Model Predictive Control of Mineral Column Flotation Process

Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control desig...

Full description

Bibliographic Details
Main Authors: Yahui Tian, Xiaoli Luan, Fei Liu, Stevan Dubljevic
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Mathematics
Subjects:
Online Access:http://www.mdpi.com/2227-7390/6/6/100
id doaj-16093e83e5be497484ead3394b0634d8
record_format Article
spelling doaj-16093e83e5be497484ead3394b0634d82020-11-24T21:12:34ZengMDPI AGMathematics2227-73902018-06-016610010.3390/math6060100math6060100Model Predictive Control of Mineral Column Flotation ProcessYahui Tian0Xiaoli Luan1Fei Liu2Stevan Dubljevic3Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, ChinaKey Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, ChinaKey Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, ChinaDepartment of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2V4, CanadaColumn flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs) and ordinary differential equations (ODEs), which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs) and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.http://www.mdpi.com/2227-7390/6/6/100model predictive controlcolumn flotationcoupled PDE–ODECayley–Tustin discretizationinput/state constraints
collection DOAJ
language English
format Article
sources DOAJ
author Yahui Tian
Xiaoli Luan
Fei Liu
Stevan Dubljevic
spellingShingle Yahui Tian
Xiaoli Luan
Fei Liu
Stevan Dubljevic
Model Predictive Control of Mineral Column Flotation Process
Mathematics
model predictive control
column flotation
coupled PDE–ODE
Cayley–Tustin discretization
input/state constraints
author_facet Yahui Tian
Xiaoli Luan
Fei Liu
Stevan Dubljevic
author_sort Yahui Tian
title Model Predictive Control of Mineral Column Flotation Process
title_short Model Predictive Control of Mineral Column Flotation Process
title_full Model Predictive Control of Mineral Column Flotation Process
title_fullStr Model Predictive Control of Mineral Column Flotation Process
title_full_unstemmed Model Predictive Control of Mineral Column Flotation Process
title_sort model predictive control of mineral column flotation process
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2018-06-01
description Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs) and ordinary differential equations (ODEs), which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs) and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.
topic model predictive control
column flotation
coupled PDE–ODE
Cayley–Tustin discretization
input/state constraints
url http://www.mdpi.com/2227-7390/6/6/100
work_keys_str_mv AT yahuitian modelpredictivecontrolofmineralcolumnflotationprocess
AT xiaoliluan modelpredictivecontrolofmineralcolumnflotationprocess
AT feiliu modelpredictivecontrolofmineralcolumnflotationprocess
AT stevandubljevic modelpredictivecontrolofmineralcolumnflotationprocess
_version_ 1716750624232570880