Analysis of Multicomponent Ionic Mixtures using Blind Source Separation - a Processing Case Study Dataset

Maggioni, Giovanni Maria

Title:

Analysis of Multicomponent Ionic Mixtures using Blind Source Separation - a Processing Case Study Dataset

dc.contributor.advisor
dc.contributor.author	Maggioni, Giovanni Maria
dc.contributor.corporatename	Georgia Institute of Technology. School of Chemical and Biomolecular Engineering
dc.date.accessioned	2019-09-09T13:19:20Z
dc.date.available	2019-09-09T13:19:20Z
dc.date.issued	2019-08
dc.description	This file contains the relevant data to run the Python script attached to the publication: Maggioni, G., Kocevska, S., Gover, M., Rousseau, R. Analysis of Multicomponent Ionic Mixtures using Blind Source Separation: A Processing Case Study, Industrial & Engineering Chemistry Research 2019, 58, 50, 22640–22651 DOI:https://pubs.acs.org/doi/10.1021/acs.iecr.9b03214	en_US
dc.description	Python script is available at https://github.com/john88gm/BSS_Analysis-Spectroscopy	en_US
dc.description.abstract	Management and remediation of complex nuclear waste solutions require identification and quantification of multiple species. Some of the species forming the solution are unknown and they can be different from vessel to vessel, thus limiting the utility of standard calibration approaches. To cope with such limited information, we propose a procedure based on blind source separation (BSS) techniques, in particular independent component analysis and multivariate curve resolution, with a one-point calibration library. Here we show the applicability and reliability of our procedure for on-line measurements of aqueous ionic solutions by proposing an automatic procedure to identify the number of species in the mixture, estimate the spectra of the pure species, and label the spectra with respect to a library of reference components. We test our procedure against simulated and experimental data for mixtures with six species (water plus five sodium salts) for the case of Raman and ATR-FTIR spectroscopy.	en_US
dc.description.sponsorship	Department of Energy. Consortium for Risk Evaluation with Stakeholder Participation
dc.identifier.uri	http://hdl.handle.net/1853/61832
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.issupplementto	https://pubs.acs.org/doi/10.1021/acs.iecr.9b03214
dc.subject	Hanford LAW
dc.subject	Spectroscopy
dc.subject	BSS techniques
dc.title	Analysis of Multicomponent Ionic Mixtures using Blind Source Separation - a Processing Case Study Dataset	en_US
dc.title.alternative	Data for "Analysis of Multicomponent Ionic Mixtures using Blind Source Separation - a Processing Case Study"	en_US
dc.type	Dataset	en_US
dspace.entity.type	Publication
local.contributor.corporatename	School of Chemical and Biomolecular Engineering
local.contributor.corporatename	College of Engineering
relation.isOrgUnitOfPublication	6cfa2dc6-c5bf-4f6b-99a2-57105d8f7a6f
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569