GOALI: Online Dynamic Control of Cooling in Continuous Casting of Thin Steel Slabs

本研究的目的是对大型半成品钢坯连铸过程中的温度进行准确的实时预测和控制。该方法是创建一个快速、准确的凝固过程中热传递的瞬时计算机模型，作为一个“软件传感器”，通过在线温度测量进行实时校准，以根据专门为这类问题设计的算法向控制系统提供反馈。新的软件系统将不断读取操作条件和结晶器温度，并连续调整连铸机二冷区域的喷水流量，以保持整个钢材所需的温度分布。这一轮廓将由钢厂工程师设定，以最大限度地减少裂纹和其他缺陷的形成。该系统将使用热电偶和光学温度传感器进行校准，并在运行中的美国薄板坯连铸机上进行测试和实施。这个项目很重要，因为美国每年生产的1亿吨钢中有96%是连续浇注的，而新的高速薄板坯连铸工艺产生的比例每年都在增加。在喷雾冷却过程中，由于不希望出现的温度变化，这一过程会出现许多缺陷，而这些缺陷在目前的控制系统中是不可避免的。传统的反馈控制不能使用，因为温度传感器太不准确和昂贵。本文提出的基于模型的预测控制系统必须克服许多挑战，包括过程的高速和模具凝固相对重要性的增加。该项目将使钢铁行业直接受益，通过提高钢材质量和降低废品率，使其变得更具竞争力。它将加强伊利诺伊大学连铸联盟的研究努力，这也有助于向该行业的成员公司传播新知识。此外，对建模和控制问题的更好理解将广泛地惠及其他制造过程。这项研究将教育那些将这些新技术带入工业的学生。

The objective of this research is to accurately predict and control temperature in real time during the continuous casting of large, semi-finished steel shapes. The approach is to create a fast, accurate transient computer model of heat transfer during the solidification process that serves as a "software sensor", calibrated in real time through online temperature measurements to provide feedback to a control system, based on algorithms which will be designed specifically for this class of problem. The new software system will continuously read in operating conditions and mold temperatures and continuously adjust the spray-water flow rates in the secondary cooling zone of the caster, in order to maintain the desired temperature profile throughout the steel. This profile will be set by steel plant engineers, in order to minimize the formation of cracks and other defects. The system will be calibrated using thermocouple and optical temperature sensors, tested and implemented at an operating U.S. thin slab caster. This project is important because 96% of the 100 million tons of steel produced in the U.S. each year is continuously cast, and the fraction produced by the new high-speed thin-slab casting process grows every year. This process experiences many defects caused by undesired temperature variations during spray cooling, which are unavoidable using current control systems. Conventional feedback control cannot be used because temperature sensors are too inaccurate and expensive. The model-based predictive control system proposed here must overcome many challenges, including the high speed of the process and increased relative importance of mold solidification. This project will directly benefit the steel industry, allowing it to become more competitive by increasing steel quality and lowering rejects. It will augment the research efforts of the Continuous Casting Consortium at the University of Illinois, which also helps to disseminate new knowledge to its member companies in this industry. In addition, improved understanding of the modeling and control issues will broadly benefit other manufacturing processes. The research will educate students who will take these new technologies into industry.