Stochastic multi-objective economic model predictive control of two-stage high consistency mechanical pulping processes
department or school: Applied Science, Faculty of
department or school: Chemical and Biological Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Tian, Hui
issued: 2020-03-24T14:59:40Z
issued: 2020
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Model predictive control (MPC) has attracted considerable research efforts and has been widely applied in various industrial processes. This thesis aims at developing economic MPC (econ MPC) strategies to optimize and control the nonlinear mechanical pulping (MP) process with two high consistency (HC) refiners, which is one of the most energy intensive processes in the pulp and paper industry. It possesses substantial economic motives and environmental benefits to develop advanced control techniques to reduce the energy consumption of MP processes. We propose four econ MPC schemes for nonlinear MP processes. Firstly, assuming that all the state variables are directly measurable, two different econ MPC schemes are proposed by adding different penalties on the state and input to ensure the closed-loop stability and convergence. Secondly, to address the issue of state variable off-sets from the steady-state target induced by above schemes, we further propose a multi-objective economic MPC (m-econ MPC) strategy. An auxiliary MPC controller and a stabilizing constraint are incorporated into the econ MPC. The stability of econ MPC is then achieved by preserving the inherent stability of the auxiliary MPC controller. Thirdly, to remove the assumption that all state variables are measurable, a moving horizon estimator (MHE) is employed to estimate the unmeasurable states. We then propose a practical framework integrating the m-econ MPC and MHE. Finally, we develop a tractable approximation for stochastic MPC (SMPC) to handle uncertainties associated with state variables. It can largely reduce the conservativeness or numerical instability incurred in robust or chance constraints of the traditional SMPC. The effectiveness of the proposed algorithms is validated by simulation examples of a nonlinear MP process consisting of a primary and a secondary HC refiner. It is shown that the proposed m-econ MPC schemes can significantly reduce the energy consumption (approximately 10\%-…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/73798
note: Stochastic Multi-objective Economic ModelPredictive Control of Two-stage HighConsistency Mechanical Pulping ProcessesbyHui TianB.Sc., Harbin Institute of Technology, 2011M.Sc., Harbin Institute of Technology, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Chemical and Biological Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)March 2020c© Hui Tian 2020The following individuals certify that they have read, and recommend to the Faculty of Graduate and Post-doctoral Studies for acceptance, the dissertation entitled:Stochastic multi-objective economic model predictive control of two-stage high consistency mechanicalpulping processessubmitted by Hui Tian in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin Chemical and Biological EngineeringExamining Committee:Bhushan Gopaluni, Chemical and Biological Engineering, The University of British ColumbiaSupervisorVictor Zavala, Chemical and Biological Engineering, The University of Wisconsin-MadisonSupervisory Committee MemberRyozo Nagamune, Mechanical Engineering, The University of British ColumbiaSupervisory Committee MemberMark Martinez, Chemical and Biological Engineering, The University of British ColumbiaSupervisory Committee MemberMatthew Yedlin, Electrical and Computer Engineer, The University of British ColumbiaUniversity ExaminerTsung Yuan (Tony) Yang, Department of Civil Engineering, The University of British ColumbiaUniversity ExamineriiAbstractModel predictive control (MPC) has attracted considerable research efforts and has been widely applied invarious industrial processes. This thesis aims at developing economic MPC (econ MPC) strategies to opti-mize and control the nonlinear mechanical pulping (MP) process with two high consistency (HC) refiners,which is one of the most energy intensive processes in the pulp and paper industry. It possesses substantialeconomic motives…
has type: Thesis/Dissertation
date issued: 2020-05
is shown at: 10.14288/1.0389625
language: eng
degree discipline: Chemical and Biological Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/73798