Evolutionary data driven modeling and tri-objective optimization for noisy BOF steel making data

Abstract

Evolutionary data-driven modeling and optimization play a major role in generating meta models from real-time data. These surrogate models are applied effectively in various industrial operations and processes to predict a more accurate model from the nonlinear and noisy data. In this work, the data collected from a basic oxygen furnace of TATA steel are utilized in the modeling process by using evolutionary algorithms like evolutionary neural network (EvoNN), bi-objective genetic programming (BioGP), and evolutionary deep neural network (EvoDN2) to generate the meta models. For creating surrogates out In the current scenario of the Indian plants, reduction of phosphorus to an acceptable level, limiting the carbon and controlling the temperature are the basic needs in a basic oxygen furnace (BOF) to produce steels with a suitable composition. This work focused on three essential process parameters, temperature, carbon and phosphorus contents, and created intelligent models using 91 process variables of the operational process. The analysis began with a total of around 17000 operational observations and creating surrogate models out of them is a mammoth task, for which the data-driven evolutionary algorithms were some apt choices. Even there deep learning turned out to be essential and only the EvoDN2 algorithm performed at the expected level. Once the trained models are generated, optimization work was carried on three objectives simultaneously by using a constraint-based reference vector evolutionary algorithm (cRVEA). The optimized results were analyzed in multi-dimensional hyperspace, and their effectiveness in BOF steel making is presented in this work.