Multiobjective Optimization for the Greener Synthesis of Chloromethyl Ethylene Carbonate by CO2 and Epichlorohydrin via Response Surface Methodology

Abstract

In this paper, a statistical analysis with response surface methodology (RSM) has been used to investigate and optimize process variables for the greener synthesis of chloromethyl ethylene carbonate (CMEC) by carbon dioxide (CO 2 ) and epichlorohydrin (ECH). Using the design expert software, a quadratic model was developed to study the interactions effect between four independent variables and the reaction responses. The adequacy of the model was validated by correlation between the experimental and predicted values of the responses using an analysis of variance (ANOVA) method. The proposed Box-Behnken design (BBD) method suggested 29 runs for data acquisition and modelling the response surface. The optimum reaction conditions of 353 K, 11 bar CO 2 pressure, and 12 h using fresh 12% (w/w) Zr/ZIF-8 catalyst loading produced 93% conversion of ECH and 68% yield of CMEC. It was concluded that the predicted and experimental values are in excellent agreement with ±1.55% and ±1.54% relative errors from experimental results for both the conversion of ECH and CMEC yield, respectively. Therefore, statistical modelling using RSM can be used as a reliable prediction technique for system optimization for greener synthesis of chloromethyl ethylene carbonate via CO 2 utilization.

Optimisation of greener and more efficient 1,7-octadiene epoxidation catalysed by a polymer-supported Mo(vi) complex via response surface methodology

“…This phenomenon may be explained by an increase in the catalyst's surface area, which increases the surface area available for contact between the catalyst's active sites and the reactant 1,7-octadiene. 38 The increase in catalyst loading from 0.15 mol% Mo to 0.42 mol% enhances the epoxide yield from 48% to 53.1% for lower feed molar ratios (for example, at 2.5 : 1). An increase in feed molar ratio 2.5 : 1 to 8.5 : 1 gave 53% and 67.1% yield of 1,2-epoxy-7-octene, respectively at 0.42 mol% Mo.…”

“…While using RSM, determining the best reaction parameters for a single answer is rather straightforward, while optimising many responses is more difficult. 38 An optimisation process of the epoxidation reaction has been carried out to define the optimum values for the independent variables affecting the dependent response variable. Design Expert software has been used to develop the numerical optimisation step by combining the desirability of each independent variable into a single value and then searching for optimum values for the response goals.…”