Hybrid nonlinear autoregressive neural network-Weibull statistical model applied to the supercritical extraction of lanolin from raw wool

摘要

Supercritical extraction of lanolin from raw wool with modified CO2 (5% ethanol) at temperatures above the melting point of lanolin (T = 36–42 °C) is difficult to model because of the multicomponent diffusion in the liquid layer. In this work, a neural network model is proposed based on the experiments previously published by our research group. Experimentally, the extraction of a 100-cm~3 packed bed of raw wool depends on five variables, i.e., temperature (60–80 °C), pressure (120–200 bar), solvent mass flow rate (3–5 kg/h), wool packing density (127–318 kg/m~3), and time (～ 1 h). A nonlinear autoregressive exogenous (5,3,1) neural network was designed and trained with the experimental data augmented using an empirical Weibull statistical function. This correctly predicts the lanolin breakthrough at the extractor exit with only ± 0.42% error. The simple arithmetics of neural network allows a fast optimization with Genetic Algorithm to find optimum operation conditions for the extraction process.