An artificial neural network model for predicting volumetric mass transfer coefficient in the biological aeration unit

Author:

Muloiwa Mpho1ORCID,Dinka Megersa Olumana2,Nyende‐Byakika Stephen3

Affiliation:

1. Faculty of Engineering and the Built Environment, Department of Civil Engineering Tshwane University of Technology Pretoria West South Africa

2. Department of Civil Engineering Science University of Johannesburg Johannesburg South Africa

3. Independent Researcher

Abstract

AbstractThe solubility of oxygen in a liquid is limited/restricted by the gas–liquid film that prevents gas from dissolving in wastewater. Oxygen in the biological aeration unit (BAU) is required by microorganisms to survive and eliminate organic and inorganic matter. This study developed a volumetric mass transfer coefficient (KLa) model using Artificial Neural Network (ANN) algorithm. The performance of the KLa model was evaluated using coefficient of determination (R2), mean squared error (MSE), and root mean squared error (RMSE). KLa model produced R2 (0.852), MSE (0.0006), and RMSE (0.0245) during the testing phase. Biomass concentration (22.29%), aeration period (20.55%), and temperature (19.63%) contributed the highest towards the KLa model. KLa model showed that the BAU should be operated at high temperatures (35°C), low biomass concentration (1.65 g/L), and low aeration period (1 h) instead of high airflow (30 L/min). Temperature should be included in the modelling of the BAU, to achieve optimum KLa.

Publisher

Wiley

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