IIT Mandi develops algorithms for HVAC systems in buildings

IIT Mandi develops algorithms for HVAC systems in buildings

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New Delhi, Updated on Mar 19, 2020 13:29 IST

The researchers of Indian Institute of Technology Mandi (IIT Mandi) have designed new algorithms for detection and diagnosis of component failure. This can be helpful to improve the energy-efficient operation of Heating, Ventilation and Air Conditioning (HVAC) systems.

The researchers of the Indian Institute of Technology Mandi (IIT Mandi) have designed new algorithms for the detection and diagnosis of component failure. This can be helpful to improve the energy-efficient operation of Heating, Ventilation and Air Conditioning (HVAC) systems which are used in buildings. In centralised HVAC systems in buildings, ventilation and climate control are done at a centralised place outside the building by an Air Handling Unit (AHU) which leads to better maintenance and removal of indoor noise. The treated air is dispersed to every room by means of controlled ducts and extra air in the room is recirculated through the unit.

The efficient functioning of centralised HVAC systems asks for the careful instrumentation of several machineries. As per the researchers of IIT Mandi, the algorithm designed for recognition and approximation of the scale of the failure of VAV dampers uses logical models that apply over a wide range of unpredictable operating conditions, such as zone occupancy profile, weather dynamics, outside air temperature, and so on.

The researchers have displayed efficiency by means of comprehensive simulation studies. They have reflected that the developed algorithm can fruitfully sense and evaluate the extent of VAV multiple damper faults. The research team is working towards designing regionalised and disseminated fault diagnosis algorithms and fault-tolerant control approach for large scale buildings in order to ensure more energy-efficient operation of HVAC systems. It also aspires to outspread this work to real-time testing and substantiation on a real building monitoring platform. The outcomes of the team’s recent work have been published in the Journal of Building Engineering, Elsevier.

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