To simplify formulation tasks when searching for hot-melt solutions, Stepan Co. developed a design of experiments (DOE) mapping' approach to their new ortho-phlathic-based polyol products. They wanted to discover how the products work within their specific applications. Requires login to view.
Changing and then re-testing just one parameter at a time still seems to be the norm for the formulation chemist, but it is not the most productive approach. In fact, this shotgun approach increases the probability of missing the best possible finished product. A more reliable and expedient method of optimizing a formulation is by design of experiments (DOE).
Researchers at Exatec, LLC, determined that the oscillating sand test reveals better wear assessment information for polycarbonates. This finding has allowed the company to optimize coating processes.
New ABET criteria require that chemical engineering students be able to ﾓdesign and conduct experiments. Experimental design may be interpreted as either developing methods and procedures to achieve experimental goals, or as statistical Design of Experiments (DOE). Auburn University's Dept. of Chemical Engineering incorporates both approaches in their unit operations lab course to satisfy EC2000 criteria and achieve beneficial learning outcomes.
Weak subgrade soils are often chemically treated, or modified, to add strength and stability to support the heavy construction vehicles required for building highways. DOE was used to develop a treatment that enabled contractors to quickly establish stable subgrades.
Sigma-Aldrich Biotechnology used mixture-DOE to increase cell growth and productivity for a particular recombinant CHO clone. Their simple, yet powerful, media-mixing experiments, combined with DOE software, allowed the researchers to rapidly elucidate an optimal medium mixture.
We can improve experimentation results by studying organizations that have experienced both frustrations due to poor experimentation methodology and satisfaction from successful applications. This paper identifies eight factors essential to successful experimentation. A solid understanding of these key factors is the foundation to a successful design of experiments program.
For many central composite designs (CCDs), particularly large ones, the usual alphas put the axial points outside the region of operability. A CCD with an alpha of one, known as a "face centered design" (FCD), avoids this problem by drawing the axial point back onto the face of the hyper cube. However, as the number of FCD factors increase, the correlation among the squared terms in the quadratic in the face-centered cube also increases. For k>5 this causes the variance inflation factors (VIFs) associated with the squared terms to become quite high. As a compromise between FCD and standard CCD, this white paper provides the case for a "practical" alpha of the fourth root of the number of factors (k). For k of 5 or more, this practical alpha balances statistical properties with operational necessities.
This article deals with thorny issues that confront every experimenter how to handle individual results that do not appear to fit with the rest of the data. (A somewhat modified version of this article was published in Quality Engineering. April 2007.)