Machine learning based position‐rendering algorithms for radioactive particle tracking experimentation. Issue 6 (9th March 2020)

• Record Type: Journal Article
• Authors: Yadav, Ashutosh; Gaurav, Tuntun Kumar; Pant, Harish J.; Roy, Shantanu
• Is Part Of: AIChE journal. Volume 66:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-09
• Subjects: artificial neural networks (ANN) -- radioactive particle tracking (RPT) -- relevance vector regression (RVR) -- support vector regression (SVR); Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.16954
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: Radioactive particle tracking (RPT) is one of the most widely used non‐intrusive velocimetry technique for multiphase reactors. The large volume of interrogation and the presence of internals limit the application of RPT in large‐scale real‐world systems. The main challenge lies in having fast reconstruction algorithms applicable to conventional (i.e., bubble columns, fluidized beds, etc.) as well as new vessels. In this contribution, a reconstruction methodology is proposed based on machine learning. Three machine‐learning algorithms, namely, artificial neural network (ANN), support vector regression (SVR), and relevance vector regression (RVR), have been employed for RPT reconstruction. The results show that the position reconstruction accuracy of SVR was best for all cases and that the accuracy of RVR was comparable to SVR for large training datasets. Whereas, in terms of reconstruction speed, RVR outperforms SVR significantly, owing to sparser RVR model. SVR and RVR based reconstruction algorithms expedite the position reconstruction.