AbstractHeat integration in crude oil refineries is a key process that aims for decreasing the large energy consumption in crude distillation units (CDU). A system consisting of a heat exchanger network (HEN), known as the pre-heat train is used for achieving this goal. Unfortunately, given the chemical characteristics of crude oil, the pre-heat train is severely affected by fouling deposition. Fouling deposition directly impacts the thermal and hydraulic performance of the pre-heat train, decreasing the overall heat transfer coefficient and increasing the pressure drop and emission of greenhouse gases. Fouling deposition is mainly mitigated via equipment cleaning or operational op- timisation. The effect of fouling is quantified using process measurements such as stream flow rates, temperatures and pressures. Previous studies have developed semi-empirical models relating specific operating conditions and the severity of fouling. However, these models require a set of parameters that needs to be esti- mated for each individual crude oil. In addition, the use of process measurements poses a further challenge, as each measurement contains measurement error. This error is associated to different sources such as signal transmission (random errors) and measurement bias (gross errors). Moreover, the number of measured process states plays an important role, as the estimation of unmeasured variables would not take place if the set of initial measurements were not correctly selected. This Thesis provides an integrated methodology for determining fouling model pa- rameters in crude oil pre-heat trains using operating data subject to random and gross errors. A detailed HEN model along with data reconciliation and gross error detection are used for minimising measurement error, identifying faulty instru- ments and estimating unmeasured variables. Additionally, an optimisation-based parameter estimation procedure is implemented for determining specific fouling models. The proposed methodology is tested in several industrially-relevant case studies, indicating that the appropriate processing of measured data increases the accuracy of fouling-related predictions, and that the incorporation of fouling deposition into heat transfer modelling provides a more realistic context for HEN design and optimisation.
|Date of Award||31 Dec 2019|
|Supervisor||Megan Jobson (Supervisor) & Robin Smith (Supervisor)|
- Heat Integration
- Pre-heat Train
- Measurement Error