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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
Author(s) -
Jha Rajesh,
Sen Prodip Kumar,
Chakraborti Nirupam
Publication year - 2014
Publication title -
steel research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.603
H-Index - 49
eISSN - 1869-344X
pISSN - 1611-3683
DOI - 10.1002/srin.201300074
Subject(s) - blast furnace , genetic programming , genetic algorithm , productivity , pareto principle , mathematical optimization , pareto optimal , algorithm , artificial neural network , evolutionary algorithm , multi objective optimization , computer science , mathematics , materials science , artificial intelligence , metallurgy , economics , macroeconomics
Data‐driven models were constructed for the Productivity, CO 2 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. The models were utilized to compute the optimum tradeoff between the level of CO 2 emission and productivity at different Si levels, using a Predator–Prey Genetic Algorithm, well tested for computing the Pareto‐optimality. The results were pitted against some similar calculations performed with commercial softwares and also compared with the results of thermodynamics‐based analytical models.