Publication:
Economic model predictive control of an industrial fluid catalytic cracker

dc.contributor.coauthorAri, Asli
dc.contributor.coauthorDogan, Ibrahim
dc.contributor.coauthorHarmankaya, Murat
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorArkun, Yaman
dc.contributor.kuauthorŞıldır, Hasan
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T23:45:31Z
dc.date.issued2014
dc.description.abstractFluid catalytic cracking (FCC) is an important refinery process by which heavy hydrocarbons are cracked to form lighter valuable products over catalyst particles. FCC plants consist of the riser (reactor), the regenerator, and the fractionator that separates the riser effluent into the useful end products. In FCC plants the product specifications and feedstocks change due to varying economic and market conditions. In addition, FCC plants operate with large throughputs and a small improvement realized by optimization and control yields significant economic return. In previous work, we developed a nonlinear dynamic model and validated it with industrial data. In this study, our focus involves the development and application of a real-time optimization framework. We propose a hierarchical structure which includes a two-layer implementation of economic model predictive control (EMPC). EMPC provides the optimal riser and the regenerator temperature reference trajectories which are determined from a dynamic optimization problem maximizing the plant profit. A regulatory model predictive controller (RMPC) manipulates the catalyst circulation rate and the air flow rate to track the reference trajectories provided by EMPC. We consider changes in product prices and the feed content, both of which necessitate online optimization. Dynamic simulations show that the proposed hierarchical control structure achieves optimal tracking of plant profit during transitions between different operating regimes thanks to the combined efforts of EMPC and RMPC.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue45
dc.description.openaccessNO
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipTUPRAS Refineries The authors gratefully acknowledge the financial support of TUPRAS Refineries.
dc.description.volume53
dc.identifier.doi10.1021/ie502271r
dc.identifier.issn0888-5885
dc.identifier.scopus2-s2.0-84910151076
dc.identifier.urihttps://doi.org/10.1021/ie502271r
dc.identifier.urihttps://hdl.handle.net/20.500.14288/13851
dc.identifier.wos344906400018
dc.keywordsAnimal feed
dc.keywordsCatalysts
dc.keywordsComputational chemistry
dc.keywordsManufacturing
dc.keywordsOptimization
dc.language.isoeng
dc.publisherAmer Chemical Soc
dc.relation.ispartofIndustrial and Engineering Chemistry Research
dc.subjectEngineering, chemical
dc.titleEconomic model predictive control of an industrial fluid catalytic cracker
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorArkun, Yaman
local.contributor.kuauthorŞıldır, Hasan
local.publication.orgunit1College of Engineering
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit2Department of Chemical and Biological Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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