Predicting Key Process Parameters For a Large, Integrated Steel Manufacturer

THE BACKGROUND

The client is a Fortune 500 company that manufactures steel. In steel manufacturing, pig iron and other raw materials are combined through a series of processes with the desired result of transforming the pig iron into a specific steel grade. At many of the intermediate stages of this process, the operator’s goal is to satisfy a variety of sometimes competing constraints, including the temperature of the infrastructure, the throughput of material, and the quality of the product leaving the process. The state of the process can impact the quantity of product produced and the efficiency of the process; it can result in reduced mixing that can lead to instability in the process, and it can even put the infrastructure at risk.

A key parameter for understanding the health of the process is the chemical abundance of the material coming out of the process. When the process finishes, a sample is taken, and the chemical abundance can be measured at a laboratory. This process of taking a sample, transporting it to the laboratory, and measuring the chemical abundance takes an extended period of time. As a result of this delay, the operators are forced to use outdated information about the chemical abundances when making their decisions about how to optimize the process.

The goal of this project was for Soothsayer Analytics to use the historical data on chemical abundances and the measured process parameter to predict what the chemical abundance would be at present if a sample was taken and the chemical abundance could be measured instantaneously. This prediction would give the operator more timely and accurate information to make tactical decisions on how to optimize the process