Economic optimization of chemical processes with feedback control

通过反馈控制实现化学过程的经济优化

• 批准号: 0825306
• 负责人: James Rawlings
• 金额: --
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0825306&HistoricalAwards=false
• 关键词: Economic; optimization; chemical; processes; feedback

James Rawlings 0828306The current paradigm in advanced process control systems is to decompose a plant's economic optimization into two levels. The first level performs a steady-state optimization. This level is sometimes referred to as real-time optimization or RTO. The RTO uses a steady-state model and optimizes the plant's steady-state operating point based on current operating costs, raw material costs, and product prices. The RTO determines the economically optimal plant operating conditions (setpoints) and sends these setpoints to the second level, which performs a dynamic optimization. The dynamic optimization is usually referred to as the advanced control system. Almost all advanced process control systems use some form of model predictive control or MPC. The MPC uses a dynamic model and regulates the plant dynamic behavior to meet the setpoints determined by the RTO.In this project, we will explore the PI will depart from this paradigm for two reasons:1. The steady-state model and the dynamic model used by the two levels often conflict. Methods to resolve conflicts between the steady-state models and dynamic models require significant maintenance and have not met with industrial acceptance and widespread use.2. The time-scale separation between the two levels is shrinking.The goal of this project is to develop a new MPC regulation/estimation problem formulation that optimizes dynamic economic performance in real time. This new formulation changes the classical setpoint regulation approach that has been used in this layer of the chemical process plant automation systems. Because of the increasing speed and memory, and decreasing cost of computational hardware, one is able to address for the first time the dynamic economic optimization of industrially relevant process models in real time.Intellectual Merit The intellectual merit in lies in the development of new systems theory, to complement and extend MPC theory of constrained systems. The approach admits unbounded cost functions and establishes the asymptotic stability (convergence) of the closed-loop system to the optimal steady state under this new type of feedback control. This extensions will enable model predictive control to address dynamic optimization of process economics subject to settling at the optimally constrained steady state. The theory will be implemented in the high-level, freely available computational language Octave, thus enabling industrial practitioners to evaluate the new theory with low costs and technical risks associated with software installation, maintenance, and future development.Broader ImpactThis project offers an improvement for optimizing the economic performance of large scale manufacturing in the process industries. Rather than optimizing distance from an optimal steady state, the method enables direct, dynamic optimization of the process economics. The systems theory and algorithms developed are completely general and can be applied to any dynamic manufacturing process instrumented with sensors and actuators. Using feedback control to directly optimize economic performance rather than simply maintaining steady operation has broad implications across all automated manufacturing sectors of the economy.

在先进的过程控制系统中，当前的范例是将工厂的经济优化分解为两个层次。第一级执行稳态优化。这个级别有时被称为实时优化或RTO。RTO使用稳态模型，并根据当前运行成本、原材料成本和产品价格优化工厂的稳态工作点。RTO确定经济上最优的工厂运行条件（设定值），并将这些设定值发送给二级，二级执行动态优化。动态优化通常被称为高级控制系统。几乎所有先进的过程控制系统都使用某种形式的模型预测控制或MPC。MPC采用动态模型，调节植物动态行为以满足RTO确定的设定值。在这个项目中，我们将探讨PI将偏离这种范式的两个原因：1。这两个层次所使用的稳态模型和动态模型经常发生冲突。解决稳态模型和动态模型之间冲突的方法需要大量的维护，并且没有达到工业接受和广泛使用。这两个层次之间的时间尺度差距正在缩小。该项目的目标是开发一种新的MPC调节/估计问题公式，以实时优化动态经济性能。这种新的配方改变了在化学过程工厂自动化系统的这一层中使用的经典设定值调节方法。由于速度和内存的增加以及计算硬件成本的降低，人们能够第一次实时地解决工业相关过程模型的动态经济优化。知识价值在于发展新的系统理论，以补充和扩展约束系统的MPC理论。该方法允许无界代价函数，并建立了在这种新型反馈控制下闭环系统对最优稳态的渐近稳定性（收敛性）。这种扩展将使模型预测控制，以解决动态优化的过程经济受制于解决在最优约束稳态。该理论将在高级、免费的计算语言Octave中实现，从而使工业从业者能够以低成本和与软件安装、维护和未来开发相关的技术风险来评估新理论。更广泛的影响本项目为优化过程工业中大规模制造的经济绩效提供了改进。该方法不是优化与最优稳态的距离，而是实现过程经济的直接动态优化。所开发的系统理论和算法是完全通用的，可以应用于任何带有传感器和执行器的动态制造过程。使用反馈控制直接优化经济绩效，而不是简单地保持稳定运行，对所有自动化制造部门都有广泛的影响。