Blend Scheduling Solutions in Petroleum Refineries towards Automated Decision-Making in Industrial-like Blend-Shops
A major operation in petroleum refinery plants, blend scheduling management of stocks and their mixtures, known as blend-shops, is aimed at feeding process units (such as distillation columns and catalytic cracking reactors) and production of finished fuels (such as gasoline and diesel). Crude-oil, atmospheric residuum, gasoline, diesel, or any other stream blending and scheduling (or blend scheduling) optimization yields a non-convex mixed-integer nonlinear programming (MINLP) problem to be solved in ad hoc propositions based on decomposition strategies. Alternatively, to avoid such a complex solution, trial-and-error procedures in simulation-based approaches are commonplace. This article discusses solutions for blend scheduling (BS) in petroleum refineries, highlighting optimization against simulation, continuous (simultaneous) and batch (sequential) mixtures, continuous- and discrete-time formulations, and large-scale and complex-scope BS cases. In the latter, ordinary least squares regression (OLSR) using supervised machine learning can be utilized to pre-model blending of streams as linear and nonlinear constraints used in hierarchically decomposed blend scheduling solutions. Approaches that facilitate automated decision-making in handling blend scheduling in petroleum refineries must consider the domains of quantity, logic, and quality variables and constraints, in which the details and challenges for industrial-like blend-shops, from the bulk feed preparation for the petroleum processing until the production of finished fuels, are revealed.
Other Information
Published in: Processes
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/pr12030561
Additional institutions affiliated with: Blend-Shops Company - QSTP
Funding
Open Access funding provided by the Qatar National Library.
History
Language
- English
Publisher
MDPIPublication Year
- 2024
License statement
This Item is licensed under the Creative Commons Attribution 4.0 International License.Institution affiliated with
- Hamad Bin Khalifa University
- College of Science and Engineering - HBKU
- Qatar Science & Technology Park