Handling Constraints and Uncertainty in Chemical Process Operation Using Nonlinear Model Predictive Control

化学过程的控制长期以来一直在努力应对各种挑战，例如对输入的约束（阀门和泵的容量有限），非线性和不确定性（干扰或工厂模型不匹配）。虽然在过去的几十年里，控制理论已经有了重要的研究，但现代传感和计算能力的可用性已经开辟了控制设计的可能性，而这些控制设计在过去是计算上难以处理的。* 使用控制策略来实现过程的预期操作的概念是一个公认的概念。控制算法的实现所依赖的三个“支柱”是传感器（例如温度传感器）、模型和控制致动器或输入（例如阀或泵）。各种系统的期望操作根据测量变量进行量化。连接这些部分是过程的模型，或者本质上是对控制致动器如何影响测量的过程变量的理解（通常是定量的）。然后，控制器需要确定如何最好地移动控制致动器以实现期望的行为。* 早期的控制方法往往忽视非线性，其他操纵变量对过程变量的影响，并且通常倾向于采取不太积极的控制措施，以避免这些不可测量的影响或干扰的负面影响。因此，整个过程自动化领域将受益于更新的控制方法，这些方法充分利用可用的计算资源来解决非线性、约束、故障处理等重要问题，并得到改进模型的帮助。拟议的研究，通过解决未解决的问题，确定“最佳方向”，以推动系统实现预期的操作受致动器的限制，将影响每一个过程中涉及自动化（从炼油厂建设温度控制车辆控制）。据保守估计，效率提高了1%，这很容易转化为加拿大数十亿美元的节省，在世界范围内则更多。改进模型的另一个重点是将统计建模工具与基于状态空间的控制工具结合起来，用于批量过程控制，节省从生物制药到特种化学品的生产过程的成本。最后，故障检测和处理的问题，将解决大规模的化学过程，积极影响环境以及加拿大工业的安全问题。 直接技术转让将通过与麦克马斯特先进控制联盟的工业合作伙伴合作进行，PI是该联盟的成员。鉴于在该地区过程控制培训的学生的快速安置，从赠款产生的25个HQP将满足加拿大工业当前和未来的需求。**

Control of chemical processes has long grappled with challenges such as the presence of constraints on the inputs (valves and pumps limited in capacity), nonlinearity and uncertainty (disturbances or plant model mismatch). While there has been significant research in control theory over the last several decades, the availability of modern sensing and computational capabilities have opened up the possibilities of control designs that were in the past computationally intractable. ***The notion of use of control strategies to achieve the desired operation of a process is a well established concept. The three `pillars' on which the implementation of control algorithm stands are sensors (such as temperature sensors), a model, and a control actuator or input (such as valves or pumps). The desired operation of various systems is quantified in terms of the measured variables. Connecting these pieces is a model of the process, or essentially an understanding (typically quantitative) of how the control actuators effect the measured process variables. The controller then needs to determine how best to move the control actuators around to achieve the desired behavior. ***Early control approaches often neglected nonlinearity, the effect of other manipulated variables on the process variable in question, and generally favored making less aggressive control actions to avoid the negative impact of these unmeasured effects or disturbances.*The entire field of process automation thus stands to benefit from newer control approaches that fully harness the available computational resources to address the important problems of nonlinearity, constraints, handling of faults, and aided by improved models. The proposed research, by addressing the unsolved problem of determining the `best direction' to push the system to achieve desired operation subject to actuator limitations, will impact every process where automation is involved (from refineries to building temperature control to vehicle control). With a conservative estimate of one percent improvement in efficiency, this readily translates into billions of dollars in savings in Canada, and much more worldwide. The other thrust on improved models will unite statistical modeling tools with state-space based control tools for batch process control saving costs for production processes ranging from bio-pharmaceuticals to specialty chemicals. Finally, the problem of fault-detection and handling will be addressed for large scale chemical processes positively impacting environmental as well as safety issues in Canadian industry. Direct technology transfer will occur through collaboration with the industrial partners of the McMaster Advanced Control Consortium, of which the PI is a member. Given the rapid placement of students trained in the area process control, the 25 HQP resulting from the grant will fulfill current and future need in Canadian industry.*** **