In this application, a supplier of industrial equipment wanted to market an existing product to companies producing ethanol from corn. There was anecdotal evidence indicating that the device increased ethanol yield. However, prior to marketing the device the company wanted to find the best operating conditions and determine what performance ethanol producers could expect from the product. Researchers performed a design of experiments (DOE) that quantified the effects of three key factors, singly and in combination, on sample preservation. The sample preservation method recommended by the DOE functioned well for ethanol plants across the Midwest.
An industrial equipment supplier made great improvements to their corn-ethanol measurement process using DOE.
Because interactions abound in the coatings industry, the multifactor and multicomponent test matrices provided by the design of experiments (DOE) approach is very appealing. However, carrying out DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends, such as aging of materials, increasing humidity, and the like. But what if complete randomization proves to be inconvenient or impossible? In this case, a specialized form of design called “split plot” becomes attractive because of its ability to effectively group hard-to-change (HTC) factors. A split plot accommodates both HTC factors and those factors that are easy to change (ETC).
Researchers worked to fine-tune the conditions that best promote peptide bond formation in an uncatalyzed aqueous phase reaction. We felt that we should be able to obtain a better yield than our initial 20% and had a hunch that one or more interactions between variables might be playing a role that was obscured by the OFAT (one factor at a time) method.
The large number of interactive ingredients makes developing metalworking fluids (MWFs) a complex process. Design of experiment (DOE) methodology helped chemists develop an MWF using half the number of formulations typically necessary. Their DOE software--Design-Expert--accurately projected that the emulsion stability of the optimized formulation would be substantially better than the current product.
This article explains how Codexis used design of experiments to develop the manufacturing process for (2S, 3R)-Epoxide (1), a key intermediate used in the production of Atazanavir (marketed as Reyataz), an antiretroviral drug used to treat human immunodeficiency virus (HIV).
Carrying out a DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends. If complete randomization proves to be impossible, however, a specialized form of design—called a split plot—is useful because of its ability to effectively group hard-to-change (HTC) factors. It accommodates both HTC and easy-to-change (ETC) factors in the design.
Formulation development often boils down to determining the optimum combination of ingredients in a mixture, which can make the difference between success and failure in many diverse fields of research, such as materials, pharmaceuticals, adhesives and coatings. The traditional approach to experimentation changes only one process factor at a time (OFAT) or one component in a formulation. However, with this approach, it’s easy to overlook interactions of factors or components, a likely occurrence in developing formulations.
By sizing experiment designs properly, test and evaluation (T&E) engineers can assure they specify a sufficient number of runs to reveal any important effects on the system. For factorial designs laid out in an orthogonal matrix this can be done by calculating statistical power. However, when a defense system behaves in a nonlinear fashion, then response surface method experiment (RSM) designs must be employed. The test matrices for RSM generally do not exhibit orthogonality, thus the effect calculations become correlated and degrade the statistical power. This in turn leads to inflation in the number of test runs needed to detect important performance differences that may be generated by the experiment. A generally acceptable alternative to sizing designs makes use of fraction of design space (FDS) plots. This article details the FDS approach and explains why it works best to serve the purpose of RSM experiments done for T&E.
The efficiency of a gasoline fueled engine is highly influenced by the fuels antiknock characteristics, which depend essentially on the chemical composition. The adequate performance of a vehicle depends on a minimal volatility of the fuel, which can be expressed by several characteristics such as distillation curves, vapor pressure, vaporization enthalpy and the vapor/liquid ratio. The vapor pressure of gasoline is directly related to the emission of volatile compounds from gasoline and the ensuing combustion processes, especially in starting the engine on cold days and in continuous operation in hot days. This prospective study experimented on the product distribution at chemical equilibrium for the simultaneous liquid-phase etherification of isobutene and isoamylenes with ethanol over Amberlyst™ 35.