White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Optimal control and scheduling of an experimental laboratory-scale hot-rolling mill using intelligent systems-based paradigms.

Gama-Valdez, Miguel Angel (2008) Optimal control and scheduling of an experimental laboratory-scale hot-rolling mill using intelligent systems-based paradigms. PhD thesis, University of Sheffield.

[img] Text (489660.pdf)
489660.pdf

Download (16Mb)

Abstract

A novel mechanism for the optimal scheduling and the control of the hot-rolling of steel is presented in this work. Such a mechanism provides optimal rolling parameters to set-up an experimental laboratory-scale hot-rolling mill and thus produce metals with desired microstructural and mechanical characteristics. The proposed methodology combines physically-based models and those associated with 'intelligence' such as Neural Networks, Fuzzy Systems, and Genetic Algorithms, to systematically calculate the optimal rolling schedule so as to achieve a right-first-time production of steel alloys, a challenge for academia in general. Unlike current design methods, the scheduling problem is here treated as an optimisation problem which aims at satisfying multiple process objectives and a set of user-defined requirements. Such objectives are expressed in terms of the quantitative elements of the steel microstructure and its mechanical properties' such as strength and toughness. Three main aspects are considered to define and approach the optimisation problem: (1) THE PROCESS MODEL, which includes integrated knowledge of the stock microstructure, the mechanical properties, and the processing route; (2) THE PHYSICAL CONSTRAINTS associated with the metal due to its chemical composition, as well as the mill operating limitations; and (3) a set of OPTIMALITY CRITERIA which is used as a performance index to evaluate the quality and feasibility of each solution. In order to show the efficacy of this methodology, extensive experimental studies, metallographic analyses, and laboratory mechanical tests, are presented using the commercial type C-Mn Steel alloy (Bright Mild Steel) grade 080A15. The results from such experimental studies showed that the final product was in good agreement with the desired design in terms of the microstructure and the mechanical properties. The experiment results also demonstrated the advantages of the proposed methodology over current methods which are generally ad hoc and lack adequate capabilities for finding the optimal process parameters. The software SISSCOR is also introduced as a friendly graphical user interface for a fast experiment design and analysis of the dynamic performance associated with the rolling mill. This work will also review the application of a modified model-based approach in the form of the Generalised Predictive Control to reflect a Fuzzy Model of the mill. Such a hybrid strategy was implemented in order to provide robustness and flexibility to the overall control system, and to guarantee an optimal control performance during real-time operations.

Item Type: Thesis (PhD)
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Automatic Control and Systems Engineering (Sheffield)
Identification Number/EthosID: uk.bl.ethos.489660
Depositing User: EThOS Import Sheffield
Date Deposited: 11 Sep 2019 13:34
Last Modified: 11 Sep 2019 13:34
URI: http://etheses.whiterose.ac.uk/id/eprint/21813

You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.

Actions (repository staff only: login required)