Neural network kinetic prediction of coke burn-off on spent MnO2/CeO2 wet oxidation catalysts

Abstract

Combustion kinetics of coke laydown on wet oxidation catalysts is studied by means of temperature-programmed oxidation “TPO” and mass spectrometry “MS” in the temperature range from 30 to 600°C. The study is designed to allow a better understanding of the influence of wet oxidation conditions (catalyst-phenol contacting time and temperature) on the combustion kinetics of coke deposited on MnO2/CeO2 and 1wt.% Pt-MnO2/CeO2 catalysts during phenol degradation. In this respect, the experimental procedure involves the continuous monitoring of carbon oxides and O2 fluxes resulting form the combustion of carbonaceous deposits in a 5% O2/He mixture. Based on the experimental data, an artificial neural network-based (ANN) modeling approach is implemented to represent, as accurately as possible, the complex combustion phenomenon so as to provide the opportunity to predict its evolution.In this context, the resulting ANN model is used as a “black-box” to approximate the complex non-linear conversion rate of the wet oxidation coke. The conversion rate is thus, expressed in terms of the TPO ramp temperature, running oxygen concentration, wet oxidation temperature, and phenol oxidation time.The proposed ANN-based modeling approach proved to be an accurate, reliable and effective tool for the quantification of the coke burn-off kinetics. The method has then great potential as a means to compensate for the lack of efficient phenomenological kinetic modeling techniques. It can also be used in the design of regenerative units downstream of catalytic wastewater treatment reactors based on wet oxidation.