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Asymmetric Probability Distribution Function-Based Distillation Curve Reconstruction and Feature Extraction for Industrial Oil-Refining Processes
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posted on 2020-01-16, 21:30 authored by Yongfei Xue, Yalin Wang, Bei SunA distillation curve is an essential
property for petroleum. Its
features are beneficial for the modeling and optimization of oil-refining
processes. To capture these features with a small number of parameters,
an asymmetric probability distribution function-based distillation
curve reconstruction and feature extraction method is proposed for
the industrial oil-refining process. In our research, the expressive
power of several frequently used probability distribution functions
are first tested with some available distillation data. According
to the statistics, the Kumaraswamy distribution function, one of the
asymmetric probability distribution functions with four parameters,
is identified as the best. Because not all distillation data are directly
obtainable in the industry, the total probability theory-based data
synthesis technique is adopted to estimate the key distillation points
of unsampled streams, especially for the unmeasurable intermediate
products at the outlet of a reaction system. Along with the distillation
curve reconstruction, features of the synthetic distillation data
are extracted by optimizing the parameters of the Kumaraswamy distribution
function using the state transition algorithm. Industrial experiments
were carried out to demonstrate the effectiveness of our proposal.
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distillation curve reconstructionparameterfeature extraction methodIndustrial Oil-Refining Processesprobability theory-based data synthesis techniqueprobability distribution functionsdistillation dataKumaraswamy distribution functionAsymmetric Probability Distribution Function-Based Distillation Curve Reconstructionstate transition algorithmprobability distribution function-based distillation curve reconstruction
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