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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

基本信息

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

项目摘要

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.

模型预测控制在化工厂和炼油厂等许多行业得到了广泛的应用，使操作得到了实质性的改善。通过基于模型的传感器进行过程监控的使用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，可以对即将发生的问题发出警告。这项研究项目涉及开发一种过程改进模型，不仅对化工和石化行业有用，而且通过识别潜在的危险，还将使食品、核能、飞机和石油行业受益。部署这一模式将挽救生命，减少工作场所伤害，并带来经济效益。研究人员正在与液化空气公司合作，该公司将确保这项研究成果的产业相关性和实用性，并将加强研究成果的传播。这项研究项目产生的数据还将提高工业运营的安全性。此外，研究人员正在根据这项研究的结果开发教育模块和项目，用于德雷克塞尔大学和宾夕法尼亚大学的研究生和本科工程学课程。该研究项目的目标是研究：(1)稳健的大规模状态估计预测(对过程模型失配和未测量的输入稳健)，(2)基于离线优化的过程模型参数值和最极端控制操作组合的计算，(3)大型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通过模拟在蒸汽-甲烷转化/变压吸附一体化装置的汽包系统上实现和测试，然后在液化空气装置的实际蒸汽-甲烷转化/变压吸附装置系统中实时实现和测试。该研究小组还在制定添加和维护模型预测型安全系统的行业指南，作为对现有功能(安全仪表)系统的补充。产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项目相结合，本科生将被招募参加为期六个月的研究实习，他们最好是来自代表性较低的群体。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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.
影响因子：
0
作者：
L. S. Masooleh;Jeffrey E. Arbogast;W. Seider;U. Oktem;M. Soroush
通讯作者：
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

DOI：
发表时间：
2020
期刊：
Proc. of American Contr. Conf.
影响因子：
0
作者：
Soroush, Masoud
通讯作者：
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
通讯作者：
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
影响因子：
0
作者：
Soroush, Masoud
通讯作者：
Soroush, Masoud

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Masoud Soroush其他文献

Mathematical Modeling and Optimization of a Semi-Batch Polymerization Reactor

DOI：
10.1016/s1474-6670(17)38668-8
发表时间：
2000-06-01
期刊：
Research article
影响因子：
作者：
Dwayne Tyner;Masoud Soroush;Michael C. Grady;John Richards;John P. Congalidis
通讯作者：
John P. Congalidis