An adaptive neural network model for predicting the post roughing mill temperature of steel slabs in the reheating furnace

Laurinen, Perttu; Röning, Juha

doi:10.1016/j.jmatprotec.2004.12.002

Cited by 44 publications

(17 citation statements)

References 16 publications

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“…The data mining challenge is to use these variables to predict the postroughing mill temperature of the slabs while they are still in the furnace. A neural network model for predicting the temperature accurately was first developed [12]. The production line implementation of the model was started out by building the first version of the software from scratch.…”

Section: Case Study: Implementing a Data Mining Application For A Stementioning

confidence: 99%

Smart archive: a component-based data mining application framework

Laurinen

Tuovinen

Röning

2005

5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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Implementation of data mining applications is a challenging and complicated task, and the applications are often built from scratch. In this paper, a component-based application framework, called Smart Archive (SA) designed for implementing data mining applications, is presented. SA provides functionality common to most data mining applications and components for utilizing history information. Using SA, it is possible to build high-quality applications with shorter development times by configuring the framework to process application-specific data. The architecture, the components, the implementation and the design principles of the framework are presented. The advantages of a framework-based implementation are demonstrated by presenting a case study which compares the framework approach to implementing a real-world application with the option of building an equivalent application from scratch. In conclusion, the paper presents a lucid framework for creating data mining applications and illustrates the importance and advantages of using the presented approach.

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Section: Case Study: Implementing a Data Mining Application For A Stementioning

confidence: 99%

Smart archive: a component-based data mining application framework

Laurinen

Tuovinen

Röning

2005

5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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“…Data-driven modeling has been applied in rolling mill applications quite extensively as well. Neural networks have been used in many cases, including plate width set-up value estimation in a hot plate mill (Lee et al 2000), temperature prediction for steel slabs (Laurinen and Röning 2005), steel hardness prediction (Das and Datta 2007) and the prediction of work roll thermal expansion (Alaei et al 2016). Furthermore, Faris et al (2013) used genetic programming to predict rolling force, torque, and slab temperature.…”

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

et al. 2017

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The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties.

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“…Up to now, as the development of modern technique, especially the computer technology, improved approaches for optimizing the slab reheating process are developed, e.g., optimization for slab reheating with prediction [4][5][6][7] and pure optimization. [8][9][10][11] However, among these researches, most of them just optimize the temperature of slab in their problems, without considering the energy and oxidation.…”

Section: Introductionmentioning

confidence: 99%

Operation Optimization of Slab Reheating Process Based on Differential Evolution

2016

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In the slab reheating process, the temperature distribution of walking beam furnace is critical for the temperature of slab, as well as the subsequent hot rolling. Besides, this process is dynamic, nonlinear, and time varying due to coupling with complex physical and chemical reactions. In this paper, we focus on an operation optimization problem of tuning furnace temperature to optimize the slab reheating process, which considers the reheating quality and production cost. For this purpose, we develop a novel operation optimization method. Specifically, first, from the view of mechanism, a heat transmit model based on the heat-exchange of slab and furnace, an energy consumption function, and especially, an oxidation loss function are established. Therefore, incorporating the mechanism models, we build an optimization model to minimize temperature deviation of slab, energy consumption, and oxidation loss to describe our problem. Then, based on the features of the problem, we propose a modified differential evolution algorithm, which includes a space contraction scheme, a new self-adaptive parameter strategy, and a new mutation operator. Finally, to evaluate the performance of our method, extensive numerical experiments are implemented by comparing it with other well-known evolutionary algorithms based on practical data. The experimental results demonstrate the effectiveness of proposed operation optimization method on solving our problem.

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An adaptive neural network model for predicting the post roughing mill temperature of steel slabs in the reheating furnace

Cited by 44 publications

References 16 publications

Smart archive: a component-based data mining application framework

Smart archive: a component-based data mining application framework

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

Operation Optimization of Slab Reheating Process Based on Differential Evolution

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