Data-driven modelling and monitoring of industrial processes with applications in the nuclear waste processing industry

在过程监测和控制领域，建立准确可靠的模型是其基础。这些模型可用于控制方案或关键过程变量的估计。由于需要确保产品质量并监控不必要的过程变化，因此监控过程变量对工业工厂非常重要。对于许多公司来说，超出某些流程限制不仅会导致财务损失，还会违反规则和法规。特别是，近年来对减少环境污染的重视导致公司必须遵守更严格的法律。在许多过程中，使用硬件传感器监测大范围的过程变量可能是非常昂贵的，并且在某些情况下，在线测量可能不可行。离线采样是一种替代方案，然而，这通常会产生较低的采样率和显著的延迟。此外，硬件传感器也可能出现故障，而计划性维护会使传感器暂时无法正常工作。“软传感器”的替代方法是通过计算从易于测量的过程变量预测难以测量的质量变量。软传感器的优点是它们可以比硬件传感器便宜得多，提供比硬件传感器具有更低延迟的在线预测，并且可以与硬件传感器结合使用以帮助故障检测和在硬件维护期间接管。机械模型利用第一原理数学方程来描述该过程。许多现代工业过程是高度复杂的，并且机械模型的产生可能非常耗时，并且需要许多可能导致次优模型的假设。数据驱动的软测量在其生产中使用过程数据。有许多技术可用于创建数据驱动的软测量，它们通常基于多变量统计和计算智能方法。由于新技术的发展和计算硬件的进步，数据驱动的软传感器领域正在不断发展，这些技术和硬件使更复杂的模型能够在没有过多计算工作的情况下构建。创建更准确和更鲁棒的模型的需求对于过程工业来说非常重要，以通过增强的过程控制和监控来降低运营成本。该项目的基础是创新和开发工业过程的非线性多变量数据分析和数据驱动建模技术，特别是塞拉菲尔德有限公司的核废料玻璃化过程。长期储存高水平核废料的挑战对于确保没有放射性泄漏到环境中至关重要。尖端的数据驱动建模技术的应用可以改善废物的长期储存以及玻璃化过程本身。研究的方法将包括非线性多元统计、计算智能和混合技术。此外，提高这些数据驱动的模型的可靠性和概括性的方法将进行调查。通过与塞拉菲尔德有限公司的合作，开发准确和可靠的数据驱动模型将是该项目的核心，因为这将为核废料处理行业提供应用。该项目是纽卡斯尔大学和塞拉菲尔德有限公司根据EPSRC工业案例奖励计划进行的一项学生资助项目。该奖学金由EPSRC和塞拉菲尔德有限公司资助，为期四年。

In the area of process monitoring and control, building accurate and reliable models is at its foundation. These models can be used in control schemes or in the estimation of key process variables. Monitoring process variables is of importance on industrial plants because of the need to ensure product quality and to monitor for unwanted process variations. For many companies, falling outside of certain process limits can not only lead to financial losses, but also breaching rules and regulations. In particular, the emphasis on the reduction of environmental pollution in more recent years has led to stricter laws that companies have to adhere to. In many processes, monitoring a wide range of process variables using hardware sensors can be of great expense and in some cases, on-line measuring may not be feasible. Off-line sampling is an alternative, however, this typically produces a lower rate of samples and significant delays. In addition, faults can arise in hardware sensors and planned maintenance leaves sensors out of order for a time.The alternative arises in "soft sensors", which predict difficult to measure quality variables from easy to measure process variables using computation. The advantages of soft sensors are that they can be much cheaper than hardware sensors, provide on-line predictions with a lower delay than hardware sensors, and can be used in conjunction with hardware sensors to aid in fault detection and take over during hardware maintenance.The two primary ways to produce soft sensors are through using mechanistic models and data-driven models. Mechanistic models utilise first principle mathematical equations to describe the process. Many modern industrial processes are highly complex and the production of a mechanistic model can be very time consuming and require many assumptions that can lead to a sub-optimal model. Data-driven soft sensors make use of process data in their production. There are many techniques that can be used to create a data-driven soft sensor and they are typically based around multivariate statistics and computation intelligence methods. The area of data-driven soft sensors is constantly evolving due to the development of new techniques and advances in computational hardware that enable more complex models to be built without excessive computational effort. The need to create more accurate and robust models is of great importance to the process industry to reduce operational costs through enhanced process control and monitoring. The basis of this project is to innovate and develop non-linear multivariate data analysis and data-driven modelling techniques for industrial processes, in particular the nuclear waste vitrification process at Sellafield Ltd. The challenge of long term storage for high level nuclear waste is of great importance to ensure there is no radioactive leakage into the environment. The application of cutting edge, data-driven modelling techniques can lead to improvements in long term storage of the waste, as well as the vitrification process itself. Methods to be investigated will include non-linear multivariate statistics, computational intelligence and hybrid techniques. In addition, methods for improving the reliability and generalisation of these data-driven models will be investigated. Development of accurate and robust data-driven models will be at the heart of the project through the collaboration with Sellafield Ltd, since this will provide application to the nuclear waste processing industry.The project is a studentship that is undertaken between Newcastle University and Sellafield Ltd under the EPSRC Industrial CASE award scheme. The studentship is funded by the EPSRC and Sellafield Ltd for a period of four years.