Neural networks for the identification and control of blast furnace hot metal quality

By V. Radhakrishnan, A. Mohamed. 2000

172 citations recorded by the source corpus.

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A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, background): “Recently the efficacy of data-driven approaches involving strategies like neural network, support vector machine, fuzzy logic and certainly various evolutionary algorithms are gaining ground, as evidenced in a huge volume of blast furnace related articles in these areas, some of which are referred h”
Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, methodology): “Radhakrishnan andMohamed [188] used neural networks for the identification and control of blast furnace hot metal quality.”
A systematic approach for soft sensor development (2007, methodology): “A dynamic partial least squares (DPLS) model is implemented to address the issue of auto-correlation n process data and thus to provide smoother estimation than using a static regression model.”
Data-driven Soft Sensors in the process industry (2009, background): “Radhakrishnan and Mohamed (2000) publishes an extensive discussion of application aspects of MLP to steel industry data modelling.”