A Dive into the Specific Electric Energy Consumption in Steelworks
The paper describes an application of optimization techniques for the minimization of the specific electrical energy consumption related to the production of steel for a steelworks situated in Italy. The major electrical consumption derives from two internal plants: the Electric Arc Furnace and the Ladle Furnace. This work addresses the problem of understanding the best settings (based on predefined models) to produce a specific steel, which is mainly characterized by its steelgrade and quality, with the minimum energy consumption.
The roles of energy and material efficiency in meeting steel industry CO2 targets.
Identifying strategies for reducing greenhouse gas emissions from steel production requires a comprehensive model of the sector but previous work has either failed to consider the whole supply chain or considered only a subset of possible abatement options. In this work, a global mass flow analysis is combined with process emissions intensities to allow forecasts of future steel sector emissions under all abatement options. Scenario analysis shows that global capacity for primary steel production is already near to a peak and that if sectoral emissions are to be reduced by 50% by 2050, the last required blast furnace will be built by 2020. Emissions reduction targets cannot be met by energy and emissions efficiency alone, but deploying material efficiency provides sufficient extra abatement potential.
Data-driven prediction of sintering burn-through point based on novel genetic programming
An empirical dynamic model of burn-through point (BTP) in sintering process was developed. The K-means clustering was used to feed distribution according to the cold bed permeability, which was estimated by the superficial gas velocity in the cold stage. For each clustering, a novel genetic programming (NGP) was proposed to construct the empirical model of the waste gas temperature and the bed pressure drop in the sintering stage. The least square method (LSM) and M-estimator were adopted in NGP to improve the ability to compute and resist disturbance. Simulation results show the superiority of the proposed method.