skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

Abstract

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that themore » proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

Authors:
 [1];  [1];  [2];  [1]
  1. Northeastern Univ., Shenyang (China). State Key Lab. of Synthetical Automation for Process Industries
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE; National Natural Science Foundation of China (NNSFC)
OSTI Identifier:
1413453
Report Number(s):
PNNL-SA-128830
Journal ID: ISSN 2168-2216
Grant/Contract Number:
AC05-76RL01830; 61473064; 61621004; 61333007; N160805001; N160801001
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Additional Journal Information:
Journal Name: IEEE Transactions on Systems, Man, and Cybernetics: Systems; Journal ID: ISSN 2168-2216
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 97 MATHEMATICS AND COMPUTING; Load modeling; Data models; Optimization; Production; Economics; Predictive models; Biological system modeling; normalsize High consistency (HC) refining system; nonlinear multiobjective model predictive control (MPC); optimal operation; pulp quality; specific energy (SE) consumption

Citation Formats

Li, Mingjie, Zhou, Ping, Wang, Hong, and Chai, Tianyou. Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking. United States: N. p., 2017. Web. doi:10.1109/TSMC.2017.2748722.
Li, Mingjie, Zhou, Ping, Wang, Hong, & Chai, Tianyou. Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking. United States. doi:10.1109/TSMC.2017.2748722.
Li, Mingjie, Zhou, Ping, Wang, Hong, and Chai, Tianyou. Tue . "Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking". United States. doi:10.1109/TSMC.2017.2748722.
@article{osti_1413453,
title = {Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking},
author = {Li, Mingjie and Zhou, Ping and Wang, Hong and Chai, Tianyou},
abstractNote = {As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.},
doi = {10.1109/TSMC.2017.2748722},
journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 19 00:00:00 EDT 2017},
month = {Tue Sep 19 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on September 19, 2018
Publisher's Version of Record

Save / Share: