Integrating statistical methods and nonlinear control tools for modeling, control and fault-handling in batch processes

批处理过程通常用于制造业，或者专门用于生产小批量、高价值的产品，或者作为主要连续操作的固有操作方面而存在(启动和关闭是主要的例子)。然而，与连续过程的建模、控制和故障处理问题相比，间歇过程控制问题受到的关注明显较少。这项研究将集中于利用第一原理建模和经验建模之间的协同作用来开发一个集成的、专门的间歇过程控制框架。将考虑几个实际问题，如缺少测量、约束、固有的过程非线性以及传感器和执行器故障的发生。拟议的研究结果预计将为从特种化学品到制药再到石化等一系列行业带来重大好处，这既是由于高效的运营和控制而节省的成本，也是防止因故障导致的环境事故的形式。拟议中的研究将导致在间歇过程控制领域特别是在过程控制领域培养高素质的人员，满足加拿大工业当前和未来的重要需求。

Batch processes are commonly used in the manufacturing industry either specifically to produce low volume, high value product, or exist as inherent operational aspects of primarily continuous operation (start-ups and shutdowns being prime examples). Yet, compared to the problem of modeling, control and fault handling of continuous processes, significantly less attention has been paid to the problem of batch process control. This research will focus on the problems of utilizing the synergies between first principles modeling and empirical modeling to develop an integrated and dedicated framework for batch process control. Several practical issues such as lack of measurements, constraints, inherent process non linearity and the occurrence of sensor and actuator faults will be considered. The results of the proposed research are expected to bring significant benefits to a range of industries from specialty chemicals to pharmaceutical to petrochemicals, both in the form of savings due to efficient operation and control, but also in the form of preventing environmental accidents resulting from faults. The proposed research will result in the development of highly qualified personnel in the area of batch process control in particular, and process control in general, fulfilling an important current and future need in Canadian industry.