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Factorial design, variables affecting

In the past, the scale-up was carried out by selecting best guess process parameters. The recent trend is to employ the Factorial and Modified Factorial Designs and Search Methods. These statistically designed experimental plans can generate mathematical relationships between the independent variables, such as process factors, and dependent variables, such as product properties. This approach still requires an effective laboratory/pilot scale development program and an understanding of the variables that affect the product properties. [Pg.309]

In factorial designs we have n variable factors which we are able to adjust at fixed levels. Sometimes the factors only exist in discrete levels but sometimes we are interested in or are only able to set up these discrete levels. The assignable causes, the factors which we assume will affect the recorded responses, may be quantitative or qualitative features. The quantitative character of temperature, pH, stirring velocity, etc. is obvious. Examples of qualitative factors are the age of membranes in ion selective electrodes or filter devices (with the levels old and new) or a certain medication or noxious agent (present or absent). In this situation the notion version of a factor makes sense (ISO 3534/3 in [ISO STANDARDS HANDBOOK, 1989]). [Pg.73]

A fully central comp osite design was applied to optimize monolaurin synthesis. A three factorial design was proven effective to establish the influence of the variables on the monolaurin synthesis. The central composite design procedure was adopted to optimize variables affecting the monolaurin molar fraction. [Pg.443]

Just because an input might affect the output does not mean that it does. The next step is to reduce the larger list of candidate input variables into a smaller list of the critical inputs. A type of designed experiment called a screening experiment (3) can be used for this purpose. They are also commonly referred to as fractional factorial designs. [Pg.179]

Ruotolo LAM, Gubulin JC (2005) A factorial-design study of the variables affecting the electrochemical reduction of Cr(VI) at polyaniline-modilied electrodes. Chem Eng J 110 113-121. doi 10.1016/J. cej.2005.03.019... [Pg.1247]

In many experimental design problems, it is necessary to design the trials or runs in such a way that the variability arising from some nuisance factors can be controlled. These nuisance factors (or "blocking variables ) may affect the response but are neither factors nor fixed variables. They can be analysts, instruments, reagents, sample materials, working day, etc. Let us consider the simple case of a 2 factorial design to improve an automated extraction step in a... [Pg.151]

The two-level factorial design method is very convenient in screening for the important variables affecting a measured quantity, and assessing their relative importance [10]. In this method a number of variables or factors are chosen, and a range of values selected for each. Only the extreme limits of the ranges are used (denoted as + and - values). The experiments or simulations are run for all possible combinations of factor values (2 experiments for n factors) and the values of quantities of interest, the responses, calculated. [Pg.754]

Factorial designs are used to identify the significant variables (factors) affecting the selected response and as a tool to explore and model the responses as a function of these significant experimental factors. Two-level full factorial designs are a powerful alternative to find the adequate experimental conditions to produce the best response of the chemical system. This type of design fits the response to a linear model. For a two-factor case, the response surface is given by the linear model ... [Pg.211]

In order to evaluate the affect of five major variables within the RIM process a 2" fractional factorial design was run. A fractional factorial was chosen to reduce the number of runs by half. The un-coded values are shown in Table 1. [Pg.2202]

Table I compares results achieved when seven variables that may affect the performance of a particular catalyst were tested one-at-a-time with results from a statistical design (fractional factorial) approach. In this comparison, a shift in measured performance is assumed to be real if it represents at least twice the uncertainty of the measuring technique. The one-at-a-time strategy, still prevalent among many catalyst researchers, requires 48 experiments to determine with 95% confidence which variables significantly impact catalyst performance. Whereas, with the fractional factorial approach, this same information was obtained in only 16 experiments with a 98.5% confidence level. The fractional factorial approach also shows possible interactions among the variables the classical one-at-a-time approach does not. Table I compares results achieved when seven variables that may affect the performance of a particular catalyst were tested one-at-a-time with results from a statistical design (fractional factorial) approach. In this comparison, a shift in measured performance is assumed to be real if it represents at least twice the uncertainty of the measuring technique. The one-at-a-time strategy, still prevalent among many catalyst researchers, requires 48 experiments to determine with 95% confidence which variables significantly impact catalyst performance. Whereas, with the fractional factorial approach, this same information was obtained in only 16 experiments with a 98.5% confidence level. The fractional factorial approach also shows possible interactions among the variables the classical one-at-a-time approach does not.

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