FLSmidth recently installed two turnkey SuperCell flotation machines the world's largest flotation cells at Rio Tinto's Kennecott Utah Copper concentrator near Salt Lake City, Utah. They used DOE to substantially reduce the amount of testing and fine-tuning required after installation.
In this presentation design of experiments (DOE) was applied to a chemical process. DOE together with computer modeling lead to a better understanding of the process and the defining of new conditions.
When performing experiments on surface conductivity, a material supplier used DOE software to get answers quicker and more efficiently.
DOE was used to determine the optimum setting conditions for three components leading to a high Hg Yield at a lower temperature.
Pure populations of cardiomyocytes derived from embryonic stem cells offer great promise as potential cell replacement therapies, as well as for use in pharmaceutical studies. The differentiation process, however, is frequently inefficient and nonspecific. We sought to improve upon the current processes by coupling automation and Design of Experiment (DOE). DOEgenerated combinations of pro-cardiomyocyte compounds were converted to dispensing volumes using Automated Assay Optimization (AAO) for BioRAPTR* software, and reagents and cells were dispensed into 384-well plates using the BioRAPTR FRD* Microfluidic Workstation.
After five days of culture, the resulting embryoid bodies (EBs) were transferred to 96-well gelatin-coated plates using the Biomek FXP* liquid handler. Over an additional three days, the wells were observed for spontaneously beating regions of cardiomyocytes, and the differentiated cells were further analyzed using flow cytometry to detect myosin heavy chain as a marker of cardiomyocytes. This bulletin illustrates the potential of a system that can both automate and optimize the differentiation of murine embryonic stem cells.
Statistical methods are becoming increasingly important for the pharmaceutical industry. The FDA and other regulatory and standard-setting organizations are moving swiftly to establish Quality by Design (QbD) guidance relevant to the needs of pharmaceutical manufacturing. The FDA suggests the use of design of experiments (DoE) because it provides a structured, organized method for determining the relationship between factors affecting a process and the response of that process.
This article discusses what to look for in DOE software in the pharmaceutical industry.
The statistical design of experiments is an essential ingredient of successful product development and improvement, and provides an efficient and scientific approach to obtaining meaningful information. In contrast to traditional vary one-factor-at-a-time (OFAT) experimentation, variables are changed together, permitting evaluation of interactions. Standard texts give details about the construction of specific test plans, such full and fractional factorial, and response surface designs, and the analysis of the resulting data. This article gives a brief overview. The focus here is on the fundamental elements of experimental design: defining the purpose and scope of the experiment, differentiating between alternative types of experimental variables, understanding the underlying environment and constraints, and conducting stage-wise experimentation. Brief discussions dealing with the statistical analysis tools, multiple response variables, and some historical background are also provided.
Schiff Nutrition used DOE to solve an intermittent problem with a multimineral tablet sticking inside the dies of a high-speed rotary tablet press. (To see the figures, download the manuscript from link below.)
The Dresser Waukesha development team adopted the design of experiments (DOE) approach to increase the accuracy of performance estimates and reduce the number of required tests.