Asymmetric Probability Distribution Function-Based Distillation Curve Reconstruction and Feature Extraction for Industrial Oil-Refining Processes

Abstract

A 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.

Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

“…Simple Bias classification is one of the commonly used methods in machine learning. The simple Bias classification method divides training instance I into feature vector W and decision category variable C. The simple Bias classification assumes that the components of the eigenvectors are relatively independent relative to the decision variables, that is, each feature is independent of the category, which is the characteristic independence hypothesis [30]. For text categorization, it assumes that each word is independent between T i and T j .…”

Virtual Soft Sensor of the Feedstock Composition of the Catalytic Reforming Unit

“…As can be seen from the graphs, the distribution within each homologous group (paraffins, iso-paraffins, olefins, naphthenes, and aromatics) did not statistically obey any distribution function. This made it impossible to apply known models [9,[47][48][49][50] based on the assumption of a change in composition in accordance with the known statistical distribution within the homologous group. The unevenness in the composition of raw materials and distribution by homologous groups can also be seen.…”

Optimal Modeling of Anti-Breast Cancer Candidate Drugs Based on Graph Model Feature Selection

“…Yang et al [ 4 ] used nonlinear simulation feature extraction to complete the tasks of speech detection and keyword location in inference sensor system with low power consumption. Xue et al [ 5 ] proposed a feature extraction method based on asymmetric probability distribution function to reconstruct the distillation curve in industrial refining process, which is beneficial to the modeling and optimization of the oil refining process. Liu et al [ 6 ] used feature extraction method to classify the biogenetic mechanism of circular RNA, which confirmed the view that multiple biogenetic mechanisms of different subsets of human CircRNA coexist.…”