Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

MULTIVARIABLE SYSTEM IDENTIFICATION OF A CONTINUOUS BINARY DISTILLATION COLUMN

BALOCH, MOHAMMAD ADNAN (2011) MULTIVARIABLE SYSTEM IDENTIFICATION OF A CONTINUOUS BINARY DISTILLATION COLUMN. Masters thesis, UNIVERSITI TEKNOLOGI PETRONAS.

[img]
Preview
PDF
Download (27Kb) | Preview
[img]
Preview
PDF
Download (347Kb) | Preview
[img]
Preview
PDF
Download (1197Kb) | Preview
[img]
Preview
PDF
Download (1442Kb) | Preview
[img]
Preview
PDF
Download (653Kb) | Preview
[img]
Preview
PDF
Download (18Kb) | Preview

Abstract

Distillation is a process that is commonly used in industries for separation purpose. A distillation column is a multivariable system which shows nonlinear dynamic behavior due to its nonlinear vapor-liquid equilibrium. In order to gain better product quality and lower energy consumption of the distillation column, an effective model based control system is needed to allow the process to be operated over a certain operating range. In control engineering, System Identification is considered as a well suited approach for developing an approximate model for the nonlinear system. In this study, System Identification technique is applied to predict the top and bottom product composition by focusing the temperature of the distillation column. The process in the column is based on the distillation of a binary mixture of Isopropyl Alcohol and Acetone. The experimental data obtained from the distillation column was used for estimation and validation of simulated models. During analysis, different types of linear and nonlinear models were developed and are compared to predict the best model which can be effectively used for designing the control system of the distillation column. Among the linear models such as; Autoregressive with Exogenous Input (ARX), Autoregressive Moving Average with Exogenous inputs (ARMAX), Linear State Space (LSS) model and Continuous Process Model were developed and compared with each other. The results of this comparison reveals that the performance of LSS model is efficient and hence it was further used to improve the modeling approach and compared with other nonlinear models. A Nonlinear State Space (NSS) model was developed by the combination of LSS and Neural Network (NN) and is compared solely with NN and ANFIS identification model. The simulation results show that the developed NSS model is well capable of defining the dynamics of the plant based on the best fit criteria and residual performance. In addition to this, NSS model predicted the best statistical measurement of the nonlinear system. This approach is helpful for designing the efficient control system for online separation process of the plant.

Item Type: Thesis (Masters)
Subject: UNSPECIFIED
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 5 not found.
Date Deposited: 05 Jun 2012 11:05
Last Modified: 25 Jan 2017 09:42
URI: http://utpedia.utp.edu.my/id/eprint/3043

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...