Mycoremediation of Synthetic Dyes: An Insight into the Mechanism, Process Optimization and Reactor Design - Microbial Degradation of Synthetic Dyes in Wastewaters - page 15

Taguchi method of statistical optimization involves fractional factorial experi-
mental design which is a part of total possible combinations which are required to
estimate the important factors affecting the process and their interaction (Kim et al.
2004 ). Taguchi method utilizes orthogonal array method, the matrices of which
vary with the number of factors and interactions. For instance, 8-trial orthogonal
array (L-8 matrix) is used when the number of factors is less than 7 and 16-trial
orthogonal array (L-16 matrix) is used when the number of factors is less than 15.
Taguchi method takes into account the

ratio to measure
the quality characteristic of the process or system which deviates from the desired
value. The

“ signal (S) ” and “ noise (N) ”

represents the undesirable
values for the output characteristics. This S/N ratio varies according to the type of
characteristics and can be calculated as follow:
if nominal is the best characteristic;

“ signal ” represents the desirable and “ noise ”

S
N ¼

10 log S y

if smaller is the best characteristic;

n X y 2

S
N ¼

1

10 log

and if larger is the best characteristic;

n X 1

S
N ¼ log

1

y 2

' Σ ' '

S y

' ' ' ' '

where, is the average of observed data, is the variation of y , n is the

number of observations and is the observed data.

Although Taguchi method provides better graphic visualization in determining
the optimal conditions, the extent of in

' y '

uence each factor exerts in the output of any
process needs further analysis through ANOVA. Nevertheless, this method requires
less data to

nd optimum conditions as compared to RSM. Also, since Taguchi
method minimizes the experimental runs, it is recommended to use this method
when the number of variables under study is large and also when the experimental
time run is lengthy and costly.
Thus, it can be concluded that application of various optimization tools is
desirable in a process like dye removal which is effected by multiple factors and
their interaction. Thus, optimizing the conditions to obtain the best possible com-
bination of the process parameters and nutrients, makes the process viable and
economic in terms of cost, time and waste production. For example, a process
optimization study as described by Kaushik and Malik ( 2011 ), resulted in 85 % cost
reduction, wherein the yeast extract from the unoptimized media was replaced by