(148c) Optimising Productivity and Energy Consumption in Crude Oil Distillation Units with Investment-Free Solutions

Labanca, L. - Presenter, Process Integration Limited
Ochoa-Estopier, L. M., The University of Manchester
Chen, L., Process Integration Limited
Crude oil distillation is central to petroleum refining. Crude oil distillation units typically consist of various complex columns (prefractionator or preflash, atmospheric and vacuum distillation columns) and their interlinked heat exchanger network. Distillation units are characterised by high product flow rates and high energy consumption, which means that their operation has serious implications with respect to product revenue and operating costs. Moreover, distillation units need to adapt to different operating scenarios, such as changes in throughput, crude and product quality as well as different prices of utilities, crude oil and distillation products. For these reasons, it becomes essential to develop and implement approaches that maximise productivity and minimise energy consumption in these ever-changing operating scenarios.

This work presents a new approach to optimise crude oil distillation units. Operational changes (e.g. on furnace outlet temperatures, flow rates of pump-arounds, distillation products and stripping steam) are systematically identified to increase net profit and to improve the unit’s flexibility in order to process different types of crude oils. The main feature of this approach is the simultaneous consideration of the distillation process and heat exchanger network during optimisation. This feature is facilitated by the development of surrogate models using artificial neural networks to represent the distillation process. Exploiting the synergy between the distillation process and heat exchanger network delivers more practicable and cost-effective solutions than the conventional approach of considering the distillation unit or heat exchanger network individually.

The proposed methodology consists of three main steps: the analysis, modelling and optimisation steps. In the analysis step, data from plant measurements and reconciled simulations are analysed to define representative operating scenarios and to identify underlying trends that can be used to guide the optimisation (e.g. identify the operational variables that dominate the process economics, define the most critical system limitations). In the modelling stage, comprehensive and accurate models are created using artificial neural network models. These models are developed to represent the distillation process using data from rigorous simulations; while mass and energy balances are used to represent the heat exchanger network. In the optimisation step, the models and the understanding from data analysis are implemented in the optimisation framework to find investment-free solutions that improve the performance of the unit.

The approach has been implemented in a complex Chinese refinery consisting of two prefractionators, two atmospheric columns, one vacuum column and a heat exchanger network. Results verified on-site indicate an increase in profits of more than $ 3 million USD per year mainly by increasing the yield of jet product by 1 %, improving the separation gap between jet and diesel products by about 36°C, and increasing the inlet temperature to the atmospheric furnaces by approximately 1°C.