Artificial neural network model for predicting wet scrubber performance - Danzomo, Bashir Ahmed; Salami, Momoh Jimoh Eyiomika; Khan, Md. Raisuddin (IJSER, 2012)
Increased public awareness posed for global climate change has led to greater concern over the impact of environmental changes due to constant emissions of air pollutants from industrial production. Wet scrubbers have important advantages when compared to other air pollution control devices. They can collect particulates like flammable and explosive dusts, foundry dusts, cement dusts, large volume of gaseous pollutants, acid mists and furnace fumes. In this study, a three layer feed forward neural network has been used to predict the performance of wet scrubber system for air pollution control. The theoretical performance, ηperf of the system was calculated using 206 scenarios for 8 data sets for the operating variables with nonlinear and complex characteristics. The performance fitness of the neural network (MSE = 0.00000107 and R-value = 0.9979) describes the effectiveness of the ANN model in predicting the performance of the scrubber system and the model follows the pattern of the theoretical data describing the scrubber performance at a higher efficiency range.