Optimal designs for estimating the parameters in weighted power-mean-mixture models

In the mixing of fluids, a mixture may be viewed conceptually as a hypothetical collection of fluid clusters. In this context, a mixture model is defined by prescriptions for (a) estimating fluid cluster properties and (b) combining them to yield an overall mixture property. A particular flexible form is obtained from using generalized weighted-power-means with the weighting based on global mole fractions equation image. Optimal designs for estimating the parameters in Scheffé S- and K-polynomials are well known. In this paper, we present optimal designs for estimating the parameters in the generalized weighted-power-mean mixture models, which may be nonlinear in the pure and binary interaction parameters. We illustrate the practical value of applying optimal designs for mixture variables through design efficiencies. The designs are derived for modeling viscosity from three-component mixtures.Institutional Research Development Programme (IRDP) of the National Research Foundation of South Africa and Xyris Technology CC

Simulation experiments for maximising the availability of a commercial octene production facility

Overall availability of a chemical process is of critical importance in industry. In this paper we evaluate the process design factors that influence the availability of a new chemical production facility by performing computer experiments on a stochastic simulation model. Experimental designs commonly used in the Design and Analysis of Computer Experiments (DACE) and Classical Design of Experiments (DOE) are evaluated and compared for application by means of simulation experiments. Furthermore, response surface and kriging models are evaluated for the approximation of the input-output relationships. The most accurate experimental design by approximation model combination is used to explore the design space, both in terms of the overall availability and the percentage time offline. We illustrate how the design and analysis of simulation experiments (DASE) are used for minimizing the risks in the design of a new 1-octene production facility in terms of maximising the overall availability and minimizing the percentage time offline simultaneously

Efficient maximin distance designs for experiments in mixtures

ArticleIn this paper, different dissimilarity measures are investigated to construct maximin designs for compositional data. Specifically, the effect of different dissimilarity measures on the maximin design criterion for two case studies is presented. Design evaluation criteria are proposed to distinguish between the maximin designs generated. An optimization algorithm is also presented. Divergence is found to be the best dissimilarity measure to use in combination with the maximin design criterion for creating space-filling designs for mixture variables. © 2012 Copyright Taylor and Francis Group, LLC

Response surface models for synthetic jet fuel properties

Jet fuel is a mixture of different hydrocarbon groups, and the mass contribution of each of these groups toward the overall chemical composition of the fuel dictates the bulk physical properties of the fuel after completion of the refining and blending processes. The fluidity properties of jet fuel mixtures at low temperatures are critical in understanding and mitigating the safety risks and performance attributes of aircraft engines, which may lead to the introduction of more stringent specification limits in the near future. Therefore, in this study the low-temperature viscosity and freeze point properties of jet fuels were investigated by variation of the linear to branched chain paraffin mass ratio, in conjunction with variation of the carbon number distribution according to a mixture by process variables experimental design. Furthermore, response surface models were developed and discussed for the two main fluidity properties of interest and inferences were made from the models for the potential generation of optimal jet fuel mixture

Flexible graphical assessment of experimental designs in R: The vdg package

textabstractThe R package vdg provides a flexible interface for producing various graphical summaries of the prediction variance associated with specific linear model specifications and experimental designs. These methods include variance dispersion graphs, fraction of design space plots and quantile plots which can assist in choosing between a catalogue of candidate experimental designs. Instead of restrictive optimization methods used in traditional software to explore design regions, vdg utilizes sampling methods to introduce more flexibility. The package takes advantage of R’s modern graphical abilities via ggplot2 (Wickham 2009), adds facilities for using a variety of distance methods, allows for more flexible model specifications and incorporates quantile regressions to help with model comparison