In fact, the way that energy and resources are used during the production process can have important effects over the complete value chain. Two aspects can be highlighted here: the sometimes “hidden” opportunities existing at the interfaces between different processes, and the direct contribution of energy and resources to improve product quality and asset health.
About the first aspect, Viridis can manage not only individual processes, but the entire plant, helping analyze the energy performance of specific equipment, as well as the interfaces between adjacent process lines. For instance, if the temperature with which a batch of direct reduced iron reaches the melt shop is below a specified target, the energy consumption at the melt shop may increase. At the other end, if steel slabs or billets can be fed into reheating furnaces as soon as they leave the caster strands, the consumption of fuel gases can be lowered. In some cases, Viridis was used to optimize the power dispatch of a thermoelectric cogeneration unit of an integrated steel plant by balancing the supply and demand of cogeneration gases, by taking into consideration the electricity and natural gas contract pricing rules, as well as their respective market spot prices and seeking minimal global operational costs. This can only be made possible by ensuring a deeply integrated perspective of energy and resources efficiency, planning, and management.
About the second aspect, Viridis has also been used to identify golden process batches, which correlate, using specialized machine learning algorithms, energy and resource consumption to key performance indicators of a production batch (e.g. heat), such as product quality and safe asset working conditions. Viridis can therefore, in combination with a deep knowledge about the process itself, support process engineers in determining performance benchmarks and the corresponding process variables, setpoints, and execution guidelines that would achieve them. Then, those guidelines could be delivered to operators for real-time orientation in process execution and, by doing that, avoiding quality issues or downtimes which would clearly incur in production reclassifications and inevitable losses in productivity.