Multivariate Optimization of Biosynthesis of Triethanolamine-Based Esterquat Cationic Surfactant Using Statistical Algorithms

Triethanolamine (TEA)-based esterquats are potential high-value products used as surfactants and softening agents. Chemical synthesis is the typical means to esterify triethanolamine and fatty acids. However, the reaction rate of triethanolamine- fatty acid esterification is usually low under high temperature. In this work, triethanolamine and oleic acid were chosen as substrates to design an optimal model reaction which will lead to high conversion rate utilizing lipase from Candida antacrctica (Novozyme 435) as biocatalyst in the organic solvent system. The investigated reaction conditions included enzyme amount, reaction time, reaction temperature, molar ratio of substrates and agitation speed. The major aim of this study was to model the effect of process parameters on the reaction yield. The most important stages in a process were modeling and optimization to improve a system and increase the efficiency of the process without increasing the cost. All process parameters are selected to conduct the optimization by using some statistical algorithms such as Artificial Neural networks (ANNs), Response Surface Methodology (RSM), Wavelet Neural Network (WNN) and Partial Least Squares (PLS). In this regard, the related parameters of developed model were determined by applying chemometrics techniques on the experimental data. The optimized conditions was validated and applied to the synthesis of product. The next objective of the current study was to compare the performance of aforementioned algorithms with regard to predicting ability. The investigation of TEA-based esterquat cationic surfactant synthesis was started in a 50 ml scale. The optimum condition derived from Taguchi experimental design were: enzyme loading 5.50 wt% of oleic acid, amount of oleic acid 17.70 mmol, amount of triethanolamine 8.85 mmol (molar ratio of substrates 1:2), reaction time of 14.44 hours and reaction temperature of 61°C. Comparison of predicted and actual values (48.42 and 49.94, respectively) revealed good correspondence between them, implying that empirical model derived from Taguchi experimental design can be used to adequately describe the relationship between the factors and response in Novozyme-catalyzed synthesis of TEA-based esterquat cationic surfactant at 50 ml scale. The relative deviation was obtained at 3.14% derived from Taguchi experimental design at 50 ml scale. The optimum reaction condition derived from Taguchi experimental design was then employed in the 2000 ml scale. The effects of five independent variables (enzyme amount, reaction time, reaction temperature, molar ration of substrates and agitation speed) were investigated, along with the mean predicted values for enzymatic reaction product. For this purpose, the response surface methodology (RSM), ANN-Quick Propagation (ANN-QP) and wavelet Neural network (WNN), using a central composite design (CCD), were adopted for predicting conversion reaction in optimal condition. Experiment was then carried out under the recommended condition and resulting response was compared to the predicted values. The optimum reaction parameters were: enzyme amount of 4.77 wt%, reaction time of 24 h, reaction temperature of 61.9°C, substrates molar ratio (OA:TEA) of 1:1mol (708 mmol of OA and TEA) and agitation speed of 480 r.p.m. The corresponding predicted value of percentage conversion was 62.64% as compared to the actual experimental value of 63.57%.

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