MPEC Problem Formulations in Chemical Engineering Applications

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100Performance Profile9080Percent Of Models Solved7060504030CONOPT-PenaltyIPOPT-Penalty20CONOPT-MultIPOPT-MultCONOPT-Reg10IPOPT-RegIPOPT-CCAN_SOLVE01 10 100 1000Time FactorFigure 1: Performance Profiles of several reformulations on all of the 92MPECLib Test Problems100Performance Profile9080Percent Of Models Solved70605040CONOPT-Penalty30IPOPT-PenaltyCONOPT-MultIPOPT-MultCONOPT-Reg20IPOPT-RegIPOPT-CCAN_SOLVE101 10 100 1000Time FactorFigure 2: Performance Profiles of several reformulations on 22 Select Test Problems12
Problem Name Objective Function Value Constraints Variables Complementaritiesbard2 -6600 10 13 8bard3 -12.67872 6 7 4bartruss3 0 3.54545E-07 29 36 26bartruss3 1 3.54545E-07 29 36 11bartruss3 2 10166.57 29 36 6bartruss3 3 3E-07 27 34 26bartruss3 4 3E-07 27 34 11bartruss3 5 10166.57 27 34 6desilva -1 5 7 4ex9 1 4m -61 5 6 4findb10s 2.02139E-07 203 198 176fjq1 3.207699 7 8 6gauvin 20 3 4 2kehoe1 3.6345595 11 11 5outrata31 2.882722 5 6 4outrata33 2.888119 5 6 4outrata34 5.7892185 5 6 4qvi 7.67061E-19 3 5 3three 2.58284E-20 4 3 1tinque dhs2 N/A 4834 4805 3552tinque sws3 12467.56 5699 5671 4480tollmpec -20.82589 2377 2380 2376Table 1: Objective Function Values of the 22 MPECLib Test Problems ThatConverged to Same Solutions13
Page 1: MPEC Problem Formulations in Chemic
Page 6 and 7: following relaxed problem:mind∇f(
Page 8 and 9: RegEq(ǫ): min f(w) (19a)s.t. h(w)
Page 10 and 11: 4.1 Automatic Reformulation of Comp
Page 14 and 15: etter understanding of NLP-based so
Page 16 and 17: Simplifying the complementarity con
Page 18 and 19: the following inner minimization pr
Page 20 and 21: 6 Case StudiesFrom the results of S
Page 22 and 23: Objective SBB CONOPT-PF IPOPT-CNFE
Page 24 and 25: of the feed. The reflux ratio is al
Page 26 and 27: 7 ConclusionsWith the development a
Page 28: [18] Mittelmann, H.D. and A. Pruess

MPEC Problem Formulations in Chemical Engineering Applications

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