Modeling basic fraction data of petroleum distillation

doi:10.11949/j.issn.0438-1157.20171453

CIESC Journal ›› 2018, Vol. 69 ›› Issue (3): 931-935.DOI: 10.11949/j.issn.0438-1157.20171453

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Modeling basic fraction data of petroleum distillation

MEI Hua^1,2, HUANG Biao², QIAN Feng¹

1 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2 Department of Chemical & Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1R1, Canada

Received:2017-11-01 Revised:2017-11-07 Online:2018-03-05 Published:2018-03-05
Supported by:
supported by the National Natural Science Foundation of China (61333010, 61422303) and the China Scholarship Council (201606745013).

石油馏分基础数据模型建模方法

梅华^1,2, 黄彪², 钱锋¹

1 华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237;
2 Department of Chemical & Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1R1, Canada

通讯作者: 梅华
基金资助:
国家自然科学基金重点项目（61333010）；国家自然科学基金优秀青年基金项目（61422303）；国家留学基金委资助项目（201606745013）。

Abstract

Abstract:

Properties of petroleum fractions are important data of petrochemical processes. However, tremendous on-site data containing redundant information and measurement errors pose a great challenge to routine operation of chemical processes. A basic fraction data modelling method was proposed from characterization techniques of state space of petroleum fractions, in which an initial basic fraction data model was obtained via non-negative matrix factorization and updated by an iterative strategy so that the scale of the model base set was minimized as much as possible under circumstance of assured modelling accuracy. The results of simulation study verify that the proposed method is effective and suitable for a wide application to petrochemical processes.

Key words: petroleum fractions, state space representation, basic fraction data model, non-negative matrix factorization, iterative optimization

摘要：

石油馏分属性数据是石油化工生产过程中的重要基础数据，但是海量的现场数据包含了大量的冗余信息和测量误差，给化工过程实际生产带来很大的困扰。基于石油馏分状态空间表征法提出一种石油馏分基础数据模型建模方法。该方法通过非负矩阵分解算法得到一组初始基础馏分数据模型并在此基础上采用迭代更新策略，在保证模型预测精度的前提下尽可能地减少模型库的规模。仿真结果验证了本方法的有效性和实用性，在石油化工生产过程中具有广阔的应用前景。

关键词: 石油馏分, 状态空间表征, 基础馏分数据模型, 非负矩阵分解, 迭代优化

CLC Number:

TE622

MEI Hua, HUANG Biao, QIAN Feng. Modeling basic fraction data of petroleum distillation[J]. CIESC Journal, 2018, 69(3): 931-935.

梅华, 黄彪, 钱锋. 石油馏分基础数据模型建模方法[J]. 化工学报, 2018, 69(3): 931-935.

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Modeling basic fraction data of petroleum distillation

石油馏分基础数据模型建模方法

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