scholarly journals Catalytic Production of Glycolic Acid from Glycerol Oxidation: An Optimization Using Response Surface Methodology

Catalysts ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 257
Author(s):  
Claudia Patricia Tavera Ruiz ◽  
Franck Dumeignil ◽  
Mickaël Capron
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Glycolic Acid ◽  
Liquid Fraction ◽  
Operating Conditions ◽  
Molar Ratio ◽  
Glycerol Solution ◽  
Glycerol Concentration ◽  
Optimal Conditions ◽  
Glycerol Conversion

This study aimed at optimizing the production of glycolic acid from glycerol catalytic oxidation over a silver catalyst supported on a mixed cerium-zirconium oxide, to progress towards the industrialization of a derived process. Optimization of the operating conditions was performed using the response surface methodology. We concluded that the production of glycolic acid depends mainly of glycerol concentration, NaOH/glycerol ratio, catalyst/glycerol ratio, and O2/glycerol ratio. The optimal conditions we found were a temperature of 60 °C, a NaOH/glycerol molar ratio of 2, an O2/glycerol molar ratio of 0.23, and a catalyst/glycerol mass ratio of 0.07. With these optimal conditions, it was possible to increase the glycerol concentration from 0.3 M to 2 M, obtaining an increase in the concentration of glycolic acid in the liquid fraction, from 0.27 mol/L of glycolic acid (with initial glycerol solution 0.3 M) to 0.88 mol/L (with initial solution 2 M), while keeping a 100% glycerol conversion.

Optimization of biodiesel synthesis under simultaneous ultrasound-microwave irradiation using response surface methodology (RSM)

2015 ◽  
Vol 4 (4) ◽  
Author(s):  
Seyed Mohammad Safieddin Ardebili ◽  
Teymor Tavakoli Hashjin ◽  
Barat Ghobadian ◽  
Gholamhasan Najafi ◽  
Stefano Mantegna ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Microwave Irradiation ◽  
Response Surface ◽  
Palm Oil ◽  
Catalyst Concentration ◽  
Irradiation Time ◽  
Operating Conditions ◽  
Molar Ratio ◽  
Reaction Conditions ◽  
Biodiesel Synthesis

AbstractThis work investigates the effect of simultaneous ultrasound-microwave irradiation on palm oil transesterification and uncovers optimal operating conditions. Response surface methodology (RSM) has been used to analyze the influence of reaction conditions, including methanol/palm oil molar ratio, catalyst concentration, reaction temperature and irradiation time on biodiesel yield. RSM analyses indicate 136 s and 129 s as the optimal sonication and microwave irradiation times, respectively. Optimized parameters for full conversion (97.53%) are 1.09% catalyst concentration and a 7:3.1 methanol/oil molar ratio at 58.4°C. Simultaneous ultrasound-microwave irradiation dramatically accelerates the palm oil transesterification reaction. Pure biodiesel was obtained after only 2.2 min while the conventional method requires about 1 h.

scholarly journals Optimization Study in Biodiesel Production via Response Surface Methodology Using Dolomite as a Heterogeneous Catalyst

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Regina C. R. Santos ◽  
Rômulo B. Vieira ◽  
Antoninho Valentini
Keyword(s):  
Response Surface Methodology ◽  
Soybean Oil ◽  
Response Surface ◽  
Heterogeneous Catalyst ◽  
Methyl Esters ◽  
Operating Conditions ◽  
Biodiesel Production ◽  
Molar Ratio ◽  
High Catalytic Activity ◽  
Calcium Leaching

A carbonate mineral, dolomite, was used as a heterogeneous catalyst to produce methyl-esters from soybean oil. The samples were analyzed by XRF, TGA, XRD, TPD-CO2, and SEM. The calcination of dolomite at 800°C/1 h resulted in a highly active mixed metal oxides. In addition, the influence of the reaction variables such as the temperature, catalyst amount, and methanol/soybean oil molar ratio in methyl-ester production was optimized by the application of a central composite design in conjunction with the response surface methodology (RSM). The XRF analysis is carried out after the reuses procedure which shows that the deactivation process is mainly due to the selective calcium leaching. Overall, the calcined dolomite exhibited high catalytic activity at moderate operating conditions for biodiesel production.

scholarly journals Optimization of an Ultrasonic-Assisted Biodiesel Production Process from One Genotype of Rapeseed (TERI (OE) R-983) as a Novel Feedstock Using Response Surface Methodology

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2656 ◽  
Author(s):  
Sara Almasi ◽  
Barat Ghobadian ◽  
Gholam Hassan Najafi ◽  
Talal Yusaf ◽  
Masoud Dehghani Soufi ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Reaction Time ◽  
Response Surface ◽  
Methyl Esters ◽  
Operating Conditions ◽  
Biodiesel Production ◽  
Molar Ratio ◽  
Climate Conditions ◽  
Ultrasonic Assisted ◽  
Optimized Conditions

In recent years, due to the favorable climate conditions of Iran, the cultivation of rapeseed has increased significantly. The aim of this study was to investigate the possibility of biodiesel production from one genotype of rapeseed (TERI (OE) R-983). An ultrasonic approach was used in order to intensify the reaction. Response surface methodology (RSM) was applied to identify the optimum conditions of the process. The results of this research showed that the conversion of biodiesel was found to be 87.175% under the optimized conditions of a 4.63:1 molar ratio (methanol to oil), 56.50% amplitude, and 0.4 s pulses for a reaction time of 5.22 min. Increasing the operating conditions, such as the molar ratio from 4:1 to 5.5:1, amplitude from 50% to 72.5%, reaction time from 3 min to 7 min, and pulse from 0.4 s to 1 s, increased the FAME (fatty acid methyl esters) yield by approximately 4.5%, 2.3%, 1.2%, and 0.5%, respectively. The properties of the TERI (OE) R-983 methyl ester met the requirements of the biodiesel standard (ASTM D6751), indicating the potential of the produced biodiesel as an alternative fuel.

scholarly journals Application of Response Surface Methodology (RSM) for optimizing removal of Cr(VI) wastewater using Cr(VI)-reducing biofilm systems

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Nurfadilah Mohammed ◽  
Wan Azlina Ahmad
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Operating Conditions ◽  
Optimal Conditions ◽  
Optimum Conditions ◽  
Nutrient Supplementation ◽  
Dependent Variables ◽  
Reduction Efficiency ◽  
Central Composite ◽  
Experimental Runs

Response surface methodology (RSM) involving central composite design (CCD) was employed to obtain optimal conditions for Cr(VI) wastewater treatment by Cr (VI) reducing biofilm systems. On the basis of a CCD, RSM was used to determine the effect of initialmetal concentrations (40-100 mgL-1), nutrient supplementations (10-20%) and flowrate (3-6 mLmin-1) on the levels of response, i.e. Cr(VI) reduction efficiency. A set of 20 experimental runs were needed for optimizing of the operating conditions. Quadratic regressionmodels with estimated coefficients were developed to describe the Cr (VI) reduction. Analysis of variance (ANOVA) showed a highcoefficient of determination (R2) value of 0.9941, thus ensuring a satisfactory adjustment of the second-order regression model with theexperimental data. Cr (VI) reduction had significant effect on all the three dependent variables. The experimental results show that Cr(VI)-reducing biofilm systems could effectively reduce Cr (VI), 100% at the optimum conditions of initial metal concentration of 100mgL-1, nutrient supplementation of 20% and flowrate of 3 mLmin-1. The experimental observations were in reasonable agreement withthe modelled values.

scholarly journals A Comparison between Several Response Surface Methodology Designs and a Neural Network Model to Optimise the Oxidation Conditions of a Lignocellulosic Blend

Biomolecules ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 787
Author(s):  
Roberto López ◽  
Camino Fernández ◽  
Fernando J. Pereira ◽  
Ana Díez ◽  
Jorge Cara ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Molar Ratio ◽  
Good Prediction ◽  
Optimal Conditions ◽  
Total Flow ◽  
Resource Requirement ◽  
Prediction Ability ◽  
Central Composite

In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn–rape blend. The ignition temperature (Te) and burnout index (Df) were selected as the responses to be optimised, while the CO2/O2 molar ratio, the total flow, and the proportion of rape in the blend were chosen as the influencing factors. For the RSM designs, complete, Box–Behnken, and central composite designs were performed to assess the experimental results. By applying the RSM, it was found that the principal effects of the three factors were statistically significant to compute both responses. Only the interactions of the factors on Df were successfully described by the Box–Behnken model, while the complete design model was adequate to describe such interactions on both responses. The central composite design was found to be inadequate to describe the factor interactions. Nevertheless, the three methods predicted the optimal conditions properly, due to the cancellation of net positive and negative errors in the mathematical adjustment. The ANN presented the highest regression coefficient of all methods tested and needed only 20 experiments to reach the best predictions, compared with the 32 experiments needed by the best RSM method. Hence, the ANN was found to be the most efficient model, in terms of good prediction ability and a low resource requirement. Finally, the optimum point was found to be a CO2/O2 molar ratio of 3.3, a total flow of 108 mL/min, and 61% of rape in the biomass blend.

scholarly journals Optimization of Vegetable Oil-Based Biodiesels by Multi-Response Surface Methodology (MRS) using Desirability Functions

2020 ◽  
Vol 45 (5) ◽  
Author(s):  
A. O. Mustapha ◽  
R.A. Adepoju ◽  
Y. T. Afolabi
Keyword(s):  
Response Surface Methodology ◽  
Reaction Time ◽  
Response Surface ◽  
Vegetable Oil ◽  
Vegetable Oils ◽  
Maximum Yield ◽  
Molar Ratio ◽  
Optimal Conditions ◽  
Catalyst Dosage ◽  
Reaction Speed

Environmental concerns associated with petroleum resources have propelled the development of sustainable and renewable alternatives to petroleum based products. Vegetable oil is one amongst the foremost abundant bio-based feedstocks. The interest in using vegetable oils and low molecular weight alcohols by direct transesterification have shown great potential as alternatives to petroleum-based diesel, and the production of bio-based diesel continues to increase. Utilization of multi-response surface methodology (MRS) for the most effective combination effect or response from the uses of input to output variables to optimize the yield and higher heating values (HHV) of biodiesels was investigated. In this work, utilizing variety of non-edible vegetable oils like castor (Ricinus communis L), jatropha (Jatropha curcas), and neem (Azadirachta indica) seeds and several process variables or inputs, including mixing time, mixing speed, process temperature and catalyst dosage to formulate high quality renewable fuels were further explored. The outputs were yield, viscosity, higher heating value, density and turbidity. The proposed optimization scenarios for biodiesel using the statistical (MRS) models was aimed to optimize the processes to achieved high conversion and higher heating values, while reducing the reaction time, turbidity, density, and viscosity in the samples. The results showed catalyst dosage as the most important variable for all the three samples. For maximum yield of 100%, the molar ratio of 6.25, catalyst of 0.75 wt.%, reaction speed of 499.99 rpm, reaction time of 19.88 min and temperature of 24.50 oC were found as optimal conditions; while the molar ratio of 5.60, catalyst of 1.01 wt.%, the reaction speed of 499.5 rpm, reaction time of 20.00 min and temperature of 35.50 oC were optimal conditions for maximum biodiesel yield.

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.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

Optimization of the Ultrasonic Extraction of Gypenoside III from Gynostemma pentaphyllum by Response Surface Methodology

2012 ◽  
Vol 550-553 ◽  
pp. 1866-1870
Author(s):  
Xiao Dan Tang ◽  
Hai Yang Hang ◽  
Shao Yan Wang ◽  
Jing Xiang Cong
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Extraction Yield ◽  
Raw Material ◽  
Extraction Temperature ◽  
Optimal Conditions ◽  
Gynostemma Pentaphyllum ◽  
Bioactive Component ◽  
Yield Value ◽  
Ultrasonic Time

Gypenosides III is a major bioactive component which is rich in Gynostemma pentaphyllum. For better utilization of the native resource, response surface methodology was used to optimize the extraction conditions of gypenosides III from G. pentaphyllum. The effects of three independent variables on the extraction yield of gypenosides III were investigated and the optimal conditions were evaluated by means of Box-Behnken design. The optimal conditions are as follows: ratio of ethanol to raw material 25, extraction temperature 58°C and ultrasonic time 25min. Under these conditions, the yield of gypenoside III is 1.216±0.05%, which is agreed closely with the predicted yield value.

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