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.

模型预测控制在化工厂和炼油厂等许多行业中得到广泛应用，通过基于模型的传感器进行过程监控使各行业能够预测和改进过程。使用模型生成警报信号的安全系统，可以对未决问题提供警告。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，而且还将有利于食品、核能、飞机和工业。石油工业通过识别潜在危险。研究人员正在与液化空气公司合作，部署该模型将能够挽救生命、减少工伤并产生经济效益，这将确保这项研究结果的工业相关性和实用性，并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，研究人员正在根据该研究成果开发教育模块和项目，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。 .本研究的目的项目将研究：（1）稳健的大规模状态估计预测（对过程模型不匹配和未测量的输入具有鲁棒性），（2）基于离线优化的过程模型参数值最坏情况组合的计算​​和最极端的控制措施，（3）大型电厂模型预测高效安全系统的实施，以及（4）首先在综合蒸汽的汽包系统上实施和测试模型预测安全系统-甲烷该研究团队还通过模拟对重整器/变压吸附器单元进行模拟，然后在真正的集成蒸汽-甲烷重整器/变压吸附器系统中的蒸汽鼓系统上实时添加和使用。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。工业合作者的参与丰富了参与该项目的研究生和本科生的培训。德雷克塞尔合作项目和本科生（最好来自代表性不足的群体）正在招募进行为期六个月的研究实习。

项目成果

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

第一原理建模，用于能量转换和存储系统的优化设计、操作和集成

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

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: 2020-06
• 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.

Distributed State Estimation in Large-scale Processes Decomposed into Observable Subsystems Using Community Detection

使用社区检测分解为可观察子系统的大规模过程中的分布式状态估计

• DOI: 10.1016/j.compchemeng.2021.107544
• 发表时间: 2021-09
• 期刊: Computers & Chemical Engineering
• 影响因子: 4.3
• 作者: Masooleh, Leila Samandari;Arbogast, Jeffrey E.;Seider, Warren D.;Oktem, Ulku;Soroush, Masoud
• 通讯作者: Soroush, Masoud

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: 2020-07
• 期刊: American Control Conference
• 作者: Soroush; Masoud
• 通讯作者: Masoud

Tuning Guidelines for Model-Predictive Control

模型预测控制的调整指南

• DOI: 10.1021/acs.iecr.9b05931
• 发表时间: 2020-03
• 期刊: Industrial & Engineering Chemistry Research
• 影响因子: 4.2
• 作者: Alhajeri, Mohammed;Soroush, Masoud
• 通讯作者: Soroush, Masoud