Design of Crude Oil Distillation Systems with Preflash Units

Abstract

Refining technology has evolved considerably over the last century in response to the need for more energy-efficient processes. Crude oil distillation is a complex and energy-intensive process with a large number of degrees of freedom that interact. A crude oil distillation system comprises one or more complex distillation units with side strippers and pump-arounds, a preheat train (heat recovery system) and a furnace providing fired heating. In addition, pre-separation units may be introduced, where a preliminary separation of some low-boiling components is carried out at a relatively low temperature, reducing the high-temperature heating requirements of the process. Design of these complex, integrated systems is challenging due to a large number of degrees of freedom and process constraints involved. The high operating costs dominated by the need for fired heating in the furnace and the complexity of the crude oil distillation system motivates the development of systematic approaches for optimal system design. In this work, the design methodology is developed using simulation models in Aspen HYSYS v8.8; these models are linked to MATLAB R2016a through an interface that allows communication between the two software packages. A simulation file is created for two different configurations with and without a preflash unit upstream of the atmospheric column. Two optimisation-based design approaches are proposed, the first one extracts streams and column information needed to perform the optimisation directly from the simulation model in Aspen HYSYS while in the second approach, artificial neural networks (ANN) are developed to represent the distillation process. The scope of the methodology consists of finding optimal operating and structural conditions for the crude oil distillation system that minimises hot utility demand in the furnace accounting for product quality and yield. Current design systematic methods have not focused on the role of preflash units. The strong interactions between the crude oil distillation unit, the preflash unit, and the heat recovery system make this a challenging optimisation case, especially because both operational and structural variables are to be optimised simultaneously. Therefore, a stochastic optimisation method (a genetic algorithm) is applied. As the simulation-optimisation approach is computationally intensive, it motivates the use of surrogate models that have the advantage of performing the optimisation of the system in less time (i.e. 4-6 hours vs 1.6 hours). In industrial practice, heat integration is of prime importance for the energy-efficient operation of crude oil distillation systems. This work applies pinch technology using the grand composite curve to evaluate the minimum heating and cooling requirements for each converged simulation rather than addressing detailed aspects of design and costing of the heat recovery system so that no details about investment costs and the complexity of the heat recovery system are taken into account. The novel optimisation-based design approach developed is extended to minimise fired heating demand. To date, no previous research studies focused on minimising the total fuel consumption of the system and the design or operation of crude oil distillation columns have been reported. Results obtained from industrially-relevant case studies indicate that introducing a preflash unit within a crude oil distillation system can reduce its energy demand by 14% to 16%. Using surrogate models, instead of rigorous models, considerably reduce the computational time (from 6.1 hours to 103 seconds per each optimisation run for the case with a preflash). Excellent agreement between the surrogate and rigorous models is also reported; rigorous optimised results vs ANN-optimised results for the case without a preflash unit are 44.5 MW and 44.6 MW respectively, demonstrating the effectiveness of the new approaches. On the other hand, it is demonstrated that minimisin

Date of Award	31 Dec 2019
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Megan Jobson (Supervisor) & Robin Smith (Supervisor)