The performance of the perforated tray aerator (PTA) was evaluated by conducting experiments in a tank of size 4 m × 4 m × 1.5 m. Based on the dimensional analysis, non-dimensional numbers related to geometric variables, viz. numbers of trays (n), ratio of consecutive width of tray to total height of aerator , ratio of perforation diameter to total height of aerator and ratio of the volume of water in the tank to total height of aerator were developed. Experiments were conducted with different numbers of trays (n): 1, 2, 3 and 4, keeping = 0.33, = 2.5 × 10−4, = 2,500 and pump flow rate (Q) = 0.010 m3/s as constants. The optimum number of perforated trays was found to be 3. Response surface methodology (RSM) and central composite rotatable design (CCRD) were used to further optimize the geometric variables with combinations of non-dimensional geometric variables , and . The flow rate (Q) of 0.013 m3/s and number of trays (n) as 3 were kept as constants. The optimum performance of PTA was obtained at = 0.665, = 1.85 × 10−4 and = 312.50 with the maximum non-dimensional standard aeration efficiency (NDSAE) and standard aeration efficiency (SAE) of 35.58 × 10−3 and 1.45 kgO2/kWh.

Aeration experiments were conducted in a masonry tank to study the effects of operating parameters on the standard aeration efficiency (SAE) of a propeller diffused aeration (PDA) system. The operating parameters included the rotational speed of shaft (N), submergence depth (h), and propeller angle (α). The response surface methodology (RSM) and an artificial neural network (ANN) were used for modelling and optimizing the standard aeration efficiency (SAE) of a PDA system. The results of both approaches were compared for their modelling abilities in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), computed from experimental and predicted data. ANN models were proved to be superior to RSM. The results indicate that for achieving the maximum standard aeration efficiency (SAE), N, h and α should be 1,000 rpm, 0.50 m, and 12°, respectively. The maximum SAE was found to be 1.711 kg O2/ kWh. Cross-validation results show that best approximation of the optimal values of input parameters for maximizing SAE is possible with a maximum deviation (absolute error) of ±15.2% between the model predicted and experimental values.