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Licensed Unlicensed Requires Authentication Published by De Gruyter May 11, 2017

Model-Based Design of Experiments for Kinetic Study of Anisole Upgrading Process over Pt/γAl2O3: Model Development and Optimization by Application of Response Surface Methodology and Artificial Neural Network

Majid Saidi, Mohammad Ali Roshanfekr Fallah, Nasrin Nemati and Mohammad Reza Rahimpour


The kinetic of catalytic upgrading of anisole as a lignin−derived bio−oil component is investigated experimentally over Pt/γAl2O3 at 573−673 K and 14 bar. According to experimental results, benzene, phenol, 2−methylphenol, 2,6−dimethylphenol, 2,4,6−trimethylphenol, and hexamethylbenzene are identified as the main products. The results indicated that the kinetically significant reaction classes are hydrogenolysis, hydrodeoxygenation (HDO), alkylation, and hydrogenation. The response surface methodology (RSM) is applied to optimize the experimental data which obtained at suggested conditions by design of experiment (DOE). Due to the complex nature of the system, artificial neural networks (ANNs) were employed as an efficient tool to model the behavior of the system. RSM and ANN methods were constructed based upon the DOE’s points and then utilized for generating extra−simulated data. Data simulated by the RSM/ANN method were used to fit power law kinetic rate expressions for the reactions. The coefficient of determination (R2) was obtained 0.998 and 0.973 for anisole conversion model and benzene selectivity model which represented the high accuracy of model predictions. The correlation coefficient (R) and mean square error (MSE) of ANN model equaled to 0.97 and 8.3 × 10−12 respectively means high accuracy of the developed model. The results of kinetic modeling with simulated data from the ANN and RSM models revealed that the highest reaction order during the upgrading process of anisole belongs to hydrogenolysis of anisole to phenol. Also the activation energy of hydrogenolysis reaction was lower than HDO.



Temperature (K)


Adequate Precision


Adjusted coefficient of determination


Neuron’s input data


Combined input data


WHSV (g anisole/g catalyst × h)


Degree of freedom


Activation energy (J.mol–1)


Pre−exponential factor of rate constant


Number of treatments


Mean square error


Number of observations


Predicted coefficient of determination


Universal gas constant (J.mol–1.K–1)


Determination Coefficient


Component selectivity (−)


Error sum of square


Sum of square of regression


Total sum of square


Temperature (K)


Time on stream


Weight hourly space velocity


Weight function


Anisole conversion (−)


Component yield (−)

Greek letters


Noise, error of response



Artificial neural network


Analysis of variance


Design of experiments


Genetic algorithm






Response surface methodology


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Received: 2016-10-15
Revised: 2017-1-18
Accepted: 2017-1-19
Published Online: 2017-5-11

© 2017 Walter de Gruyter GmbH, Berlin/Boston