Modeling for integrated refinery planning with crude-oil scheduling
Chemical Engineering Research and Design, ISSN: 0263-8762, Vol: 192, Page: 141-157
2023
- 9Citations
- 15Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
This work aims to solve the integrated optimization for a complete refinery, which spans from crude oil operations to the refining processes and blending operations. We define a multi-period refinery planning problem by considering the scheduling for crude oil in which the objective function is to maximize the net profit. The optimization procedure simultaneously determines the variables of crude oil scheduling, refinery planning, and blending recipes in each time period. A hierarchical hybrid continuous-discrete time representation is proposed for the integrated optimization problem. This integrated optimization problem yields a Mixed-Integer Nonlinear Programming model for the refinery-wide multi-period optimization. The main contribution of this work is the novel mathematical optimization model proposed for the entire process optimization of refinery production. Computational results with the solvers DICOPT and BARON illustrate the scope of integrated optimization solutions for a real refinery plant with different horizon lengths, demonstrating the tractability, validity, and capability for obtaining near-optimal solutions of the proposed integrated model.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0263876223000710; http://dx.doi.org/10.1016/j.cherd.2023.02.008; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85148914431&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0263876223000710; https://dx.doi.org/10.1016/j.cherd.2023.02.008
Elsevier BV
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