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Estimation and control of froth flotation units used in Coal Handling and Preparation Plants (CHPPs) Open Access


Other title
model predictive control
multiple model estimation
froth flotation
economic model predictive control
Type of item
Degree grantor
University of Alberta
Author or creator
Arulmaran, Kevin
Supervisor and department
Liu, Jinfeng (Chemical and Materials Engineering)
Examining committee member and department
Li, Zukui (Chemical and Materials Engineering)
Liu, Jinfeng (Chemical and Materials Engineering)
Dubljevic, Stevan (Chemical and Materials Engineering)
Department of Chemical and Materials Engineering
Chemical Engineering
Date accepted
Graduation date
2017-06:Spring 2017
Master of Science
Degree level
The processing of coal in coal handling and preparation plants (CHPPs) produces a significant amount of valuable fine coal which is then recovered using froth flotation. As froth flotation units are regulated using simple, conventional control methods the application of modern control methods is one option to improve the efficiency and profitability of these units. There are various issues to consider before modern control methods can be applied, namely: state estimation, handling model plant mismatch (MPM) and handling measurement delays. This thesis compares the performance of various modern control methods in the nominal case (no MPM and no measurement delays) and in the case of MPM with no measurement delays with the aim of identifying the best control method for each scenario. State estimation methods are also tested in the nominal case, with MPM and with measurement delays to identify the best one for each scenario. For state estimation, three methods are investigated: an extended Kalman filter (EKF), moving horizon estimator (MHE) and nonlinear observer. The EKF is found to give the best estimation performance in the nominal case. Although MHE has the potential to give better estimates, it does not do so due to the small estimation window. The estimation window needs to be small to keep computation time reasonable as there are a lot of system states. The nonlinear observer performs the worst because it does not use noise information. All three methods have similar performance when there is MPM as the MPM masks noise information. All three methods also have similar performance when measurements are delayed as the large measurements delays in the froth flotation system mean that state prediction dominates over state estimation. The EKF is picked as the best estimator for this system on the basis of its estimation performance and computation time. A multiple model (MM) based approach is proposed to obtain unbiased state estimates in the presence of MPM caused by parameter mismatch. For a deterministic linear system operating at steady state, it is proved that unbiased output estimates guarantee unbiased states estimates provided certain conditions are met. This approach is successfully applied to the froth flotation system. A model predictive controller (MPC) and economic model predictive controller (EMPC) are tested for the nominal case of no MPM and accurate state estimates available. Both are found to stabilize the system and give similar economic performance. For the case of offset free control in the presence of parameter mismatch, we present two methods: an offset free MPC using augmented models and a model identification based method which combines the MM state estimation method we propose with a conventional MPC. The offset free MPC fails to achieve offset free control when using an output disturbance model. Although the model ID based approach achieves offset free control there is no guarantee that it will work in the general case.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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