Ultrasonic determination of water concentration in ethanol fuel using artificial neural networks.

摘要

Detection of water concentration in ethanol is important for several industries because of ethanol's widespread use in a number of products, including alcoholic beverages, solvents, and transportation fuels. The miscibility of ethanol with water and other polar liquids makes ethanol susceptible to adulteration beyond the specifications. In this study, adulteration of ethanol was simulated by mixing anhydrous ethanol with water at known concentrations. Ultrasound speed and temperature measurements were used to estimate the water concentration in ethanol using two mathematical models. The first model was based on statistical curve fitting to the experimental data, and the second model was based on a feed-forward, back-propagation neural network algorithm. A total of 651 data sets containing ultrasound speed and temperature as inputs and water concentration in ethanol as output were used to train the neural network algorithm. Both models were validated by preparing ethanol and water mixtures at known concentrations. Validation experiments showed that water concentration in ethanol-water mixtures can be determined by using ultrasound speed and mixture temperature with a standard error of prediction of 8.6% with neural network model and 12.4% with an empirical model. Improving the accuracy and increasing the number of data points for neural network training would reduce the prediction error. The measurements of ultrasound speed and temperature do not involve any moving parts, and the method is rapid and non-invasive, which makes the ultrasound measurement system suitable for online monitoring of water concentration in ethanol.