AI BASED SMART AIR FILTRATION SYSTEM AND METHOD FOR IMPROVED FILTRATION EFFICIENCY

Abstract

ABSTRACT AI BASED SMART AIR FILTRATION SYSTEM AND METHOD FOR IMPROVED FILTRATION EFFICIENCY The present invention relates to an AI based smart air filtration system (100) and method for improved filtration efficiency. The system (100) comprises of a plurality of sensors (101) to sense a plurality of parameters in atmospheric air; a filtration unit (102) to filter atmospheric air; and a processing unit (103) utilizing Artificial Intelligence for controlling the operation of the AI based smart air filtration system (100). The processing unit (103) analyses the sensor data using AI and machine learning technique of Artificial neural networks, Convolutional Neural Networks or deep learning techniques. The AI based smart air filtration system (100) activates filtration of air only when air pollutant crosses a prespecified threshold. By combining the strengths of each component, it offers a powerful solution for addressing modern air purification challenges, ensuring cleaner, safer air in various environments.

Specification

Description:AI BASED SMART AIR FILTRATION SYSTEM AND METHOD FOR IMPROVED FILTRATION EFFICIENCY
FIELD OF THE INVENTION
[0001] The present invention in general relates to air filtration. More particularly, the present invention relates to an AI based smart air filtration system and method for improved filtration efficiency.
BACKGROUND
[0002] Air quality has become a critical concern in both residential and commercial environments due to increasing pollution levels, allergens, and pathogens in the air. Traditional air filtration systems, while effective to a degree, often fail to adapt to fluctuating air quality conditions, resulting in inefficiencies that can compromise health and comfort.
[0003] The health implications of poor indoor air quality are significant. Research has shown that exposure to indoor pollutants can exacerbate asthma, trigger allergies, and contribute to chronic health conditions. Vulnerable populations, such as children and the elderly, are particularly at risk. Therefore, improving air filtration not only enhances comfort but also plays a crucial role in public health.
[0004] Carbon based particles have inhalable sizes ranges from PM10 with diameters that are 10 micrometers to smaller PM1 or 1 micrometer or smaller. PM2.5 or 2.5 micrometers or smaller is of primary focus and concern as this size is the most efficient at traversing filtration mediums and represents a serious health risk if inhaled in lungs. In addition, to carbon particles and particulates, carbon monoxide also needs to be measured and eliminated for healthy air indoors. CO is highly toxic and can lead to severe health effects or even death when inhaled in significant quantities. It binds with hemoglobin in the blood more effectively than oxygen, reducing the blood's ability to carry oxygen to vital organs.
[0005] Prior art studies and patents have explored air purification systems to eliminate carbon content and CO from air. For example: Patent application number US20100070086 discloses Indoor air quality controllers and user interfaces. Controllers for controlling heating, ventilating, air conditioning, and cooling (HVAC) systems are provided. The controllers include graphical user interfaces for user adjustment of system settings. The controllers also include communication interfaces for receiving air data. The controllers also may govern operation of air treatment devices within the HVAC systems.
[0006] Another patent application CN108778455B discloses air filter containing polymeric adsorbent for reactive gases such as CO. The air filter includes a filter support supporting polymeric sorbent particles. The polymeric adsorbent is the reaction product of a divinylbenzene/maleic anhydride precursor polymeric material and a nitrogen-containing compound. Air filters may be used to capture, for example, reactive gases.
[0007] These prior works highlight the potential of effective use of filtration systems. However, these systems face limitations as the existing air sensing and managing systems are not yet energy efficient. The currently available air cleaning systems can only be turned on manually, or at a regular time, instead of being turned on necessarily when air cleaning is most needed, resulting in wasted power.
[0008] The shortcomings of prior art have demonstrated the benefits of smart air filtration systems. Therefore, a system which can be turned on only when air filtration is needed is disclosed, resulting in efficient energy utilization and filtration efficiency.
[0009] The present invention discloses an AI based smart air filtration system for improved filtration efficiency. The AI based smart air filtration system activates filtration of air only when an air carbon content or CO level crosses a prespecified threshold. By combining the strengths of each component, it offers a powerful solution for addressing modern air purification challenges, ensuring cleaner, safer air in various environments.
OBJECTS OF THE INVENTION
[0010] The object of the present invention is to provide an AI based smart air filtration system and method for improved filtration efficiency.
[0011] It is another object of the present invention to develop an air filtration system that significantly improves the ability to capture carbon content in air without wasting energy.
[0012] Another object of the present invention is to provide a system which can be turned on only when air filtration is needed is disclosed, resulting in efficient energy utilization and filtration efficiency.
SUMMARY OF THE INVENTION
[0013] The present invention relates to an AI based smart air filtration system (100) for improved filtration efficiency. The system (100) comprises of a plurality of sensors (101) to sense a plurality of parameters in atmospheric air; a filtration unit (102) to filter atmospheric air, wherein the filtration unit (102) comprising a plurality of filtration chambers including a nanoparticles-based filtration chamber and a catalytic adsorption chamber, wherein the nanoparticles-based filtration chamber utilizing a silver or gold nanoparticles based filtration of carbon particles in air, and wherein the catalytic adsorption chamber utilizing palladium-based catalytic adsorption of Carbon monoxide gas in air; and a processing unit (103) utilizing Artificial Intelligence for controlling the operation of the AI based smart air filtration system (100), wherein the AI based smart air filtration system (100) is having a manual mode of operation by user and an automatic mode of operation by presetting in the processing unit (103).
[0014] In one aspect the plurality of sensors (101) includes a carbon sensor and a carbon monoxide sensor.
[0015] In another aspect plurality of parameters includes air carbon content and carbon monoxide gas content.
[0016] Yet another aspect of the invention is that the processing unit (103) is configured to receive data from the plurality of sensors (101) for analysis of the plurality of parameters to make a decision from a plurality of decisions.
[0017] In another aspect the plurality of decisions includes switching ON/OFF the nanoparticles-based filtration chamber and/or switching ON/OFF the catalytic adsorption chamber depending upon levels of the plurality of parameters of air.
[0018] Yet another aspect of the invention, the processing unit (103) is configured to utilize AI for training and learning from manual mode of operation by the user.
[0019] In another aspect, the present invention also discloses a method of air filtration by AI based smart air filtration system (100). The method comprises of sensing a plurality of parameters in atmospheric air by a plurality of sensors (101); sending data from the plurality of sensors (101) to a processing unit (103); receiving data from the plurality of sensors (101) by the processing unit (103) for estimation of the plurality of parameters; and making a decision from a plurality of decisions utilizing AI by the processing unit (103) based on estimation result.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The accompanying drawings are included to provide a further understanding of the present disclosure and is incorporated in and constitutes a part of this specification. The drawings illustrate exemplary embodiment of the present disclosure and, together with the, serve to explain the principles of the present disclosure.
[0021] Figure 1 illustrates a block diagram to detail the embodiments of the AI based smart air filtration system (100) for improved filtration efficiency. This diagram depicts the interconnected components and flow of the gas generation through the system.
[0022] Figure 2 illustrates the flowchart of the method of air filtration by AI based smart air filtration system (100).
DETAILED DESCRIPTION
[0023] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents.
[0024] Figure 1 illustrates a block diagram to detail the embodiments of an AI based smart air filtration system (100) for improved filtration efficiency. The system (100) comprises of a plurality of sensors (101) to sense a plurality of parameters in atmospheric air; a filtration unit (102) to filter atmospheric air; and a processing unit (103) utilizing Artificial Intelligence for controlling the operation of the AI based smart air filtration system (100).
[0025] The filtration unit (102) comprising of a plurality of filtration chambers including a nanoparticles-based filtration chamber and a catalytic adsorption chamber. The nanoparticles-based filtration chamber utilizes a silver or gold nanoparticles based filtration of carbon particles in air. The catalytic adsorption chamber utilizes palladium-based catalytic adsorption of Carbon monoxide gas in air.
[0026] The plurality of sensors (101) includes a carbon sensor and a carbon monoxide sensor. The plurality of parameters includes air carbon content and carbon monoxide gas content. The carbon sensor senses the carbon content or pollution in the surrounding air. The carbon monoxide sensor senses the presence of carbon monoxide in surrounding air. The sensor (101) continuously senses the surrounding air and measure the carbon content and CO levels in air. The sensed data is sent to the processing unit (103). The system includes transmitter and receiver for sending data wirelessly.
[0027] The processing unit (103) analyses the data using AI and machine learning technique. The processing unit (103) utilizes Artificial neural networks, Convolutional Neural Networks or deep learning techniques. Both the air carbon content and carbon monoxide gas content are estimated. Based on the air carbon content and carbon monoxide gas content, the processing unit (103) makes a decision from a plurality of decisions. If the carbon content and the carbon monoxide levels crosses a pre specified threshold, then the filtration unit (102) starts filtering air. The thresholds are specified for desirable air quality.
[0028] Table 1 shows the decision making of the processing unit (103) based upon the threshold levels of the air carbon content.
Table 1: Decision making of processing unit based upon threshold levels of the air carbon content
Air carbon level (ppm) Decision by Processing unit
≤ 400 Nanoparticles-based Filtration chamber is kept OFF
≥ 400 Nanoparticles-based Filtration chamber is turned ON

[0029] Table 2 shows the decision making of the processing unit (103) based upon the threshold levels of the carbon monoxide content in air.
Table 2: Decision making of processing unit based upon threshold levels of the air carbon monoxide gas content
Carbon monoxide level (ppm) Decision by Processing unit
≤ 25 Catalytic adsorption chamber is kept OFF
≥ 25 Catalytic adsorption chamber is turned ON
[0030] If the surrounding air is high in carbon content and/or carbon monoxide, then the processing unit (103) commands the filtration unit (102) to start filtering air. If the carbon content crosses a pre specified threshold, then the nanoparticles-based filtration chamber of the filtration unit (102) is turned ON to start filtering air. If the carbon monoxide crosses a pre specified threshold, then the catalytic adsorption chamber of the filtration unit (102) is turned ON to start filtering air. If both the levels cross thresholds, then both the chambers are started. The thresholds are specified for desirable air quality.
[0031] The filtration unit (102) includes plurality of filtration chambers including the nanoparticles-based filtration chamber and the catalytic adsorption chamber. The nanoparticles-based filtration chamber utilizes a silver or gold nanoparticles based filtration of carbon particles in air. When the nanoparticles-based filtration chamber is turned ON, then the silver or gold particle are released and makes the carbon content in air as conductive to neutralise them.
[0032] The catalytic adsorption chamber utilizes palladium-based catalytic adsorption of Carbon monoxide gas in air. When the catalytic adsorption chamber is turned ON, then the carbon monoxide is adsorbed using palladium-based catalyst which removes CO from the air.
[0033] If the carbon content and the carbon monoxide remains within the limit and doesn't cross the threshold, then there is no filtration. This ensures that the system (100) is not operating unnecessarily when there is no need for air purification. This optimizes the use of the filtration unit (102) by saving energy and increasing efficiency.
[0034] The AI based smart air filtration system (100) is having a manual mode of operation by user and an automatic mode of operation by presetting in the processing unit (103). The manual mode can be operated by the user. The user can turn ON the filtration unit (102) or the nanoparticles-based filtration chamber and the catalytic adsorption chamber. The processing unit (103) is configured to utilize AI and machine learning for training and learning from manual mode of operation by the user. The user can turn ON the filtration unit (102) when there is discomfort to user and/or there is a specific level of carbon content/CO in air. The user preferences for turning ON both or anyone of the chambers will be learnt by the processing unit (103). The processing unit (103) can then turn ON the chamber(s) automatically when the specific level of carbon content/CO in air is reached. The processing unit (103) utilizes supervised or unsupervised Machine Learning techniques and deep learning techniques.
[0035] In an embodiment of the invention, the AI based smart air filtration system (100) also includes filtration membranes to filter various pollutants of air. Users can also adjust settings and monitor air quality via a mobile app, providing convenience and control over their environment. Notifications about air quality and system status keep users informed and engaged, promoting proactive air quality management.
[0036] Table 3 shows the various air pollutant types and sizes filtered by the filtration membranes.
Table 3: Air pollutant types filtered by AI based smart air filtration system (100)
Air pollutant type Air pollutant size (micron)

Dust, dust mites, smoke, Pet dander 5-10
Chemical pollutants, fine particles and nanoparticles 0.01-5

Microbes (viruses, bacteria, and mould spores) 0.01-5

[0037] The AI based smart air filtration system (100) represents a significant advancement in air quality technology. By combining the strengths of each component, it offers a powerful solution for addressing modern air purification challenges, ensuring cleaner, safer air in various environments.
[0038] Advantages of AI based smart air filtration system (100) for improved filtration efficiency:
1. Efficient Energy Utilization:
Targeted Operation: The system activates only when air quality deteriorates or pollutants are detected, reducing unnecessary energy consumption during times of clean air.
Cost Savings: Lower energy usage translates to reduced utility bills, making the system economically advantageous over time.
2. Enhanced Filtration Efficiency:
Real-Time Monitoring: Continuous air quality assessments ensure that the system operates at optimal settings, maximizing the removal of specific pollutants when needed.
Adaptive Filtration Strategies: By adjusting filtration methods based on real-time data, the system can focus on removing the most harmful contaminants effectively.
3. Prolonged Filter Life:
Reduced Wear and Tear: Operating only when necessary minimizes the strain on filters, extending their lifespan and reducing replacement frequency.
Lower Maintenance Costs: With longer-lasting filters, users save on both filter replacement and maintenance services.
4. Improved Indoor Air Quality (IAQ):
Responsive to Changes: The ability to turn on and off based on air quality allows for timely responses to changes in indoor pollution, ensuring a consistently healthier environment.
Personalized Comfort: Users can enjoy better air quality tailored to their specific needs and living conditions.
5. User-Centric Features:
Smart Alerts: Notifications about air quality and system status keep users informed and engaged, promoting proactive air quality management.
Remote Control and Monitoring: Users can adjust settings and monitor air quality via a mobile app, providing convenience and control over their environment.
6. Sustainability Benefits:
Lower Carbon Footprint: Reduced energy consumption contributes to overall sustainability goals, aligning with efforts to lower environmental impact.
Minimized Waste: Longer filter life and targeted operation lead to less waste and fewer resources expended on manufacturing and disposing of filters.
7. Health and Well-Being:
Immediate Response to Pollutants: By ensuring that the system activates when needed, users are protected from potential health hazards associated with poor air quality, such as carbon content and CO.
Peace of Mind: Knowing that the air filtration system is actively working to maintain a healthy indoor environment contributes to overall well-being and comfort.
[0039] Although the present invention has been particularly described with reference to implementations discussed above, various changes, modifications and Substitutes are can be made. Accordingly, it will be appreciated that in numerous instances some features of the invention can be employed without a corresponding use of other features. Further, variations can be made in the number and arrangement of components illustrated in the figures discussed above. 
, Claims:I/We Claim:
1. An AI based smart air filtration system (100) for improved filtration efficiency comprising of:
a plurality of sensors (101) to sense a plurality of parameters in atmospheric air;
a filtration unit (102) to filter atmospheric air, wherein the filtration unit (102) comprising a plurality of filtration chambers including a nanoparticles-based filtration chamber and a catalytic adsorption chamber, wherein the nanoparticles-based filtration chamber utilizing a silver or gold nanoparticles based filtration of carbon particles in air, and wherein the catalytic adsorption chamber utilizing palladium-based catalytic adsorption of Carbon monoxide gas in air; and
a processing unit (103) utilizing Artificial Intelligence for controlling the operation of the AI based smart air filtration system (100), wherein the AI based smart air filtration system (100) is having a manual mode of operation by user and an automatic mode of operation by presetting in the processing unit (103).
2. The AI based smart air filtration system (100) for improved filtration efficiency as claimed in claim 1, wherein the plurality of sensors (101) includes a carbon sensor and a carbon monoxide sensor.
3. The AI based smart air filtration system (100) for improved filtration efficiency as claimed in claim 1, wherein the plurality of parameters includes air carbon content and carbon monoxide gas content.
4. The AI based smart air filtration system (100) for improved filtration efficiency as claimed in claim 1, wherein the processing unit (103) is configured to receive data from the plurality of sensors (101) for analysis of the plurality of parameters to make a decision from a plurality of decisions.
5. The AI based smart air filtration system (100) for improved filtration efficiency as claimed in claim 4, wherein the plurality of decisions includes switching ON/OFF the nanoparticles-based filtration chamber and/or switching ON/OFF the catalytic adsorption chamber depending upon levels of the plurality of parameters of air.
6. The AI based smart air filtration system (100) for improved filtration efficiency as claimed in claim 1, wherein the processing unit (103) is configured to utilize AI for training and learning from manual mode of operation by the user.
7. A method of air filtration by AI based smart air filtration system (100), wherein the method comprising steps of:
sensing a plurality of parameters in atmospheric air by a plurality of sensors (101);
sending data from the plurality of sensors (101) to a processing unit (103);
receiving data from the plurality of sensors (101) by the processing unit (103) for estimation of the plurality of parameters; and
making a decision from a plurality of decisions utilizing AI by the processing unit (103) based on estimation result.
8. The method of preparation of filtration membrane for air purification as claimed in claim 7, wherein the sensing by the plurality of parameters including air carbon content and carbon monoxide gas content, and wherein the plurality of sensors (101) including a carbon sensor and a carbon monoxide sensor.
9. The method of preparation of filtration membrane for air purification as claimed in claim 7, wherein the plurality of decisions including switching ON/OFF a nanoparticles-based filtration chamber and/or switching ON/OFF a catalytic adsorption chamber depending upon levels of the plurality of parameters of air.
10. The method of preparation of filtration membrane for air purification as claimed in claim 9, wherein the nanoparticles-based filtration chamber utilizing a silver or gold nanoparticles based filtration of carbon particles in air, and wherein the catalytic adsorption chamber utilizing palladium-based catalytic adsorption of Carbon monoxide gas in air.

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