Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis

Artificial neural networks (ANNs) are computer based systems that are designed to simulate the learning process of neurons in the human brain. ANNs have been attracting great interest during the last decade as predictive models and pattern recognition. Artificial neural networks possess the ability to "learn" from a set of experimental data (e.g. processing conditions and corresponding responses) without actual knowledge of the physical and chemical laws that govern the system. Therefore, ANNs application in data treatment is especially important where systems present nonlinearities and complex behavior. In recent years "advanced oxidation processes" (AOPs), including homogeneous and heterogeneous nanocatalytic processes, have been proposed to oxidize quickly and non-selectively a broad range of water pollutants. Due to the complexity of reactions in AOPs, the effect of different operational parameters involved are very difficult to determine, leading to uncertainties in the design and scale-up of chemical reactors of industrial interest. It is evident that this problem can not be solved by simple linear multivariate correlation. Artificial neural networks are a promising alternative modeling technique. This paper briefly describes the application of artificial neural networks for modeling of water and wastewater treatment using various homogeneous and heterogeneous nanocatalytic processes. Examples of early applications of ANNs in modeling and simulation of photocatalytic, photooxidative and electrochemical treatment processes are reviewed.