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

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Majid Saidi ◽  
Mohammad Ali Roshanfekr Fallah ◽  
Nasrin Nemati ◽  
Mohammad Reza Rahimpour
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Model Development ◽  
Simulated Data ◽  
High Accuracy ◽  
Coefficient Of Determination ◽  
Complex Nature ◽  
Conversion Model ◽  
Selectivity Model ◽  
Artificial Neural

AbstractThe kinetic of catalytic upgrading of anisole as a lignin−derived bio−oil component is investigated experimentally overPt/γAl2O3at 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.RSMandANNmethods were constructed based upon theDOE’s points and then utilized for generating extra−simulated data. Data simulated by theRSM/ANNmethod 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) ofANNmodel equaled to 0.97 and 8.3 × 10−12respectively means high accuracy of the developed model. The results of kinetic modeling with simulated data from theANNandRSMmodels 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 thanHDO.

scholarly journals Prediction of standard aeration efficiency of propeller diffused aeration system using response surface methodology and artificial neural network

2021 ◽  
Author(s):  
Subha M. Roy ◽  
Mohammad Tanveer ◽  
Debaditya Gupta ◽  
C. M. Pareek ◽  
B. C. Mal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Maximum Deviation ◽  
Operating Parameters ◽  
Aeration Efficiency ◽  
Artificial Neural

Abstract Aeration experiments were conducted in a masonry tank to study the effects of operating parameters on standard aeration efficiency (SAE) of a propeller diffused aeration (PDA) system. The operating parameters include the rotational speed of shaft (N), submergence depth (h), and propeller angle (α). The response surface methodology (RSM) and artificial neural network (ANN) were used for modelling and optimizing the standard aeration efficiency (SAE) of a PDA system. The results of the both approaches were compared for their modelling abilities in terms of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), computed from experimental and predicted data. ANN models were proved to be superior to RSM. The results indicate that for achieving the maximum standard aeration efficiency (SAE), N, h and α should be 1,000 rpm, 0.50 m, and 12°, respectively. The maximum SAE was found to be 1.711 kg O2/ kWh. The cross-validation results show that the best approximation of optimal values of input parameters for maximizing SAE is possible with a maximum deviation (absolute error) of ±15.2% between the model predicted and experimental values.

scholarly journals Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

2021 ◽  
Author(s):  
Jorge Marcos Rosa ◽  
Flavio Guerhardt ◽  
Silvestre Eduardo Rocha Rocha Ribeiro Júnior ◽  
Peterson Adriano Belan ◽  
Gustavo Araujo Lima ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Coefficient Of Determination ◽  
Main Process ◽  
Modeling And Optimization ◽  
Dyeing Process ◽  
Artificial Neural ◽  
Cotton Dyeing

Abstract This work explores the modeling and optimization of the conditions to obtain a set of blue pigments for dyeing reactive cotton, by means of an approach that combines the techniques response surface methodology (RSM) and artificial neural network (ANN). By means of RSM technique the interactions and the effects of the main process variables (factors) on the behavior of coloristic intensity (K.S-1) were investigated. For this, a 26 central composite rotational design (CCRD) was carried out considering the factors temperature, NaCl, Na2CO3, NaOH, processing time and RB5 concentration. The results obtained show that all investigated factors have considerable effect on the behavior of K.S-1. The data produced in the dyeing experiments were used to build and train a Multilayer Perceptron ANN (MLP-ANN) to predict K.S-1, being the input layer of the MLP-ANN designed according to the results achieved by the RSM. The non-linear behavior of dyeing with RB5 was successfully modeled by a three-layer MLP-ANN comprising 6 input neurons, 15 hidden neuros, and 1 output neuron to indicate the value of K.S-1. The results achieved in the performed simulations confirmed the ANN effectiveness to predict K.S-1 values in RB5 the dyeing process, with high coefficient of determination (R2=0.942). The developed approach allowed the composition of a table containing optimized conditions to obtain a set of colors of the blue palette using RB5 dye, varying from sky blue to oxford blue, which will facilitate the assembly of the dyes. Finally, the experiments conducted in this work allowed the development of a computational tool to support the dyeing process, saving chemical inputs and time in cotton dyeing with specific dyestuff.

scholarly journals Effective Chromium Adsorption From Aqueous Solutions and Tannery Wastewater Using Bimetallic Fe/Cu Nanoparticles: Response Surface Methodology and Artificial Neural Network

2021 ◽  
Vol 14 ◽  
pp. 117862212110281
Author(s):  
Ahmed S. Mahmoud ◽  
Nouran Y. Mohamed ◽  
Mohamed K. Mostafa ◽  
Mohamed S. Mahmoud
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Industrial Effluent ◽  
Oxygen Demand ◽  
Operating Conditions ◽  
Tannery Wastewater ◽  
P Value ◽  
Cu Nanoparticles ◽  
Artificial Neural ◽  
Cr Removal

Tannery industrial effluent is one of the most difficult wastewater types since it contains a huge concentration of organic, oil, and chrome (Cr). This study successfully prepared and applied bimetallic Fe/Cu nanoparticles (Fe/Cu NPs) for chrome removal. In the beginning, the Fe/Cu NPs was equilibrated by pure aqueous chrome solution at different operating conditions (lab scale), then the nanomaterial was applied in semi full scale. The operating conditions indicated that Fe/Cu NPs was able to adsorb 68% and 33% of Cr for initial concentrations of 1 and 9 mg/L, respectively. The removal occurred at pH 3 using 0.6 g/L Fe/Cu dose, stirring rate 200 r/min, contact time 20 min, and constant temperature 20 ± 2ºC. Adsorption isotherm proved that the Khan model is the most appropriate model for Cr removal using Fe/Cu NPs with the minimum error sum of 0.199. According to khan, the maximum uptakes was 20.5 mg/g Cr. Kinetic results proved that Pseudo Second Order mechanism with the least possible error of 0.098 indicated that the adsorption mechanism is chemisorption. Response surface methodology (RSM) equation was developed with a significant p-value = 0 to label the relations between Cr removal and different experimental parameters. Artificial neural networks (ANNs) were performed with a structure of 5-4-1 and the achieved results indicated that the effect of the dose is the most dominated variable for Cr removal. Application of Fe/Cu NPs in real tannery wastewater showed its ability to degrade and disinfect organic and biological contaminants in addition to chrome adsorption. The reduction in chemical oxygen demand (COD), biological oxygen demand (BOD), total suspended solids (TSS), total phosphorus (TP), total nitrogen (TN), Cr, hydrogen sulfide (H2S), and oil reached 61.5%, 49.5%, 44.8%, 100%, 38.9%, 96.3%, 88.7%, and 29.4%, respectively.

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