Mycoremediation of Synthetic Dyes: An Insight into the Mechanism, Process Optimization and Reactor Design - Microbial Degradation of Synthetic Dyes in Wastewaters - page 13

4 Statistical Tools Required for Process Optimization

Design of experiment (DOE) is an important tool to study any process involving
multiple variables that affect it. Through this approach, a process under study is
described through a mathematical model and an experimental design is created to
obtain a set of experiments to collect the data to be analyzed using the model
equations. DOE approach enables the researcher to optimize the conditions for
maximizing the process and aids in selecting the principle factor affecting it. DOE
methodologyis superior to the conventional approach of one variable at a time
(OVAT) analysis, being less labour intensive and time consuming. It has an added
advantage of providing the interaction studies of various variables affecting the
process which is not possible through OVAT analysis. The two most widely
applied tools of DOE utilized indye removal process are Taguchi method and
response surface methodology (Fig. 4 ).

4.1 Response Surface Methodology (RSM)

RSM is a method that utilizes statistical and mathematical techniques to optimize a
process in which the output or response is in uenced by different factors or vari-

ables. RSM analyzes the effect of independent variables alone and in combination
and generates a mathematical model (Bas and Boyaci 2007 ). The term independent
variables refer to theexperimental variables that can be changed independently of
each other. In a typical dye removal process, these variables can be pH, tempera-
ture, initial dye concentration, nutrient concentration, contact time etc. A response
is de ned as a dependent variable which is measured as an output of the

Fig. 4 Statistical approach involving design of experiment methodology for optimizing dye
removal process