skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Bayesian Regression of Thermodynamic Models of Redox Active Materials

Abstract

Finding a suitable functional redox material is a critical challenge to achieving scalable, economically viable technologies for storing concentrated solar energy in the form of a defected oxide. Demonstrating e ectiveness for thermal storage or solar fuel is largely accomplished by using a thermodynamic model derived from experimental data. The purpose of this project is to test the accuracy of our regression model on representative data sets. Determining the accuracy of the model includes parameter tting the model to the data, comparing the model using di erent numbers of param- eters, and analyzing the entropy and enthalpy calculated from the model. Three data sets were considered in this project: two demonstrating materials for solar fuels by wa- ter splitting and the other of a material for thermal storage. Using Bayesian Inference and Markov Chain Monte Carlo (MCMC), parameter estimation was preformed on the three data sets. Good results were achieved, except some there was some deviations on the edges of the data input ranges. The evidence values were then calculated in a variety of ways and used to compare models with di erent number of parameters. It was believed that at least one of the parameters was unnecessary and comparingmore » evidence values demonstrated that the parameter was need on one data set and not signi cantly helpful on another. The entropy was calculated by taking the derivative in one variable and integrating over another. and its uncertainty was also calculated by evaluating the entropy over multiple MCMC samples. Afterwards, all the parts were written up as a tutorial for the Uncertainty Quanti cation Toolkit (UQTk).« less

Authors:
 [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Fuel Cell Technologies Office (EE-3F)
OSTI Identifier:
1389915
Report Number(s):
SAND2017-9526R
656761
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 36 MATERIALS SCIENCE

Citation Formats

Johnston, Katherine. Bayesian Regression of Thermodynamic Models of Redox Active Materials. United States: N. p., 2017. Web. doi:10.2172/1389915.
Johnston, Katherine. Bayesian Regression of Thermodynamic Models of Redox Active Materials. United States. doi:10.2172/1389915.
Johnston, Katherine. 2017. "Bayesian Regression of Thermodynamic Models of Redox Active Materials". United States. doi:10.2172/1389915. https://www.osti.gov/servlets/purl/1389915.
@article{osti_1389915,
title = {Bayesian Regression of Thermodynamic Models of Redox Active Materials},
author = {Johnston, Katherine},
abstractNote = {Finding a suitable functional redox material is a critical challenge to achieving scalable, economically viable technologies for storing concentrated solar energy in the form of a defected oxide. Demonstrating e ectiveness for thermal storage or solar fuel is largely accomplished by using a thermodynamic model derived from experimental data. The purpose of this project is to test the accuracy of our regression model on representative data sets. Determining the accuracy of the model includes parameter tting the model to the data, comparing the model using di erent numbers of param- eters, and analyzing the entropy and enthalpy calculated from the model. Three data sets were considered in this project: two demonstrating materials for solar fuels by wa- ter splitting and the other of a material for thermal storage. Using Bayesian Inference and Markov Chain Monte Carlo (MCMC), parameter estimation was preformed on the three data sets. Good results were achieved, except some there was some deviations on the edges of the data input ranges. The evidence values were then calculated in a variety of ways and used to compare models with di erent number of parameters. It was believed that at least one of the parameters was unnecessary and comparing evidence values demonstrated that the parameter was need on one data set and not signi cantly helpful on another. The entropy was calculated by taking the derivative in one variable and integrating over another. and its uncertainty was also calculated by evaluating the entropy over multiple MCMC samples. Afterwards, all the parts were written up as a tutorial for the Uncertainty Quanti cation Toolkit (UQTk).},
doi = {10.2172/1389915},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 9
}

Technical Report:

Save / Share:
  • The goals of this program have been to develop a series of new compounds that act as redox recyclable heavy metal ion selective materials. This has been a preliminary exploration into the viability of creating materials that act as selective exchange media. We have historically been involved in the separation of ionic pollutants such as radionuclides or toxic heavy metal ions from water by designing extractants with high selectivities and large capacities. We have also recognized that there is a more urgent need to develop processes that allow the target pollutants to be recovered in a minimal volume of secondarymore » waste and that allow the extractants to be reused or recycled. We have been studying redox active transition-metal-containing extractants that undergo reversible electron transfer activation and deactivation as the target ions are extracted and recovered or that undergo efficient, selective ion exchange.« less
  • This paper is concerned with the selection of subsets of ''predictor'' variables in a linear regression model for the prediction of a ''dependent'' variable. We take a Bayesian approach and assign a probability distribution to the dependent variable through a specification of prior distributions for the unknown parameters in the regression model. The appropriate posterior probabilities are derived for each submodel and methods are proposed for evaluating the family of prior distributions. Examples are given that show the application of the Bayesian methodology. 23 refs., 3 figs.
  • This report presents methods designed to permit habitat classification of reservoirs that contain coolwater, coldwater, and seasonal two-story fisheries. Multiple regression equations describing relations between reservoir environmental characteristics and biomass harvest of selected sport fish species or groups are presented. Cumulative frequency plots of known harvest estimates from the various classes of reservoirs are presented to facilitate conversion of harvest predictions to Habitat Suitability Indices.
  • A simple physical model of residential energy consumption provides the framework for an exploration of segmented regression models fit by least squares. The energy model is a generalization of a linear, single change-point model such as that considered by Hinkley. Some simple geometric measures of nonlinearity and nondifferentiability are proposed. These measures are related to the construction of approximate confidence regions for the parameters of a general segmented model. In addition, the relation shown between these measures and those proposed by Bates and Watts may be useful in analyzing continuously differentiable models.
  • Asymptotic results are given for the problem of testing goodness-of-fit for any specified distribution of errors in multiple regression models. In particular, the results apply to the case of testing for normality in standard regression and experimental design models. For a very wide class of goodness-of-fit statistics, in particular those which depend only on the empirical distribution of the residuals, it is shown that the limiting distributions under the null hypothesis are precisely the same in regression models as in ordinary location-scale models. 10 references.