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.

中文翻译：

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工业油精制过程中基于不对称概率分布函数的蒸馏曲线重构和特征提取

蒸馏曲线是石油的基本特性。其特征有利于炼油工艺的建模和优化。为了用少量参数捕获这些特征，提出了一种基于不对称概率分布函数的精馏曲线重构和特征提取方法，用于工业炼油过程。在我们的研究中，首先使用一些可用的蒸馏数据来测试几种常用概率分布函数的表达能力。根据统计数据，具有四个参数的不对称概率分布函数之一的Kumaraswamy分布函数被确定为最佳。由于并非所有蒸馏数据都可以在行业中直接获得，采用基于总概率理论的数据合成技术来估计未采样流的关键蒸馏点，特别是对于反应系统出口处不可测量的中间产物。除蒸馏曲线重建外，还使用状态转换算法通过优化Kumaraswamy分布函数的参数来提取合成蒸馏数据的特征。进行了工业实验以证明我们的建议的有效性。通过使用状态转移算法优化Kumaraswamy分布函数的参数来提取合成蒸馏数据的特征。进行了工业实验以证明我们的建议的有效性。通过使用状态转移算法优化Kumaraswamy分布函数的参数来提取合成蒸馏数据的特征。进行了工业实验以证明我们的建议的有效性。