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

这一研究项目旨在解决过程工程师执行的三项常见任务的创新转变：过程系统的设计、调度和控制。这些任务通常彼此独立地执行，而不考虑它们之间的交互和权衡。开发和使用用于过程系统工程决策的新策略、程序和工具，作为智能制造和过程改进努力的一部分，有可能为可持续的未来做出重大贡献。本研究项目的主要目的是为设计、控制和调度任务的集成提供一个有用的概念框架和软件工具。该框架和软件平台已应用于某住宅冷热电联产网络系统的优化。该项目的研究结果正被纳入德克萨斯农工大学的研究生课程。开发的工具正作为一种开放获取软件工具部署，以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项开放的重大挑战。虽然在过去的20年里，人们已经做出了大量的研究努力，以顺序地将设计与控制相结合，最近又将控制与调度相结合，但仍然缺乏一种普遍接受的方法来统一该领域。正在开发的多尺度框架具有(1)高保真动态模型表示，还包括全局灵敏度分析、参数估计和混合整数动态优化能力；(2)模型近似方法套件/工具箱；(3)用于混合连续/整数问题的大量多参数规划求解器；(4)用于调度和控制问题的状态空间建模表示能力；以及(5)用于多参数/显式模型预测控制和移动时段反应调度问题的高级工具包。该活动的智力优势在于将三个任务(设计、控制和调度)集成在一个统一的多尺度框架内，并能够通过单一的优化公式获得最优操作策略、基于最优模型的控制器和过程系统的最优设计。此外，通过与原始模型交叉验证结果来闭合回路的能力也具有优势，从而确保最佳和稳定的运行。

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.