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。这一级别有时被称为实时优化或RTO。RTO使用稳态模型，并根据当前的运行成本、原材料成本和产品价格优化工厂的稳态运行点。RTO确定经济上最优的工厂运行条件(设置点)，并将这些设定值发送到第二级，第二级执行动态优化。动态优化通常被称为先进控制系统。几乎所有的先进过程控制系统都使用某种形式的模型预测控制或MPC。MPC使用动态模型来调节对象的动态行为，以满足RTO确定的设定值。在本项目中，我们将探索PI将偏离这一范例的原因有两个：1.两个层次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，还没有得到工业上的接受和广泛使用。这两个层次之间的时间尺度差距正在缩小。本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绩效。这一新的公式改变了在化工加工厂自动化系统的这一层中使用的经典设定值调节方法。由于计算硬件的速度和内存的增加以及成本的降低，首次能够实时地解决与工业相关的过程模型的动态经济优化问题。智力的优点在于发展了新的系统理论，补充和扩展了约束系统的MPC理论。该方法允许无界代价函数，并在这种新型反馈控制下证明了闭环系统渐近稳定(收敛)到最优稳态。这一扩展将使模型预测控制能够在满足最优约束的稳态时解决过程经济的动态优化问题。该理论将用高级、可免费获得的计算语言Octave实现，从而使工业从业者能够以与软件安装、维护和未来开发相关的低成本和技术风险来评估新理论。该方法不是优化距离最优稳态的距离，而是实现过程经济性的直接、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表现，而不是简单地保持稳定运行，对经济中的所有自动化制造部门都有广泛的影响。