Tamoghna Mitra

Multiobjective Optimization of Top Gas Recycling Conditions in the Blast Furnace by Genetic Algorithms

Limited natural resources and a growing concern about the potential effect of carbon dioxide emissions on the worlds climate have triggered a search of ways to suppressing the emissions of CO2 in primary steelmaking. A possible future solution is to strip CO2 from the blast furnace top gas, feeding back the gas to the tuyere level. The work reported in this article explores states of an integrated steel plant that arise if both production costs and emissions are simultaneously minimized. This multiobjective problem is tackled by genetic algorithms using a predator–prey strategy for constructing the Pareto-frontier of nondominating solutions. Four alternative ways of treating the top gas recycling problem are explored, and the resulting solutions are analyzed with respect to the two objectives and to the internal states of the plant they correspond to. Conclusions are drawn concerning the solutions in terms of technical feasibility and complexity.

Model for Fast Evaluation of Charging Programs in the Blast Furnace

A mathematical model for fast evaluation of charging programs in bell-less top blast furnaces is presented. The model describes the burden formation and descent procedures in the blast furnace, and can be used for designing charging programs. Experimental results in small scale were used to validate the model. The model was applied to a real charging program from a reference blast furnace. Through comparison between the estimated burden distribution and gas temperatures from an above-burden probe it was concluded that the model has captured the main features of the distribution of coke and pellets. The potential of using the model for the design of new charging programs was finally illustrated by analyzing the effect of small changes in the positions of the rings on the arising burden distribution.

Evolution of Charging Programs for Achieving Required Gas Temperature Profile in a Blast Furnace

This article presents an approach by which charging programs in the blast furnace can be evolved. The core of the method is a mathematical model, which on the basis of a given charging program estimates the two-dimensional distribution of burden layers in the shaft. A gas flow model uses this information to estimate the gas distribution, applying a simplified treatment of the conditions in the upper shaft. The aim is to find the charging program that gives a state of the furnace shaft matching a target for the radial temperature profile at the level of an in-burden probe. This is accomplished by applying a genetic algorithm (GA) that makes an efficient search among the huge number of potential charging programs, executing the burden and gas flow models in the function evaluations. The method is illustrated by six cases, where targets for the gas temperature distribution are given and the GA evolves the charging sequence and the chute settings for the dumps. It is demonstrated that the algorithm efficiently can evolve charging programs which yield temperatures in agreement with the targets, which holds promise for a practical application of the method in the steel plant.

Thermal modelling of 3D multicore systems in a flip-chip package

Three-dimensional (3D) technology offers greater device integration, reduced signal delay and reduced interconnect power. It also provides greater design flexibility by allowing heterogeneous integration. In this work, a 3D thermal model of a multicore system is developed to investigate the effects of hotspot, and placement of silicon die layers, on the thermal performance of a modern flip-chip package. In this regard, both the steady-state and transient heat transfer analysis has been performed on the 3D flip-chip package. Two different thermal models were evaluated under different operating conditions. Through experimental simulations, we have found a model which has better thermal performance. The optimal placement solution is also provided based on the maximum temperature attained by the individual silicon dies. We have also provided the improvement that is required in the heat sink thermal resistance of a 3D system when compared to the single-die system.

Blast furnace charging optimization using multi-objective evolutionary and genetic algorithms

ABSTRACT Charging programs giving rise to desired burden and gas distributions in the ironmaking blast furnace were detected through an evolutionary multi-objective optimization strategy. The Pareto optimality condition traditionally used in such studies was substituted by a recently developed k-optimality criterion that allowed for simultaneous optimization of a large number of objectives, leading to a significant improvement over the results of earlier studies. A large number of optimum charging strategies were identified through this procedure and thoroughly analyzed, in view of an efficient blast furnace operation.