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

模型预测控制广泛应用于化工厂、炼油厂等工业领域，极大地提高了生产效率。 通过基于模型的传感器使用过程监控使工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，这些模型生成警报信号，可以提供未决问题的警告。该研究项目涉及开发一个过程改进模型，该模型不仅对化学和石化行业有用，而且通过识别潜在危害，还将使食品，核，航空和石油行业受益。部署这种模式将拯救生命，减少工伤，并带来经济效益。研究人员正在与液化空气公司合作，这将确保这项研究成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运营的安全性。此外，研究人员正在开发教育模块和项目的基础上，这项研究的成果，用于研究生和本科工程课程在德雷克塞尔大学和宾夕法尼亚大学。该研究项目的目标是研究：（1）鲁棒大规模状态估计预测（对过程模型失配和未测量的输入是鲁棒的），（2）过程模型参数值和最极端控制动作的最坏情况组合的基于离线优化的计算，（3）用于大规模工厂的模型预测安全系统的有效实施，以及（4）首先通过模拟在集成的蒸汽-甲烷重整器/变压吸附器单元的蒸汽鼓系统上实施和测试模型预测安全系统，然后在液化空气公司的真实的时间在真实的集成的蒸汽-甲烷重整器/变压吸附器系统中的蒸汽鼓系统上实施和测试。该研究小组还正在制定工业指南，用于添加和维护模型预测安全系统，作为对现有功能（安全仪表）系统的补充。工业合作者的参与丰富了参与该项目的研究生和本科生的培训。 该研究项目也正在与德雷克塞尔合作社计划相结合，本科生，最好是来自代表性不足的群体，正在招募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.