Statistical Optimization of Ammonium Sulfamate and Urea-Based Fire …
119
–NEC acrylic poly-carboxylate binder to improve the wash stability of the said fireretardant formulation 1e). Finally, from this preliminary study, the superiority of
formulation 1e (15 % AS + 12.5 % urea + 3 % MgCl 2 catalyst) is considered
to be the most acceptable and more particularly modified formulation 1e, that is,
formulation 4, is more acceptable for its better wash stability. But still, it needs to
optimize the concentration of ammonium sulfamate and urea with the study of their
interactive effect for maximizing LOI and minimizing both char length and loss of
fabric tenacity, following response surface methodology with user-defined Quadratic
Model (UDQM) model of statistical optimization technique.
3.5 Statistical Optimization of the Fire-Retardant
Formulation Using Urea and Ammonium Sulfamate
Combination by Using UDQM (User-Defined Quadratic
Model) Under Response Surface Methodology
Response surface methodology (RSM) has recently emerged as useful mathematical and statistical tools for empirical model building for evaluation of interactive
effects amongst process factor, process variables for assessing the optimal process
conditions [13]. In statistics, response surface methodology (RSM) explores the relationships between several experimental input variables and the required number of
one, two or more response or resultant output variables. The main idea of RSM
is to use a sequence of designed experiments to obtain an optimal response. By
careful selection of the suitable design of experimental model such as Box and
Behnkan or UDQM (user-defined quadratic model), the objective is to generate
useful response surface equations to optimize the responsive resultant/output variable by using Response surface methodology (RSM), which is influenced by several
independent input process variables.
The design of the experiment is a series of experimental tests, called runs, designed
in a planned scientific manner in which equal differences of variables are changed in
the input variables in order to identify the assessment of corresponding changes in
the resultant output response. As revealed by a series of preliminary experiments, the
required average dosages of flame-retardant chemicals and additives/binder, etc., and
average conditions of treatment, etc., are to be identified before selecting critical input
variables to vary and other variables to be pre-fixed at test run set of experiments of
design. Hence, in this part of the work, by use of this response surface methodology
(RSM), a suitable statistical optimization model such as UDQM technique of design
of experiment has been used for determining the two selective input process variables
such as the concentration of Ammonium sulfamate and concentration of urea for
optimizing three important resultant output process variables such as limiting oxygen
index (LOI) value, char length and loss of fabric Tenacity for corresponding treated
fabrics, for the said selective flame retardant formulation along with pre-fixed dosages
of catalyst and other additives (e.g. for formulation 1e: in this case, it is concentrations
119
–NEC acrylic poly-carboxylate binder to improve the wash stability of the said fireretardant formulation 1e). Finally, from this preliminary study, the superiority of
formulation 1e (15 % AS + 12.5 % urea + 3 % MgCl 2 catalyst) is considered
to be the most acceptable and more particularly modified formulation 1e, that is,
formulation 4, is more acceptable for its better wash stability. But still, it needs to
optimize the concentration of ammonium sulfamate and urea with the study of their
interactive effect for maximizing LOI and minimizing both char length and loss of
fabric tenacity, following response surface methodology with user-defined Quadratic
Model (UDQM) model of statistical optimization technique.
3.5 Statistical Optimization of the Fire-Retardant
Formulation Using Urea and Ammonium Sulfamate
Combination by Using UDQM (User-Defined Quadratic
Model) Under Response Surface Methodology
Response surface methodology (RSM) has recently emerged as useful mathematical and statistical tools for empirical model building for evaluation of interactive
effects amongst process factor, process variables for assessing the optimal process
conditions [13]. In statistics, response surface methodology (RSM) explores the relationships between several experimental input variables and the required number of
one, two or more response or resultant output variables. The main idea of RSM
is to use a sequence of designed experiments to obtain an optimal response. By
careful selection of the suitable design of experimental model such as Box and
Behnkan or UDQM (user-defined quadratic model), the objective is to generate
useful response surface equations to optimize the responsive resultant/output variable by using Response surface methodology (RSM), which is influenced by several
independent input process variables.
The design of the experiment is a series of experimental tests, called runs, designed
in a planned scientific manner in which equal differences of variables are changed in
the input variables in order to identify the assessment of corresponding changes in
the resultant output response. As revealed by a series of preliminary experiments, the
required average dosages of flame-retardant chemicals and additives/binder, etc., and
average conditions of treatment, etc., are to be identified before selecting critical input
variables to vary and other variables to be pre-fixed at test run set of experiments of
design. Hence, in this part of the work, by use of this response surface methodology
(RSM), a suitable statistical optimization model such as UDQM technique of design
of experiment has been used for determining the two selective input process variables
such as the concentration of Ammonium sulfamate and concentration of urea for
optimizing three important resultant output process variables such as limiting oxygen
index (LOI) value, char length and loss of fabric Tenacity for corresponding treated
fabrics, for the said selective flame retardant formulation along with pre-fixed dosages
of catalyst and other additives (e.g. for formulation 1e: in this case, it is concentrations