Multi-objective optimization of iron ore induration process using optimal neural networks

Miriyala, Srinivas Soumitri and Mitra, Kishalay (2019) Multi-objective optimization of iron ore induration process using optimal neural networks. Materials and Manufacturing Processes. pp. 1-8. ISSN 1042-6914

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Abstract

Induration in steel industries is the process of pelletizing iron ore particles. It is an important unit operation which produces raw materials for a subsequent chemical reduction in Blast Furnace. Of the enormous amount of energy consumed by Blast Furnace, a large portion is utilized in processing the raw materials. High-quality raw materials, therefore, ensure less consumption of energy in the Blast Furnace. Thus, optimization of induration process is necessary for conservation of a significant amount of energy in steelmaking industries. To realize this, a highly non-linear, industrially validated, 22 dimensional first principles based model for induration is created and a multi-objective optimization problem is designed. However, the physics-based model being computationally expensive, Multi-layered Perceptron Networks (MLPs) are trained to emulate the induration process. Novelty in this work lies with the optimal architecture design of MLPs through a multi-objective integer non-linear programming (MO-INLP) problem and with simultaneous training size estimation through four different Sobol sampling-based algorithms. Successful emulation of induration process resulted in 10-fold speed increment in optimization through surrogate models. To justify the parsimonious behavior of resultant MLPs, five different tests are performed for checking whether they are over-fitted. Comparison with Kriging adds to other highlights.

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IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Article
Uncontrolled Keywords: Iron, ores, steels, energy, optimization, neural, genetic, algorithm, computation, simulations, data, nonlinear, Indexed in WoS
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 16 Dec 2019 11:59
Last Modified: 16 Dec 2019 11:59
URI: http://raiith.iith.ac.in/id/eprint/7160
Publisher URL: http://doi.org/10.1080/10426914.2019.1643476
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