NSF Award
1013790
This Small Business Innovation Research Phase I project proposes to develop the Data-fusion Predictive Control for the Flaws in the Bulk of the Continuously Cast Products ("DPC") in which (a) various sensors are used to acquire surface conditions of the cast products in a steel mill, (b) a diagnostic module predicts whether the cast products meets quality requirements in both internal and surface conditions, and (c) a software application suggests corrective actions to enable reduction or elimination of defects. The DPC will be a product that is commercially viable and have high impact in the continuous casting, resulting in a new energy efficient control paradigm in the operations through improved yield, reduced material removal and enhanced direct charge. The current practice by continuous casters, which is the primary steel making process in the U.S., has room to improve for better efficiency and energy savings. <br/><br/>The boarder/commercial impact of this project will be in-line sensors; the DPC has the potential of over $10 million per annum per installation in yield improvement or energy savings, along with the savings of 130 million KWh of energy and 1.5 billion gallons of water reduction, as well as the reduction of 37,500 tons of CO2 emission. This project represents a unique multi-model data fusion (soft as well as hard sensors, hydrogenous data, in-line/off-line information) approach to controlling a highly stochastic and non-linear process. This predictive system approach will have wide applications to other processes that are difficult to monitor and control by conventional statistical methods.
