A coupled computational fluid dynamics and back-propagation neural network-based particle swarm optimizer algorithm for predicting and optimizing indoor air quality

In the modern era, people spend approximately 90% of their time in indoor settings, such as offices and residential buildings. As prolonged exposure to indoor environments can significantly impact health outcomes, it is important to ensure that good indoor air quality (IAQ) is achieved. The main obstacles to achieving effective control of IAQ are twofold. First, it is very difficult to monitor the values of IAQ parameters, especially within a person's breathing zone. Second, current heating ventilation and air conditioning systems are unable to rapidly predict and optimize IAQ. This study aims to obtain accurate indoor environmental parameters for rapidly predicting and optimizing IAQ. To achieve this, a computational fluid dynamics (CFD)-based back propagation neural network (BPNN) combined with a particle swarm optimizer (PSO) algorithm is proposed. Notably, the BPNN-PSO algorithm can rapidly predict and optimize IAQ while using a limited number of CFD runs. In comparison with other state-of-the-art methods for controlling IAQ, such as the artificial neural network (ANN) genetic algorithm (GA), the BPNN-PSO algorithm further reduces the concentration of indoor air pollutants by up to 6.44% and computational costs by as much as 23.53%. The use of CFD ensures that the information obtained is accurate and allows for rapid prediction of indoor environmental conditions. The BPNN-PSO algorithm has the potential to contribute very effective and intelligent control strategies for ventilating indoor environments.

Automated summary

This study aims to rapidly predict and optimize indoor air quality (IAQ) by using computational fluid dynamics (CFD) combined with back propagation neural network (BPNN) and particle swarm optimizer (PSO) algorithm. The BPNN-PSO algorithm reduces indoor air pollutants concentration and computational costs compared to other methods.