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On the design and solution of an online refinery scheduling algorithm for open-loop and closed-loop strategies

Grant number: 18/04943-0
Support Opportunities:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): July 01, 2018
Effective date (End): November 14, 2018
Field of knowledge:Engineering - Chemical Engineering - Industrial Operations and Equipment for Chemical Engineering
Principal Investigator:Jorge Andrey Wilhelms Gut
Grantee:Robert Eduard Franzoi Junior
Supervisor: Christos T. Maravelias
Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Research place: University of Wisconsin-Madison (UW-Madison), United States  
Associated to the scholarship:17/03310-1 - Integrated scheduling optimization in the crude-oil refinery industry: from crude-oil unloading to fuel deliveries, BP.DD


So far, the literature on crude-oil scheduling optimization covered the problem from the crude-oil unloading and storage up to the distillation straight-run streams considering an offline environment. To go further, this research extends the scope of the problem from the raw material deliveries up to product liftings through the refinery process-shop in an online environment integrated within the scheduling cycle. Routine process feedback data and laboratory measurements from the field are considered in both open- and closed-loop strategies for better process predictions. In the open-loop strategy, the inputs of the process are updated in the online scheduling cycle such as catalyst activities, residence time of reactors and fill-draw-delays in tanks, the latter for better mixing or segregations of streams. On the other hand, in a closed-loop strategy rather than only updating the inputs, measured process outputs can calibrate gains and biases as ymeasured = gain × ymodel + bias, whereby the data updating in y considers both process yields and variables such as throughputs, flows, holdups and properties, whose effects propagate throughout the process network. After a simultaneous data reconciliation and estimation computation, the parameter feedback will be applied in a complete crude-oil refinery blend scheduling problem considering real tank topology, cascaded distillation towers, process-shops and blend-shops to effectively optimize the complex process system. The feedback strategy is solved for the refinery production scheduling within an iterative mixed-integer linear and nonlinear programming (MILP-NLP) decomposition by updating NLP results of process-shop's yields and properties, and blend-shops' recipes in the next MILP solution until convergence is achieved. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FRANZOI, ROBERT E.; MENEZES, BRENNO C.; KELLY, JEFFREY D.; GUT, JORGE A. W.. A moving horizon rescheduling framework for continuous nonlinear processes with disturbances. CHEMICAL ENGINEERING RESEARCH & DESIGN, v. 174, p. 276-293, . (18/04943-0, 17/03310-1)

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