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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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Agarwal, A.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2020Ternary Lead Chalcogenide Alloys for Mid-Infrared Detectorscitations
- 2016Simulation studies of recombination kinetics and spin dynamics in radiation chemistry
- 2016Fe-substituted Co-Li bismuth borate glassescitations
- 2010Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithmscitations
- 2006High energy density processing of a free form nickel-alumina nanocompositecitations
- 2006High energy density processing of a free form nickel-alumina nanocompositecitations
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article
Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithms
Abstract
<p>Approximately one year's operational data of a TATA Steel blast furnace were subjected to a multiobjective optimisation using genetic algorithms. Data driven models were constructed for productivity, CO<sub>2</sub> content of the top gas and Si content of the hot metal, using an evolutionary neural network that itself evolved through a multiobjective genetic algorithm as a tradeoff between the accuracy of training and the network complexity. The final networks were selected using the corrected Akaike information criterion. Bi-objective optimisation studies were subsequently carried out between the productivity and CO<sub>2</sub> content with various constraints at the Si level in the hot metal. The results indicate that a productivity increase would entail either a compromise of the CO<sub>2</sub> fraction in the top gas or the Si content in the hot metal. The Pareto frontiers presented in this study provide the best possible parameter settings in such a scenario.</p>