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Application of Artificial Neural Networks for Prediction of Photocatalytic Reactor
Author(s) -
Delnavaz Mohammad
Publication year - 2015
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/werd1400430.1
Subject(s) - photocatalysis , phenol , slurry , wastewater , pollutant , chemical engineering , materials science , kinetics , nano , artificial neural network , chemistry , reaction rate constant , process (computing) , catalysis , pulp and paper industry , chromatography , environmental engineering , environmental science , organic chemistry , computer science , composite material , physics , quantum mechanics , machine learning , engineering , operating system
In this paper, forecasting of kinetic constant and efficiency of photocatalytic process of TiO 2 nano powder immobilized on light expanded clay aggregates (LECA) was investigated. Synthetic phenolic wastewater, which is toxic and not easily biodegradable, was selected as the pollutant. The efficiency of the process in various operation conditions, including initial phenol concentration, pH, TiO 2 concentration, retention time, and UV lamp intensity, was then measured. The TiO 2 nano powder was immobilized on LECA using slurry and sol‐gel methods. Kinetics of photocatalytic reactions has been proposed to follow the Langmuir–Hinshelwood model in different initial phenol concentration and pH. Several steps of training and testing of the models were used to determine the appropriate architecture of the artificial neural network models (ANNs). The ANN‐based models were found to provide an efficient and robust tool in predicting photocatalytic reactor efficiency and kinetic constant for treating phenolic compounds.