Model input and structure selection in multivariable dynamic modeling of batch distillation column pilot plant / Ilham Rustam

In the wake of fast soft computing processing, advances in hardware interface provisioning and demand over cost efficiency, a substantial acquiesce over the interests in nonlinear control and optimal process operation can been seen in recent academic and industrial study particularly in multivariable control processes. Among such are the distillation processes which are susceptible to various operation perturbations that would directly influence the desired end product outcome. There are several recorded control approaches that have been successfully employed to meet with the end product quality requirement however the current study put the bulk of its focus on a control approach performed based on the reflux ratio as manipulated variable with the top tray temperature as the controlled variable. It is recognized that there is a need to have an offline representation of the pilot plant to allow for anticipation of experiment results to reduce operation error, hence, the main purpose of this study. The pilot plant used as reference process platform in this study is a binary mixture, batch process bubble cap distillation column that suffers from the time-varying nature of its process. It is established that a valid nonlinear multivariable case study is presented by the process plant via a systematic experiment conducted to justify the adopted nonlinear system identification. The implementation of Nonlinear Auto-Regressive with eXogenous input (NARX) technique was then explored in this study to prove its reliability as a comprehensive system identification approach which has been cited across various recorded process identification platforms…