# Optimizing the Sodium Hydroxide Conversion Using Regression Analysis in CSTR
## 1 Introduction  
Stepwise regression is a prevailing technique to forecast unidentified predicted variables from predictor variables. The independent variables are also known as predicators. The predicted variable is known as a dependent variable. The regression analysis is applied to study the impact of predictor variables on response (output). The main advantage of stepwise regression is the ability to manage large amounts of potential predictor variables and to select the best predictor variables from available options. The stepwise regression model is used to determine the most dominating factors among the variables. The central composite design (CCD) is useful in surface response methodology for developing a quadratic model to predict the response variable, whereas the Box-Behnken design (BBD) applies to obtain high order response surfaces using little needed runs than normal factorial design. The statistical designs are comprehensively applied to advance process identification and optimization. Good quality preferred products are produced frugally applying well acknowledged statistical design of experiment in chemical and other allied industries [1, 2]. The computational attempts are essential to study the model behavior, and the cost can be optimized by evolving an effectual procedure. A factorial design was applied for process enhancement to minimalize process time [3]. A batch reactor performance was studied using statistical tools for methodical procedure [4]. It has been stated that developments in the design of experiments have made it conceivable to use the technique for wider applications [5], process enhancement of hydrolysis of ethyl acetate [6], and batch reactor process development [7]. The selection of experimental design decisively relies on the objective of experimentation [2, 8]. The response surface methodology (RSM) and screen designs are the most broadly used designs for optimizing the processes. The aim ofselection design is to remove the irrelevant variables and then relating RSM to determine the optimal values of the substantial variables. The surface response methods (CCD and BBD) were applied to compare the efficiency and analyze the prime interacting parameters of anaerobic sludge reactor [9]. It was suggested that RSM can be applied for optimizing the wastewater treatment process. The polynomial regression model has been optimized by multiple regressions. Sodium acetate produced in the current work is profitable and valuable carboxylic salt. The literature [10; 11; 12; 13; 14; 15; 16] have specifically focused on reaction mechanics for the hydrolysis of CH\({}_{3}\)COOC\({}_{2}\)H\({}_{5}\). An experimental study on saponification of ethyl acetate applying different reactive systems was performed, and the influence of reactor-type on reactor performance was examined [17]. It was suggested that a high-pressure drop was exhibited by microreactors. It was perceived that hydrolysis of CH\({}_{3}\)COOC\({}_{2}\)H\({}_{5}\) continues by direct attack of the ion of CH\({}_{3}\)COOC\({}_{2}\)H\({}_{5}\) on the carbon atom [18]. The fast hydrolysis process exploits dichloromethane/methanol in 1:9 ratio as a solvent with a small concentration of sodium hydroxide [19]. A simulation study for an ethyl acetate hydrolysis was examined in detail [20]. A factorial design was applied to study the optimization of ethyl acetate [21; 22]. The process intensification of applying a design approach with the aims of reducing the formation of unwanted products and increasing conversion was described [23]. A parametric study for ethyl acetate saponification was performed using a batch reactor and the impact of temperature, volume, rate of agitation, and initial reagents concentration was examined [24]. It was suggested that increased initial concentration resulted in reduced sodium hydroxide (NaOH) conversion.