SASS applied to optimum work roll profile selection in the hot rolling of wide steel

Authors:
Lars Nolle
Affiliations:
Computational Optimisation and Applications Research Group, School of Computing and Informatics, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
Venue:
Knowledge-Based Systems
Year:
2007

Citing 2
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Optimum Work Roll Profile Selection in the Hot Rolling of Wide Steel Strip Using Computational Intelligence

Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days

Self-adaptive stepsize search for automatic optimal design

Knowledge-Based Systems

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Abstract

The quality of steel strip produced in a wide strip rolling mill depends heavily on the careful selection of initial ground work roll profiles for each of the mill stands in the finishing train. In the past, these profiles were determined by human experts, based on their knowledge and experience. In this research, SASS, a novel heuristic optimisation algorithm that has only one control parameter, has been used to find the optimum profiles for a simulated rolling mill. The best set of profiles found by SASS clearly outperformed the original set and performed equally well as conventional algorithms without the need of finding a suitable set of control parameters.