(6ig) Optimal Design of Petroleum Refinery Configuration Using a Model Based Mixed-Integer Programming Approach with Practical Approximation
- Conference: AIChE Annual Meeting
- Year: 2018
- Proceeding: 2018 AIChE Annual Meeting
- Group: Meet the Faculty Candidate Poster Session – Sponsored by the Education Division
- Time: Sunday, October 28, 2018 - 1:00pm-3:30pm
Teaching Interests: Petroleum Refining, Mass Transfer Unit Operations
We present a model-based optimization approach to determine the configuration of a petroleum refinery for grassroots (new) or existing site that considers a large number of commercial technologies particularly for heavy oil processing of crude oil residue from an atmospheric distillation unit. First, we develop a superstructure representation for the refinery configuration to encompass all possible topology alternatives comprising 96 technologies and their interconnectivities. The superstructure is postulated by decomposing it to incorporate representative heavy oil processing scheme alternatives that center on the technologies for atmospheric residual hydrodesulfurization (ARDS), vacuum residual hydrodesulfurization (VRDS), and residual fluid catalytic cracking (RFCC). We formulate a mixed-integer linear program (MILP) based on the superstructure by devising logic propositions on design and structural specifications that represent these processing options to aid convergence to an optimal refinery configuration. A numerical example is illustrated to implement the proposed technique in which an equivalent of more than two million refinery plot plans is evaluated. To assess the applicability and value of the approach, we validate the results against the literature as well as compare with existing real-world refinery configurations. A main contribution of this work is to demonstrate how a mixed-integer programming approach can be applied to a large-scale petroleum refinery design problem with suitable approximations informed by practical considerations to obtain results with reasonable computational load.