Modelling and prediction of aeration efficiency of the venturi aeration system using ANN-PSO and ANN-GA - Yadav, Anamika; Roy, Subha M.; Biswas, Abhijit; Swain, Bhagaban; Majumder, Sudipta (Frontiers in Water, 2024)
The significance of this work involves optimization of the aeration efficiency (AE) of the venturi aerator using artificial neural network (ANN) technique integrated with optimization algorithm i.e. particle swarm optimization (PSO) and genetic algorithm (GA). To optimize the effects of operational factors on aeration efficiency utilising a venturi aeration system, aeration experiments were conducted in an experimental tank having dimensions of 90 cm × 55 cm × 45 cm. The operating parameters of venturi aerator includes throat length (TL), effective outlet pipe (EOP), and flow rate (Q) to estimate the efficacy of venturi aerator in terms of AE. A 3-6-1 ANN model was developed and integrated with PSO and GA technique to find out the best possible optimal operating variables of the venturi aerator. The coefficient of determination (R 2 ), root mean square error (RMSE), and mean absolute error (MAE) determined from the experimental and estimated data were used to assess and compare the performance of the ANN-PSO and ANN-GA modelling. It's showed that ANN-PSO provides a better result as compared to ANN-GA. The operational parameters, TL, EOP, and Q, were determined to have the most optimum values at 50 mm, 6 m, and 0.6 l/s, respectively. The optimised aeration efficiency of venturi was found to be 0.105 kg O2/kWh at optimum operational circumstances. In fact, the neural network having an ideal design of (3-6-1) and a correlation coefficient value that is extremely close to unity has validated the results indicated above.