期刊
JOURNAL OF MOLECULAR CATALYSIS A-CHEMICAL
卷 331, 期 1-2, 页码 86-100出版社
ELSEVIER
DOI: 10.1016/j.molcata.2010.07.016
关键词
TiO2 nanoparticles; ZnO nanoparticles; Neural network; Photocatalysis; Fenton; Photo-Fenton; Electro-oxidation
资金
- University of Tabriz
- Tabriz Islamic Art University, Iran
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. (C) 2010 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据