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The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to model biomass growth. Three metrics: coefficient of determination ( R 2 ), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the model. Sensitivity analysis was applied to confirm variables that have a strong influence on biomass growth. The results of the study showed that MLP ANN algorithm was able to model biomass growth successfully. R 2 values were 0.844, 0.853, and 0.823 during training, validation, and testing phases, respectively. RMSE values were 0.7476, 1.1641, and 0.7798 during training, validation, and testing phases respectively. MSE values were 0.5589, 1.3551, and 0.6081 during training, validation, and testing phases, respectively. Sensitivity analysis results showed that temperature (47.2%) and dissolved oxygen (DO) concentration (40.2%) were the biggest drivers of biomass growth. Aeration period (4.3%), chemical oxygen demand (COD) concentration (3.2%), and oxygen uptake rate (OUR) (5.1%) contributed minimally. The biomass growth model can be applied at different wastewater treatment plants by different plant managers/operators in order to achieve optimum biomass growth. The optimum biomass growth will improve the removal of organic and inorganic matter in the biological treatment process.

The biological treatment process (BTP) is responsible for removing COD and ammonia using microorganisms present in wastewater. The BTP consumes large quantities of energy due to the transfer of oxygen using air pumps/blowers. Energy consumption in the BTP is due to low solubility of oxygen, which results in low aeration efficiency (AE). The aim of the study was to develop an AE model that can be used to monitor the performance of the BTP. MLP ANN algorithm was used to model AE in the BTP. The performance of the AE model was evaluated using R2, MSE, and RMSE. Sensitivity analysis was performed on the AE model to determine variables that drive AE. The results of the study showed that MLP ANN algorithm was able to model AE. R2, MSE, and RMSE results were 0.939, 0.0025, and 0.05 respectively during testing phase. Sensitivity analysis results showed that temperature (34.6%), COD (21%), airflow rate (19.1%), and OTR/KLa (15.7%) drive AE. At high temperatures, the viscosity of wastewater is low which enables oxygen to penetrate the wastewater, resulting in high AE. The AE model can be used to predict the performance of the BTP, which will assist in minimizing energy consumption.

The biological aeration unit consumes the highest energy (67.3%) in wastewater treatment compared with physical (18.8%) and chemical (13.9%) treatment processes. The high energy consumption is caused by the supply of oxygen using air pumps/blowers and temperature that controls microorganisms' growth. The purpose of this study was to model and optimize energy consumption in the biological aeration unit. The multilayer perceptron (MLP) artificial neural network (ANN) algorithm was used to model energy consumption. The particle swarm optimization (PSO) algorithm was used to optimize the energy consumption model. Sensitivity analysis was performed to determine the percentage contribution of input variables towards energy consumption. The MLP ANN algorithm modelled energy consumption successfully and produced R², RMSE, and MSE of 0.89, 0.0265, and 0.00070, respectively, during the testing phase. The PSO algorithm optimized energy consumption successfully and produced a global solution of 0.993 kWh/m³. The percentage reduction between the lowest measured and optimized energy consumption was 38.4%. Aeration period (81%) and temperature (10.7%) contributed the highest towards energy consumption. In conclusion, the temperature played a significant role in energy consumption compared with the airflow rate (4.2%). When the temperature is conducive to allowing the growth of microorganisms, the removal of COD and ammonia will be rapid resulting in low energy consumption.