SusChEM: An integrated framework for process design, control and scheduling [PAROC]

SusChEM：过程设计、控制和调度的集成框架 [PAROC]

基本信息

批准号：
1705423
负责人：
Efstratios Pistikopoulos
金额：
$ 29.52万
依托单位：
Texas A&M Engineering Experiment Station
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1705423&HistoricalAwards=false
关键词：
SusChEM integrated framework process design

项目摘要

This research project aims for an innovative shift in addressing three common tasks performed by process engineers: design, scheduling, and control of process systems. These tasks are typically performed independently of each other, without taking into consideration the interactions and trade-offs amongst them. The development and use of novel strategies, procedures, and tools for decision making for process system engineering has the potential, as part of smart manufacturing and process improvement efforts, to contribute significantly to a sustainable future. The primary aim of this research project is to provide a useful conceptual framework and software tool for the integration of design, control and scheduling tasks. The framework and software platform is being applied to the optimization of a residential combined cooling, heating and power generation network system. The project findings are being incorporated into graduate courses at Texas A&M University. The developed tool is being deployed as an open access software tool for the benefit of the academic and industrial communities. The integration of process design, control and scheduling remains an open grand challenge in process systems engineering. While significant research efforts have been made in the last twenty years to sequentially integrate design with control, and more recently control with scheduling, a generally accepted methodology to unify the field is still lacking. The multi-scale framework being developed features (1) a high-fidelity dynamic model representation, also involving global sensitivity analysis, parameter estimation and mixed integer dynamic optimization capabilities; (2) a suite/toolbox of model approximation methods; (3) a host of multi-parametric programming solvers for mixed continuous/integer problems; (4) a state-space modeling representation capability for scheduling and control problems; and (5) an advanced toolkit for multi-parametric/explicit Model Predictive Control and moving horizon reactive scheduling problems. The intellectual merit of the activity lies in the integration of the three tasks (design, control and scheduling) within a unified multiscale framework and in the ability to acquire optimal operation policies, optimal model based controllers and optimal designs of process systems through a single optimization formulation. Additionally, there is merit in the capability to close the loop with a cross validation of the outcomes with the original model, ensuring optimal and stable operation.

该研究项目旨在解决流程工程师执行的三项常见任务的创新转变：流程系统的设计、调度和控制，这些任务通常是相互独立执行的，而不考虑它们之间的相互作用和权衡。作为智能制造和流程改进工作的一部分，流程系统工程决策的新颖策略、程序和工具的开发和使用有可能为可持续的未来做出重大贡献。为设计、控制的集成提供有用的概念框架和软件工具该框架和软件平台正在应用于住宅联合制冷、供暖和发电网络系统的优化，该项目的研究结果正在被纳入德克萨斯农工大学的研究生课程中。开放访问软件工具，造福于学术界和工业界。过程设计、控制和调度的集成仍然是过程系统工程中的一个开放的巨大挑战，尽管在过去的二十年中已经做出了大量的研究工作来顺序集成设计。控制，以及最近的调度控制，这是一种普遍接受的方法正在开发的多尺度框架仍然缺乏统一的特征：（1）涉及动态模型表示的高保真度，以及全局敏感性分析、参数估计和混合整数动态优化功能；（2）模型套件/工具箱；近似方法；(3) 一系列用于混合连续/整数问题的多参数规划求解器；(4) 用于调度和控制问题的状态空间建模表示功能；以及 (5) 用于多参数/显式问题的高级工具包；模型预测控制和移动最优水平反应调度问题的智能优点在于在统一的多尺度框架内集成三个任务（设计、控制和调度）以及获取操作策略、基于最优模型的控制器和能力。此外，通过对结果与原始模型进行交叉验证来闭环的能力也具有优点，从而确保最佳和稳定的运行。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The impact of model approximation in multiparametric model predictive control

模型逼近对多参数模型预测控制的影响

DOI：
10.1016/j.cherd.2018.09.034
发表时间：
2018-11
期刊：
Chemical Engineering Research and Design
影响因子：
3.9
作者：
Katz, Justin;Burnak, Baris;Pistikopoulos, Efstratios N.
通讯作者：
Pistikopoulos, Efstratios N.

Integrated process design, scheduling, and control using multiparametric programming

使用多参数编程集成过程设计、调度和控制

DOI：
10.1016/j.compchemeng.2019.03.004
发表时间：
2019-06
期刊：
Computers & Chemical Engineering
影响因子：
4.3
作者：
Burnak, Baris;Diangelakis, Nikolaos A.;Katz, Justin;Pistikopoulos, Efstratios N.
通讯作者：
Pistikopoulos, Efstratios N.

Integrating deep learning models and multiparametric programming

集成深度学习模型和多参数编程