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

Bashista Kumar Mahanta, Nirupam Chakraborti

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

DOI:

https://doi.org/10.7494/cmms.2021.3.0733

Abstract:

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).

Cite as:

Mahanta, B. K., & Chakraborti, N. (2021). Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process. Computer Methods in Materials Science, 31(3), pages. https://doi.org/10.7494/cmms.2021.3.0733

Article (PDF):

Accepted Manuscript – final pdf version coming soon

Key words:

Deep Learning, Reference vector, Neural net, Genetic programming, Blast furnace

References: