflecting the learning of its own experience; the third term c2·r2·(g j - x ij (t)) drives the particle to move towards the global optimal position, leveraging the collective wisdom;

[0122] According to the updated velocity, through x ij (t + 1) = xij (t)+v ij (t+1) Calculate the new particle position and obtain a new set of working parameters of the photocatalytic and biological reaction modules;

[0123] Evaluate the performance index of the system under the new position. If the performance corresponding to the new position of the particle is better than its individual optimal position P i , then update P i =X i (t+1), if the performance of the new position is better than the global optimal position G, update G = X i (t+1);

[0124] Termination condition judgment: Repeat the iterative update steps until the preset number of iterations is reached, or the performance index change corresponding to the global optimal position for multiple consecutive times is less than the set threshold. At this time, the algorithm is considered to have converged, and the working parameters corresponding to the global optimal position G are output as the optimal configuration.

[0125] The particle swarm initially "flies" randomly in the parameter space, and by constantly comparing itself, its companions and historical experience, it dynamically adjusts its flight direction and speed, and gradually gathers in the high-performance parameter area. For example, when a particle finds that moving closer to its own historical best parameters can improve the degradation efficiency of the system, it will increase the trend of moving in this direction; if a particle in the group finds a better global solution, other particles will also be attracted to move closer, and the optimization will be iterated repeatedly. Compared with the traditional exhaustive method, there is no need to traverse all possible parameter combinations. Through collaboration and self-learning between particles, it can quickly locate the photocatalytic and biological reaction working parameters close to the optimal level, greatly reducing the amount of calculation and time cost, and improving the system debugging and optimization efficiency. As environmental parameters (such as pollutant concentration, temperature and humidity) change, the particle swarm can start the optimization process again and re-find the best parameters to adapt to the new working conditions, ensuring that the system always maintains efficient and coordinated operation and effectively responds to complex and changeable air purification needs.

[0126] Sensor Monitoring Module-Multi-objective Optimization Algorithm:

[0127] Assume that the objective to be optimized is the air quality prediction accuracy f1 (which can be measured by the mean square error between the predicted value and the actual value:

[0128]

[0129] Where m is the number of samples, y i is the actual air quality value, is the predicted value), sensor energy consumption f2 (energy consumption per unit time), monitoring data real-time f3 (data delay time) and other goals;

[0130] Define the comprehensive objective function F(X):

[0131] F(X) = w1·f1(X) + w2·f2(X) + w3·f3(X)

[0132] Where X is the decision variable, including factors such as sensor sampling frequency and data transmission strategy that affect each objective; w1, w2, and w3 are the weights corresponding to the objectives, and w1 + w2 + w3 = 1. The weights reflect the relative importance of each objective in the optimization process and are set by the user according to actual needs.

[0133] Implementation steps:

[0134] Initialization: Given the initial decision variable X0, including setting the initial sampling frequency, data transmission method, etc., and calculating the values of each objective function f1(X0), f2(X0), f3(X0) according to the initial settings, and then obtaining the value of the comprehensive objective function F(X0);

[0135] Neighborhood search: By changing the value of the decision X, such as fine-tuning the sampling frequency, switching the transmission protocol, etc., generate a set of neighborhood solutions X i (i = 1, 2,..., k), and calculate their corresponding comprehensive objective function values F(X i );

[0136] Comparison and selection: Compare the comprehensive objective function values of the neighborhood solutions with the current solution F(X0). If there exists a neighborhood solution X j such that F(X j ) < F(X0), then update the current solution X0 = X j ; otherwise, keep the current solution unchanged;

[0137] Iteration: Repeat the neighborhood search, comparison and selection steps until the termination condition is met, such as reaching the preset number of iterations, the comprehensive objective function value converges to a certain accuracy, or the values of each objective function reach the satisfactory interval set by the user.

[0138] Starting from the initial monitoring configuration, continuously explore possible improvement solutions in the surrounding area. Each time, evaluate the advantages and disadvantages of the new solution based on the comprehensive objective function, and gradually optimize in the direction of accurate air quality prediction, low sensor energy consumption, and good data real-time performance. For example, if it is found that appropriately reducing the sampling frequency (within the acceptable prediction error) can significantly reduce energy consumption, update the configuration; if the new transmission protocol can balance real-time performance improvement and energy consumption control, also switch in time, dynamically adapt to the requirements, overcome the limitations of single-objective optimization, take into account the multi-faceted needs of air quality monitoring, avoid neglecting one thing while attending to another, achieve the coordinated improvement of accurate monitoring, energy-saving and high-efficiency, and timely response, and lay a solid foundation for the stable operation of the entire air purification system. Through weight adjustment, it fits the key requirements of different application scenarios and different stages. Whether it is high-precision monitoring in scientific research, energy-saving operation in business, or rapid response in an emergency, it can accurately optimize the sensor monitoring strategy and maximize the system benefits.

[0139] Data analysis and optimization module - Support Vector Machine (SVM) algorithm:

[0140] Assume the training sample set:

[0141] {(x1,y1),(x2,y2),…,(x m ,y m )}

[0142] Among them, x i is the sample feature vector (including various operating data collected by the sensor, such as pollutant degradation efficiency, module working parameters, equipment temperature, etc.); i ∈{-1, 1} is the class label of the sample (-1 indicates failure, 1 indicates normal);

[0143] The goal of SVM is to find a hyperplane ω·x+b=0 so that the two types of samples are on both sides of the hyperplane and the interval is the largest, by solving the following optimization problem:

[0144]

[0145] subjecttoy i (ω·x i +b)≥1, fora / / i=1, 2,…,m

[0146] Introducing the Lagrange multiplier α i (i=1,2,…,m), transforming the original problem into a dual problem:

[0147]

[0148] Solving the dual problem yields α i , and then the hyperplane parameters can be obtained Then choose a support vector (satisfying y i (ω·x i +b)=1) find b to determine the position of the hyperplane, and for the new sample x new , by judging f(x new )=ω·x new +b's sign to predict its category;

[0149] ||ω|| 2 Represents the regularization term in the objective function, controls the complexity of the hyperplane, prevents overfitting, ensures the generalization ability of the model, and strikes a balance between fitting training data and dealing with unknown samples;

[0150] x i ·x j It represents the inner product operation between samples, reflects the similarity of samples, plays a core role in the construction of dual problems, optimizes the hyperplane based on sample association, and explores the fault discrimination rules hidden in the data;

[0151] f(x new ) represents the new sample prediction function. According to the hyperplane parameters obtained through training, the newly collected operation data can be quickly classified, and potential system failures can be warned in real time to ensure operation reliability.

[0152] Based on the historical data learning system's normal and faulty operating modes, a distinguishing hyperplane is constructed. During operation, new data is continuously compared with the hyperplane. Once a new sample falls on the fault side, the anomaly is detected immediately. For example, data combinations such as abnormal continuous increase in equipment temperature and sudden drop in pollutant degradation efficiency trigger SVM judgment, which prevents system faults from worsening in advance. In the complex and high-dimensional system operation data space, the fault boundary is accurately outlined, and the normal and faulty states are effectively distinguished. Potential faults are keenly observed, the risk of false alarms and missed alarms is reduced, and the stable and long-term operation of the purification system is guaranteed. It does not rely too much on specific sample details. After regularization constraints, it can adapt to changes in operating conditions within a certain range. It can still accurately predict new data with similar historical fault characteristics, reducing the trouble of frequent re-modeling.

[0153] This series of algorithms work together to comprehensively improve the performance of the efficient air purification system of hybrid photocatalysis and biological reactions. From precise monitoring, intelligent regulation to fault prediction and maintenance, they ensure that the system can stably and efficiently purify the air in complex environments and continue to meet people's demand for high-quality air. Specific embodiment four:

[0155] like Figures 1 to 9 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0156] Based on the system architecture, a series of experiments are conducted to evaluate the air purification efficiency, energy efficiency, system stability and self-maintenance capability of the system under different environmental conditions. The following is a detailed experimental design content, which aims to reflect the innovation and advantages of the system through experiments.

[0157] 1. Purpose of the experiment

[0158] 1. Compare the purification efficiency of this system with that of traditional air purification technology (HEPA filter and activated carbon adsorption device) under the same pollutant concentration;

[0159] 2. Evaluate the system's responsiveness and intelligent adjustment capabilities under changing pollutant concentrations;

[0160] 3. Compare the energy efficiency, system stability and maintenance cost of the two systems;

[0161] 2. Experimental Equipment and Materials

[0162] 1. Experimental setup:

[0163] This system: includes photocatalytic reaction module (TiO2, ZnO nanomaterials), biological reaction module (VOCs degradation bacteria), intelligent control module (particle swarm optimization and reinforcement learning), sensor monitoring module (gas, temperature and humidity, particulate matter sensors), etc.

[0164] Control system: traditional HEPA filter + activated carbon adsorption device;

[0165] 2. Pollutant sources: VOCs (such as formaldehyde, benzene, xylene), PM2.5;

[0166] 3. Control environment: Closed indoor environment (volume about 50m 3 );

[0167] 4. Sensors and data analysis equipment: real-time monitoring of pollutant concentrations in the air, recording of system operating data, and analysis using data collectors and optimization models.

[0168] 3. Experimental steps

[0169] Sp1: Pollutant input and environmental settings:

[0170] Inject known concentrations of VOCs (such as formaldehyde, benzene, xylene) and PM2.5 into the experimental environment, with the concentration range set at 50-500 ppb of VOCs and 100-500 μg / m 3 PM2.5, the ambient temperature is set to 22℃, the humidity is 50%, ensure that the experiment is carried out under standard indoor conditions, start the comparative experimental system, including this system and the control system;

[0171] Sp2: Purification system operation:

[0172] Start the system, including the photocatalytic reaction module and the biological reaction module. According to the data monitored by the sensor, the intelligent control module will adjust the photocatalytic reaction time and light source intensity, and dynamically adjust the microbial metabolic pathway in the biological reaction module according to the concentration of VOCs and PM2.5. Start the control system (HEPA filter and activated carbon adsorption device) to simulate the traditional air purification operation and record the degradation of pollutants during the purification process.

[0173] Sp3: Pollutant concentration monitoring and data recording:

[0174] The pollutant concentration is monitored by sensors every 30 minutes, and the VOCs (formaldehyde, benzene, xylene) and PM2.5 concentration data are recorded in real time. The purification efficiency, reaction time, energy consumption and other data of the two systems are compared. The data analysis and optimization module is used to evaluate the changes in the purification efficiency of the system under different pollutant concentrations, as well as energy consumption, system stability and maintenance requirements;

[0175] Sp4: Experimental continuity and system stability test:

[0176] The two systems were operated continuously for 48 hours to observe the changing trends of pollutant concentrations and the stability of the two systems in long-term operation, and to record the self-maintenance capabilities of the systems in long-term operation, including self-cleaning of photocatalytic reaction materials, self-regulation of microbial communities, and self-maintenance of reactor components.

[0177] IV. Experimental Data Recording and Analysis

[0178] 1. Comparison of purification efficiency:

[0179]

[0180] Table 1

[0181] Note: The data are the purification effect data of the system after the purification by photocatalysis and biological reaction for each pollutant experiment. The calculation method is: purification rate = (initial concentration - concentration after purification) / initial concentration × 100%;

[0182] 2. Energy efficiency analysis:

[0183] System Type Pollutant purification (g / h) Energy consumption (W) Purification efficiency (g / W·h) This system 0.35g / h 50W 7.0g / W·h Control system 0.20g / h 40W 5.0g / W·h

[0184] Table 2

[0185] Note: Data is calculated based on the degradation of pollutants per unit time and the total energy consumption of the system;

[0186] 3. System stability and self-maintenance

[0187] This system: After 48 hours of operation, the photocatalytic reaction module remains highly efficient, and the microbial community is able to self-regulate to adapt to the different characteristics of pollutants. During continuous use, the photocatalytic material and microbial community have not shown significant performance degradation, and the system maintains a purification efficiency of more than 90%;

[0188] Control system: After 24 hours of operation, the HEPA filter and activated carbon adsorption device had a significant decline in effectiveness and needed to be manually cleaned or replaced, resulting in a reduction in purification efficiency to around 60%;

[0189] 4. System self-cleaning and self-maintenance

[0190] This system: The photocatalytic material uses an automatic cleaning mechanism to trigger a cleaning cycle every 12 hours to ensure continuous reaction efficiency. The microbial population of the bioreactor module is dynamically adjusted through genetic algorithms and reinforcement learning algorithms to adaptively optimize when pollutant concentrations change;

[0191] Control system: The traditional system has no self-cleaning function and requires regular manual maintenance.

[0192] 5. Experimental Conclusion

[0193] 1. Purification efficiency: This system is superior to traditional air purification systems in terms of degradation efficiency of formaldehyde, benzene, xylene and PM2.5. Especially in high-concentration pollutant environments, it can achieve a degradation rate of more than 85%, while the degradation efficiency of traditional systems is 60%-75%;

[0194] 2. Energy efficiency: The energy efficiency of this system is significantly higher than that of the control system. Per unit energy consumption, this system can degrade more pollutants and show a better energy efficiency ratio (7.0g / W·h vs 5.0g / W·h);

[0195] 3. System stability and self-maintenance capability: This system has self-cleaning and self-adjusting capabilities, and can maintain efficient purification capabilities during long-term operation, while traditional systems require frequent manual intervention and cleaning;

[0196] 4. Intelligent adjustment: This system dynamically adjusts the operating parameters through the intelligent control module, responds quickly when the pollutant concentration changes, and maintains the optimal operating state of the system. The control system cannot achieve such precise adjustment under changing conditions.

[0197] Through the above experimental data, we can see the significant advantages of this system in air purification efficiency, energy efficiency, stability and maintenance capabilities.

[0198] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0199] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency air purification system combining photocatalysis and biological reaction, characterized in that: The system includes a photocatalytic reaction module, a biological reaction module, an intelligent control module, a sensor monitoring module, a data analysis and optimization module, and a modularization and self-maintenance module, wherein: The photocatalytic reaction module is used to perform photocatalytic degradation reaction on pollutants in the air, including nanostructured photocatalytic materials and high-efficiency light source components, oxidizing and decomposing organic pollutants into small molecules or harmless gases through light, and adjusting the light source intensity and reaction time through fuzzy logic control algorithm to adapt to changes in pollutant concentration in the air; The bioreaction module is used to process the intermediate products after photocatalytic degradation to achieve secondary degradation and thorough purification, including microbial communities and reactor components, further degrading pollutants through biological metabolism, dynamically optimizing the selection of microbial communities through genetic algorithms, and adjusting metabolic pathways in combination with reinforcement learning to improve the efficiency of bioreactions; The intelligent control module is used to realize the coordination and scheduling of various modules of the system, including an intelligent coordination mechanism and a real-time controller, realizes the synchronous operation of photocatalysis and biological reaction through a rule base and a control strategy, and dynamically adjusts the working parameters of photocatalysis and biological reaction through a particle swarm optimization algorithm to ensure the maximization of the synergistic effect; The sensor monitoring module is used to monitor air quality, pollutant concentration, temperature and humidity environmental parameters in real time, including multi-modal sensors and monitoring networks, obtain multi-dimensional data through high-sensitivity sensing technology, analyze environmental parameter changes through multi-objective optimization algorithms, and provide real-time control basis for the system; The data analysis and optimization module is used to analyze system operation data and pollutant degradation efficiency, provide intelligent optimization solutions, including data collectors and optimization models, predict failure risks and make maintenance suggestions through support vector machine (SVM) algorithms, learn historical data and environmental characteristics through reinforcement learning algorithms, optimize system operation strategies, and ensure long-term stable operation; The modular and self-maintenance module is used to realize modular design and automatic maintenance of the system, including an independent photocatalytic module, a biological reaction module and a control module, and simplifies system expansion and upgrading through modular design.

2. The high-efficiency air purification system of hybrid photocatalysis and biological reaction according to claim 1, characterized in that: The light source component of the photocatalytic reaction module adopts a high-intensity ultraviolet light source with an adjustment function, which can intelligently adjust the light intensity under different pollutant concentrations. It adopts a nano-photocatalytic material with a high surface area, optimizes the photocatalytic performance of the material through surface chemical modification and nanostructure design, and combines a fuzzy logic control algorithm to dynamically adjust the light source intensity and reaction time according to real-time sensor monitoring data, including pollutant concentration, temperature and humidity, to adapt to different changes in air quality. The free radicals and gaseous oxides generated in the photocatalytic degradation reaction can interact with the microorganisms in the biological reaction module to promote further degradation of pollutants.

3. The high-efficiency air purification system of hybrid photocatalysis and biological reaction according to claim 1, characterized in that: The bioreactor module uses intelligent control of microbial populations, and optimizes the metabolic pathways and flora structures of microorganisms through genetic algorithms and reinforcement learning algorithms. Specific species in the microbial population can efficiently degrade organic pollutants and their degradation products, and have strong environmental adaptability. The genetic algorithm optimizes the genome of microorganisms by simulating natural selection, so that they can adapt to the characteristics of different pollutants and increase the degradation rate. The reinforcement learning algorithm further improves the efficiency of the bioreactor by dynamically adjusting the metabolic pathways of microorganisms, learning the characteristics of pollutants and changes in environmental conditions.

4. The high-efficiency air purification system of hybrid photocatalysis and biological reaction according to claim 1, characterized in that: The rule base of the intelligent control module controls the working mode of each module in real time by intelligently analyzing the pollutant type, concentration and environmental parameters. The particle swarm optimization algorithm optimizes the collaborative working time and intensity of photocatalysis and biological reaction according to the sensor monitoring data to achieve the best degradation efficiency, provide adaptability to different working conditions, and automatically adjust the working ratio of each module when the pollutant concentration changes to ensure efficient operation of the system.

5. The high-efficiency air purification system of hybrid photocatalysis and biological reaction according to claim 1, characterized in that: The multimodal sensor network of the sensor monitoring module integrates gas sensors, temperature and humidity sensors, and particulate matter sensors, and can monitor the concentrations of various pollutants in the air in real time. The multi-objective optimization algorithm performs real-time analysis on multi-source sensor data, predicts air quality change trends, and provides adjustment suggestions to the intelligent control module. The highly sensitive sensing technology ensures that pollutant changes can be accurately captured and responded to in a timely manner even in low-concentration pollutants.

6. The high-efficiency air purification system of hybrid photocatalysis and biological reaction according to claim 1, characterized in that: The data collector of the data analysis and optimization module collects data from sensors, reaction modules, and control modules in real time to ensure the timeliness and accuracy of the information. The SVM algorithm classifies the operating status of the equipment, predicts possible system failures and makes maintenance suggestions. The reinforcement learning algorithm automatically adjusts the system's operating strategy based on historical operating data and environmental characteristics to ensure long-term stable operation.

7. The high-efficiency air purification system of hybrid photocatalysis and biological reaction according to claim 1, characterized in that: The modularization and self-maintenance module makes each module of the system independent, and dynamically switches or increases or decreases modules according to different pollutant concentrations. Through automatic detection and self-maintenance technology, the system can realize self-cleaning, automatic repair and maintenance of modules to ensure long-term and efficient operation of the system. Each module adopts a standardized interface design to support rapid expansion and upgrading of the system. The self-maintenance mechanism includes automatic cleaning of photocatalytic materials, self-regulation of microbial populations and periodic maintenance of reactor components. The health status of the equipment is predicted and diagnosed in combination with the SVM algorithm, and potential faults are discovered and repaired in advance.

8. The high-efficiency air purification system of hybrid photocatalysis and biological reaction according to claim 1, characterized in that: The photocatalytic reaction module and the biological reaction module together constitute a pollutant multi-stage degradation and efficient decomposition module. The pollutant multi-stage degradation and efficient decomposition module maximizes the degradation efficiency in multiple stages through the synergistic effect of photocatalysis and biological reaction. The pollutant multi-stage degradation and efficient decomposition module uses a multi-objective optimization algorithm (MOGA) to treat pollutants in stages to ensure that each stage can achieve the optimal effect, thereby achieving efficient synergy between the reaction stages. The photocatalytic reaction module first performs a preliminary degradation of the pollutants and converts them into intermediate products. The biological reaction module further decomposes the intermediate products through microorganisms to achieve a thorough purification effect. The MOGA algorithm optimizes multiple objectives in the multi-stage degradation process, including degradation rate, energy consumption, and system stability, and performs real-time regulation.

Images