SBIR Phase I:DATA-FUSION PREDICTIVE CONTROL FOR THE FLAWS IN THE BULK OF THE CONTINUOUSLY CAST PRODUCTS

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$150,000.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
1013790
Award Id:
99078
Agency Tracking Number:
1013790
Solicitation Year:
n/a
Solicitation Topic Code:
M2
Solicitation Number:
n/a
Small Business Information
4300 VARSITY DR STE C, ANN ARBOR, MI, 48108
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
072247088
Principal Investigator:
Tzyy-ShuhChang
MA
(734) 973-7500
chang@ogtechnologies.com
Business Contact:
Tzyy-ShuhChang
MA
(734) 973-7500
chang@ogtechnologies.com
Research Institute:
n/a
Abstract
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. 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.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government