Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural Network Article Swipe
Jun Lu
,
Jinliang Ding
,
Changxin Liu
,
Yaochu Jin
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2018.09.349
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2018.09.349
Prediction of physical properties of crude oil plays a key role in the petroleum refining industry, therefore, it is of great significance to establish the prediction model of physical properties of crude oil. In this paper, we propose an ensemble random weights neural network based prediction model whose inputs are nuclear magnetic resonance (NMR) spectra and outputs are carbon residual and asphaltene of crude oil. The model uses random vector functional link (RVFL) networks as the basic components and employs the regularized negative correlation learning strategy to build neural network ensemble and the online method to learn the new data. The experiment using the practical data collected from a refinery is carried out and compared with the decorrelated neural network ensembles with random weights (DNNE), least squares support vector machine (LS-SVM), partial least squares regression (PLS) and multiple linear regression (MLR). The results indicate the effectiveness of the proposed approach.
Related Topics
Concepts
Artificial neural network
Support vector machine
Oil refinery
Multivariate random variable
Partial least squares regression
Linear regression
Computer science
Artificial intelligence
Crude oil
Least squares support vector machine
Refinery
Compositional data
Correlation coefficient
Regression
Machine learning
Mathematics
Random variable
Statistics
Chemistry
Engineering
Petroleum engineering
Organic chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2018.09.349
- OA Status
- diamond
- Cited By
- 6
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2896427442
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2896427442Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ifacol.2018.09.349Digital Object Identifier
- Title
-
Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Jun Lu, Jinliang Ding, Changxin Liu, Yaochu JinList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ifacol.2018.09.349Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.ifacol.2018.09.349Direct OA link when available
- Concepts
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Artificial neural network, Support vector machine, Oil refinery, Multivariate random variable, Partial least squares regression, Linear regression, Computer science, Artificial intelligence, Crude oil, Least squares support vector machine, Refinery, Compositional data, Correlation coefficient, Regression, Machine learning, Mathematics, Random variable, Statistics, Chemistry, Engineering, Petroleum engineering, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 3, 2021: 2, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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