Abstract
Developing a suitable nonlinear model is the most challenging problem in the application of nonlinear model based controllers to distillation column. Hammerstein model consists of a nonlinear static element described by wavenet based nonlinear function, followed by a linear dynamic element described by the Output Error(OE) model was used in this study to represent the nonlinear dynamics of the distillation column. The model parameters were identified using iterative prediction-error minimization method. The model validation results proved that the Hammerstein model was capable of capturing the nonlinear dynamics of distillation column.
Highlights
Distillation is a complex multivariable system and exhibits highly nonlinear dynamic behavior due to its nonlinear vapor-liquid equilibrium relationships
Two multiple-input single-output (MISO) Hammerstein models are developed in this work to model the dynamics of the distillation column
In both the MISO models used this work, wavenet function is used as static nonlinear element and output error (OE) model is used as linear dynamic element
Summary
Distillation is a complex multivariable system and exhibits highly nonlinear dynamic behavior due to its nonlinear vapor-liquid equilibrium relationships. Fruzzetti et al [9] have proposed a nonlinear model using the Hammerstein model structure to control a binary distillation column They modeled the nonlinear static portion of the system by a power series and linear dynamic function by a linear transfer function model. Nugroho et al [10] have studied the nonlinear identification of aqueous ammonia binary distillation column based on Hammerstein model They employed simple quadratic function to represent the nonlinear static portion of the system and linear transfer function model to represent the linear dynamic function. The Narendra and Gallman (1966) proposed an iterative algorithm which is referred as Narendra Gallman Algorithm (NGA) rovided the initial momentum to block oriented modeling This algorithm updates the linear dynamic element and the nonlinear gain polynomial separately and sequentially. In both the MISO models used this work, wavenet function is used as static nonlinear element and output error (OE) model is used as linear dynamic element
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
