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SBIR Phase I:DATA-FUSION PREDICTIVE CONTROL FOR THE FLAWS IN THE BULK OF THE…

Award Information

Agency:
National Science Foundation
Branch:
N/A
Award ID:
99078
Program Year/Program:
2010 / SBIR
Agency Tracking Number:
1013790
Solicitation Year:
N/A
Solicitation Topic Code:
M2
Solicitation Number:
N/A
Small Business Information
Og Technologies, Inc.
4300 Varsity Drive, Suite C Ann Arbor, MI 48108-5010
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2010
Title: SBIR Phase I:DATA-FUSION PREDICTIVE CONTROL FOR THE FLAWS IN THE BULK OF THE CONTINUOUSLY CAST PRODUCTS
Agency: NSF
Contract: 1013790
Award Amount: $150,000.00
 

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.

Principal Investigator:

Tzyy-Shuh Chang
MA
7349737500
chang@ogtechnologies.com

Business Contact:

Tzyy-Shuh Chang
MA
7349737500
chang@ogtechnologies.com
Small Business Information at Submission:

OG Technologies, Inc.
4300 VARSITY DR STE C ANN ARBOR, MI 48108

EIN/Tax ID: 383447449
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No