Application of MLP-ANN as novel tool for estimation of effect of inhibitors on asphaltene precipitation reduction |
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| Authors: | Alireza Baghban Rasoul Hekmati Mahdi Hajiali Majid Kamyab |
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| Affiliation: | 1. Department of Chemical Engineering, Amirkabir University of Technology, Mahshahr Campus, Mahshahr, Iran;2. Department of Mathematics, University of Houston, Houston, TX, USA;3. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran;4. Computer Aided Process Engineering Lab (CAPE), School of Chemical Engineering, Iran;5. University of Science and Technology, IUST, Tehran, Iran |
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| Abstract: | Sedimentation of heavy fractions of oil such as asphaltene is the main concern in different parts of petroleum industries like production and transportation. Due to this fact, the inhibition of asphaltene precipitation becomes one of the great interests in the petroleum industry. In the present investigation, multi-layer perceptron artificial neural network (MLP-ANN) was developed to estimate asphaltene precipitation reduction as a function of concentration and kind of inhibitors and oil properties. To this end, a total number of 75 data points were extracted from reliable source for training and validation of predicting algorithm. The outputs of MLP-ANN were compared with experimental data graphically and statistically, the determined coefficients of determination (R2) for training and testing are 0.996522 and 0.995239 respectively. These comparisons expressed the high quality of this algorithm in the prediction of asphaltene precipitation reduction. so the MLP-ANN can be used as a powerful machine for estimation of different processes in petroleum industries. |
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| Keywords: | asphaltene inhibitor MLP-ANN precipitation predicting algorithm |
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