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Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process
Author(s) -
Mahanta Bashista Kumar,
Chakraborti Nirupam
Publication year - 2018
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.201800121
Subject(s) - blast furnace , tuyere , multi objective optimization , process (computing) , artificial neural network , pareto principle , genetic algorithm , steelmaking , evolutionary algorithm , computer science , engineering , mathematical optimization , mathematics , materials science , artificial intelligence , metallurgy , operating system
Data driven models are constructed for the tuyere cooling heat loss, total blast furnace gas flow, tuyere velocity, productivity, and coke rate for an operational blast furnace of an integrated steel plant by using evolutionary computation methods like bi objective genetic programming (BioGP) and evolutionary neural network (EvoNN), which serve as the objectives for their optimization. The models are used to compute the Pareto tradeoff between these conflicting objectives with the help of predator prey genetic algorithm well tasted for computing the Pareto optimality earlier. The results of optimization and the performances of these models are thoroughly analyzed and accessed and a discussion regarding these data driven models and their influence in the ironmaking process are presented. The results are also compared against a similar calculation performed with the commercial software KIMEME.