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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

Abstract

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.

Nomenclatures

A

Temperature (K)

Adeq−Prec

Adequate Precision

Adj−R2

Adjusted coefficient of determination

ai

Neuron’s input data

cj

Combined input data

B

WHSV (g anisole/g catalyst × h)

df

Degree of freedom

E

Activation energy (J.mol–1)

ko

Pre−exponential factor of rate constant

m

Number of treatments

MSE

Mean square error

n

Number of observations

Pred−R2

Predicted coefficient of determination

R

Universal gas constant (J.mol–1.K–1)

R2

Determination Coefficient

Si

Component selectivity (−)

SSE

Error sum of square

SSReg

Sum of square of regression

SST

Total sum of square

T

Temperature (K)

TOS

Time on stream

WHSV

Weight hourly space velocity

wji

Weight function

X

Anisole conversion (−)

Yi

Component yield (−)

Greek letters

ε

Noise, error of response

Abbreviations

ANN

Artificial neural network

ANOVA

Analysis of variance

DOE

Design of experiments

GA

Genetic algorithm

HDO

Hydrodeoxygenetion

L.M

Levenberg−Marquardt

RSM

Response surface methodology

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

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