In this study, an Autoregressive with eXogenous input (ARX) model and an Autoregressive Moving Average with eXogenous input (ARMAX) model are developed to predict the overhead temperature of a distillation column. The model parameters are estimated using the recursive algorithms. In order to select an optimal model for the process, different performance measures, such as Aikeke's Information Criterion (AIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE), are calculated.

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