Synthesis and tribological properties of Guerbet alcohol from a mixture of C12–C14 fatty alcohol: Modeling using RSM, ANN

Abstract

Guerbet alcohol (GA) is β-branched primary alcohol having excellent physiochemical properties like lower pour point (PP) and higher kinematic viscosity (KV) in comparison to linear alcohol. Although synthesis of GA has been extensively studied to evaluate the role of various catalysts and effect of reaction conditions, statistical modeling and optimization of the synthesis process has not been reported. In the present work, the optimization of the synthesis of GA using a mixture of lauryl and myristyl alcohol was carried out with the aid of response surface methodology (RSM) considering the conversion of the reaction, PP and KV at 40 and 100°C as dependent variables. The optimal reaction conditions were temperature, pressure, and time of 220°C, 300 mbar, and 10 hours respectively. The optimum conversion was 99.14%, including dimer yield of 81.76%, PP of −3°C, KV at 40 and 100°C of 34.12 and 7.22 cSt, respectively. The results obtained by the RSM were then authenticated, applying artificial neural networks (ANN) generated with the help of MATLAB. The ability of the generated model to predict the response variables was validated by less than 5% error for almost all the models, confirming their statistical significance. Also, the tribological potential for linear Ginol-12,14 (FA) and synthesized branched GA as lubricant additive was evaluated by determining its physiochemical, thermal and tribological properties.