Self-adaptive modelling of large-scale process systems using machine learning

过程系统已经可以通过各种手段来实现，通常这些模型最终将变成真实的系统的不准确表示，这导致工厂的次优操作。这种不匹配可能由于许多原因而开始发生，例如底层系统的变化（例如结垢和设备变化）。因此，需要训练有素的工程师重新审视问题并维护底层模型。这可能会产生重大费用，并且对于存在不匹配的操作期间，这也可能导致次优的过程操作，从而导致重大利润的损失。此外，这种对工程工作的依赖为大型过程工厂部署基于模型的解决方案带来了缩放问题。所有这些问题都是特别相关的，如果目标是达到一个更高程度的自主工业，因为这些问题作为实现自主工业系统的目标的障碍。出于上述原因，本博士的目标将是开发数据驱动的自适应建模框架，可以处理底层系统的变化，并相应地适应，以保证过程模型的准确性。如果能够做到这一点，对大规模工业生产的影响可能是巨大的。这可以通过多种方式实现，但这个博士学位的主要重点将是利用统计学习技术，从简单的机器学习方法到更复杂的深度学习算法。虽然这些技术中的许多已经显示出很大的希望的任务，仍然存在一些问题，这些技术的适用性，大规模的过程系统的动态建模，其可扩展性，训练的稳定性和所需的数据量和种类，以实现这些目标。因此，在本博士论文中，我们将研究这些开放的问题。

process systems can already be achieved by various means, often these models will eventually become an inaccurate representation of the real system, which results in the sub-optimal operation of the plant. This mismatch can begin to occur due to a number of reasons such as changes in the underlying system (e.g. fouling and equipment changes). Consequently, there is a need for highly-trained engineers to re-visit the problem and maintain the underlying models. This can incur major expenses and for the period of operation where the mismatch existed, this can also result in sub-optimal process operation resulting in the loss of significant profits. Additionally, this dependence on engineering effort creates a scaling problem for deploying model-based solutions for large process plants. All of these issues are especially relevant if the objective is to reach a higher degree of autonomy within industry as these issues act as obstacles to achieve the goal of autonomous industrial systems.For the above reasons, the objective of this PhD will be to develop data-driven self-adaptive modelling frameworks which can handle changes in the underlying system and adapt accordingly to guarantee the accuracy of the process model. If this can be achieved, the impact on large-scale industrial processes could be significant. This could potentially be achieved in a number of ways, but the main focus of this PhD will be to utilize statistical learning techniques ranging from simple machine learning methods to more complex deep learning algorithms. While many of these techniques have shown great promise for the task, some questions still remain regarding the applicability of these techniques to dynamic modelling of large-scale process systems, their scalability, the stability of training and the amount and variety of data required to achieve these objectives. Consequently, in this PhD we will investigate such open problems.