Optimization of the Biodegradation of Aliphatic, Aromatic, and UCM Hydrocarbons from Light Crude Oil in Marine Sediment Using Response Surface Methodology (RSM)

: 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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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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Application of the response surface methodology for modeling demulsification of crude oil emulsion using a demulsifier

Journal of Dispersion Science and Technology ◽

10.1080/01932691.2017.1385480 ◽

2017 ◽

Vol 39 (5) ◽

pp. 700-710 ◽

Cited By ~ 10

Author(s):

Negin Roshan ◽

Sattar Ghader ◽

Mohammad Reza Rahimpour

Keyword(s):

Response Surface Methodology ◽

Crude Oil ◽

Response Surface ◽

Oil Emulsion

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

British Biotechnology Journal ◽

10.9734/bbj/2015/16810 ◽

2015 ◽

Vol 7 (2) ◽

pp. 68-78 ◽

Cited By ~ 2

Author(s):

K. Salam ◽

S. Agarry ◽

A. Arinkoola ◽

I. Shoremekun

Keyword(s):

Response Surface Methodology ◽

Mild Steel ◽

Crude Oil ◽

Response Surface ◽

Operating Conditions ◽

Microbiologically Influenced Corrosion

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Optimization of Biomethane Production via Fermentation of Chicken Manure Using Marine Sediment: A Modeling Approach Using Response Surface Methodology

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182211988 ◽

2021 ◽

Vol 18 (22) ◽

pp. 11988

Author(s):

Fatma Abouelenien ◽

Toyokazu Miura ◽

Yutaka Nakashimada ◽

Nooran S. Elleboudy ◽

Mohammad S. Al-Harbi ◽

...

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Marine Sediment ◽

Incubation Time ◽

Methane Production ◽

Total Solid ◽

Solid Content ◽

Chicken Manure ◽

Total Solid Content ◽

Semi Solid

In this study, marine sediment (MS) was successfully used as a source of methanogenic bacteria for the anaerobic digestion (AD) of chicken manure (CM). Using MS showed high production in liquid and semi-solid conditions. Even in solid conditions, 169.3 mL/g volatile solids of chicken manure (VS-CM) was produced, despite the accumulation of ammonia (4.2 gNH3-N/kg CM). To the best of our knowledge, this is the highest methane production from CM alone, without pretreatment, in solid conditions (20%). Comparing MS to Ozouh sludge (excess activated sewage sludge) (OS), using OS under semi-solid conditions resulted in higher methane production, while using MS resulted in more ammonia tolerance (301 mL/gVS-CM at 8.58 gNH3-N/kg). Production optimization was carried out via a response surface methodology (RDM) model involving four independent variables (inoculum ratio, total solid content, NaCl concentration, and incubation time). Optimized methane production (324.36 mL/gVS-CM) was at a CM:MS ratio of 1:2.5 with no NaCl supplementation, 10% total solid content, and an incubation time of 45 days.

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High efficiency degradation crude oil by a novel mutant irradiated from Dietzia strain by 12C6+ heavy ion using response surface methodology

Bioresource Technology ◽

10.1016/j.biortech.2013.03.097 ◽

2013 ◽

Vol 137 ◽

pp. 386-393 ◽

Cited By ~ 15

Author(s):

Xiang Zhou ◽

Zhi-Jun Xin ◽

Xi-Hong Lu ◽

Xian-Peng Yang ◽

Mei-Rong Zhao ◽

...

Keyword(s):

Response Surface Methodology ◽

Crude Oil ◽

Response Surface ◽

High Efficiency ◽

Heavy Ion

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Catalytic deacidification optimization of Korean crude oil based on response surface methodology

Clean Technologies and Environmental Policy ◽

10.1007/s10098-014-0878-5 ◽

2014 ◽

Vol 17 (6) ◽

pp. 1513-1522 ◽

Cited By ~ 3

Author(s):

Norshahidatul Akmar Mohd Shohaimi ◽

Jafariah Jaafar ◽

Wan Azelee Wan Abu Bakar

Keyword(s):

Response Surface Methodology ◽

Crude Oil ◽

Response Surface

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Optimisation of reaction parameters for a novel polymeric additives as flow improvers of crude oil using response surface methodology

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01349-1 ◽

2021 ◽

Author(s):

Ibrahim Elganidi ◽

Basem Elarbe ◽

Norida Ridzuan ◽

Norhayati Abdullah

Keyword(s):

Response Surface Methodology ◽

Reaction Time ◽

Regression Model ◽

Crude Oil ◽

Response Surface ◽

Reaction Temperature ◽

Yield Response ◽

Initiator Concentration ◽

Oil Pipeline ◽

Polymeric Additives

AbstractIn recent years, polymeric additives have received considerable attention as a wax control approach to enhance the flowability of waxy crude oil. Furthermore, the satisfactory model for predicting maximum yield in free radical polymerisation has been challenging due to the complexity and rigours of classic kinetic models. This study investigated the influence of operating parameters on a novel synthesised polymer used as a wax deposition inhibitor in a crude oil pipeline. Response surface methodology (RSM) was used to develop a polynomial regression model and investigate the effect of reaction temperature, reaction time, and initiator concentration on the polymerisation yield of behenyl acrylate-co-stearyl methacrylate-co-maleic anhydride (BA-co-SMA-co-MA) polymer by using central composite design (CCD) approach. The modelled optimisation conditions were reaction time of 8.1 h, reaction temperature of 102 °C, and initiator concentration of 1.57 wt%, with the corresponding yield of 93.75%. The regression model analysis (ANOVA) detected an R2 value of 0.9696, indicating that the model can clarify 96.96% of the variation in data variation and does not clarify only 3% of the total differences. Three experimental validation runs were carried out using the optimal conditions, and the highest average yield is 93.20%. An error of about 0.55% was observed compared with the expected value. Therefore, the proposed model is reliable and can predict yield response accurately. Furthermore, the regression model is highly significant, indicating a strong agreement between the expected and experimental values of BA-co-SMA-co-MA yield. Consequently, this study’s findings can help provide a robust model for predicting maximum polymerisation yield to reduce the cost and processing time associated with the polymerisation process.

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