An air quality system handled by artificial intelligence internet of things is provided and includes a gas detection device, an artificial intelligent internet of things handling device, and a user demand platform. In the system, the user demand platform transmits an instruction to the artificial intelligent internet of things handling device. By using self deep-learning analysis of data computing and algorithms of the AI device, second digital data is generated. The second digital data is fed back to the gas detection device through the network. Accordingly, the actuating actions of the micro pumps and the detecting actions of the sensors can be generated. Hence, the actuation and the detection of air quality can be achieved more effectively.

An air quality monitor connected to a server that can identify individual people as they move from room to room count the people in each room; the information is then used to determine each person's individual exposure to pollutants.

Air purification and dehumidification apparatus includes a first cooler that cools air introduced through a first inlet, a first rotor that primarily adsorbs and absorbs VOCs and moisture contained in the air cooled by the first cooler, an air conditioning unit that cools or heats the air primarily purified and dehumidified by the first rotor, a blower that moves the air cooled or heated by the air conditioning unit, a second rotor that adsorbs and absorbs VOCs and moisture remaining in the air moved by the blower, a second cooler that re-cools the air secondarily purified and dehumidified by the second rotor, a first heating unit that heats air that is introduced through a second inlet and is then supplied to the first rotor, using sequentially solar energy and electric energy, and a third cooler that condenses air containing the VOCs and moisture that are released from the first rotor.

A heating, ventilation, and/or air conditioning (HVAC) system includes a heat exchanger configured to exchange heat between a refrigerant and an air flow, a blower configured to induce the air flow across the heat exchanger, and a control board configured to receive an input indicative of a presence of refrigerant external to the heat exchanger and adjust operation of the blower based on the input.

A system for climate control comprises a controller, comprising a processor and a non-transitory computer-readable medium with instructions stored thereon, a plurality of sensors communicatively connected to the controller, and at least one HVAC component communicatively connected to the controller, wherein the instructions, when executed by the processor, perform steps comprising receiving sensor data from at least one sensor of the plurality of sensors, executing a machine learning model using the received sensor data as inputs, calculating a predicted temperature, humidity, or occupancy state from the machine learning model, and sending a control instruction to the at least one HVAC component based on the calculated temperature, humidity, or occupancy state. A method for training a machine learning algorithm for a climate control system and a method for HVAC control in a building are also described.

A system that includes a heating, ventilation, and air conditioning (HVAC) system that includes a sensor system within the HVAC system. The refrigerant sensor system includes a refrigerant sensor that detects the presence of an amount of refrigerant in the HVAC system and a control system that receives the amount of refrigerant detected from the refrigerant sensor. The control system also determines the amount of refrigerant exceeds a threshold value. Additionally, the control system sends one or more output signals to one or more components that are configured to couple to one or more devices that are part of the HVAC system. The one or more components are configured to cause the one or more devices to adjust operations based on the state of the components. The sensor system also includes a housing that encloses the refrigerant sensor and control system.

The present invention relates to an air quality monitoring and controlling system and method for enclosed spaces, which provides an innovative closed loop (i.e., inline real time feedback) air management process, utilizing learning and prediction capabilities to provide with effective air purification and reliable alerting when required.

A central controller for an intelligent environmental air management system includes at least one processor and memory storing instructions executable by the at least one processor. The instructions, when executed, cause the central controller to detect and communicate with an air hazard sensor, an environmental condition monitor, and an air hazard mitigation device of the intelligent environmental air management system.

An indoor air quality (IAQ) system for a building includes an IAQ sensor that is located within the building and that is configured to measure an IAQ parameter. The IAQ parameter is one of: an amount of particulate of at least a predetermined size present in air; an amount of volatile organic compounds (VOCs) present in air; and an amount of carbon dioxide present in air. A mitigation module is configured to: selectively turn on a mitigation device based on a comparison of the IAQ parameter with a first ON threshold and a second ON threshold; and selectively turn off the mitigation device based on a comparison of the IAQ parameter with an OFF threshold. A clean module is configured to determine a clean value for the IAQ parameter. A thresholds module is configured to, based on the clean value, determine the first ON threshold and the OFF threshold.