Use of Artificial Neural Network to Develop Surrogates for Hydrotreated Vegetable Oil with Experimental Validation in Ignition Quality Tester

Abstract

<div>This article presents surrogate mixtures that simulate the physical and chemical properties in the auto-ignition of hydrotreated vegetable oil (HVO). Experimental investigation was conducted in the Ignition Quality Tester (IQT) to validate the auto-ignition properties with respect to those of the target fuel. The surrogate development approach is assisted by artificial neural network (ANN) embedded in MATLAB optimization function. Aspen HYSYS is used to calculate the key physical and chemical properties of hundreds of mixtures of representative components, mainly alkanes—the dominant components of HVO, to train the learning algorithm. Binary and ternary mixtures are developed and validated in the IQT. The target properties include the derived cetane number (DCN), density, viscosity, surface tension, molecular weight, and volatility represented by the distillation curve. The developed surrogates match the target fuel in terms of ignition delay and DCN within 6% error range. This investigation will be of value to developing high-fidelity models to investigate HVO combustion and spray behavior. This will be beneficial to researchers advancing the design and development of compression ignition engines to efficiently operate on renewable fuels such as HVO.</div>