Artificial neural network (ANN) for prediction indoor airborne particle concentration. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Gheziel, Athmane; Hanini, Salah; Mohamedi, Brahim
• Is Part Of: International journal of ventilation. Volume 21:Issue 1(2022)
• Page Start: 74
• Page End: 87
• Publication Date: 2022-01-02
• Subjects: Numerical simulation -- concentration distribution -- fine particles -- indoor air -- ANN model -- velocity; Ventilation -- Periodicals; Air conditioning -- Periodicals; Ventilation -- Périodiques; Ventilation; Electronic journals; Periodicals; 697.905
• DOI: 10.1080/14733315.2021.1876408
• Languages: English
• ISSNs: 1473-3315

Abstract

Abstract: Due to experimental data insufficiency for results validation realized by Computation Fluid Dynamics method (CFD), we are proposed new numerical simulations to determined concentration distribution of fine particles in indoor air for transient regime. The ANN model approach of multi-layer perceptron type with three layers is applied successfully. This model requires learning through a database which deduced from the bibliographic literature, composed by 2271 measurement points of which 80% assigned to ANN model training, 10% to test model and so the remaining (10%) assigned to validation part. The ANN model developed in this paper is beneficial and easy to predict fine particles distribution in air indoor when compared to the CFD method. The results average error found by this model does not reach 5%, when compared to the CFD method with an error of 16%. This model is used to treat the effect of the velocity and air exhaust section positions on the stability and flow regime establishment time.