Improving energy cost performance of steel production mills

Abstract

The global steel supply capacity is more than the demand. This has caused an increase in competition and imports to some countries. South Africa is one of the affected countries and the effects have been seen in the closing down of four local steel producers since 2009. The South African iron and steel industry is under immense pressure to reduce operational costs to remain competitive. Energy consumption contributes about 20% to the operational costs of an integrated steelmaking facility. In the production of steel profiles, the finishing rolling operations are a large energy consumer. These operations consume 20% of the energy in steelmaking. Hot rolling operations are equipped with reheating furnaces that operate on fuel gas. An integrated steelmaking facility produces by-product gases that can be consumed as an energy source throughout the works. Reheating furnaces can be designed to operate on these gases. When a by-product gas supply shortage occurs, the gas can be supplemented with purchased gases like natural gas. This occurs frequently in the older plants of South Africa. A human operator is responsible for controlling the by-product gases distributed in a complex network throughout the works. Quick reactions are required in this process where changes to the system occur frequently. The operator cannot always distribute the gases optimally based on the energy efficiency of the reheating furnaces. Energy efficiency losses occur that increase the costs of production. Research has shown that existing furnace simulation and optimisation models do not allow for changes to the gas supply type, as the primary focus is on the control of temperature in the furnace. Optimisation models of the whole facility focus on the complete utilisation of thermal energy or the improvement of production scheduling. Real-time optimisation systems require complex measurements that are often unavailable in older facilities, therefore, requiring expensive refurbishment of the furnaces. The existing systems do not take into account the effect of other components in the network. A methodology has been developed in this study that characterises the energy consumption of reheating furnaces. The outcome of the model is to simulate the energy consumption of the furnace under different workloads and the effect of changing fuel supply. These furnace models are configured in a network so that changes to the gas supply can be simulated. This model is then used to develop a real-time optimisation system that can optimise the by-product gas distribution to the reheating furnaces for improved cost performance on purchased gases. The methodology is validated on a case study steelmaking facility based in South Africa. The facility has five reheating furnaces in four rolling mills. By-product gas is supplemented with natural gas in the case of shortages. The gas consumption for the rolling operations comprised of 38% natural gas and 62% by-product gas for the year 2016. Implementing the optimisation model on historical data indicated a 9% possible reduction in natural gas. The methodology was validated by implementing the real-time optimisation system for a test period. Results showed daily natural gas consumption improvements of up to 13%. The overall improvement in natural gas consumption was 3% when including all data and 4% when excluding operational restrictions. Based on the natural gas consumption for 2016, the cost saving projection at a 4% natural gas reduction is R 2.3 million per year, excluding other charges.