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
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Majid Saidi
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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Articles in the same Issue
- TBP Assisted Uranyl Extraction in Water-Dodecane Biphasic System: Insights from Molecular Dynamics Simulation
- Optimization of Pistachio Nut Drying in a Fluidized Bed Dryer with Microwave Pretreatment Applying Response Surface Methodology
- Multi-Period Water Network Synthesis for Eco Industrial Parks considering Regeneration and Reuse
- Prediction of Pressure Drop in Venturi Scrubbers by Multi-Gene Genetic Programming and Adaptive Neuro-Fuzzy Inference System
- Hydrogen Production via Low Temperature Water Gas Shift Reaction: Kinetic Study, Mathematical Modeling, Simulation and Optimization of Catalytic Fixed Bed Reactor using gPROMS
- Maximum Sensitivity Based New PID Controller Tuning for Integrating Systems Using Polynomial Method
- 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
- Prediction of Experimental Measurement Data for High Density Polyethylene and Polypropylene Solubility in Organic Solvents
