Optimization of Operating Conditions Affecting Microbiologically Influenced Corrosion of Mild Steel Exposed to Crude Oil Environments Using Response Surface Methodology

: In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. As well, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables are 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

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

Air Soil and Water Research ◽

10.1177/11786221211028162 ◽

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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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 ◽

10.3390/membranes11010070 ◽

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.

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Optimization of Crude Oil and PAHs Degradation by Stenotrophomonas rhizophila KX082814 Strain through Response Surface Methodology Using Box-Behnken Design

Biotechnology Research International ◽

10.1155/2016/4769542 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Praveen Kumar Siddalingappa Virupakshappa ◽

Manjunatha Bukkambudhi Krishnaswamy ◽

Gaurav Mishra ◽

Mohammed Ameenuddin Mehkri

Keyword(s):

Response Surface Methodology ◽

Crude Oil ◽

Response Surface ◽

Oxygen Demand ◽

Inoculum Size ◽

Coefficient Of Determination ◽

Oil Removal ◽

Box Behnken Design ◽

Stenotrophomonas Rhizophila ◽

Crude Oil Removal

The present paper describes the process optimization study for crude oil degradation which is a continuation of our earlier work on hydrocarbon degradation study of the isolate Stenotrophomonas rhizophila (PM-1) with GenBank accession number KX082814. Response Surface Methodology with Box-Behnken Design was used to optimize the process wherein temperature, pH, salinity, and inoculum size (at three levels) were used as independent variables and Total Petroleum Hydrocarbon, Biological Oxygen Demand, and Chemical Oxygen Demand of crude oil and PAHs as dependent variables (response). The statistical analysis, via ANOVA, showed coefficient of determination R2 as 0.7678 with statistically significant P value 0.0163 fitting in second-order quadratic regression model for crude oil removal. The predicted optimum parameters, namely, temperature, pH, salinity, and inoculum size, were found to be 32.5°C, 9, 12.5, and 12.5 mL, respectively. At this optimum condition, the observed and predicted PAHs and crude oil removal were found to be 71.82% and 79.53% in validation experiments, respectively. The % TPH results correlate with GC/MS studies, BOD, COD, and TPC. The validation of numerical optimization was done through GC/MS studies and % removal of crude oil.

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

Green Processing and Synthesis ◽

10.1515/gps-2015-0029 ◽

2015 ◽

Vol 4 (4) ◽

Cited By ~ 3

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.

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