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Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model

ZareNezhad, Bahman, Aminian, Ali
Energy conversion and management 2011 v.52 no.2 pp. 911-916
algorithms, combustion, condensation, corrosion, dewpoint, energy recovery, gases, hydrochloric acid, neural networks, nitrogen dioxide, pollution control, power plants, prediction, sulfur dioxide, temperature
This paper presents a new approach based on using an artificial neural network (ANN) model for predicting the acid dew points of the combustion gases in process and power plants. The most important acidic combustion gases namely, SO₃, SO₂, NO₂, HCl and HBr are considered in this investigation. Proposed Network is trained using the Levenberg–Marquardt back propagation algorithm and the hyperbolic tangent sigmoid activation function is applied to calculate the output values of the neurons of the hidden layer. According to the network’s training, validation and testing results, a three layer neural network with nine neurons in the hidden layer is selected as the best architecture for accurate prediction of the acidic combustion gases dew points over wide ranges of acid and moisture concentrations. The proposed neural network model can have significant application in predicting the condensation temperatures of different acid gases to mitigate the corrosion problems in stacks, pollution control devices and energy recovery systems.