Dual Optimization in Phase Transfer Catalyzed Synthesis of Dibenzyl Sulfide using Response Surface Methodology

Abstract

A new reaction protocol has been developed to prepare dibenzyl sulfide (DBS), a value-added organosulfur fine chemical, by utilizing toxic hydrogen sulfide (H 2 S). H 2 S absorbed in monoethanolamine (MEA) has been used as a sulfiding agent for benzyl chloride (BC) under liquid−liquid phase-transfer-catalyzed condition. Response surface methodology was used to model and optimize the process parameters for simultaneous dual-maximization of BC conversion and DBS selectivity. BC/sulfide mole ratio, MEA/sulfide mole ratio, temperature, and catalyst concentration were chosen as independent variables, and conversion of BC and selectivity of DBS were chosen as responses. A quadratic regression model was derived with satisfactory prediction. Dual optimization with desirability function predicts a maximum BC conversion of 100% and a maximum DBS selectivity of 95.2% under experimental conditions: temperature 353 K, catalyst concentration 0.14 kmol/m 3 , BC/sulfide mole ratio 2.83, MEA/sulfide mole ratio 3.7. The analysis of variance and regression with R 2 values of 0.9996 for BC conversion and 0.9833 for DBS selectivity confirm the good agreement of experimental results with predicted values and therefore, the models can be successfully used to predict the synthesis of DBS successfully.

Efficient One-Step Conversion of a Low-Grade Vegetable Oil to Biodiesel over a Zinc Carboxylate Metal–Organic Framework

“…Furthermore, the very low value of the coefficient of variation (CV = 1.72%) and the considerably high adequate precision (i.e., signal-to-noise ratio > 4) point out that the model provides an accurate fit of the observed response and can navigate the design space. 31 , 32 …”

“…The predicted R 2 value of 0.91 may reflect that the obtained model adequately approximates the response values under the experimental range studied without overfitting. Furthermore, the very low value of the coefficient of variation (CV = 1.72%) and the considerably high adequate precision (i.e., signal-to-noise ratio > 4) point out that the model provides an accurate fit of the observed response and can navigate the design space. , …”

“…Furthermore, the very low value of the coefficient of variation (CV = 1.72%) and the considerably high adequate precision (i.e., signal-tonoise ratio > 4) point out that the model provides an accurate fit of the observed response and can navigate the design space. 31,32 The ANOVA test results reveal that all the linear, quadratic, and interaction parameters are statistically significant in the implemented model with P values lower than 0.05 (highlighted with an asterisk (*) in Table 1), except for the quadratic X 1 2 (P = 0.5239) and interactive X 2 X 3 effects (P = 0.1525). The linear effect of the methanol/oil molar ratio (X 2 ) emerges as the most important contributor to increasing FAME yields (P < 0.0001 and F value = 334.1) among the other linear effects, namely, reaction temperature (F value = 97.17) and time (F value = 93.54).…”

Chemometrics-Assisted Optimization of Beta-/Gamma-Tocol Separation on a C30 Stationary Phase in Reversed-Phase LC

“…In most method development studies, analysts have traditionally followed the practice of altering "one-variable-ata-time" or a "step-by-step" optimization. These approaches for method optimization are laborious, time consuming, and unsuccessful for detecting the accurate optimum conditions as they do not take into account interactions among the experimental factors [28][29][30]. Multi-factor experimental designs, such as the response surface methodology (RSM), are widely used for method optimization since they are less laborious and faster as they allow considering mutually influencing experimental factors in a rapid manner.…”

“…Multi-factor experimental designs, such as the response surface methodology (RSM), are widely used for method optimization since they are less laborious and faster as they allow considering mutually influencing experimental factors in a rapid manner. The central composite design (CCD) method, a second-order technique of RSM, has been successfully used to determine the interaction effects among experimental factors and provides closer information of the influencing factors [28,31,32]. Many experimental factors affect the separation efficiency of reversed-phase columns for the tocol isomers.…”