A Genetic Algorithm Evolving Charging Programs in the Ironmaking Blast Furnace

Pettersson, Frank; Saxén, Henrik; Hinnelä, Jan

doi:10.1081/amp-200053420

Cited by 15 publications

(9 citation statements)

References 6 publications

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“…In the current industrial scenario, the ferrous production industries need to run the processes under very tight optimization, in order to stay competitive and continue strategies of high productivity and less emission along with the other requirements. As demonstrated in a number of recent studies [27][28][29][30][31][32][33] the use of data driven evolutionary approaches, like what has been adopted here, are clearly emerging as one of the very effective strategies to reach that goal.…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Mahanta

Chakraborti

2018

steel research int.

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Data driven models are constructed for the tuyere cooling heat loss, total blast furnace gas flow, tuyere velocity, productivity, and coke rate for an operational blast furnace of an integrated steel plant by using evolutionary computation methods like bi objective genetic programming (BioGP) and evolutionary neural network (EvoNN), which serve as the objectives for their optimization. The models are used to compute the Pareto tradeoff between these conflicting objectives with the help of predator prey genetic algorithm well tasted for computing the Pareto optimality earlier. The results of optimization and the performances of these models are thoroughly analyzed and accessed and a discussion regarding these data driven models and their influence in the ironmaking process are presented. The results are also compared against a similar calculation performed with the commercial software KIMEME.

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Section: Resultsmentioning

confidence: 99%

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Mahanta

Chakraborti

2018

steel research int.

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“…The ironmaking blast furnace is an extremely complex metallurgical process, and information from it therefore lends itself perfectly to be used for tests of novel modeling techniques [41,42]. In this paper a difficult forecasting problem, i.e., the prediction of the silicon content of the hot metal, has been used as an example to demonstrate the potential of the method to both finding appropriate inputs and to evolving a sparse connectivity of the lower layer of the networks.…”

Section: Discussionmentioning

confidence: 98%

Evolving Nonlinear Time-Series Models of the Hot Metal Silicon Content in the Blast Furnace

Saxén

Pettersson

Gunturu

2007

Materials and Manufacturing Processes

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Neural networks are versatile tools for nonlinear modeling, but in time-series modeling of complex industrial processes the choice of relevant inputs and time lags can be a major problem. A novel method for the simultaneous detection of relevant inputs and an appropriate structure of the lower part of the networks has been developed by evolving neural networks by a genetic algorithm, where the approximation error and the number of weights are minimized simultaneously by multiobjective optimization. The networks on the Pareto front are considered possible candidate models that are evaluated on an independent test set. In order to consider the problem of drift in the variables, which may cause parsimonious models to perform poorly on the test set, the weights in the upper layer of the networks are recursively estimated by a Kalman filter. The method is illustrated on a data set from ironmaking industry, where time-series models of the hot metal silicon content in a blast furnace are evolved. The technique is demonstrated to synthesize models with a choice of inputs in agreement with findings presented in the literature and process know-how.

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“…artificial neural networks, principal component analysis, Bayesian networks, and support vector machines have been used for the prediction and control of hot metal quality produced in the blast furnace [9]- [14]. Genetic algorithms have been used to find charging plans which optimize the burden distribution [15]. This paper describes for the first time the application of genetic programming for system identification of blast furnace processes and compares the quality of models obtained with genetic programming with models obtained by linear regression and support vector regression.…”

Section: Related Workmentioning

confidence: 99%

System Identification of Blast Furnace Processes with Genetic Programming

Kronberger

Feilmayr

Kommenda

et al. 2009

2009 2nd International Symposium on Logistics and Industrial Informatics

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The blast furnace process is the most common form of iron ore reduction. The physical and chemical reactions in the blast furnace process are well understood on a high level of abstraction, but many more subtle inter-relationships between injected reducing agents, burden composition, heat loss in defined wall areas of the furnace, inhomogeneous burden movement, scaffolding, top gas composition, and the effect on the produced hot metal (molten iron) or slag are not totally understood. This paper details the application of data-based modeling methods: linear regression, support vector regression, and symbolic regression with genetic programming to create linear and non-linear models describing different aspects of the blast furnace process. Three variables of interest in the blast furnace process are modeled: the melting rate of the blast furnace (tons of produced hot metal per hour), the specific amount of oxygen per ton of hot metal, and the carbon content in the hot metal. The melting rate is largely determined by the qualities of the hot blast (in particular the amount of oxygen in the hot blast). Melting rate can be described accurately with linear models if data of the hot blast are available. Prediction of the required amount of oxygen per ton of hot metal and the carbon content in the hot metal is more difficult and requires non-linear models in order to achieve satisfactory accuracy.

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A Genetic Algorithm Evolving Charging Programs in the Ironmaking Blast Furnace

Cited by 15 publications

References 6 publications

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Evolving Nonlinear Time-Series Models of the Hot Metal Silicon Content in the Blast Furnace

System Identification of Blast Furnace Processes with Genetic Programming

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