Learning-Based Optimization of Copper Converter Predictive Maintenance

Konferenz: ANNA '18 - Advances in Neural Networks and Applications 2018
15.09.2018 - 17.09.2018 in St. St. Konstantin and Elena Resort, Bulgaria

Tagungsband: ANNA '18

Seiten: 6Sprache: EnglischTyp: PDF

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Hadjiski, Mincho (Bulgarian Academy of Sciences and University of Chemical Technology and Metallurgy, Sofia, Bulgaria)
Bosnhakov, Kosta (University of Chemical Technology and Metallurgy, Sofia, Bulgaria)

In the presented work, the development of a hybrid intelligent system for Condition-Based Maintenance (CBM) of Peirce-Smith Converter (PSC) is considered as an integral part of a plant-wide operational system. PSC is considered as a nonlinear, stochastic, time-varying and with a big uncertainty. Thus Neural Networks (NN) for modeling the degradation of each tuyer is accepted. Data reconciliation is performed in order to reduce the gross errors. A method for the candidate-tuyer for a possible blocking is proposed. The quantity of blocked tuyers and the blocking time is determined at the higher hierarchical level as a trade-off between learning procedure, Case-Based Reasoning Model Predictive Control and a Threshold-Based Maintenance. To estimate the minimal residual size of the PSC refractory lining during the final series of the process cycle, a NN-based approach is proposed using signals from the hottest spots image processing and pattern recognition. Some representative experimental and stimulation results are presented.