Single-factor design methods

The simplest experimental design is one in which the effect of a single factor is investigated in two experiments. For example, the effect of method of agitation on the rate of solution of a solid in a liquid is being investigated. The experimental design is a one-factor factorial at two levels, three runs at each level, shown at the left. 

Simple comparative designs Categorical/qualitative and quantitative Check of method, testing of single factor effect... 

The radiation dose should not be over the value of 3.5+4 Mrd. The simplex optimization method was selected since response and single factor limitations do not interfere with this method. Simplex design matrix is obtained from Table 2.208 for k=2. Matrix elements are divided by the highest value of 0.578 so as to get initial simplex matrix, Table 2.218. Formulas for transformation from coded to real factor values have these forms ... 

The optimization of the atomic absorption method of determining metals in particulates found in the air of workplace is described. The Plackett-Burman Youden-Steiner balanced incomplete block designs as well as single-factor experiments were utilized with ten metals Be, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, and Pd. Of the parameters tested, perchloric acid digestion, flame-stoichiometry, and the composition of the calibration standards were the most significant. Perchloric acid affected the recoveries of chromium. This was attributed to the formation of volatile chromylchloride. Flame-related phenomena and interelemental effects were brought under control using lanthanum flame buffer. 

According to the results of the single factor experiment, reaction time was set in the range of 10-20 h, reaction temperature 20°C-40°C, dialysis time 6-10 h, DS and yield were employed as response value. A response surface method experiment was designed by a software — Design Expert 6.0. The results are shown in Table 5.2. 

Establish simple sequences. Using methods described in Chap. 5, sequences of simple columns with low overall vapor load are established. Consideration should not be restricted to the single sequence with the lowest overall vapor load, since many factors need to be considered in finally arriving at the best design. 

The practice of estabHshing empirical equations has provided useflil information, but also exhibits some deficiencies. Eor example, a single spray parameter, such as may not be the only parameter that characterizes the performance of a spray system. The effect of cross-correlations or interactions between variables has received scant attention. Using the approach of varying one parameter at a time to develop correlations cannot completely reveal the tme physics of compHcated spray phenomena. Hence, methods employing the statistical design of experiments must be utilized to investigate multiple factors simultaneously. 

---