GOALI: Collaborative Research: Model-Predictive Safety Systems for Predictive Detection of Operation Hazards

模型预测控制在许多行业中得到了广泛的应用，如化工厂和炼油厂，从而大大改善了操作。通过基于模型的传感器使用过程监控使行业能够预测和改进过程。先前的研究已经引入了使用模型的新型安全系统，该系统产生警报信号，可以对即将发生的问题提供警告。这个研究项目包括开发一个过程改进模型，不仅证明对化学和石化工业有用，而且通过识别潜在的危害，对食品、核、飞机和石油工业也有好处。这种模式的部署将挽救生命，减少工作场所的伤害，并带来经济效益。研究人员正在与液化空气公司合作，这将确保这项研究结果的工业相关性和实用性，并将加强研究成果的传播。该研究项目产生的数据还将提高工业操作的安全性。此外，研究人员正在开发基于这项研究成果的教育模块和项目，用于德雷塞尔大学和宾夕法尼亚大学的研究生和本科生工程课程。本研究项目的目的是研究：(1)稳健的大规模状态估计预测（对过程模型失配和未测量输入具有鲁棒性）；(2)基于离线优化的过程模型参数值和最极端控制动作的最坏情况组合计算；(3)大规模工厂模型预测安全系统的有效实施；(4)模型预测安全系统首先在液化空气公司的蒸汽-甲烷转化炉/变压吸附器集成装置的蒸汽-鼓系统上进行仿真，然后在实际的蒸汽-甲烷转化炉/变压吸附器集成系统的蒸汽-鼓系统上进行实时实现和测试。研究小组还正在制定工业指南，以增加和维护模型预测安全系统，作为现有功能（安全仪表）系统的补充。工业合作伙伴的参与丰富了参与该项目的研究生和本科生的培训。该研究项目还与德雷塞尔合作项目（Drexel Co-op Program）相结合，并招募本科生（最好是来自代表性不足的群体）进行为期六个月的研究实习。

Model predictive control is widely being implemented in many industries, such as chemical plants and oil refineries, leading to substantial improvement in operations. The use of process monitoring through model-based sensors has enabled industries to predict and improve processes. Prior research has introduced novel safety systems using models, which generate alarm signals that can provide warnings of pending problems. This research project involves developing a process improvement model will not only prove useful for the chemical and petrochemical industries, but will also benefit the food, nuclear, aircraft, and petroleum industries by identifying potential hazards. Deployment of this model would result in saving lives, reducing workplace injuries, and economic benefits. The researchers are collaborating with the Air Liquide Corporation, which will ensure the industrial relevance and practicality of the results of this research and will enhance the dissemination of research results. The data resulting from this research project will also provide improved security of industrial operations. Additionally, the researchers are developing educational modules and projects based on the outcomes of this research for use in graduate and undergraduate engineering courses at Drexel University and the University of Pennsylvania.The objectives of this research project are to study: (1) robust large-scale state-estimate prediction (robust to process-model mismatch and unmeasured inputs), (2) offline optimization-based calculation of the worst-case combinations of process-model parameter values and the most extreme control actions, (3) efficient implementation of the model-predictive safety system for large-scale plants, and (4) implementation and testing of the model-predictive safety system first on the steam-drum system of an integrated steam-methane reformer/pressure-swing adsorber unit through simulations, and then on a steam-drum system in a real integrated steam-methane reformer/pressure-swing adsorber system in real time at Air Liquide. The research team also is developing industrial guidelines for adding and maintaining model-predictive safety systems as a complement for existing functional (safety-instrumented) systems. The involvement of the industrial collaborator enriches the training of graduate and undergraduate students involved in the project. The research project also is being integrated with the Drexel Co-op Program, and undergraduate students, preferably from underrepresented groups, are being recruited for six-month long research internships.