Showing papers by "Nirupam Chakraborti published in 2022"

Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning

Abstract: Optimization techniques are applied widely in iron and steel making process to solve complicated process-related problems. These methods and the models created through them are regularly used in this field to find out the optimum operating conditions in terms of cost, quality, quantity and effectiveness of the process. The various blast furnace parameters like burden distribution, oxygen enrichment, productivity improvement, composition of the top gas, quality of hot metal production, etc., are very difficult to effectively optimize. In recent times data-driven models of diverse nature have been quite successfully applied for this purpose, where evolutionary approaches have made a significant impact in simultaneous optimization of multiple number of objectives in problems related to the iron and steel making industry. In this chapter implementation of various evolutionary strategies are discussed, which are recently applied in this domain to tackle some real-world problems.

Novel Strategies for Data-Driven Evolutionary Optimization

Abstract: Novel learning algorithms like Evolutionary Neural Net (EvoNN), Bi-objective Genetic Programming (BioGP), and Evolutionary Deep Neural Net (EvoDN2) developed in our laboratory are being widely used in diverse areas of engineering metamodeling and multi-objective optimization of practical interest. These are intelligent algorithms, based on a nature-inspired approach, trying to mimic some basic aspects of evolutionary biology in a non-biological context, and follow the principles of multi-objective optimization. In this article, the basic working principles of these algorithms are explained.