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

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.