A compounding-model comprising back propagation neural network and genetic algorithm for performance prediction of bio-based lubricant blending with functional additives

Abstract

Purpose The amount, type and addition conditions of additives of lubricants should be continuously adjusted to obtain appealing performance. To obtain the optimal pretreatment parameters and reduce the cost of time-consuming experiments, the purpose of this paper is to establish an optimal back propagation neural network (BPNN) model combined with genetic algorithm (GA) in this work. Design/methodology/approach Using trimethylolpropane trioleate as the base oil and three types of phosphorus compounds as additives, 25 sets of lubricant formulas were designed regarding lubricant performances of average friction coefficient, average spot diameter, disk wear volume and extreme pressure. The data set was used for training and learning of BPNN and then combined with GA to optimize BPNN with continuously optimization by adjusting various parameters. Findings Comparing prediction data of BPNN with actual test data, correlation coefficients were above 90%, indicating that the model could accurately predict the performance of lubricants. When combined with GA, all performance errors were less than 5%, indicating that BPNN could be optimized by GA to obtain an accurate combined model for prediction of lubricant performance. The best additive formula with excellent performances was obtained from the BPNN–GA model. Originality/value This work developed a new method to study lubricant compounding. The combined model was expected to provide a theoretical basis and guidance for the compounding optimization of lubricant additives with high efficiency and low cost and to expand the scope to practical applications. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2020-0165/