Fractional factorial design

About: Fractional factorial design is a research topic. Over the lifetime, 2393 publications have been published within this topic receiving 90097 citations.

Analytical Quality-by-Design-Based Systematic Optimization of RP-HPLC Method Conditions to Analyze Simultaneously Chloroquine Phosphate and Flavopiridol in Stress-Induced Combined Drug Solution and Pharmaceutical Emulsions.

Abstract: A design of experiments (DoE)-driven RP-HPLC method conditions was employed to analyze simultaneously chloroquine (CQ) phosphate and flavopiridol (FLAP) in emulsions and solution. After subjecting the various critical method attributes to preliminary risk assessment and screening by Pareto-chart-based fractional factorial design, the 17 runs were produced in Box-Behnken design for optimization. Analysis of variance, lack of fit, prediction equations, 3D response surface plots and contour plots were used to evaluate the critical analytical attributes such as retention time, tailing factor and theoretical plate count. The optimized RP-HPLC method conditions include 262 nm as detection wavelength, 37°C temperature for column, 20-μl injection volume, 1-ml/min flow rate and mobile phase mixture [70:30 ratio of 0.4% triethylamine in methanol&sodium phosphate buffer (11 mM, pH 3.0)]. The studied validation parameters were found within the ICH-prescribed limits. Exposing the combined drug solution at oxidative stress condition resulted to diminish the FLAP recovery value (53.39 ± 0.86) and arrival of an extra chromatographic peak. However, the % drug entrapment efficiency values of 96.22 ± 2.47 and 85.86 ± 3.66, respectively, were noticed for CQ phosphate and FLAP in emulsions. Thus, DoE-driven approach could be helpful for systematically optimizing RP-HPLC method conditions.

Optimization of chromium and lead biosorption in wastewater using 33 factorial design

Abstract: In this study removing heavy metals, Cr (III), and Pb (II) from wastewater, Microorganism Trichoderma sp. biosorption was performed using Cr (III), and Pb (II) removal was taken into account. For this study, 3^3 Factorial Experiment Designs were used, and temperature (°C), biosorbent dosage (g/L), and pH were selected as the main factors for Cr (III), and Pb (II) metals and three levels of these factors were determined as low, medium, and high. In this study, which was carried out to increase the metal removal efficiency and biosorption capacity, the main factors and the significance of each interaction of these factors were examined with 3^3 Factorial Design. For this purpose, by conducting Analysis of Variance (ANOVA) via Response Surface Methodology and optimization, more detailed results were obtained regarding the factors affecting the efficiency of metal removal from wastewater.

Comparisons of Bayes factors for 𝟐𝟒 full, fractional, and reduced factorial designs

Abstract: The effect of factors in full and fractional factorial designs is being studied ubiquitously in all fields of science and engineering. At times, researchers would want to gather additional information than the fractional factorial design provided, there is no restriction to conducting more experimental runs. In this study, we propose a reduced fractional factorial design consisting of all significant factors. This paper illustrates the effectiveness of factors through real data application and simulation by comparing the full factorial, reduced factorial, and fractional factorial designs. The actual weightage of the main/interaction effects in these three designs was found by identifying and quantifying the Bayes factors through the simulation datasets. It is observed that the reduced factorial design produces better results when there are no constraints to select or add factors to the model.

Improved results processing methodology factorial experiment

Abstract: In the process of processing the results of experimental studies of any, in particular, technical processes, there is a necessity to establish a correlation between independent and dependent variables. During the analysis of experimental data, such a connection is established by using certain computer programs. The authors proposed the App program. 1 to calculate the parameters of ten empirical regression equations using the method of least squares, which is developed in the Visual Studio programming environment in the C# (Сі Sharp) programming language using the “Windows Form Application” framework using Windows operating systems. This program can be used in processing the results of studies conducted both according to the classical, and factorial (rational) plans. Making analysis data of experiments, conducted according to the factor plan with the help of this program the parameters of partial empirical dependencies of the studied factor Y on independent external factors are determined. The basic version of the method of creating an empirical multifactorial model of multiple nonlinear correlations based on the data obtained by the method of rational planning of the experiment is the version proposed in the work "Methodology of processing the results of a factorial experiment". The authors supplemented this method by determining the parameters of partial empirical dependencies based on logarithmic experimental data for averaging of which the geometric mean is used for each independent factor. It is proposed to determine the parameters of partial empirical dependencies, which are used to create a multifactorial model, based on the antilogarithms of the averaged values.