Data Challenges in Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Luis A Ocampo Giraldo; Edna S Cardenas; Mitchell Greenhalgh; Jay D Hix; James T Johnson; Katherine Neis Wilsdon; Cody McBroom Walker

Data Challenges in Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Luis A Ocampo Giraldo, Edna S Cardenas, Mitchell Greenhalgh, Jay D Hix, James T Johnson, Katherine Neis Wilsdon, Cody McBroom Walker

Research output: Contribution to conference › Poster › peer-review

Abstract

Idaho National Laboratory (INL) is maintaining and gaining knowledge into the nuclear fuel cycle by building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying solvent extraction processes that use centrifugal contactors. As part of INL’s mission, the goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multisensory data can support the development of safeguards by design, provide operator process awareness, and discover process anomalies. This poster will highlight some of the data collection and analytics challenges for the multi-sensor system as well as the mitigation strategies to build a robust system. Additionally, some preliminary data from the first testing campaign will be shown to help illustrate the data needs of the system.

Original language	English
State	Published - Mar 8 2023

Access to Document

https://www.osti.gov/biblio/1984509

Cite this

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title = "Data Challenges in Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process",

abstract = "Idaho National Laboratory (INL) is maintaining and gaining knowledge into the nuclear fuel cycle by building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying solvent extraction processes that use centrifugal contactors. As part of INL{\textquoteright}s mission, the goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multisensory data can support the development of safeguards by design, provide operator process awareness, and discover process anomalies. This poster will highlight some of the data collection and analytics challenges for the multi-sensor system as well as the mitigation strategies to build a robust system. Additionally, some preliminary data from the first testing campaign will be shown to help illustrate the data needs of the system.",

author = "{Ocampo Giraldo}, {Luis A} and Cardenas, {Edna S} and Mitchell Greenhalgh and Hix, {Jay D} and Johnson, {James T} and Wilsdon, {Katherine Neis} and Walker, {Cody McBroom}",

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T1 - Data Challenges in Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

AU - Ocampo Giraldo, Luis A

AU - Cardenas, Edna S

AU - Greenhalgh, Mitchell

AU - Hix, Jay D

AU - Johnson, James T

AU - Wilsdon, Katherine Neis

AU - Walker, Cody McBroom

PY - 2023/3/8

Y1 - 2023/3/8

N2 - Idaho National Laboratory (INL) is maintaining and gaining knowledge into the nuclear fuel cycle by building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying solvent extraction processes that use centrifugal contactors. As part of INL’s mission, the goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multisensory data can support the development of safeguards by design, provide operator process awareness, and discover process anomalies. This poster will highlight some of the data collection and analytics challenges for the multi-sensor system as well as the mitigation strategies to build a robust system. Additionally, some preliminary data from the first testing campaign will be shown to help illustrate the data needs of the system.

AB - Idaho National Laboratory (INL) is maintaining and gaining knowledge into the nuclear fuel cycle by building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying solvent extraction processes that use centrifugal contactors. As part of INL’s mission, the goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multisensory data can support the development of safeguards by design, provide operator process awareness, and discover process anomalies. This poster will highlight some of the data collection and analytics challenges for the multi-sensor system as well as the mitigation strategies to build a robust system. Additionally, some preliminary data from the first testing campaign will be shown to help illustrate the data needs of the system.

M3 - Poster

ER -