Case Studies

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  1. Technology for Corn Ethanol Measurement

    August 2018
    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. The industrial equipment supplier contracted with SALLC to develop a method for measuring how well the supplier’s product improved performance to the corn ethanol market.
    Authors: Jerry Fireman
    Publication: Laboratory Focus
  2. Design of Experiments Approach Halves Development Time

    August 2018

    Optimizing the formulation of a new metalworking fluid required assessing multiple additives. Houghton International used design of experiments (DoE) to develop — in half the time required by the company’s previous methods—a line of multi-metal cutting fluids that handle steel, cast iron, aluminum and cast aluminum.

    Authors: David Slinkman and Yixing (Philip) Zhao
    Publication: Chemical Processing
  3. Design of Experiments Helps Develop New Chemical Specialty Product

    May 2018

    Design of experiments (DOE) is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. This article describes how DOE helps in developing a new specialty chemical product.

    Authors: Kip Howard
    Publication: Chemical Engineering World
  4. DOE, HPLC Validate Corn Ethanol Measurement Technology

    February 2018
    Mark Anderson, Stat-Ease Principal, discusses how an industrial equipment supplier made great improvements to their corn ethanol measurement process using DOE and HPLC methods.
    Authors: Mark Anderson
    Publication: Laboratory Equipment
  5. How to Handle Hard-to-Change Factors or Components in a Designed Experiment

    February 2018
    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).
    Authors: Mark J. Anderson
    Publication: CoatingsTech
  6. Design of Experiments Improves Peptide Bond Yield from 20% to 76%

    February 2018

    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 method.

    Authors: Professor Palwinder Singh & Dr. Manpreet Singh Bhatti
    Publication: Laboratory Focus
  7. Design of Experiment Reduces Development Time for Higher-Performing Metal-Cutting Fluids

    January 2018
    The large number of interactive ingredients makes developing metalworking fluids (MWFs) a complex process. Design of Experiment (DOE) methodology recently helped chemists develop an MWF using half the number of formulations typically necessary. The DOE software accurately projected that the emulsion stability of the optimized formulation would be substantially better than the current product.
    Authors: David Slinkman and Yixing (Philip) Zhao
    Publication: Tribology & Lubrication Technology
  8. Design of Experiments Improves Throughput of Key Intermediate

    December 2017
    This article explains how Codexis developed 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).
    Authors: Jerry Fireman
    Publication: Express Pharma
  9. How to Handle Hard-to-Change Factors Using a Split Plot

    September 2016
    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 factors in the design.
    Authors: Mark J. Anderson
    Publication: Chemical Engineering
  10. DOE Reduces Bioequivalent Generic Development Time to 4 Months

    July 2016

    A generic pharmaceutical manufacturer recently hired VerGo Pharma Research Laboratories Pvt. Ltd to develop a bioequivalent with different polymorphic forms for an anti-depressant drug that had previously been patented in crystalline form only. Bioequivalence requires that a drug be pharmaceutically equivalent and that it be delivered at the same rate and same level of bioavailability so that its efficacy and safety can be expected to be the same as the original product. By using design of experiments (DOE) to reduce the number of tests required (to determine the effects of inactive ingredients on bioavailability in both fed and fasting conditions), VerGo was able to cut the development process from several years to only four months.

    A variation of this case study was also printed in R&D magazine, July 2016, as Scientists Develop Bioequivalent Drug in Months instead of Years.

    See also How Design of Experiments Can Improve Formulation Development by Jerry Fireman, March 2016, Scientific Computing magazine.

    Authors: Subrata Kundu
    Publication: Pharmaceutical Manufacturing
  11. How Design of Experiments Can Improve Formulation Development

    March 2016
    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.
    Authors: Jerry Fireman
    Publication: R&D Magazine
  12. How to Properly Size Response Surface Method Experiment (RSM) Designs for System Optimization

    March 2016
    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 (Anderson and Whitcomb, 2014). However, when a defense system behaves in a nonlinear fashion, then response surface method experiment (RSM) designs must be employed (Anderson and Whitcomb, 2005). 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.
    Authors: Mark J. Anderson, Wayne F. Adams, Pat J. Whitcomb
    Publication: ITEA Journal
  13. Equilibrium conversion, selectivity and yield optimization of the simultaneous liquid-phase etherification of isobutene and isoamylenes with ethanol over Amberlyst™ 35

    February 2016
    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.
    Authors: R. Soto, C. Fité, E. Ramírez, R. Bringué, F. Cunill
    Publication: Fuel Processing Technology
  14. How To Get More Learning With Less Experimentation

    January 2016
    Experimentation informs every part of the biologic lifecycle. But it is costly and time consuming — especially when you are using outdated methods. As you strive for more efficiency from your scientists and engineers, can you streamline your work processes to get more learning with less experimentation? Both multivariate analysis (MVA) and design of experiments (DoE) methods have numerous applications for simplifying the learning from large data sets and experimentation. However, many scientists and engineers still perceive these methods to be complex. Today’s newer, intuitive software applications make these techniques user-friendly for even non-statisticians. Early adopters are seeing decreased time to market, reduced development and production costs, and improved quality and reliability.
    Authors: Frank Westad
    Publication: CAMO Analytics
  15. Conquer Pilot Plant Challenges

    November 2015
    A pilot plant usually plays a key role in process development by providing essential data related to operation, safety, scale-up and other issues. The value of the pilot plant depends on the validity of the data captured. Planned experimentation is crucial to gathering meaningful data, and design of experiments (DOE) is the gold standard for finding results which are statistically significant.
    Authors: Ron Stites
    Publication: Chemical Processing

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