Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process

• Autorzy: Mahanta Bashista Kumar , Chakraborti Nirupam
• Języki publikacji: EN

Abstrakty

EN

Optimization of process parameters in modern blast furnace operation, where both control and accessing large data set with multiple variables and objectives is a challenging task. To handle such non-linear and noisy data set deep learning techniques have been used in recent time. In this study an evolutionary deep neural network algorithm (EvoDN2) has been applied to derive a data driven model for blast furnace. The optimal front generated from deep neural network is compared against the optimal models developed from bi-objective genetic programming algorithm (BioGP) and evolutionary neural network (EvoNN). The optimization process is applied to all the training models by using constraint based reference vector evolutionary algorithm (cRVEA).

Słowa kluczowe

EN

deep learning; reference vector; neural net; genetic programming; blast furnace

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2021
• Tom: Vol. 21, No. 3
• Strony: 163--175
• Opis fizyczny: Bibliogr. 39 poz., rys.

Twórcy

autor

Mahanta Bashista Kumar

• Department of Metallurgical and Materials Engineering Indian Institute of Technology, Kharagpur, India

autor

Chakraborti Nirupam

• Department of Metallurgical and Materials Engineering Indian Institute of Technology, Kharagpur, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).