scholarly journals Predicting the Viscosity of Petroleum Emulsions Using Gene Expression Programming (GEP) and Response Surface Methodology (RSM)

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Abubakar A. Umar ◽  
Ismail M. Saaid ◽  
Aliyu A. Sulaimon ◽  
Rashidah M. Pilus
Keyword(s):  
Gene Expression ◽  
Response Surface Methodology ◽  
Crude Oil ◽  
Response Surface ◽  
Aging Time ◽  
Gene Expression Programming ◽  
Transport Systems ◽  
Oil Viscosity ◽  
Emulsified Water ◽  
Water Cut

This paper summarizes an investigation of certain operating parameters on the viscosity of petroleum emulsions. The production of crude oil is accompanied by emulsified water production, which comes along with various challenges like corroding the transport systems and catalysts poisoning during petroleum refining in the downstream. Several process variables are believed to affect the ease with which emulsified water can be separated from emulsions. Some of the issues have not been extensively examined in the literature. The simplicity with which water is separated from petroleum changes with age (after formation) of the emulsion; notwithstanding, this subject has not been investigated broadly in literature. This study tries to assess the correlation between aging time, water cut, crude oil viscosity, water viscosity and amount of solids and viscosity of petroleum emulsions. To achieve that, a response surface methodology (RSM) based on Box-Behnken design (BBD) was used to design the experiment. Synthetic emulsions were prepared from an Offshore Malaysian Crude oil based on the DoE design and were aged for 7 days. The emulsions viscosities were measured at 60-degree Celsius using an electromagnetic viscometer (EV100). The broad pressure and temperature range of the HPHT viscometer permit the imitation of acute conditions under which such emulsions may form. The data obtained from the RSM analysis was used to develop a prediction model using gene expression programming (GEP). It was discovered that the viscosity of water has no effect on the viscosities of the studied emulsions, as does the water cut and amount of solids. The most significant factor that affects emulsion viscosity is the aging time, with the emulsion becoming more viscous over time. This is believed to be imminent because of variations in the interfacial film structure. This is followed by the amount of solids, also believed to be as a result of increasing coverage at the interface of the water droplets, limiting the movements of the dispersed droplets (reduced coalescence), thereby increasing the viscosity of the emulsions.

Demulsification of a Water-in-crude Oil Emulsion with a Corn Oil BasedDemulsifier using the Response Surface Methodology: Modelling and Optimization

2021 ◽  
Vol 14 ◽  
Author(s):  
Abed Saad ◽  
Nour Abdurahman ◽  
Rosli Mohd Yunus
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Water Content ◽  
Response Surface ◽  
Separation Efficiency ◽  
Corn Oil ◽  
Oil Emulsion ◽  
Optimal Values ◽  
Input Variables ◽  
Oil Emulsions

: 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.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
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.

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

2013 ◽  
Vol 137 ◽  
pp. 386-393 ◽  
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

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

2014 ◽  
Vol 17 (6) ◽  
pp. 1513-1522 ◽  
Author(s):  
Norshahidatul Akmar Mohd Shohaimi ◽  
Jafariah Jaafar ◽  
Wan Azelee Wan Abu Bakar
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Response Surface

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

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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