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
Contract: 1013790
Agency Tracking Number: 1013790
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solitcitation Year: 2010
Solitcitation Topic Code: NM
Solitcitation Number: NSF 09-609
Small Business Information
OG Technologies, Inc.
4300 VARSITY DR STE C, ANN ARBOR, MI, 48108
Duns: 072247088
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Tzyy-Shuh Chang
 MA
 (734) 973-7500
 chang@ogtechnologies.com
Business Contact
 Tzyy-Shuh Chang
Title: MA
Phone: (734) 973-7500
Email: chang@ogtechnologies.com
Research Institution
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.

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