research-article

A mathematical model of a cold rolling mill by symbolic regression alpha-beta

Authors:

L. A. Alvarado,

L. M. Torres-Treviño,

F. Gonzalez,

L. NievesAuthors Info & Claims

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1347 - 1352

https://doi.org/10.1145/2598394.2609858

Published: 12 July 2014 Publication History

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Abstract

Improvement of processes in metallurgical industry is a constant of competitive enterprises, however, changes made in a process are risky and involves high cost and time, considering this, a model can be made even using inputs usually not presented in real process and its analysis could be useful for the improvement of the process. In this work, a mathematical model is built using only experimental data of a four high tandem cold rolling mill, a set of input variables involving characteristics of the process. The performance of the model is determined by residual analysis considering new data. Results are a non black box model with a good performance; by this way, the model is a good representation of the process under study.

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Cited By

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Vuković MKoutroulis GMutlu BKrahwinkler PThalmann S(2024)Local machine learning model-based multi-objective optimization for managing system interdependencies in production: A case study from the ironmaking industryEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108099133(108099)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108099
Paulina de Souza Ferreira Mda Silva Arantes Mda Silva Arantes JBonnard RFabiano Motta Toledo C(2022)Stochastic model for setpoint of a rolling mill: an application in the soybean oil productionThe International Journal of Advanced Manufacturing Technology10.1007/s00170-022-09439-y121:3-4(2773-2786)Online publication date: 9-Jun-2022
https://doi.org/10.1007/s00170-022-09439-y

Index Terms

A mathematical model of a cold rolling mill by symbolic regression alpha-beta
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Vuković MKoutroulis GMutlu BKrahwinkler PThalmann S(2024)Local machine learning model-based multi-objective optimization for managing system interdependencies in production: A case study from the ironmaking industryEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108099133(108099)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108099
Paulina de Souza Ferreira Mda Silva Arantes Mda Silva Arantes JBonnard RFabiano Motta Toledo C(2022)Stochastic model for setpoint of a rolling mill: an application in the soybean oil productionThe International Journal of Advanced Manufacturing Technology10.1007/s00170-022-09439-y121:3-4(2773-2786)Online publication date: 9-Jun-2022
https://doi.org/10.1007/s00170-022-09439-y

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Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression α-β

Application of symbolic regression on blast furnace and temper mill datasets

1450 Cold Rolling Mill Rack Finite Element Modal Analysis