| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Pettersson, Frank
Åbo Akademi University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (28/28 displayed)
- 2024Mathematical optimization modeling for scenario analysis of integrated steelworks transitioning towards hydrogen-based reductioncitations
- 2024Optimizing the transition pathway of a steel plant towards hydrogen-based steelmaking
- 2017Blast furnace charging optimization using multi-objective evolutionary and genetic algorithmscitations
- 2015Optimal operation strategy and gas utilization in a future integrated steel plantcitations
- 2015Sustainable development of primary steelmaking under novel blast furnace operation and injection of different reducing agents
- 2013Optimization of a steel plant with multiple blast furnaces under biomass injectioncitations
- 2013Evolution of charging programs for optimal burden distribution in the blast furnace
- 2013Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnacecitations
- 2012Steelmaking integrated with a polygeneration plant for improved sustainability
- 2012Optimal resource allocation in integrated steelmaking with biomass as auxiliary reductant in the blast furnace
- 2011Multiobjective optimization of top gas recycling conditions in the blast furnace by genetic algorithmscitations
- 2011Optimization of blast furnace steelmaking process from a process integration perspective
- 2011Nonlinear modeling method applied to prediction of hot metal silicon in the ironmaking blast furnacecitations
- 2011Optimization study of steelmaking under novel blast furnace operation combined with methanol productioncitations
- 2010Multi-objective optimization of ironmaking in the blast furnace with top gas recycling
- 2010Optimization of top gas recycling conditions under high oxygen enrichment in the blast furnace
- 2010Optimisation study of ironmaking using biomasscitations
- 2010Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithmscitations
- 2009Optimization of Blast Furnace Operation Under Top Gas Recycling
- 2009Mathematical Optimization of Ironmaking with Biomass as Auxiliary Reductant in the Blast Furnace
- 2009Future potential for biomass use in blast furnace ironmakingcitations
- 2009Genetic Algorithm-Based Multicriteria Optimization of Ironmaking in the Blast Furnacecitations
- 2009Analyzing Sparse Data for Nitride Spinels Using Data Mining, Neural Networks, and Multiobjective Genetic Algorithmscitations
- 2007Evolving nonlinear time-series models of the hot metal silicon content in the blast furnacecitations
- 2007Neural networks analysis of steel plate processing augmented by multi-objective genetic algorithmscitations
- 2006Model for economic optimization of iron production in the blast furnace
- 2006Modelling noisy blast furnace data using genetic algorithms and neural networkscitations
- 2005A genetic algorithm evolving charging programs in the ironmaking blast furnacecitations
Places of action
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article
Modelling noisy blast furnace data using genetic algorithms and neural networks
Abstract
<p>Noisy blast furnace data from a Finnish steel plant was modelled by artificial neural networks, which relied upon a novel Genetic Algorithm for training. It allowed the neural networks the flexibility of evolving their optimum architectures both in terms of their weights and the utilized neurons and neuron connections. The important alloying elements in the hot metal, C, S and Si, were monitored as a function of five input variables related to the two reducing agents: coke and injected oil. The analysis indicated an intricate interaction between the variables and also highlighted the importance of lagged data in describing the complex relations. Despite these complexities the models developed were able to quantify relationships that have been generally observed and reported in the literature.</p>