INTEMA   05428
INSTITUTO DE INVESTIGACIONES EN CIENCIA Y TECNOLOGIA DE MATERIALES
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
BAYESIAN ANALYSIS APPLIED TO THE ESTIMATION OF PARTICLE SIZE DISTRIBUTION FROM LIGHT SCATTERING MEASUREMENTS
Autor/es:
F.A. OTERO; G.L. FRONTINI; G.E. ELICABE; HELCIO ORLANDE
Lugar:
Joao Pessoa
Reunión:
Simposio; Inverse Problems, Design and Optimization Symposium, 2010; 2010
Resumen:
<!-- /* Font Definitions */ @font-face {font-family:CCAPKA+TimesNewRoman; panose-1:0 0 0 0 0 0 0 0 0 0; mso-font-alt:"Times New Roman"; mso-font-charset:0; mso-generic-font-family:roman; mso-font-format:other; mso-font-pitch:auto; mso-font-signature:3 0 0 0 1 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US; mso-no-proof:yes;} p.MsoBodyText, li.MsoBodyText, div.MsoBodyText {mso-style-noshow:yes; margin:0cm; margin-bottom:.0001pt; text-align:justify; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US; mso-no-proof:yes;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 3.0cm 70.85pt 3.0cm; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> This article presents a Bayesian approach to the solution of a scattering inverse problem. Our particular interest is the estimation of the particle size distribution (PSD) of a colloidal system of spherical particles using measurements of Static Light Scattering (SLS). For concentrated particle systems, with low contrast between media and particles, i.e., polymeric particles suspended in a different polymeric medium, the approximate model due to Pedersen [1] can be appropriate. We assume that the distribution can be well described by a log-normal function, with only two unknowns related to the mean and variance. The developed implementation  makes use of the Metropolis-Hastings algorithm. We explored the effects of the algorithm parameters on the obtained estimations.  We have also analyzed the reduction on the number of parameters in the used models. The solutions confidence intervals were estimated.