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Development of methods for modelling, parameter and state estimation for nonlinear processes
Author(s)
Dube, Ntuthuko Marcus
Date Issued
2017
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
Industrial processes tend to have very complex mathematical models that in
most instances result in very model specific optimal estimation and designs of
control strategies. Such models have many composition components, energy
compartments and energy inventories that result in many process variables that
are intertwined and too complex to separate from one another. Most of the
derived mathematical process models, based on the application of first principles,
are nonlinear and incorporate unknown parameters and unmeasurable states.
This fact results in difficulties in design and implementation of controllers for a
majority of industrial processes. There is a need for the existing parameter and
state estimation methods to be further developed and for new methods to be
developed in order to simplify the process of parameters or states calculation and
be applicable for real-time implementation of various controllers for nonlinear
systems.
The thesis describes the research work done on developing new parameter and
state estimation methods and algorithms for bilinear and nonlinear processes.
Continuous countercurrent ion exchange (CCIX) process for desalination of
water is considered as a case study of a process that can be modelled as a
bilinear system with affine parameters or as purely nonlinear system. Many
models of industrial processes can be presented in such a way. The ion
exchange process model is developed based on the mass balance principle as a
state space bilinear model according to the state and control variables.
The developed model is restructured according to its parameters in order to
formulate two types of parameter estimation problem – with process models
linear and nonlinear according to the parameters. The two models developed are
a bilinear model with affine and a nonlinear according to the parameters model.
Four different methods are proposed for the first case: gradient-based
optimization method that uses the process output measurements, optimization
gradient based method that uses the full state vector measurements, direct
solution using the state vector measurements, and Lagrange’s optimization
technique. Two methods are proposed for the second case: direct solution of the
model equation using MATLAB software and Lagrange’s optimisation
techniques.
most instances result in very model specific optimal estimation and designs of
control strategies. Such models have many composition components, energy
compartments and energy inventories that result in many process variables that
are intertwined and too complex to separate from one another. Most of the
derived mathematical process models, based on the application of first principles,
are nonlinear and incorporate unknown parameters and unmeasurable states.
This fact results in difficulties in design and implementation of controllers for a
majority of industrial processes. There is a need for the existing parameter and
state estimation methods to be further developed and for new methods to be
developed in order to simplify the process of parameters or states calculation and
be applicable for real-time implementation of various controllers for nonlinear
systems.
The thesis describes the research work done on developing new parameter and
state estimation methods and algorithms for bilinear and nonlinear processes.
Continuous countercurrent ion exchange (CCIX) process for desalination of
water is considered as a case study of a process that can be modelled as a
bilinear system with affine parameters or as purely nonlinear system. Many
models of industrial processes can be presented in such a way. The ion
exchange process model is developed based on the mass balance principle as a
state space bilinear model according to the state and control variables.
The developed model is restructured according to its parameters in order to
formulate two types of parameter estimation problem – with process models
linear and nonlinear according to the parameters. The two models developed are
a bilinear model with affine and a nonlinear according to the parameters model.
Four different methods are proposed for the first case: gradient-based
optimization method that uses the process output measurements, optimization
gradient based method that uses the full state vector measurements, direct
solution using the state vector measurements, and Lagrange’s optimization
technique. Two methods are proposed for the second case: direct solution of the
model equation using MATLAB software and Lagrange’s optimisation
techniques.
Additional information
Thesis (DTech (Electrical Engineering))--Cape Peninsula University of Technology, 2018.
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DEVELOPMENT OF METHODS FOR MODELLING & ESTIMATION (Final Version).pdf
Description
Thesis
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Adobe PDF
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Sponsor(s)
National Research Foundation (NRF)