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Nirupam Chakraborti
Researcher at Indian Institute of Technology Kharagpur
Publications - 153
Citations - 3122
Nirupam Chakraborti is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Multi-objective optimization & Genetic algorithm. The author has an hindex of 28, co-authored 146 publications receiving 2813 citations. Previous affiliations of Nirupam Chakraborti include Pohang University of Science and Technology & Indian Institutes of Technology.
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Dynamic process modelling of iron ore sintering
TL;DR: In this paper, a mathematical model was developed for the iron ore sintering process considering all the major thermochemical phenomena in the system, assuming both the static and moving bed configurations.
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Effect of reactive‐ion bombardment on the properties of silicon nitride and oxynitride films deposited by ion‐beam sputtering
TL;DR: In this article, a dual ion-beam sputtering technique was employed to control the composition of silicon oxynitride and silicon nitride films at low temperatures (150-200°C).
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Multi‐Objective Genetic Algorithms and Genetic Programming Models for Minimizing Input Carbon Rates in a Blast Furnace Compared with a Conventional Analytic Approach
TL;DR: In this article, a data-driven model was constructed for the Productivity, CO2 emission, and Si content for an operational Blast furnace using evolutionary approaches that involved two recent strategies based upon bi-objective genetic programming and neural nets evolving through Genetic Algorithms.
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Multiobjective Optimization of Top Gas Recycling Conditions in the Blast Furnace by Genetic Algorithms
TL;DR: In this article, a genetic algorithm is used to solve the problem of top gas recycling in an integrated steel plant, where both production costs and emissions are simultaneously minimized, and the resulting solutions are analyzed with respect to the two objectives and to the internal states of the plant they correspond to.
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Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms
Baidurya Bhattacharya,G.R. Dinesh Kumar,Akash Agarwal,Şakir Erkoç,Arunima K. Singh,Nirupam Chakraborti +5 more
TL;DR: Failure behavior of Zn coated Fe is simulated through molecular dynamics (MD) and the energy absorbed at the onset of failure along with the corresponding strain of the Zn lattice are computed for different levels of applied shear rate, temperature and thickness.