A. L. Brambilla, A. Deidda, and D. Semino, Optimal operation of a sepa-921 ration plant using artificial neural networks, Computers & Chemical Engineering, vol.922, pp.5939-5942, 1998.

K. Amouzgar and N. Strilmberg, Radial basis functions as surrogaoe models wlth a 924 priori bias in comparison with a posteriori bias, Structural and Multidisciplinary, vol.925, issue.4, pp.1453-1469, 2017.

. J. Ancheyta-j?rez and E. Villafuerte-macw, Experirnental validation of a kinetic 927 mode! for naphtha reforming, Studies in Surface Science and Catalysis, vol.133, p.618, 2001.

. E. Anderson and . Bai,

C. Bischof, S. Blackford, . J. Demmel, . J. Dongarra, and C. J. Du, , p.930

A. Greenbaum, S. Hammarling, A. Mckenney, and D. Sorensen, , 1999.

. User's-guide, Society for lndustrial and Applied Mathematics, issue.SIAM

C. Audet,

I. Digabel, S. Tribes, and C. ,

. Rochon-montplaisir, The NOMAD project. 933 Software available at

. B. Beykal, . F. Boukoulava, and C. A. Floudas,

E. N. Pistikopoulos, Optimal design of 935 energy systems using constrained grey-box multi-objective optimization. Corn-936 puters a, Chemical Engineering, vol.116, pp.488-502, 2018.

B. Beykal, . F. Boukoulava, C. A. Floudas, N. 5orek, H. Zalavadia et al., 938 Global optimization of grey-box computational systems using surrogaoe func-939 tiens and application to highly constrained oil-field operations, Computers Il, p.940, 2018.

, Chemical Engineering, vol.114, pp.99-110

. A. Bhosekar and M. Lerapetritou, Advances in surrogate ba.sed modeling, feasi-942 bility analysis, and optimization: A review, Computers & Chemical Engineering, vol.943, pp.250-267, 2018.

A. Mencarelli, P. Pagot, and . Duchêne, Surrogate-based modeling techniques with application to catalytic reforming and isomerization processes, Computers and Chemical Engineering
URL : https://hal.archives-ouvertes.fr/hal-02553492

. M. Bouhlel,

N. Bartoli, A. Otsmane, and J. Morlier, Improving kriging surrogates 946 of high-dimensional design models by Partial Least Squares dimension reduc-947 tion, Structural and Multidisciplinary Optimization, vol.53, issue.5, pp.935-952, 2016.

F. Boukoulava and C. A. Floudas, ARGONAlJI": Alg'ORithms for Global Optimization 949 of coNstrAined grey-box compTJTational problems, Optimization Letters, vol.11, issue.5, pp.950-895, 2017.

F. Boukoulava, M. M. Hasilll, and C. A. Floudas, Global optimization of general 952 constrained grey-box models: new method and its application to constrained 953 PDEs for pressure swing adsorption, Journal of Global Optimization, vol.67, issue.1-2, pp.3-42, 2017.

F. Boukoulava, R. Misener, and C. A. Floudas, Global optimization advances in 956 mixed-integer nonlinear programming, MINLP, and constrained derivative-free 957 optimization, CDRJ. EuropeanJournal of Operational Research, vol.255, issue.3, pp.701-727, 2016.

. G. Box, .. S. Hunter, and W. G. Hunter, Statistics for experimenters: Design, in-959 novation, and discovery, 2005.

D. S. Broomhead and D. Lowe, Radial basis functions, multi-variable functional 961 interpolation and adaptive networks. 4148. Royal Signal & Radar Establishment, 1988.

M. D. Buhmann, Radial basis functions, Acta Numerica, vol.9, pp.1-38, 2000.

J. A. Caballero and I. E. Grossmann, An algorithm for the use of surrogate models 964 in modular flowsheet optimization, AIChE Journal, vol.54, issue.10, pp.2633-2650, 2008.

S. Clarke, J. H. Griebsch, and T. W. Simpson, Analysis of support vector regres-966 sion for approximation of complex engineering analyses, Journal of Mechanical 967 Design, vol.127, issue.6, pp.1077-1087, 2004.

R. Coetzer,

L. M. Haines, The construction of D-and !-optimal designs for mix-969 ture experiments with linear constraints on the components, Chemometrics and 970 Intelligent Laboratory Systems, vol.171, pp.112-124, 2017.

A. R. Conn and S. Le-digabel, Use of quadratic models with mesh-adaptive di-972 rect search for constrained black box optimizatimL Optimization Methods and 973 Software, vol.28, pp.139-158, 2013.

J. A. Comell, Ex.periments with mixtures: Designs, models, and the analysis of 975 mixture data, 2002.

A. Cozad, N. V. Sahinidis, and D. C. Miller, Leaming surrogate models for 977 slmulation-based optimlzation, AIChE Journal, vol.60, issue.6, pp.2211-2227, 2014.

A. Cozad, N. Sahinidis, and D. C. Miller, A combined first-principles and data-979 driven approach to mode! building, Computers & Chemical Engineering, vol.73, pp.116-980, 2015.

P. Cunningham, Dimension reduction. UCD-CSl-2007-7, 2007.

S. E. Davis, S. Cremaschi, and M. R. Eden, Efficient surrogate mode! development: 984 Impact of sample slze and underlying model dimensions, Computer Aided 985 Chemical Engineering, vol.44, pp.979-984, 2018.

E. R. Van-dam, Two-dimensional minimax Latin hypercube designs, Discrete 987 Applied Mathematics, vol.158, issue.18, pp.3483-3493, 2008.

E. R. Van-dam, B. G. Husslage, D. Hertog, and . Melissen, Maximin Latin 989 hypercube design in two dimensions, Operations Research, vol.55, issue.1, pp.158-169, 2007.

, Design Expert

J. Eason and S. Cremaschl, Adaptive sequential sampling for surrogate mode! gen-992 eration with artificial neural networks, Computers & Chemical Engineering, vol.68, issue.4, pp.220-232, 2014.

!. Fahmi and S. Cremaschi, Process synthesis of biodiesel production plant us-995 ing artificial neural networks as the surrogate models, Computers & Chenûcal 996 Engineering, vol.46, pp.105-123, 2012.

I. K. Fodor, 998 cenoer for Applied Scientific Computing. Lawrence Uvermore National Labora-999 tory, 2002.

A. I. Forrester and A. J. Keane, Recent advances in surrogate--based optimization. 1001 Progress in, Aerospace Sciences, vol.45, issue.1-3, pp.50-79, 2009.

A. 1. Forrester, A. Sobester, and A. J. Keane, Engineering design via surrogate mod-1003 elling: A practical guide, 2008.

S. S. Garud, I. A. Karimi, and . M. Kraft, Design of computer experiments: A review, 2017.

, Computers & Chemical Engineering, vol.106, pp.71-95

S. Garud and I. A. Karimi,

M. Kraft, Smart sampling algorithm for surrogate 1007 mode! development, Computers & Chemical Engineering, vol.96, pp.103-114, 2017.

B. Gaspar, A. P. Teixeira, and C. Soares, Adaptive surrogate mode! with 1009 active refinement combining Kriging and a trust region method, Reliability En-1010 gineering & System Safety, vol.165, pp.277-291, 2017.

. Gjetvan, R. Prestvik, and . Holmen, Ca.talytic reforming, p.1012

, Basic Principles in Applied catalysis. Sprtnger, pp.125-158

P. Goos, B. Jones, and . Syafitri, !-optimal design of mixtUre experiments, Journal 1014 of the American Statistical Association, vol.111, issue.514, pp.899-911, 2016.

R. L. Hamy, Multiquadratic equations of topographY and other irregular sur-1016 faces, Journal of Geophysical Research, vol.76, issue.8, pp.1905-1915, 1971.

T. Hastie, R. Tibshirani, and M. Wainwright, Statistical leaming with sparsity: The 1018 lasso and generalizations, 2015.

C. A. Henao,

C. T. Maravelias, Surrogate-based process synthesis, Computer 1020 Aided Chemical Engineering, vol.28, pp.1129-1134, 2010.

C. A. Henao, C. Maravelias, and . T?, Surrogate-based superstructure optimization 1022 framework, AIChE Journal, vol.57, issue.5, pp.1216-1232, 2011.

P. J. Huber, Robust statistics, 1981.

B. G. Husslage, G. Rennen, . E. Van-dam, and D. Den-hertog, Space-filling Latin 1025 hypercube designs for computer experiments, Optimlzation and Engineering, vol.12, issue.4, pp.611-630, 2011.

. Ibm-!log and . Cplex,

O. Ivanciuc, Application of support vector machine in chemistry, Reviews in Computational Chemistry, p.23, 2007.

R. Jin, W. Chen, and T. W. Simpson, Comparative studies of metamodelling tech-1031 niques under multiple modelling criteria. Structural and Multidisciplinary Opti-1032 rnization, vol.23, pp.1-13, 2001.

J. M. Moore, L. M. Ylvisaker, and D. , Minimax and maximin distance de-1034 signs, Journal of Statitical Planning and lnference, vol.26, issue.2, pp.131-148, 1990.

D. R. Jones, M. Schonlau, and W. J. Welch, Efficient global optimization of expen-1036 sive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.

V. R. Joseph and . Y. Hung, Orthogonal-maximin latin hypercube designs, Statistica, vol.1038, issue.1, pp.171-186, 2008.

S. H. Kim and .. F. Boulroulava, Machine leaming-based surrogate modeling for 1040 data-driven optirnization: A comparison of subset selection for regression tech-1041 nique, 2019.

J. P. Kleijnen, Kriging metamodeling in simulation: A review. European Jour-1043 na! of, Operational Research, vol.192, issue.3, pp.707-716, 2009.

F. Krahmer and R. Ward, A unified frarnework for linear dimensionality reduction 1045 in Il, Results in Mathematics, vol.70, issue.1-2, pp.209-231, 2016.

D. G. Krige, A statistical approach to some basic mine valuation problems on 1047 the Witwatersrand, Journal of the Southern African lnstitute of Mining and Met-1048 allurgy, vol.52, pp.201-203, 1952.

M. P. Lapinski, S. Metro, P. R. Pujad6, and M. Moser, catalytic reforming in 1050 petroleum processing, Handbook of 1051 Petroleum Processing, pp.1-25, 2014.

L. Digabel and S. , Algorithm 909: NOMAD: Nonlinear optimization with the MADS 1053 algorithm, ACM Transactions on Mathematical Software, vol.37, issue.4, p.15, 2011.

H. Li, Y. Lianh, and Q. Xu, Support vector machines and itli applications in chem-1055 lstry. Chemometrlcs and Intelligent Laboratory Systems 95 {2, pp.188-198, 2009.

G. Matheron, Principles of Geostatistics, Economie Geology, vol.58, issue.8, 1963.

K. Mcbride and K. Sundmacher, Overview of surrogate modeling in chemical pro-1058 cess engineering, Chemie lngenieur Technik, vol.91, issue.3, pp.228-239, 2019.

B. A. Mccarl, . Meeraus, P. Van-der-eijk, M. Bussieck, S. Dirkse et al., McCarl Expanded GAMS User Guide, GAMS Release 24.6.GAMS Develop-1061 ment Corporation, 1060.

M. D. Mckay, R. J. Beckman, and W. J. Conover, A comparison of three methods for 1063 selecting values of input variables in the analysis of outpUt from a computer 1064 code, Technometrlcs, vol.21, issue.2, pp.239-245, 1979.

L. Mencarelli, Q. Chen, A. Pagot, !. Grossma, and I. E. Ui, A review on superstructure 1066 optimization approaches in process system engineering, IFP 1067 Energies nouvelles, 2019.

L. Mencarelli, P. Duchêne, and A. Pagot, Opt!mlzation approaches to the lnte--1070 grated system of catalytic reforming and isomerization processes in petroleum 1071 refinery, Tech ni cal Report. IFP Energies nouvelles, 2019.

A. Miller, Subset selection in regression Chapman & Hall/CRC, 2002.

M. D. Morris and T. J. Mitchell, Exploratory designs for computational experi-1075 ments, Journal of Statitical Planning and Inference, vol.43, issue.3, pp.381-402, 1995.

J. Moller and C. A. Shoemaker, Influence of ensemble surrogate models and sam-1077 pling strategy on the solution quality of algorithms for computationally expen-1078 sive black-box global optimization problems, Journal of Global Optimization, vol.60, issue.2, pp.123-144, 2015.

C. A. Nascimiento and R. Giudici,

R. Gua!tlani, Neural network based approach 1081 for optimlzation of industrial chemical processes, Computers & Chemical Engi-1082 neering, vol.24, pp.2303-2314, 2000.

J. Park,

I. W. Sandberg, Approximation and radial-basis-function networks, Neural Computation, vol.5, issue.2, pp.305-316, 1084.

M. Petelet, B. Iooss, O. Asserin, and A. Loredo, Latin hypercube sampling with 1086 inequality constraints, AStA Advances in Statistical Analysis, vol.9, issue.4, pp.325-339, 2010.

L. Pronzato, Minimax and maximin space-filling designs: Sorne properties and 1088 methods for construction, Journal de la Soci? Française de Statistique, vol.158, issue.1 }, pp.1089-1096, 2017.

A. Psaltis, . Sinoquet, and A. Pagot, Systematic optimlzation methodology for 1091 heat exchanger network and simultaneous process design, Computers & Chem-1092 ical Engineering, vol.95, pp.146-160, 2016.

N. V. Qj.ieipo, R. T. Hilfka, W. Shyy, T. Goel, R. Vaidyanathan et al., , p.1094, 2005.

, Surrogate--based analysis and optimization. Progress in, Aerospace Sciences, vol.41, issue.1, pp.1-28

M. R. Rahimpour, M. Jafari, and . Lranshavi, Progress in catalytic naphtha reform-1097 ing process: A review, Applied Energy, vol.109, pp.79-93

J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design a.nd analysis of corn-1099 puter experiments, Statistical Science, vol.4, issue.4, pp.409-485, 1989.

W. F. Smith, Experimental design for formulation, ASA-SIAM Series on Statis-1101 tics and Applied Probability, 15. SIAM, p.1102, 2005.

A. J. Smola and B. Sch!llkopf, A tutorial on support vector regression, Statistics 1103 and Computing, vol.14, issue.3, pp.199-222, 2004.

. Sorek, E. Gildin, F. Boukoulava, B. Beykal, and C. A. Floudas, Dimensionality 1105 reduction for production optimization using polynomial approximations. Corn-1106 putational, Geosciences, vol.21, issue.2, pp.247-266, 2017.

J. Straus and S. Skogestad, Use of latent variables to reduce the dimension of 1108 surrogate models, Computer Aided Chemical Engineering, vol.40, pp.445-450, 2017.

J. Straus and S. Skogestad, Variable reduction for surrogate modelling, Pro-1110 ceeding offoundations of Computer-Aided Process Operations, 1112.

J. Straus and S. Skogestad, Surrogate model generation using self-optimizing vari-1113 ables, Computers & Chemical Engineering, vol.119, pp.143-151, 2018.

A. Mencarelli, P. Pagot, and . Duchêne, Surrogate-based modeling techniques with application to catalytic reforming and isomerization prooesses, Computers and Chemical Engineering

. Straus and S. Sko?stad, A new tennination criterion for sampling for surrogate 1116 mode! ?neration using partial least squares regression, Computers S. Chemical 1117 Engineering, vol.121, pp.75-85, 2019.

. D. Sullivan, S. Metro, P. R. Pujadli, . Jones, and . Pujad, lsomerization in Petroleum Processing. 1119 ln: Treese, 2014.

C. Springer, , pp.1-15

M. Ti!wannalmi and N. V. Sahinidis, A polyhedral branch-and-cut approach to 1122 global optimization, Mathematical Programming, vol.103, issue.2, pp.225-249, 2005.

U. T. Turaga and R. Ramanathan, catalytic naphtha reforming: Revisiting its im-1124 portance in the modem refinery, Journal of Scientific and Industrial Research, vol.62, issue.10, pp.963-978, 2003.

G. Valavarasu and B. Sairam, Light naphtha isomerization prooess: A review, Petroleum Science and Technology, vol.31, issue.6, pp.580-595, 1127.

V. Vapnik, The nature of statistical leaming theory, 1995.

V. Vapnik, S. Golowich, and A. ]. Smola, Support vector method for function ap-1129 proximation, regression estimation, and signal prooessing, Advanoes in Neural Information Processing Systems, p.1131, 1997.

F. A. Viana, A tutorial on latin hypercube design of experiments. Q.lality and 1133, Reliilbility Engineering International, vol.3, issue.5, pp.1975-1985, 2016.

. K. Vu, . D'ambrosio, Y. Hamadi, and . Uberti, Surrogate-based methods for 1135 black-box optimization, International Transactions in Operational Research, vol.24, issue.3, pp.3-4, 2017.

Z. T. Wilson and N. V. Sahinidis, The A!J\MO approach to machine learning. Corn-1138 puoers s, Chemical Engineering, vol.106, pp.785-795, 2017.