Design of Experiments: A comparison study from the non‐expert user's perspective

Abstract

In a digital era where terabytes of structured and unstructured records are created and stored every minute, the importance of collecting small amounts of high quality data is often undervalued. However, this activity plays a critical role in industrial and laboratory settings, when addressing problems from process modeling and analysis, to optimization and robust design. Implementing a screening design is usually the way to begin a systematic statistical Design of Experiments program. Its aim is to find the influential factors and establish a first description of the process under analysis. Recently, new developments have emerged in this field, especially with the appearance of a new class of designs that join screening efficiency with the possibility to estimate quadratic effects (definitive screening designs). Therefore, several alternatives are currently available for conducting screening studies, but information is still scarce on the pros and cons of each methodology. This study was thus designed to gather useful information from the user's perspective by independently considering each design with rather standard settings. For each design, its ability to recover the correct model structure was evaluated, through Monte Carlo simulations on several simulated process structures containing elements likely to be found in practice (they obey the general principles of effect sparsity, hierarchy, and heredity). As some screening designs can estimate quadratic terms, these effects were also considered in the simulated models, and three-level designs were brought to the analysis for comparison purposes, even though they are not screening designs.

Experimental Design in Polymer Chemistry—A Guide towards True Optimization of a RAFT Polymerization Using Design of Experiments (DoE)

“…Depending on the design’s geometry, certain factor combinations are required or evaded. There is a wide range of viable RSM designs with various geometries, each one offering certain assets as well as challenges [ 41 ]. The RSM of our choice was a FC-CCD, which allows for great prediction accuracy whilst examining each factor at three levels only.…”

Energy-Efficiency Assessment and Improvement—Experiments and Analysis Methods

“…Nevertheless, notice that no design can provide in one shot what is expected to be done through a sequential (screening-characterization-optimization) experimentation approach. For further guidelines and discussion on experimental design selection at various experimental stages in the response surface methodology framework, namely, when constraints exist, the reader is also referred to [39,[48][49][50][51][52][53][54][55]. To deal with multiobjective when constructing a design, the reader is referred to Lu et al [56].…”

Lipid-Based Nanocarriers for Ophthalmic Administration: Towards Experimental Design Implementation

“…The presented research on lipid-based nanocarriers has also shown that one optimisation problem can be approached following different experimental designs, still arriving, in any case, at valid polynomials [97]. For instance, the same formulation problem regarding solid lipid nanoparticles has been optimised by a three-level full factorial, central composite, Box-Behnken or Taguchi designs [120,125,134,155].…”

“…Optimisation experimental designs are also known as response surface methodologies (RSM) and create higher-order model equations that maximise product similarity to the desired CQAs. Therefore, it is important to select a low number of factors under study, so the optimisation step maintains an affordable experimental workload [ 96 , 97 ]. However, recent research in the field has resulted in the appearance of the so-called definitive screening designs, which are able, under specific circumstances, to identify active quadratic terms with minimal experimental workload [ 98 ].…”