The use of factorial design for modeling membrane distillation

Membrane Distillation: Principle, Advances, Limitations and Future Prospects in Food Industry

“…Therefore, the temperature and concentration at the liquid-vapour interface becomes closer to the corresponding values at the bulk feed solution (Winter et al, 2011). Onsekizoglu et al (2010a) studied the effects of various operating parameters on permeate flux and soluble solid content of apple juice during concentration through osmotic distillation (OD) and membrane distillation (MD) processes. They observed that the effect of feed flow rate on transmembrane flux was less than half of the influence of temperature difference across the membrane.…”

Screening of Effective Markers for Mesophilic Bacterium Growth Using Factorial Experimental Design

“…If there is little curvature in the limited region, a first-order model with interaction is appropriate for modeling. Adding interaction terms introduces curvature into the response function [15] . The first-order model with interaction terms proposed for each response variable (Yi) was based on the multiple linear regression methods.…”

“…4-9). The statistical significance was checked by the F-test [12,15] . These statistical parameters used to validate the fitness of a particular model are given by the equations below: …”

“…This is further argued by the fact that variable cannot be studied by varying one factor at a time, as it often does not allow determination of actual optimum level of different components for a particular metabolic activity, as well as enable identification of vital factors affecting a process [14] ( Ridzuan, et al, 2016). Comparatively, statistical factorial design of experiments, provide an alternative approach through screening of a particular process by considering individual and mutual interactions among the variables and give an estimate of the combined effect of these variables on the final result [15] The relationship between the response and the input is given "Refer to Equ. 1" η = f (x 1 , x 2 …..xn) +ε ………….. (1) Where η is the response, f is the unknown function of response, x 1 ,x 2 …..xn denote the independent variables, also called natural variables, n is the number of the independent variables and finally ε is the statistical error that represents other sources of variability not accounted for by f. Selected independent variables are assign levels based on different ranges each of the coded variables is assigned to a range from -1 to +1, so that they all affect the response more evenly, so the units of the parameters are insignificant.…”

Using factorial design to increase the efficiency on a small-scale ethanol distillation

“…The use of factorial design methodology for the optimization of continuous distillation process is quite scarce. In some works in literature, the use of factorial design methodology is mainly focused on batch distillation [23], vacuum distillation [24], system using dividing-wall column [25,26], and membrane distillation [27][28][29].…”