Honeywell has introduced a system with integrated early warning smoke detection and advanced IAQ parameter monitoring to identify environmental concerns before they become problems.
The problem with smoke detection systems is that they only begin after a fire has developed to an already dangerous level. Then somehow the smoke reaches the detectors and then the system alerts the whole zone. This does not sound and is not in fact desired for a firm or any other place.
‘VESDA Air’, a first-of-its-kind, samples air actively, and therefore improves building health and safety by identifying life safety, asset protection or IAQ issues before they escalate into problems, disrupt operations, or put occupants at risk.
Honeywell’s VESDA technology actively samples air – rather than passively waiting for smoke to reach traditional spot sensors. The system consists of a highly sensitive IAQ sensor that measures critical IAQ parameters, including volatile organic compounds, fine particulate matter of 1.0 micron (PM1.0) and PM2.5 or larger with unprecedented accuracy, CO and CO2 concentration, temperature and humidity.
“We challenged our engineering teams to find a way to complement the capabilities of our aspirating smoke detection systems with highly sensitive IAQ monitoring. The system identifies not only the minute presence of smoke, but also the presence of air quality contaminants of concern, allowing building operators to react and respond to out-of-bounds parameters quickly, before they escalate into unsafe situations,” said Udaya Shrivastava, vice president and chief technology officer, Honeywell Building Technologies.
The plug-and-play cartridge-based IAQ sensor works much the same as replacing a printer’s ink-jet cartridge, thus avoiding costly calibration and other maintenance. This convenience helps to reduce total cost, minimise waste and provide accurate IAQ data. Ideal for premium commercial buildings, healthcare facilities, hospitality, manufacturing and schools, the new sensor provides instrument-grade IAQ sensing for data uniformity and accuracy.
