- author = "Rajesh Jha and Prodip Kumar Sen and Nirupam Chakraborti",
- title = "Multi-Objective Genetic Algorithms and Genetic Programming Models for Minimizing Input Carbon Rates in a Blast Furnace Compared with a Conventional Analytic Approach",
- journal = "Steel Research International",
- year = "2014",
- volume = "85",
- number = "2",
- pages = "219--232",
- month = feb,
- keywords = "genetic algorithms, genetic programming, Blast furnace, CO2 emission, Si in hot metal, evolutionary algorithms, artificial neural network, multi-objective optimisation, Pareto front, BioGP, EvoNN, modeFRONTIER, KIMEME",
- ISSN = "1869-344X",
-
DOI = "
doi:10.1002/srin.201300074",
- size = "14 pages",
- abstract = "Data-driven models were 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. The models were used to compute the optimum tradeoff between the level of CO2 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 software and also compared with the results of thermodynamics-based analytical models.",
-
notes = "Department of Metallurgical and Materials Engineering,
Indian Institute of Technology, Kharagpur 721 302,
India.
Also known as \cite{SRIN:SRIN201300074}",
