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- Title
Online Sequential Extreme Learning Machine Based Soft-sensor Model of SMB Chromatographic Separation Process.
- Authors
Cheng Xing; Jie-Sheng Wang; Qing-Da Yang; Yong-Cheng Sun; Yi-Peng ShuangGuan
- Abstract
Simulated moving bed chromatography (SMB) is a proven and efficient separation tool for the complex separation needs of a variety of industries. On the basis of analysis on the SMB chromatographic separation, the appropriate indexes were selected to optimize and control the separation process. Taking the component purity and yield of extract and residual solution as prediction objects, the soft-sensor modeling was conducted based on online sequential extreme learning machine (OS-ELM), online sequential reduced kernel extreme learning machine (OS-RKELM) and regularized online sequential extreme learning machine (ReOS-ELM). The results show that different limit learning functions can effectively realize the accurate prediction of key economic and technical indexes, and can meet the real-time, efficient and robust operation of SMB chromatographic separation process.
- Subjects
MACHINE learning; SEQUENTIAL learning; CHROMATOGRAPHIC analysis; MOVING bed reactors; ECONOMIC forecasting
- Publication
IAENG International Journal of Computer Science, 2024, Vol 51, Issue 7, p813
- ISSN
1819-656X
- Publication type
Article