The goal may be clear, yet the path to achieving it can seem complex and should not be underestimated. The steel industry, for example, produces tons of gaseous by-products, such as COG (Coke-Oven Gas), BFG (Blast-Furnace Gas), and BOFG (Basic-Oxygen-Furnace Gas), which carry a huge amount of chemical and thermal energy. If these by-product gases are burned to generate electricity, the amount of electrical energy that can potentially be generated is normally higher than the energy consumed by the plant. If these by-products are used as fuel for processes inside the plant, the purchase of external fuel can be greatly reduced or even eliminated. In times like these, when natural gas availability and prices are unstable, this is a very promising concept that offers a much wider range of possibilities. In fact, the whole process can be optimized to achieve higher energy efficiency levels.
Nevertheless, despite the seemingly promising prospects, achieving these objectives is not an easy task. Such improvement opportunities come with tremendous difficulties. The problem is especially complex because it involves having to deal with huge volumes of data and variables from different areas, which, although dependent on each other, do not work in a fully integrated manner. For example, if off-gases from steelmaking are used as fuel in the process, it is difficult to align and synchronize the timing between the production and consumption of gases if the gasometer capacities are limited. Because of that and many other factors and restrictions, the gases are burned off multiple times in flares, while natural gas and other fuels need to be bought in to meet demand.