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

GOALI：协作研究：用于预测检测操作危险的模型预测安全系统

• 批准号: 1704915
• 负责人: Masoud Soroush
• 金额: $ 31.02万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704915&HistoricalAwards=false
• 关键词: GOALI; Collaborative; Research; Model; Predictive

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.

在许多行业（例如化学厂和炼油厂）中，广泛实施了模型预测控制，从而大大改善了运营。通过基于模型的传感器对过程监视的使用使行业能够预测和改善流程。先前的研究已使用模型引入了新型安全系统，该系统产生了警报信号，可以警告待处理问题。该研究项目涉及开发过程改进模型不仅对化学和石化行业有用，而且还将通过识别潜在危害来使食品，核，飞机和石油行业受益。这种模型的部署将导致挽救生命，减少工作场所伤害和经济利益。研究人员正在与空气液体公司合作，这将确保本研究结果的工业相关性和实践，并将增强研究结果的传播。该研究项目产生的数据还将提供改进的工业运营安全性。 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过程模型参数值和最严重控制的动作的组合，（3）对大型工厂的模型预测性安全系统有效实施，（4）（4）（4）对模型预测性安全系统的实施和测试，首先在蒸汽蒸汽机/压力蒸汽机上通过蒸汽机和蒸汽机进行蒸汽机和现实的模拟系统，然后在实用的蒸汽机系统上实现和测试，然后在蒸汽机上进行蒸汽机，然后在实时的蒸汽机上进行蒸汽机，并实现蒸汽系统，并在现实上进行蒸汽机，并在实时的蒸汽系统上进行，并在实时的蒸汽系统上进行，并在现有的蒸汽系统上进行实时的组合，该系统是实用的，然后在蒸汽机上进行蒸汽机，并在蒸汽机上进行蒸汽机，并在蒸汽机上进行蒸汽机，并在蒸汽机上进行蒸汽机，并在蒸汽机上进行了安装，然后在蒸汽机上进行了启动。液化空气中的吸附剂系统。研究团队还正在制定用于添加和维护模型预测的安全系统的工业准则，以作为现有功能（安全启发）系统的完成。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。该研究项目还与Drexel合作计划集成在一起，而本科生（最好是来自代表性不足的小组）正在招募六个月的研究实习。

项目成果

Distributed state estimation in large-scale processes decomposed into observable subsystems using community detection

• DOI: 10.1016/j.compchemeng.2021.107544
• 发表时间: 2021-09
• 期刊: Comput. Chem. Eng.
• 作者: L. S. Masooleh;Jeffrey E. Arbogast;W. Seider;U. Oktem;M. Soroush

Model‐predictive safety optimal actions to detect and handle process operation hazards

模型—预测安全最佳行动，以检测和处理过程操作危险

• DOI: 10.1002/aic.16932
• 发表时间: 2020
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Soroush, Masoud;Masooleh, Leila Samandari;Seider, Warren D.;Oktem, Ulku;Arbogast, Jeffrey E.
• 通讯作者: Arbogast, Jeffrey E.

Closest Feasible Points Invariance: a System Property to Characterize Systems with Actuator Limits

最近可行点不变性：用于表征具有执行器限制的系统的系统属性

• 发表时间: 2020
• 期刊: Proc. of American Contr. Conf.
• 作者: Soroush, Masoud

First‐principles modeling for optimal design, operation, and integration of energy conversion and storage systems

• DOI: 10.1002/aic.16482
• 发表时间: 2018-12
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Yuriy Y. Smolin;K. Lau;M. Soroush

Closest Feasible Points Invariance: A System Property to Characterize Systems with Actuator Saturation

最近可行点不变性：用执行器饱和来表征系统的系统属性

• DOI: 10.23919/acc45564.2020.9147437
• 发表时间: 2020
• 期刊: American Control Conference
• 作者: Soroush, Masoud