TY - JOUR
T1 - A modified DIRECT algorithm for hidden constraints in an LNG process optimization
AU - Na, Jonggeol
AU - Lim, Youngsub
AU - Han, Chonghun
N1 - Publisher Copyright:
© 2017
PY - 2017
Y1 - 2017
N2 - Optimization for process design in the chemical engineering industry has been important for energy efficiency and economic feasibility. Because many industries perform optimization with a commercial process simulator such as the Aspen HYSYS, an optimization methodology for expensive black-box functions is needed. Thus, the development of derivative free optimization algorithms has long been studied and the deterministic global search algorithm DIRECT (DIviding a hyper-RECTangle) was suggested. In this paper, a modified DIRECT algorithm with a sub-dividing step for considering hidden constraints is proposed. The effectiveness of the algorithm is exemplified by its application to a cryogenic mixed refrigerant process using a single mixed refrigerant for natural gas liquefaction and its comparison with a well-known stochastic algorithm (GA, PSO, SA), and model based search algorithm (SNOBFIT), local solver (GPS, GSS, MADS, active-set, interior-point, SQP), and other hidden constraint handling methods, including the barrier approach and the neighborhood assignment strategy. Optimal solution calculated by the proposed algorithms decreases the specific power required for natural gas liquefaction to 18.9% compared to the base case.
AB - Optimization for process design in the chemical engineering industry has been important for energy efficiency and economic feasibility. Because many industries perform optimization with a commercial process simulator such as the Aspen HYSYS, an optimization methodology for expensive black-box functions is needed. Thus, the development of derivative free optimization algorithms has long been studied and the deterministic global search algorithm DIRECT (DIviding a hyper-RECTangle) was suggested. In this paper, a modified DIRECT algorithm with a sub-dividing step for considering hidden constraints is proposed. The effectiveness of the algorithm is exemplified by its application to a cryogenic mixed refrigerant process using a single mixed refrigerant for natural gas liquefaction and its comparison with a well-known stochastic algorithm (GA, PSO, SA), and model based search algorithm (SNOBFIT), local solver (GPS, GSS, MADS, active-set, interior-point, SQP), and other hidden constraint handling methods, including the barrier approach and the neighborhood assignment strategy. Optimal solution calculated by the proposed algorithms decreases the specific power required for natural gas liquefaction to 18.9% compared to the base case.
KW - Algorithm
KW - DIRECT
KW - Derivative-free optimization
KW - Hidden constraint
KW - Liquefaction
KW - Single mixed refrigerant (SMR)
UR - http://www.scopus.com/inward/record.url?scp=85015381995&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2017.03.047
DO - 10.1016/j.energy.2017.03.047
M3 - Article
AN - SCOPUS:85015381995
VL - 126
SP - 488
EP - 500
JO - Energy
JF - Energy
SN - 0360-5442
ER -