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Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash
Abstract
The chapter describes a multilayer quantum backpropagation neural network (QBPNN) architecture to predict the removal of phenol from aqueous solution by orange peel ash, guided by the application of three types of activation functions and characterized by backpropagation of errors. These activation functions are Sigmoid function, tanh function and tan1.5h function. First by a classical multilayer neural network architecture with three types of activation functions is discussed in this chapter. It takes 6000000 iterations to train the network with a learning rate of 0.01. Among these three types of activation functions tan1.5 function shows the best prediction result. Next, QBPNN is discussed in this chapter. It takes 22000 iterations to train the network with the same learning rate. Here also tan1.5h function shows the best result in prediction of removal of phenol. Thus QBPNN is much faster than the classical multilayer neural network architecture. Different graphs are also given for comparison between the experimental output and network output using different activation functions. This particular chapter basically deals with a model application by which experimental results can be comparing with the model output. Because of their reliable, robust, and salient characteristics in capturing the non-linear relationships existing between variables (multi-input/output) in complex systems, it has become apparent that numerous applications of ANNs/QBNN have been successfully conducted in various parts of environmental engineering. Fuzzy Logic is also used as alternate method to predict the removal of phenol from aqueous solution by orange peel ash, but QBPNN shows the best result.
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