Deep Learning Based Soft Sensor and Its Application on a Pyrolysis Reactor for Compositions Predictions of Gas Phase Components
Document Type
Article
Publication Date
1-1-2018
Abstract
In this work, we proposed a data-driven soft sensor based on deep learning techniques, namely the convolutional neural network (CNN). In the proposed soft sensor, instead of only building time-independent correlations among the key variable with other measurements, the moving window method is utilized to describe the most recent process dynamics, where the time-dependent correlation can be located. Beyond on that, a signal recovery scheme is developed to improve the model robustness when confronting common sensor faults. The proposed soft sensoring technique was tested on the composition data of gas-phase components from an ethylene pyrolysis reactor. The model was also verified through the manually introduced sensor faults.
Publication Source (Journal or Book title)
Computer Aided Chemical Engineering
First Page
2245
Last Page
2250
Recommended Citation
Zhu, W., Ma, Y., Zhou, Y., Benton, M., & Romagnoli, J. (2018). Deep Learning Based Soft Sensor and Its Application on a Pyrolysis Reactor for Compositions Predictions of Gas Phase Components. Computer Aided Chemical Engineering, 44, 2245-2250. https://doi.org/10.1016/B978-0-444-64241-7.50369-4