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

这个小型企业创新研究第一阶段项目建议开发数据融合预测控制，用于连铸产品中的大部分缺陷(“DPC”)，其中(A)使用各种传感器来获取钢厂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)软件应用程序建议纠正措施，以减少或消除缺陷。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，在操作中产生一种新的节能控制范例。连铸机是美国的主要炼钢工艺，目前的做法还有改进的空间，以提高效率和节约能源。该项目的边界/商业影响将是串联传感器；DPC在提高产量或节省能源方面的潜力每年超过1000万美元，同时节省1.3亿千瓦时的能源和15亿加仑的水，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(软硬传感器、含氢数据、在线/离线信息)方法，以控制高度随机和非线性的过程。这种预测系统方法将广泛应用于传统统计方法难以监测和控制的其他过程。

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.