SBIR Phase I: Data-Fusion Predictive Control for the Flaws in the Bulk of the Continuously Cast Products

SBIR 第一阶段：针对连铸产品批量缺陷的数据融合预测控制

• 批准号: 1013790
• 负责人: Tzyy-Shuh Chang
• 金额: $ 15万
• 依托单位: OG TECHNOLOGIES, INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1013790&HistoricalAwards=false
• 关键词: SBIR; Phase; Data; Fusion; Predictive

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.

该小型企业创新研究第一阶段项目提出开发用于连续铸造产品的批量缺陷（“DPC”）的数据融合预测控制，其中（a）使用各种传感器来获取钢厂中的铸造产品的表面状况，（B）诊断模块预测铸造产品是否满足内部和表面条件的质量要求，以及（c）软件应用程序建议校正动作以使得能够减少或消除缺陷。DPC将是一种商业上可行的产品，在连铸中具有很高的影响力，通过提高产量、减少材料去除和增强直接装料，在操作中产生新的节能控制模式。连铸机是美国的主要炼钢工艺，有改进的空间，以提高效率和节约能源。该项目的边界/商业影响将是在线传感器; DPC每年在产量提高或节能方面的潜力超过1000万美元，沿着节省1.3亿千瓦时的能源和15亿加仑的水减少，以及减少37,500吨二氧化碳排放。该项目代表了一种独特的多模型数据融合（软传感器和硬传感器，含氢数据，在线/离线信息）方法来控制高度随机和非线性过程。这种预测系统的方法将有广泛的应用，难以监测和控制的常规统计方法的其他过程。