Development of a back-propagation neural network combined with an adaptive multi-objective particle swarm optimizer algorithm for predicting and optimizing indoor CO2 and PM2.5 concentrations

Abstract

People now spend between 80% and 90% of their lifetimes in indoor locations such as offices and residential buildings. These extended hours indoors have substantial health impacts, making it vital that people have adequate indoor air quality (IAQ). The management of IAQ presents two issues: (1) rapidly predicting and controlling IAQ using current intelligent ventilation systems is difficult, and (2) simultaneously controlling both gas pollutants (of which CO2 is a representative example) and particulate matter concentrations (for example, PM2.5) while achieving optimal outcomes is challenging. Therefore, this study aims to develop a fast and accurate optimization algorithm to simultaneously predict and control indoor CO2 and PM2.5 concentrations. To this end, a back-propagation neural network (BPNN) combined with an adaptive multi-objective particle swarm optimizer (AMOPSO) algorithm based on computational fluid dynamics (CFD) is proposed. The CFD model first creates a database of indoor CO2 and PM2.5 concentrations. Then, based on the CFD database, the BPNN model is used to predict indoor air pollutant concentrations. If the predicted concentrations do not meet predetermined limits, the AMOPSO algorithm is initialized to optimize the concentrations of the indoor air pollutants. In test examples, the proposed optimization algorithm reduces CO2 concentrations by up to 30.5%, while also reducing PM2.5 concentrations by as much as 77.1%.