Scott K. Cooley

Mixture-process variable approach to optimize a microemulsion electrokinetic chromatography method for the quality control of a nutraceutical based on coenzyme Q10

In recent years, multivariate optimization has played an increasing role in analytical method development. ICH guidelines recommend using statistical design of experiments to identify the design space, in which multivariate combinations of composition variables and process variables have been demonstrated to provide quality results. Considering a microemulsion electrokinetic chromatography method (MEEKC), the performance of the electrophoretic run depends on the proportions of mixture components (MCs) of the microemulsion and on the values of process variables (PVs). In the present work, for the first time in the literature, a mixture-process variable (MPV) approach was applied to optimize a MEEKC method for the analysis of coenzyme Q10 (Q10), ascorbic acid (AA), and folic acid (FA) contained in nutraceuticals. The MCs (buffer, surfactant-cosurfactant, oil) and the PVs (voltage, buffer concentration, buffer pH) were simultaneously changed according to a MPV experimental design. A 62-run MPV design was generated using the I-optimality criterion, assuming a 46-term MPV model allowing for special-cubic blending of the MCs, quadratic effects of the PVs, and some MC-PV interactions. The obtained data were used to develop MPV models that express the performance of an electrophoretic run (measured as peak efficiencies of Q10, AA, and FA) in terms of the MCs and PVs. Contour and perturbation plots were drawn for each of the responses. Finally, the MPV models and criteria for the peak efficiencies were used to develop the design space and an optimal subregion (i.e., the settings of the mixture MCs and PVs that satisfy the respective criteria), as well as a unique optimal combination of MCs and PVs.

Construction of a 21-Component Layered Mixture Experiment Design Using a New Mixture Coordinate-Exchange Algorithm

This article describes the solution to a unique and challenging mixture experiment design problem involving (1) 19 and 21 components for two different parts of the design, (2) many single-component and multicomponent constraints, (3) augmentation of existing data, (4) a layered design developed in stages, and (5) a no-candidate-point optimal design approach. The problem involved studying the liquidus temperature of spinel crystals as a function of nuclear waste glass composition. A D-optimal approach was used to augment existing glasses with new nonradioactive and radioactive glasses chosen to cover the designated nonradioactive and radioactive experimental regions. The traditional approach to building D-optimal mixture experiment designs is to generate a set of candidate points from which design points are D-optimally selected. The large number of mixture components (19 or 21) and many constraints defining each layer of the waste glass experimental region made it impossible to generate and store the huge number of vertices and other typical candidate points. A new coordinate-exchange algorithm applicable for constrained mixture experiments implemented in JMP® was used to D-optimally select design points without candidate points. The new coordinate-exchange algorithm for mixture experiments is described in this article.

Database and Interim Glass Property Models for Hanford HLW Glasses

The purpose of this report is to provide a methodology for an increase in the efficiency and a decrease in the cost of vitrifying high-level waste (HLW) by optimizing HLW glass formulation. This methodology consists in collecting and generating a database of glass properties that determine HLW glass processability and acceptability and relating these properties to glass composition. The report explains how the property-composition models are developed, fitted to data, used for glass formulation optimization, and continuously updated in response to changes in HLW composition estimates and changes in glass processing technology. Further, the report reviews the glass property-composition literature data and presents their preliminary critical evaluation and screening. Finally the report provides interim property-composition models for melt viscosity, for liquidus temperature (with spinel and zircon primary crystalline phases), and for the product consistency test normalized releases of B, Na, and Li. Models were fitted to a subset of the screened database deemed most relevant for the current HLW composition region.

Augmenting a Waste Glass Mixture Experiment Study with Additional Glass Components and Experimental Runs

A glass composition variation study (CVS) for high-level waste (HLW) stored in Idaho is being statistically designed and performed in phases over several years. The purpose of the CVS is to investigate and model how HLW-glass properties depend on glass composition. The resulting glass property–composition models will be used to develop desirable glass formulations and for other purposes. Phases 1 and 2 of the CVS have been completed and are briefly described. This paper focuses on the CVS Phase 3 experimental design, which was chosen to augment the Phase 1 and 2 data with additional data points, as well as to account for additional glass components not studied in Phases 1 and/or 2. In total, 16 glass components were varied in the Phase 3 experimental design. The paper describes how these Phase 3 experimental design augmentation challenges were addressed using the previous data, preliminary property–composition models, and statistical mixture experiment and optimal experimental design methods and software. The resulting Phase 3 experimental design of 30 simulated HLW glasses is presented and discussed.

Upper Tolerance Intervals Adjusted for Multiple Nuisance Uncertainties

In many product-quality situations, it is of interest to state with X% confidence that at least Y% of the product made during a given period has true (unknown) values of a property below a calculated value. Such a statistical statement can be made using an upper Y%-content tolerance interval with X% confidence, which is also referred to as an X%/Y% upper tolerance interval (X%/Y% UTI). In this article, we present the methodology and formulas for calculating X%/Y% UTIs on the true property values when there are sources of uncertainty from nuisance factors (nuisance uncertainties) in addition to the source of variation of interest. The nuisance uncertainties may include regression model uncertainty if a regression model is used to predict the product property of interest. The X%/Y% UTI methodology is developed and illustrated using an example in which there are sampling, analytical, and regression model nuisance uncertainties in addition to the source of variation for which a statistical tolerance statement is desired. The design and results of a simulation study conducted to assess the performance of the X%/Y% UTI method are described. The X%/Y% UTI method that adjusts for nuisance uncertainties is shown to generally achieve the nominal X% and Y% values and yields significantly smaller UTIs than not adjusting for the nuisance uncertainties.

Designing a mixture experiment when the components are subject to a nonlinear multiple-component constraint

ABSTRACT This article presents a case study of developing an experimental design for a constrained mixture experiment when the experimental region is defined by single-component constraints (SCCs), linear multiple-component constraints (MCCs), and a nonlinear MCC. Traditional methods and software for designing constrained mixture experiments with SCCs and linear MCCs are not directly applicable because of the nonlinear MCC. A modification of existing methodology to account for the nonlinear MCC was developed and is described in this article. The case study involves a 15-component nuclear waste glass example in which SO3 is one of the components. SO3 has a solubility limit in glass that depends on the composition of the balance of the glass. A goal was to design the experiment so that SO3 would not exceed its predicted solubility limit for any of the experimental glasses. A partial quadratic mixture model expressed in the relative proportions of the 14 other components was used to construct a nonlinear MCC in terms of all 15 components. In addition, there were SCCs and linear MCCs. This article discusses the waste glass example and how a layered design was generated to (1) account for the SCCs, linear MCCs, and nonlinear MCC and (2) meet the goals of the study.

Methods for Quantifying the Uncertainties of LSIT Test Parameters, Test Results, and Full-Scale Mixing Performance Using Models Developed from Scaled Test Data

This report discusses the statistical methods for quantifying uncertainties in 1) test responses and other parameters in the Large Scale Integrated Testing (LSIT), and 2) estimates of coefficients and predictions of mixing performance from models that relate test responses to test parameters. Testing at a larger scale has been committed to by Bechtel National, Inc. and the U.S. Department of Energy (DOE) to “address uncertainties and increase confidence in the projected, full-scale mixing performance and operations” in the Waste Treatment and Immobilization Plant (WTP).

A decision-theoretic approach to evaluate radiation detection algorithms

There are a variety of sensor systems deployed at border crossings and ports of entry throughout the world that scan for illicit nuclear material. These systems employ detection algorithms that interpret the output of the scans and determine whether additional investigation is warranted. In this work, we demonstrate an approach for comparing the performance of such detection algorithms. We optimize each algorithm by minimizing risk, which considers the probability distribution of threat sources and the consequence of detection errors. Our method is flexible and is easily adapted to many different assumptions regarding the probability of a conveyance containing illicit material and the relative consequences of false positive and false negative errors. This approach can help developers and decision makers identify optimal settings for these algorithms. We illustrate the method by comparing the risk from two families of detection algorithms and discuss the generalizability of the method.

Final Report - IHLW PCT, Spinel T1%, Electrical Conductivity, and Viscosity Model Development, VSL-07R1240-4

This report is the last in a series of currently scheduled reports that presents the results from the High Level Waste (HLW) glass formulation development and testing work performed at the Vitreous State Laboratory (VSL) of the Catholic University of America (CUA) and the development of IHLW property-composition models performed jointly by Pacific Northwest National Laboratory (PNNL) and VSL for the River Protection Project-Waste Treatment and Immobilization Plant (RPP-WTP). Specifically, this report presents results of glass testing at VSL and model development at PNNL for Product Consistency Test (PCT), one-percent crystal fraction temperature (T1%), electrical conductivity (EC), and viscosity of HLW glasses. The models presented in this report may be augmented and additional validation work performed during any future immobilized HLW (IHLW) model development work. Completion of the test objectives is addressed.

Final Report - ILAW PCT, VHT, Viscosity, and Electrical Conductivity Model Development, VSL-07R1230-1

This report describes the results of work and testing specified by the Test Specifications (24590-LAW-TSP-RT-01-013 Rev.1 and 24590-WTP-TSP-RT-02-001 Rev.0), Test Plans (VSL-02T4800-1 Rev.1 & TP-RPP-WTP-179 Rev.1), and Text Exception (24590-WTP-TEF-RT-03-040). The work and any associated testing followed established quality assurance requirements and conducted as authorized. The descriptions provided in this test report are an accurate account of both the conduct of the work and the data collected. Results required by the Test Plans are reported. Also reported are any unusual or anomalous occurrences that are different from the starting hypotheses. The test results and this report have been reviewed and verified.