Modern Building Services

FEATURE INDOOR AIR QUALITY Correctly selected multi-pocket bag filters or compact mini pleat filters, can offer this increased filtration surface area meaning that a higher grade of a filter can be used without necessarily increasing the pressure drop. These filters can often be a straight swap for conventional banks of bag filters in air handling units. This allows increased efficiency of particle reduction without modifications to the air handling unit, without increased pressure drop, and subsequently without increased energy use. That being said, when looking at improving the performance of an air handling unit, it’s often worth considering upgrading the centrifugal fans to EC plug-fans. This can also further free up space in the AHU for other IAQ improvement solutions. Proper consultation with filter and fan manufacturers will ensure that any of the above improvements are viable. Opportunities for using data DEFRA provides low-resolution data on outdoor air pollutant levels, but with pollution concentrations varying significantly over short distances, particularly in heavily built-up areas, the accuracy cannot be relied on. In the last few years, there has been a significant increase in the amount of high-quality outdoor air quality monitoring stations, particularly in London and other major cities. A number of providers can now provide highly localised historical data for levels of particulate pollution. On a simplistic level, this data is created by combining outdoor air monitoring data and adding additional layers of information about the weather and traffic. We have tested the quality of this data and found it to be sufficiently accurate compared with local outdoor air quality reference stations. This data can be gathered and then factored into the selection process for air handling unit filters. By applying this accurate annualised average outdoor air quality data along with filter dust holding capacity, energy, material, and labour costs, it is possible to predict the optimal change intervals for air handling unit filter systems. When this solution was first developed, there was a wide gap between the optimal filter change point from a total cost perspective and the optimal change point from a carbon perspective. With the rapidly increased cost of energy, these two optimal change points are getting closer in time. Both occupancy patterns and pollution levels vary hour by hour, with hybrid working only going to increase variation. When demand- controlled ventilation systems are used, it is impossible to accurately predict the dust loading of filters without more data. The latest version of Part F Building Regulations - Approved Document F: Ventilation introduced a new concept, “Reducing intakes during high levels of pollution” 2.6 Where sources of pollution vary with the time of day, such as urban road traffic, it may be acceptable, for time-limited periods, to take one of the following actions. a. Reduce the flow of external air into ventilation intakes. b. Close ventilation intakes when the concentrations of external pollutants are highest. We deploy a two-stage solution for maximising this opportunity for reducing energy, reducing filter change intervals, and improving indoor air quality. The first level of optimisation entails the deployment of indoor air quality sensors throughout the building to gauge how IAQ varies throughout the day.We then study the data to seewhere lower occupancy levels (and therefore CO2 levels) alignwith typical local outdoor air quality patterns.This information is then used tomanually configure the BMS tominimise outdoor air intakewhere possiblewhen outdoor air pollution levels are high. The volume of supply air being brought through the AHU is measured with a manometer. This data, combined with 4G-enabled pressure differential sensors accurately models the dust loading of the filters to allow them to either be changed to optimise cost, or to minimise carbon emissions. Only by measuring the operation of the air handling unit in combination with real-time outdoor air quality data can we accurately predict the optimal filter change points. The second level to optimisation is to use past, present and future indoor and outdoor air quality to control the ventilation rate. The learning algorithm is able to predict patterns of IAQ driven by occupancy levels. Outdoor pollution levels are modelled using data from local air quality monitoring station networks, traffic, weather, and wind direction. The system can then control outdoor air rates according to the relationship between indoor and outdoor air quality. This extra level of data over the stage 1 solution allows optimisations to be pushed to their limits without risking poor indoor air quality and for potential energy and filter cost savings to be made. This optimisation often includes the addition of active air purification systems which continue to keep indoor air quality at acceptable levels when ventilation rates are reduced. MODERN BUILDING SERVICES JULY 2022 23 More information can be found at www.arm-environments.com