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Development of an Intelligent Neural Model to Predict and Analyze the VOC Removal Pattern in a Photocatalytic Reactor
Abstract
Volatile organic compounds (VOCs) belong to a new class of air pollutant that causes significant effect on human health and environment. Photocatalytic oxidation is an innovative, highly efficient, and promising option to decontaminate air polluted with VOCs, at faster elimination rates. This study pertains to the application of artificial neural networks to model the removal dynamics of an annular type photoreactor for gas – phase VOC removal. Relevant literature pertaining to the experimental work has been reported in this chapter. The different steps involved in developing a suitable neural model have been outlined by considering the influence of internal network parameters on the model architecture. Anew, the neural network modeling results were also subjected to sensitivity analysis in order to identify the most influential parameter affecting the VOC removal process in the photoreactor.
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