Economic optimization of chemical processes with feedback control

詹姆斯罗林斯0828306在先进的过程控制系统的当前范例是分解成两个层次的工厂的经济优化。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，并根据当前的运营成本、原材料成本和产品价格优化工厂的稳态操作点。RTO确定经济上最佳的工厂操作条件（设定点），并将这些设定点发送到执行动态优化的第二级。动态优化通常被称为先进控制系统。 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使用动态模型并调节设备的动态行为以满足RTO确定的设定点。在本项目中，我们将探讨PI将偏离此范例的两个原因：1。这两个层次所采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的冲突的方法需要大量的维护，并且还没有得到工业上的接受和广泛使用。这个项目的目标是开发一个新的MPC调节/估计问题公式，优化动态经济性能在真实的时间。这种新的配方改变了经典的设定值调节方法，已在这一层的化工厂自动化系统中使用。由于速度和内存的增加，以及计算硬件成本的降低，人们能够在真实的time.Intellectual Merit首次解决工业相关过程模型的动态经济优化问题。该方法承认无界的成本函数，并建立了渐近稳定性（收敛）的闭环系统的最佳稳态下这种新型的反馈控制。这种扩展将使模型预测控制，以解决动态优化的过程经济性的最佳约束的稳定状态。该理论将在高层次的，免费提供的计算语言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。该方法不是优化与最佳稳态的距离，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，可以应用于任何动态制造过程中的传感器和执行器仪表。使用反馈控制来直接优化经济性能，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。

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.