Investigation of Asphaltene Precipitation using Response Surface Methodology Combined with Artificial Neural Network

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HOSSEINI DASTGERDI Zeinab; JAFARZADEH GHOUSHCHI SAEID

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Journal Paper

Paper Information

Journal: Journal of Chemical and Petroleum Engineering Year:2019 | Volume:53 | Issue:2 Page(s): 153-167

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Information Journal Paper

Title

Investigation of Asphaltene Precipitation using Response Surface Methodology Combined with Artificial Neural Network

Author(s)

HOSSEINI DASTGERDI Zeinab | JAFARZADEH GHOUSHCHI SAEID | Issue Writer Certificate

Keywords

Artificial Neural Network Asphaltene

Desirability

Precipitation

Response Surface Methodology

Abstract

The Precipitation of asphaltene, one of the components of oil, in reservoirs, transfer lines, and equipment causes many problems. Accordingly, researchers are prompted to determine the factors affecting asphaltene Precipitation and methods of avoiding its formation. Predicting Precipitation and examining the simultaneous effect of operational variables on asphaltene Precipitation are difficult because of the multiplicity, complexity, and nonlinearity of factors affecting asphaltene Precipitation and the high cost of experiments. This study combined the use of Response Surface Methodology and the artificial neural network to predict asphaltene Precipitation under the mutual effects of various parameters. The values of such parameters were determined to reach the minimum amount of Precipitation. We initially selected the appropriate algorithm for predicting asphaltene Precipitation from the two neural network algorithms. The outputs of designed experiments in Response Surface Methodology were determined using the optimum algorithm of the neural network. The effects of variables on asphaltene Precipitation were then investigated by Response Surface Methodology. According to the results, the minimum Precipitation of asphaltene achieved at zero mole percent of injected nitrogen and methane, 10– 20 mole percent of injected carbon dioxide, asphaltene content of 0. 46, the resin content of 16. 8 weight percent, the pressure of 333 psi, and temperature of 180 ℉ . Results showed that despite the complexities of asphaltene Precipitation, the combination of artificial neural network with Response Surface Methodology can be successfully used to investigate the mutual effect of different variables affecting asphaltene Precipitation.

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APA: Copy

HOSSEINI DASTGERDI, Zeinab, & JAFARZADEH GHOUSHCHI, SAEID. (2019). Investigation of Asphaltene Precipitation using Response Surface Methodology Combined with Artificial Neural Network. JOURNAL OF CHEMICAL AND PETROLEUM ENGINEERING (JOURNAL OF FACULTY OF ENGINEERING), 53(2), 153-167. SID. https://sid.ir/paper/728886/en

Vancouver: Copy

HOSSEINI DASTGERDI Zeinab, JAFARZADEH GHOUSHCHI SAEID. Investigation of Asphaltene Precipitation using Response Surface Methodology Combined with Artificial Neural Network. JOURNAL OF CHEMICAL AND PETROLEUM ENGINEERING (JOURNAL OF FACULTY OF ENGINEERING)[Internet]. 2019;53(2):153-167. Available from: https://sid.ir/paper/728886/en

IEEE: Copy

Zeinab HOSSEINI DASTGERDI, and SAEID JAFARZADEH GHOUSHCHI, “Investigation of Asphaltene Precipitation using Response Surface Methodology Combined with Artificial Neural Network,” JOURNAL OF CHEMICAL AND PETROLEUM ENGINEERING (JOURNAL OF FACULTY OF ENGINEERING), vol. 53, no. 2, pp. 153–167, 2019, [Online]. Available: https://sid.ir/paper/728886/en

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