Mikko Helle

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.

Optimisation study of ironmaking using biomass

Abstract The economic advantage of using biomass as partial substitute for fossil reductants in the blast furnace (BF) process was studied by simulation. A thermodynamic model of the BF was used in combination with simple models of the coke plant, sinter plant, hot stoves, basic oxygen furnace and power plant. Pretreatment of the biomass before its injection in the BF is considered in a pyrolysis unit where the carbon content and heating value are raised and the oxygen content is lowered, which is beneficial for the BF. The system was optimised with respect to the price of raw steel, considering costs of raw materials, energy and CO2 emissions of the unit processes. The study demonstrates that biomass in partially pyrolysed form is a potential auxiliary reductant and that the optimal states of operation within certain regions depend strongly on the price structure of the raw materials and emissions.

Simulation of tuyere-raceway system in blast furnace

Abstract A uniform distribution of the blast is an important prerequisite of a balanced blast furnace operation, because the blast is the main source of the hot gases that are needed to preheat, reduce and melt iron ores. The supply of hot gas from the raceways is not necessarily uniform along the furnace periphery, but depends on flow resistances encountered on the individual bustle main tuyere–raceway–raceway boundary routes. A model for this system has been developed in order to study and analyse the effects of changes in tuyere parameters and boundary conditions. Variables such as the total blast volume, blast pressure, tuyere diameter and the combustion degree of injected reductants in the tuyeres can be studied. An online version of the model has also been developed to track how the conditions on the tuyere level change with time in operating blast furnaces.

A method for detecting cause-effects in data from complex processes

When models are developed to aid the decision making in the operation of industrial processes, lack of understanding of the underlying mechanisms can make a first-principles modeling approach infeasible. An alternative is to develop a black-box model on the basis of historical data, and neural networks can be used for this purpose to cope with nonlinearities. Since numerous factors may influence the variables to be modeled, and all potential inputs cannot be considered, one may instead solely focus on occasions where the (input or output) variables exhibit larger changes. The paper describes a modeling method by which historical data can be interpreted with respect to changes in key variables, yielding a model that is well suited for analysis of how changes in the input variables affect the outputs.

Optimal Resource Allocation in Steel Making Using Torrefied Biomass as Auxiliary Reductant

Steelmaking is an energy intensive industrial sector and being largely coal-based it gives rise to 5–6 % of the global CO2 emissions. Energy use for producing 1 ton of crude steel has been reduced by 50 % since 1975, but the annual production rate of crude steel has been increasing more strongly. Since 2002, the production rate has increased by almost 80 % amounting to 1,510 Mt in 2012, and this trend seems to continue in the future. Therefore, making the iron production itself more efficient is not enough to reduce carbon dioxide emissions. A possible remedy is to replace part of the fossil reductants by renewables and to optimize the entire production chain from ores to steel, allowing more beneficial resource allocation in the processes involved. The present study focuses on the use of biomass as auxiliary reductant in the blast furnace, also paying attention to the effect of the introduction on the material and energy flows of the whole steel plant using a simulation model. Substituting part of the fossil coke or injected hydrocarbon by biomass may result in reduced fossil carbon dioxide emissions, as long as the biomass is harvested, transported and pre-processed in a sustainable way. As the biomass may need upgrading before it is used, a torrefaction model is included in the steel plant model. Results are presented from studies where the entire system is optimized with respect to costs, considering a penalty for CO2 emissions.

Data-Driven Analysis of Results from Blast Furnace Tuyere Core Drillings

Due to the extremely hostile environment in the lower part of the ironmaking blast furnace, where three different phases exist and interact at high temperatures, direct measurements of the prevailing conditions are not available. Tuyere core drillings reveal information about both physical and chemical conditions at the high temperature region. In this paper some results from drillings performed relatively regularly at an industrial blast furnace are analysed and correlated with cyclic changes detected in the furnace hearth lining temperatures. The analysis s based on the lengths of the different zones, such as raceway, birds nest and deadman, that can be detected from the drill core. The relation between the zone lengths and hearth state derived from the thermocouple readings is modelled and studied with neural networks using different combinations of the measured zone lengths as inputs. The final model can be used to study the relationship between the examined variables and also to quickly classify the hearth state on the basis of future core drillings.