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Operational optimization of industrial steam systems under uncertainty using data‐ D riven adaptive robust optimization
Author(s) -
Zhao Liang,
Ning Chao,
You Fengqi
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16500
Subject(s) - robust optimization , steam turbine , mathematical optimization , computer science , integer programming , process (computing) , optimization problem , robustness (evolution) , steam reforming , engineering , mathematics , mechanical engineering , hydrogen , biochemistry , chemistry , hydrogen production , organic chemistry , gene , operating system
This article addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data‐driven adaptive robust optimization approach. A semiempirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then used to construct the uncertainty sets for modeling these uncertain parameters. The data‐driven uncertainty sets are incorporated into a two‐stage adaptive robust mixed‐integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multilevel optimization model is transformed into its robust counterpart, which is a single‐level MILP problem. The proposed framework is applied to the steam system of a real‐world ethylene plant to demonstrate its applicability. © 2018 American Institute of Chemical Engineers AIChE J , 65: e16500 2019