Division of Information Technology, Engineering and the Environment
School of Advanced Manufacturing and Mechanical Engineering
School of Advanced Manufacturing and Mechanical Engineering
Thesis Abstract
Refinery crude preheat train (CPHT) is prone to fouling and ageing effects due to the complexity of processed crude feedstock preheated prior to distillation. This has serious implications on the thermal and hydraulic performance of the CPHT. As a result, efficient performance of crude preheat train is compromised and as such, optimal scheduling cleaning operations are required to restore performance. Mathematical Models (mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) models), Artificial Intelligence (AI) Models, and Heuristic Techniques are three prominent models used for optimal scheduling cleaning in CPHT. These optimization models have specifically failed to incorporate ageing effect in the scheduling formulation due to the paucity of knowledge on the impact of ageing in CPHT in the context of chemical reaction fouling. Recent research on ageing in chemical reaction fouling has reported grave impact of ageing on fouling in CPHT, subsequently, inspiring the development of some theoretical ageing models. Based on these ageing models, a robust and optimised fouling mitigation model that incorporates the impact of ageing on fouling and scheduling cleaning in CPHT will be developed to aid efficient refinery management.
