A surrogate model approach to refinery-wide optimization

THESIS
A surrogate model approach to refinery-wide optimization
Dale D. Slaback
Texas Tech University (2012)
The techniques currently used to perform refinery -wide optimization can give results that are inconsistent with the overall objectives of the refinery . A full -scale nonlinear refinery -wide optimization approach can accurately predict the overall refinery optimum , but suffers from a large computational requirement . In this study , a surrogate model approach is applied to the refinery -wide optimization problem.
The surrogate model approach to large -scale optimization involves building both detailed and approximate models for all of the processing units in the refinery . The detailed models developed in this study are rigorous first -principles models involving the material and energy balance equations . The surrogate model approach to large -scale optimization can employ approximate models of any form . However , selection of the proper form for the approximate models can greatly increase the efficiency of the optimization problem . In this study , fixed physical property phenomenological models are used as the approximate models . By fixing the values of the stream enthalpies and vapor -liquid equilibrium constants , the total number of equations in the process models is greatly reduced . This choice of approximate model form also guarantees that , at convergence , the results of the detailed and approximate models will be identical . The ASCEND IV modeling language is chosen for creating the detailed and approximate models in this study . This modeling platform provides significant advantages over a standard programming language such as Fortran . In addition to having a graphical user interface , the ASCEND software also contains an integrated solver and optimizer , making implementation of the optimization procedure more straightforward . By combining the models of each unit of the refinery together , a refinery -wide model is created . Using the CONOPT optimization routine in ASCEND , the refinery -wide optimization problem can be solved . The optimization results obtained in this work are consistent with the refinery -wide optimization results presented by Li (2000 ) . For the refinery model created in this study , the surrogate model approach decreased the required solution time by nearly an order of magnitude . An optimization was also performed for a refinery in which some product was recycled back to the crude unit . By adding this recycle stream , the system of equations was made much more complex , with each unit being affected by all others . In this case , a dramatic reduction in the optimization solution time was also observed . The refinery model in this study contains 32 decision variables and 63 constraints . An industrial -scale refinery model would be much larger , perhaps including 150 decision variables . The solution time reduction using the surrogate model approach increases with the number of decision variables . Therefore , it is projected that the time reduction for an industrial -scale refinery model could be substantially larger than for the model used in this study . The speedup obtained using the surrogate model approach would decrease the solution time for refinery -wide optimization from several days to only a few hours . By decreasing the solution time , the surrogate model approach provides a method for performing refinery -wide optimization in an industrial setting .
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