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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37714

Title: Global method for a class of operation optimization problem in steel rolling systems
Authors: Tang, Lianjie
Tang, Lixin
Liu, Jiyin
Cheng, Cong
Issue Date: 2019
Publisher: © American Chemical Society
Citation: TANG, L. ... et al, 2019. Global method for a class of operation optimization problem in steel rolling systems. Industrial and Engineering Chemistry Research, 58 (14), pp.5552-5566.
Abstract: Many steel products are produced in hot or cold rolling lines with multiple stands. The steel material becomes thinner after being rolled at each stand. Steady-state parameters for controlling the rolling line need to be set so as to satisfy the final product specifications and minimize the total energy consumption. This paper develops a generalized geometric programming model for this setting problem and proposes a global method for solving it. The model can be expressed with a linear objective function and a set of constraints including nonconvex ones. Through constructing lower bounds of some components, the constraints can be converted to convex ones approximately. A sequential approximation method is proposed in a gradually reduced interval to improve accuracy and efficiency. However, the resulting convex programming model in each iteration is still complicated. To reduce the power, it is transformed into a second-order cone programming (SOCP) model and solved using alternating direction method of multipliers (ADMM). The effectiveness of the global method is tested using real data from a hot-rolling line with seven stands. The results demonstrate that the proposed global method solves the problem effectively and reduces the energy consumption.
Description: This paper is closed access until 4 March 2020.
Sponsor: Fund for the Major International Joint Research Project of the National Natural Science Foundation of China (71520107004), the Major Program of National Natural Science Foundation of China (71790614), Innovative Research Groups of the National Natural Science Foundation of China (71621061), and the 111 Project (B16009).
Version: Accepted for publication
DOI: 10.1021/acs.iecr.8b05022
URI: https://dspace.lboro.ac.uk/2134/37714
Publisher Link: https://doi.org/10.1021/acs.iecr.8b05022
ISSN: 0888-5885
Appears in Collections:Closed Access (Business)

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