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Keywords:

  • Blast furnace;
  • CO2 emission;
  • Si in hot metal;
  • genetic algorithms;
  • genetic programming;
  • evolutionary algorithms;
  • artificial neural network;
  • multi-objective optimization;
  • pareto front;
  • BioGP;
  • EvoNN;
  • modeFRONTIER™;
  • KIMEME™

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 utilized 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 softwares and also compared with the results of thermodynamics-based analytical models.