Black box modeling of steam distillation essential oil extraction system using ARMAX structure

In this paper, the temperature response of a steam distillation essential oil extraction system with a refilling line is modeled based on data collected using a dedicated acquisition system. The autoregressive moving average with exogenous input (ARMAX) model structure is assumed and the pseudo-random binary sequences (PRBS) signal was used to perturb the process. The optimization of ARMAX model structure such as model order criterions is discussed. The model order criterions includes the normalized sum of squared error (NSSE), Akaikepsilas Information Criterion (AIC), Akaikepsilas Final Prediction Error (FPE) and Rissanenpsilas Minimum Description Length (MDL). All criterions will be used to estimate the model order of the system based on the data. The system identification will be performed based on these selected model orders and the estimated models are denoted as ARMAX-NSSE, ARMAX-AIC, ARMAX-FPE and ARMAX-MDL respectively. All the models will be validated and compared in terms of coefficient of determination (R²), adjusted-R² and normalized mean-square-error (NMSE). Overall, ARMAX-MDL provides best results. Finally, the results are discussed and concluded.