V. 결론
5.3 향후 계획
이번연구로물성분석기와조성분석기에대한소프트센서모델의예측성 능은검증되었으며,기존공정분석기와의 이중화구성을통해산업현장에적용할 수있을것으로기대한다. 반면에전기화학분석기와광학분석기에대한소프트 센서모델에대해서는공정에서발생하는외란의영향과부족한정보로인해모델 구현에한계가있었다.향후에는기존연구에서부족했던정보를추가하여변수화 함으로써 전기화학분석기와 광학분석기에 대한소프트 센서 모델구현이가능 한지확인하고자한다.또한,딥러닝을활용한소프트센서모델을구현하여공정 분석기별소프트센서모델을최적화하는연구를진행할계획이다.
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Abstract
Data-Driven Soft Sensor Modeling for Process Analyzer in Refinery
Dongjoo Noh Graduate School of Engineering Practice Seoul National University
A variety of process analyzers are used in refineries to increase the production of high value-added products, comply with environmental regulations, and maintain a safe working environment. The process analyzer measures the physical properties and ingredients of the analysis target. It controls process variables to keep the mea- sured values within the standard required for product production. In general, process analyzers rarely lead to large-scale accidents such as fires and explosions, so they are not in a redundant configuration. Therefore, as it is impossible to monitor the process analyzer’s measuring values in the event of an abnormality, the loss of production and non-compliance with environmental regulations intermittently occur.
This study attempted to develop a soft sensor model for process analyzers us- ing machine learning algorithms based on process variables data, including temper- ature, pressure and flow rate. It examined the soft sensor model performance with R2, RMSE, and MAE. It also found the best-fit algorithm for each process analyzer.
The results clearly indicated that the machine learning-based soft sensor model can