基于迁移学习的有机硅单体分馏过程能耗建模

平晓静; 赵顺毅; 栾小丽; 刘飞

doi:10.11949/0438-1157.20200402

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基于迁移学习的有机硅单体分馏过程能耗建模

过程系统工程 | 更新时间：2026-07-24

- 基于迁移学习的有机硅单体分馏过程能耗建模
- Modeling of energy consumption in organosilicon monomer fractionation process based on transfer learning
- 化工学报 2020年71卷第12期页码：5706-5714
- 作者机构：
  
  江南大学自动化研究所，轻工过程先进控制教育部重点实验室，江苏无锡 214122
- 作者简介：
  
  平晓静（1997—），女，硕士研究生，6191905011@stu.jiangnan.edu.cn
  栾小丽（1979—），女，博士，教授，xiaoli_luan@126.com
- 基金信息：
  
  国家自然科学基金项目(61991402;61973136;61722306)
- DOI：10.11949/0438-1157.20200402
  中图分类号： TP 181
- 收稿：2020-04-16，
  
  修回：2020-05-25，
  
  网络首发：2020-12-17，
  
  纸质出版：2020-12-05
- 稿件说明：
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平晓静, 赵顺毅, 栾小丽, 刘飞. 基于迁移学习的有机硅单体分馏过程能耗建模[J]. 化工学报, 2020, 71(12): 5706-5714

PING Xiaojing, ZHAO Shunyi, LUAN Xiaoli, LIU Fei. Modeling of energy consumption in organosilicon monomer fractionation process based on transfer learning[J]. CIESC Journal, 2020, 71(12): 5706-5714
平晓静, 赵顺毅, 栾小丽, 刘飞. 基于迁移学习的有机硅单体分馏过程能耗建模[J]. 化工学报, 2020, 71(12): 5706-5714 DOI： 10.11949/0438-1157.20200402.

PING Xiaojing, ZHAO Shunyi, LUAN Xiaoli, LIU Fei. Modeling of energy consumption in organosilicon monomer fractionation process based on transfer learning[J]. CIESC Journal, 2020, 71(12): 5706-5714 DOI： 10.11949/0438-1157.20200402.

摘要

有机硅单体分馏过程需分离的物质多且分离技术要求高，是有机硅单体生产过程中能耗较高的单元之一。能耗模型的建立对分馏过程的生产优化有着重要的意义。虽然分馏过程中各提纯单元的塔器设备型号、操作条件存在差异，但由于各提纯单元存在相似性，如何充分利用这些相似性进行能耗建模，从而减少建模过程中对数据量、质的要求是要解决的问题。通过引入迁移学习算法，增大源单元和目标单元数据域的相似性，实现不同数据域的知识传递，充分利用现有数据中包含的知识，建立新的能耗模型。以某有机硅企业采集的现场数据进行算法验证，结果显示，当训练样本量不足时，迁移学习可以有效提升模型性能，从而验证了该算法的有效性与实际应用价值。

Abstract

Organosilicon monomer fractionation process needs to separate many substances and the separation requirements are high

leading to high energy consumption. The modeling of energy consumption is of great significance to the optimization of fractionation process. Although the type of equipment and operating conditions of each purification unit in the fractionation process are different

each purification unit has certain similarity according to the dynamic characteristics of the distillation process. Therefore

making full use of these similarities in modeling can reduce the cost of data acquisition in the modeling process. By introducing the transfer learning algorithm

the similarity between the data domains of the source unit and the target unit is increased

the knowledge transfer in different data domains is realized

the knowledge contained in the existing data is fully utilized

and a new energy consumption model is established. Finally

the simulation experiment was carried out with the field data of an organosilicon factory

and the results showed that when the sample size of target domain is little

the transfer learning can effectively improve the performance of the model

so the effectiveness and the practical application of the algorithm were verified.

关键词

Keywords

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