Evolutionary data driven modeling and tri-objective optimization for noisy BOF steel making data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{MAHANTA:2023:dche,
-
author = "Bashista Kumar Mahanta and Prakash Gupta and
Itishree Mohanty and Tapas Kumar Roy and Nirupam Chakraborti",
-
title = "Evolutionary data driven modeling and tri-objective
optimization for noisy {BOF} steel making data",
-
journal = "Digital Chemical Engineering",
-
year = "2023",
-
volume = "7",
-
pages = "100094",
-
month = jun,
-
keywords = "genetic algorithms, genetic programming, Optimization,
Multi-objective optimization, Pareto optimality,
Evolutionary algorithms, Deep learning, Neural network,
ANN, Reference vector",
-
ISSN = "2772-5081",
-
URL = "https://www.sciencedirect.com/science/article/pii/S2772508123000121",
-
DOI = "doi:10.1016/j.dche.2023.100094",
-
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
used 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 analysed in multi-dimensional
hyperspace, and their effectiveness in BOF steel making
is presented in this work",
- }
Genetic Programming entries for
Bashista Kumar Mahanta
Prakash Gupta
Itishree Mohanty
Tapas Kumar Roy
Nirupam Chakraborti
Citations