Uncertainty in chemical process systems engineering: a critical review

References

Allwright JC, Papavasiliou GC. On linear programming and robust model-predictive control using impulse-responses. Syst Control Lett 1992; 18: 159–164.10.1016/0167-6911(92)90020-SSearch in Google Scholar

Alqurashi A, Etemadi AH, Khodaei A. Treatment of uncertainty for next generation power systems: state-of-the-art in stochastic optimization. Electr Power Syst Res 2016; 141: 233–245.10.1016/j.epsr.2016.08.009Search in Google Scholar

Alvarado-Morales M, Hamid MKA, Sin G, Gernaey KV, Woodley JM, Gani R. A model-based methodology for simultaneous design and control of a bioethanol production process. Comput Chem Eng 2010; 34: 2043–2061.10.1016/S1570-7946(09)70260-3Search in Google Scholar

Anderson E. Monte Carlo methods and importance sampling: lecture notes. 1999. Retrieved from http://ib.berkeley.edu/labs/slatkin/eriq/classes/guest_lect/mc_lecture_notes.pdf.Search in Google Scholar

Askarian M, Escudero G, Graells M, Zarghami R, Jalali-Farahani F, Mostoufi N. Fault diagnosis of chemical processes with incomplete observations: a comparative study. Comput Chem Eng 2016; 84: 104–116.10.1016/j.compchemeng.2015.08.018Search in Google Scholar

Aster RC, Borchers B, Thurber CH. Chapter Eleven – Bayesian methods. In: Aster RC, Borchers B, Thurber CH, editors. Parameter estimation and inverse problems, 2nd ed., Boston: Academic Press, 2013: 253–280. Retrieved from http://www.sciencedirect.com/science/article/pii/B9780123850485000112.10.1016/B978-0-12-385048-5.00011-2Search in Google Scholar

Azadeh A, Vafa Arani H, Dashti H. A stochastic programming approach towards optimization of biofuel supply chain. Energy 2014; 76: 513–525.10.1016/j.energy.2014.08.048Search in Google Scholar

Balci O, Sargent RG. Validation of simulation models via simultaneous confidence intervals. Am J Math Manag Sci 1984; 4: 375–406.10.1080/01966324.1984.10737151Search in Google Scholar

Baldea M, Du J, Park J, Harjunkoski I. Integrated production scheduling and model predictive control of continuous processes. AIChE J 2015; 61: 4179–4190.10.1002/aic.14951Search in Google Scholar

Ben-Tal A, Nemirovski A. Robust convex optimization. Math Oper Res 1998; 23: 769–805.10.1287/moor.23.4.769Search in Google Scholar

Ben-Tal A, Nemirovski A. Robust solutions of uncertain linear programs. Oper Res Lett 1999; 25: 1–13.10.1016/S0167-6377(99)00016-4Search in Google Scholar

Ben-Tal A, Goryashko A, Guslitzer E, Nemirovski A. Adjustable robust solutions of uncertain linear programs. Math Program 2004; 99: 351–376.10.1007/s10107-003-0454-ySearch in Google Scholar

Bertsimas D, Sim M. Tractable approximations to robust conic optimization problems. Math Program 2006; 107: 5–36.10.1007/s10107-005-0677-1Search in Google Scholar

Bertsimas D, Gupta V, Kallus N. Data-driven robust optimization. Math Program 2018; 167: 235.10.1007/s10107-017-1125-8Search in Google Scholar

Biegler LT, Thierry DM. Large-scale optimization formulations and strategies for nonlinear model predictive control. IFAC-PapersOnLine 2018; 51: 1–15.10.1016/j.ifacol.2018.10.167Search in Google Scholar

Birge J. Stochastic programming computation and applications. INFORMS J Comput 1997; 9: 111–133.10.1287/ijoc.9.2.111Search in Google Scholar

Birge J, Louveaux F. Introduction to stochastic programming. Springer series, New York: Springer, 2011.10.1007/978-1-4614-0237-4Search in Google Scholar

Blum C, Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 2003; 35: 268–308.10.1145/937503.937505Search in Google Scholar

Bojarski AD, Guillén-Gosálbez G, Jiménez L, Espuña A, Puigjaner L. Life cycle assessment coupled with process simulation under uncertainty for reduced environmental impact: application to phosphoric acid production. Ind Eng Chem Res 2008; 47: 8286–8300.10.1021/ie8001149Search in Google Scholar

Brdyś M. Finding a feasible control for real process under uncertainty. In: Cea J, editor. Optimization techniques modeling and optimization in the service of man part 2: proceedings, 7th IFIP conference nice, September 8–12, 1975. Berlin, Heidelberg: Springer Berlin Heidelberg, 1976: 656–684.Search in Google Scholar

Buyukada M. Probabilistic uncertainty analysis based on Monte Carlo simulations of co-combustion of hazelnut hull and coal blends: data-driven modeling and response surface optimization. Biores Technol 2017; 225: 106–112.10.1016/j.biortech.2016.11.045Search in Google Scholar PubMed

Calfa BA, Grossmann IE, Agarwal A, Bury SJ, Wassick JM. Data-driven individual and joint chance-constrained optimization via kernel smoothing. Comput Chem Eng 2015; 78: 51–69.10.1016/j.compchemeng.2015.04.012Search in Google Scholar

Campbell T, How JP, editors. Bayesian nonparametric set construction for robust optimization. American control conference (ACC), 2015.10.1109/ACC.2015.7171991Search in Google Scholar

Campo P, Morari M, editors. Robust model predictive control. American control conference, 1987.Search in Google Scholar

Carey Janet M, Burgman Mark A. Linguistic uncertainty in qualitative risk analysis and how to minimize it. Ann NY Acad Sci 2008; 1128: 13–17.10.1196/annals.1399.003Search in Google Scholar

Chan LLT, Chen T, Chen J. PID based nonlinear processes control model uncertainty improvement by using Gaussian process model. J Process Contr 2016; 42: 77–89.10.1016/j.jprocont.2016.03.006Search in Google Scholar

Charnes A, Cooper WW. Chance-constrained programming. Manag Sci 1959; 6: 73–79.10.1287/mnsc.6.1.73Search in Google Scholar

Chen Z, Yan Z. Scenario tree reduction methods through clustering nodes. Comput Chem Eng 2018; 109: 96–111.10.1016/j.compchemeng.2017.10.017Search in Google Scholar

Chen M, Mehrotra S, Papp D. Scenario generation for stochastic optimization problems via the sparse grid method. Comput Opt Appl 2015; 62: 669–692.10.1007/s10589-015-9751-7Search in Google Scholar

Chen Y, Yuan Z, Chen B. Process optimization with consideration of uncertainties – an overview. Chin J Chem Eng 2018; 26: 1700–1706.10.1016/j.cjche.2017.09.010Search in Google Scholar

Chisci L, Rossiter JA, Zappa G. Systems with persistent disturbances: predictive control with restricted constraints. Automatica 2001; 37: 1019–1028.10.1016/S0005-1098(01)00051-6Search in Google Scholar

Corbetta M, Pirola C, Galli F, Manenti F. Robust optimization of the heteroextractive distillation column for the purification of water/acetic acid mixtures using p-xylene as entrainer. Comput Chem Eng 2016; 95: 161–169.10.1016/j.compchemeng.2016.09.015Search in Google Scholar

Dantzig GB. Linear programming under uncertainty. Manag Sci 1955; 1: 197–206.10.1007/978-1-4419-1642-6_1Search in Google Scholar

Dempster MAH, Fisher ML, Jansen L, Lageweg BJ, Lenstra JK, Rinnooy Kan AHG. Analytical evaluation of hierarchical planning systems. Oper Res 1981; 29: 707–716.10.1287/opre.29.4.707Search in Google Scholar

Dempster MAH, Fisher ML, Jansen L, Lageweg BJ, Lenstra JK, Rinnooy Kan , AHG. Analysis of heuristics for stochastic programming: results for hierarchical scheduling problems. Math Oper Res 1983; 8: 525–537.10.1287/moor.8.4.525Search in Google Scholar

Der Kiureghian A, Thoft-Christensen P, Manners W, editors. Classification and analysis of uncertainty in structural systems: reliability and optimization of structural systems’ 90. Berlin, Heidelberg: Springer, 1991.Search in Google Scholar

Dias LS, Ierapetritou MG. Integration of scheduling and control under uncertainties: review and challenges. Chem Eng Res Des 2016; 116: 98–113.10.1016/j.cherd.2016.10.047Search in Google Scholar

Diwekar U. Optimization under uncertainty in chemical engineering. Proc Indian Natl Sci Acad 2003; 69: 267–283.Search in Google Scholar

Diwekar UM, Kalagnanam JR. Efficient sampling technique for optimization under uncertainty. AIChE J 1997; 43: 440–447.10.1002/aic.690430217Search in Google Scholar

Du X, Chen W. Methodology for managing the effect of uncertainty in simulation-based design. AIAA J 2000; 38: 1471–1478.10.2514/2.1125Search in Google Scholar

Du Y, Duever TA, Budman H. Fault detection and diagnosis with parametric uncertainty using generalized polynomial chaos. Comput Chem Eng 2015; 76: 63–75.10.1016/j.compchemeng.2015.02.009Search in Google Scholar

Du Y, Budman H, Duever T. Robust self-tuning control under probabilistic uncertainty using generalized polynomial chaos models. IFAC-PapersOnLine 2017; 50: 3524–3529.10.1016/j.ifacol.2017.08.944Search in Google Scholar

El Ghaoui L, Lebret H. Robust solutions to least-squares problems with uncertain data. SIAM J Matrix Anal Appl 1997; 18: 1035–1064.10.1137/S0895479896298130Search in Google Scholar

Ely JC, Neal CR. Method of data reduction and uncertainty estimation for platinum-group element data using inductively coupled plasma-mass spectrometry. Geostandards Newslett 2007; 26: 31–39.10.1111/j.1751-908X.2002.tb00621.xSearch in Google Scholar

Ermoliev Y. Stochastic quasigradient methods and their application to system optimization. Stochastics 1983; 9: 1–36.10.1080/17442508308833246Search in Google Scholar

Fairley RE. Tutorial: static analysis and dynamic testing of computer software. Computer 1978; 11: 14–23.10.1109/C-M.1978.218132Search in Google Scholar

Fonseca RR, Sencio RR, Franco IC, Da Silva FV. An adaptive fuzzy feedforward-feedback control system applied to a saccharification process. Chem Product Process Model 2018; 13: 20180014.10.1515/cppm-2018-0014Search in Google Scholar

Freeman RA, Gaddy JL. Quantitative overdesign of chemical processes. AIChE J 1975; 21: 436–440.10.1002/aic.690210303Search in Google Scholar

Frey HC, Rubin ES, Diwekar UM. Modeling uncertainties in advanced technologies: application to a coal gasification system with hot-gas cleanup. Energy 1994; 19: 449–463.10.1016/0360-5442(94)90123-6Search in Google Scholar

Frutiger J, Cignitti S, Abildskov J, Woodley JM, Sin G. Computational chemical product design problems under property uncertainties. In: Espuña A, Graells M, Puigjaner L, editors. Computer aided chemical engineering: 27th European symposium on computer aided process engineering, Elsevier, 2017: 973–978.10.1016/B978-0-444-63965-3.50164-1Search in Google Scholar

Gao J, You F. Deciphering and handling uncertainty in shale gas supply chain design and optimization: novel modeling framework and computationally efficient solution algorithm. AIChE J 2015; 61: 3739–3755.10.1002/aic.15032Search in Google Scholar

Gargalo CL, Sin G. Sustainable process design under uncertainty analysis: targeting environmental indicators. In: Gernaey KV, Huusom JK, Gani R, editors. Computer aided chemical engineering: 12th international symposium on process systems engineering and 25th European symposium on computer aided process engineering, Elsevier, 2015: 2579–2584.10.1016/B978-0-444-63576-1.50124-2Search in Google Scholar

Gilbert JA, Meyers LA, Galvani AP, Townsend JP. Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy. Epidemics 2013; 6: 37–45.10.1016/j.epidem.2013.11.002Search in Google Scholar PubMed PubMed Central

Gong J, You F. Optimal processing network design under uncertainty for producing fuels and value-added bioproducts from microalgae: two-stage adaptive robust mixed integer fractional programming model and computationally efficient solution algorithm. AIChE J 2017; 63: 582–600.10.1002/aic.15370Search in Google Scholar

Gong J, You F. Resilient design and operations of process systems: nonlinear adaptive robust optimization model and algorithm for resilience analysis and enhancement. Comput Chem Eng 2018; 116: 231–252.10.1016/j.compchemeng.2017.11.002Search in Google Scholar

Gorissen BL, Yanıkoğlu İ, den Hertog D. A practical guide to robust optimization. Omega 2015; 53: 124–137.10.1016/j.omega.2014.12.006Search in Google Scholar

Griffith DW, Biegler LT, Patwardhan SC. Robustly stable adaptive horizon nonlinear model predictive control. J Process Contr 2018; 70: 109–122.10.1016/j.jprocont.2018.07.014Search in Google Scholar

Grossmann IE, Floudas CA. Active constraint strategy for flexibility analysis in chemical processes. Comput Chem Eng 1987; 11: 675–693.10.1016/0098-1354(87)87011-4Search in Google Scholar

Grossmann IE, Sargent RWH. Optimum design of chemical plants with uncertain parameters. AIChE J 1978; 24: 1021–1028.10.1002/aic.690240612Search in Google Scholar

Grossmann IE, Straub DA. Recent developments in the evaluation and optimization of flexible chemical processes. In: Reklaitis GV, Sunol AK, Rippin DWT, Hortaçsu Ö, editors. Batch processing systems engineering: fundamentals and applications for chemical engineering. Berlin, Heidelberg: Springer, 1996: 495–516.10.1007/978-3-642-60972-5_22Search in Google Scholar

Grossmann IE, Halemane KP, Swaney RE. Optimization strategies for flexible chemical processes. Comput Chem Eng 1983; 7: 439–462.10.1016/0098-1354(83)80022-2Search in Google Scholar

Grossmann IE, Apap RM, Calfa BA, García-Herreros P, Zhang Q. Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty. Comput Chem Eng 2016; 91: 3–14.10.1016/B978-0-444-63578-5.50001-3Search in Google Scholar

Gupta A, Maranas CD, McDonald CM. Mid-term supply chain planning under demand uncertainty: customer demand satisfaction and inventory management. Comput Chem Eng 2000; 24: 2613–2621.10.1016/S0098-1354(00)00617-7Search in Google Scholar

Halemane KP, Grossmann IE. Optimal process design under uncertainty. AIChE J 1983; 29: 425–433.10.1002/aic.690290312Search in Google Scholar

Halvorsen-Weare EE, Norstad I, Stålhane M, Nonås LM. A metaheuristic solution method for optimizing vessel fleet size and mix for maintenance operations at offshore wind farms under uncertainty. Energy Proc 2017; 137: 531–538.10.1016/j.egypro.2017.10.382Search in Google Scholar

Hanak DP, Manovic V. Economic feasibility of calcium looping under uncertainty. Appl Energy 2017; 208: 691–702.10.1016/j.apenergy.2017.09.078Search in Google Scholar

Hanssen G, Foss B, Teixeira A. Production optimization under uncertainty with constraint handling Kristian. IFAC-PapersOnLine 2015; 48: 62–67.10.1016/j.ifacol.2015.08.011Search in Google Scholar

Hsiung K-L, Kim S-J, Boyd S. Tractable approximate robust geometric programming. Optim Eng 2008; 9: 95–118.10.1007/s11081-007-9025-zSearch in Google Scholar

Hui C-W. Optimization of heat integration with variable stream data and non-linear process constraints. Comput Chem Eng 2014; 65: 81–88.10.1016/j.compchemeng.2014.03.010Search in Google Scholar

Jiao Y, Su H, Hou W, Liao Z. Optimization of refinery hydrogen network based on chance constrained programming. Chem Eng Res Des 2012; 90: 1553–1567.10.1016/j.cherd.2012.02.016Search in Google Scholar

Johnson DR, Lenzen AJ, Zapotocny TH, Schaack TK. Numerical uncertainties in simulation of reversible isentropic processes and entropy conservation: part II. J Clim 2002; 15: 1777–1804.10.1175/1520-0442(2002)015<1777:NUISOR>2.0.CO;2Search in Google Scholar

Kall P, Wallace S. Stochastic programming. Chichester: Wiley, 1994.Search in Google Scholar

Kannan R, Solai Manohar S, Senthil Kumaran M. Nominal features-based class specific learning model for fault diagnosis in industrial applications. Comput Ind Eng 2018; 116: 163–177.10.1016/j.cie.2017.12.027Search in Google Scholar

Karimipourfard D, Nemati N, Bahrani S, Rahimpour MR. Simultaneous increase of H-2 and gasoline production by optimizing thermally coupled methanol steam reforming with Fischer-Tropsch synthesis. Chem Product Process Model 2018; 13: 20170079.10.1515/cppm-2017-0079Search in Google Scholar

Kaveh A, Dadras A. A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 2017; 110: 69–84.10.1016/j.advengsoft.2017.03.014Search in Google Scholar

Kennedy Marc C, O’Hagan A. Bayesian calibration of computer models. J R Stat Soc Ser B (Stat Methodol) 2001; 63: 425–464.10.1111/1467-9868.00294Search in Google Scholar

Khayyam H, Naebe M, Bab-Hadiashar A, Jamshidi F, Li Q, Atkiss S, Buckmaster D, Fox B. Stochastic optimization models for energy management in carbonization process of carbon fiber production. Appl Energy 2015; 158: 643–655.10.1016/j.apenergy.2015.08.008Search in Google Scholar

Kothare MV, Balakrishnan V, Morari M. Robust constrained model predictive control using linear matrix inequalities. Automatica 1996; 32: 1361–1379.10.1109/ACC.1994.751775Search in Google Scholar

Krewer U, Röder F, Harinath E, Braatz RD, Bedürftig B, Findeisen R. Review – dynamic models of li-ion batteries for diagnosis and operation: a review and perspective. J Electrochem Soc 2018; 165: A3656–A3673.10.1149/2.1061814jesSearch in Google Scholar

Laguna M. A review of: “Robust Discrete Optimization and its Applications” P. Kouvelis and G. Yu Kluwer Academic Publishers, 1997, 356 pp., ISBN 0-7923-4291-7. IIE Trans 2000; 32: 280–281.10.1080/07408170008963903Search in Google Scholar

Lappas NH, Gounaris CE. Comparison of continuous-time models for adjustable robust optimization in process scheduling under uncertainty. In: Kravanja Z, Bogataj M, editors. Computer aided chemical engineering: 26th European symposium on computer aided process engineering, Elsevier, 2016: 391–396.10.1016/B978-0-444-63428-3.50070-9Search in Google Scholar

Lappas NH, Gounaris CE. Robust optimization for decision-making under endogenous uncertainty. Comput Chem Eng 2018; 111: 252–266.10.1016/j.compchemeng.2018.01.006Search in Google Scholar

Laskey KB. Model uncertainty: theory and practical implications. IEEE Trans Syst Man Cybern Pt A Syst Humans 1996; 26: 340–348.10.1109/3468.487959Search in Google Scholar

Lee SH, Chen W. A comparative study of uncertainty propagation methods for black-box-type problems. Struct Multidisc Optim 2008; 37: 239.10.1007/s00158-008-0234-7Search in Google Scholar

Lee YI, Kouvaritakis B. Robust receding horizon predictive control for systems with uncertain dynamics and input saturation. Automatica 2000; 36: 1497–1504.10.1016/S0005-1098(00)00064-9Search in Google Scholar

Lee JY, Jang J, Hong SM, Hwnag SS, Seo Y, Kim KU. A study on the reactive compatibilization of a PBT/LCP blend: catalyst effect. Int Polym Process 1997; 12: 19–25.10.3139/217.970019Search in Google Scholar

Leibowicz BD, Lanham CM, Brozynski MT, Vázquez-Canteli JR, Castejón NC, Nagy Z. Optimal decarbonization pathways for urban residential building energy services. Appl Energy 2018; 230: 1311–1325.10.1016/j.apenergy.2018.09.046Search in Google Scholar

Li Z, Floudas CA. Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: II. Sequential reduction. Comput Chem Eng 2016; 84: 599–610.10.1016/j.compchemeng.2015.05.010Search in Google Scholar

Li Z, Ierapetritou M. Process scheduling under uncertainty: review and challenges. Comput Chem Eng 2008; 32: 715–727.10.1016/j.compchemeng.2007.03.001Search in Google Scholar

Li P, Wendt M, Arellano-Garcia H, Wozny G. Optimal operation of distillation processes under uncertain inflows accumulated in a feed tank. AIChE J 2002; 48: 1198–1211.10.1002/aic.690480608Search in Google Scholar

Li P, Wendt M, Wozny G. Optimal operations planning under uncertainty by using probabilistic programming. Proc Found Comput Aided Process Oper 2003; 289–292. Retrieved from folk.ntnu.no/skoge/prost/proceedings/focapo_2003/pdffiles/papers/035.pdf.Search in Google Scholar

Li P, Hai R, Wozny G. Feasibility analysis and optimal design using a chance constrained programming framework. Proc Found Comput Aided Process Oper 2004; 555–559. Retrieved from folk.ntnu.no/skoge/prost/proceedings/focapd_2004/pdffiles/papers/091_19.pdf.Search in Google Scholar

Li P, Arellano-Garcia H, Wozny G. Chance constrained programming approach to process optimization under uncertainty. In: Marquardt W, Pantelides C, editors. Computer aided chemical engineering: 16th European symposium on computer aided process engineering and 9th international symposium on process systems engineering. Elsevier, 2006a: 1245–1250.Search in Google Scholar

Li J, Karimi IA, Srinivasan R. Robust scheduling of crude oil operations under demand and ship arrival uncertainty. In: AIChE Annual Meeting, November 2006b, San Francisco, CA.Search in Google Scholar

Li P, Arellano-Garcia H, Wozny G. Chance constrained programming approach to process optimization under uncertainty. Comput Chem Eng 2008; 32: 25–45.10.1016/S1570-7946(06)80217-8Search in Google Scholar

Li J, Misener R, Floudas CA. Scheduling of crude oil operations under demand uncertainty: a robust optimization framework coupled with global optimization. AIChE J 2012a; 58: 2373–2396.10.1002/aic.12772Search in Google Scholar

Li J, Verderame PM, Floudas CA. Operational planning of large-scale continuous processes: deterministic planning model and robust optimization for demand amount and due date uncertainty. Ind Eng Chem Res 2012b; 51: 4347–4362.10.1021/ie202670aSearch in Google Scholar

Liao W, Garg A, Gao L. Design of robust energy consumption model for manufacturing process considering uncertainties. J Cleaner Prod 2018; 172: 119–132.10.1016/j.jclepro.2017.10.155Search in Google Scholar

Lima C, Relvas S, Barbosa-Póvoa A. Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain. Comput Chem Eng 2018; 108: 314–336.10.1016/j.compchemeng.2017.09.012Search in Google Scholar

Liu C, Lee C, Chen H, Mehrotra S. Stochastic robust mathematical programming model for power system optimization. IEEE Trans Power Syst 2016; 31: 821–822.10.1109/TPWRS.2015.2394320Search in Google Scholar

Liu J, Meng X, Xu C, Zhang D, Jiang C. Forward and inverse structural uncertainty propagations under stochastic variables with arbitrary probability distributions. Comput Methods Appl Mech Eng 2018; 342: 287–320.10.1016/j.cma.2018.07.035Search in Google Scholar

Lucia S, Engell S, editors. Robust nonlinear model predictive control of a batch bioreactor using multi-stage stochastic programming. European control conference (ECC), 2013.10.23919/ECC.2013.6669521Search in Google Scholar

Ma Y, Chen X, Biegler LT. Monte-Carlo-simulation-based optimization for copolymerization processes with embedded chemical composition distribution. Comput Chem Eng 2018; 109: 261–275.10.1016/j.compchemeng.2017.11.018Search in Google Scholar

Mahmoudzadeh H, Purdie TG, Chan TCY. Constraint generation methods for robust optimization in radiation therapy. Oper Res Health Care 2016; 8: 85–90.10.1016/j.orhc.2015.03.003Search in Google Scholar

Maleki A, Khajeh MG, Rosen MA. Two heuristic approaches for the optimization of grid-connected hybrid solar–hydrogen systems to supply residential thermal and electrical loads. Sustain Cities Soc 2017; 34: 278–292.10.1016/j.scs.2017.06.023Search in Google Scholar

Malik RK, Hughes RR. Optimal design of flexible chemical processes. Comput Chem Eng 1979; 3: 473–485.10.1016/0098-1354(79)80078-2Search in Google Scholar

Mandur J, Budman H. A robust algorithm for run-to-run optimization of batch processes. IFAC Proc Vol 2013; 46: 541–546.10.3182/20131218-3-IN-2045.00155Search in Google Scholar

Mandur J, Budman H. Robust optimization of chemical processes using Bayesian description of parametric uncertainty. J Process Contr 2014; 24: 422–430.10.1016/j.jprocont.2013.10.004Search in Google Scholar

Martín M. Methodology for solar and wind energy chemical storage facilities design under uncertainty: methanol production from CO₂ and hydrogen. Comput Chem Eng 2016; 92: 43–54.10.1016/j.compchemeng.2016.05.001Search in Google Scholar

Mayne DQ, Kerrigan EC. Tube-based robust nonlinear model predictive control. In: Proceedings of the 7th IFAC symposium on nonlinear control systems, 2007: 110–115.10.1002/rnc.1758Search in Google Scholar

Mayne DQ, Seron MM, Raković SV. Robust model predictive control of constrained linear systems with bounded disturbances. Automatica 2005; 41: 219–224.10.1016/j.automatica.2004.08.019Search in Google Scholar

McLean K, Li X. Robust scenario formulations for strategic supply chain optimization under uncertainty. Ind Eng Chem Res 2013; 52: 5721–5734.10.1021/ie303114rSearch in Google Scholar

Melchers R. Structural reliability analysis and prediction, 2nd ed., Hoboken: Wiley, 1999.Search in Google Scholar

Miller BL, Wagner HM. Chance constrained programming with joint constraints. Oper Res 1965; 13: 930–945.10.1287/opre.13.6.930Search in Google Scholar

Morgan McLean N, Bowring J, Bowring SA. An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation. Geochem Geophys Geosyst 2011; 12. doi:10.1029/2010GC003478.10.1029/2010GC003478Search in Google Scholar

Morgan LE, Titman AC, Worthington DJ, Nelson BL, editors. Input uncertainty quantification for simulation models with piecewise-constant non-stationary Poisson arrival processes. Winter simulation conference (WSC), 2016.10.1109/WSC.2016.7822104Search in Google Scholar

Mulvey JM, editor. Solving robust optimization models in finance. IEEE/IAFE conference on computational intelligence for financial engineering (CIFEr), 1996.Search in Google Scholar

Nagy ZK, Braatz RD. Distributional uncertainty analysis using power series and polynomial chaos expansions. J Process Contr 2007; 17: 229–240.10.1016/j.jprocont.2006.10.008Search in Google Scholar

Navia D, Sarabia D, Gutiérrez G, Cubillos F, de Prada C. A comparison between two methods of stochastic optimization for a dynamic hydrogen consuming plant. Comput Chem Eng 2014; 63: 219–233.10.1016/j.compchemeng.2014.02.004Search in Google Scholar

Nazari-Heris M, Mohammadi-Ivatloo B, Gharehpetian GB. A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives. Renew Sustain Energy Rev 2018; 81(Part 2): 2128–2143.10.1016/j.rser.2017.06.024Search in Google Scholar

Ning C, You F. When robust statistics meets with robust optimization: data-driven batch process scheduling in the presence of outliers. In: Espuña A, Graells M, Puigjaner L, editors. Computer aided chemical engineering: 27th European symposium on computer aided process engineering. Elsevier, 2017: 2263–2268.10.1016/B978-0-444-63965-3.50379-2Search in Google Scholar

Núñez-Serna RI, Zamora JM. NLP model and stochastic multi-start optimization approach for heat exchanger networks. Appl Thermal Eng 2016; 94: 458–471.10.1016/j.applthermaleng.2015.10.128Search in Google Scholar

O’Hagan A. Probabilistic uncertainty specification: overview, elaboration techniques and their application to a mechanistic model of carbon flux. Environ Model Softw 2012; 36: 35–48.10.1016/j.envsoft.2011.03.003Search in Google Scholar

Ostrovsky GM, Volin YM, Golovashkin DV. Optimization problem of complex system under uncertainty. Comput Chem Eng 1998; 22: 1007–1015.10.1016/S0098-1354(97)00266-4Search in Google Scholar

Ostrovsky GM, Ziyatdinov NN, Lapteva TV. One-stage optimization problem with chance constraints. Chem Eng Sci 2010; 65: 2373–2381.10.1016/j.ces.2009.09.072Search in Google Scholar

Ostrovsky GM, Ziyatdinov NN, Lapteva TV, Zaitsev I. Two-stage optimization problem with chance constraints. Chem Eng Sci 2011; 66: 3815–3828.10.1016/j.ces.2011.05.001Search in Google Scholar

Ostrovsky GM, Ziyatdinov NN, Lapteva TV. Optimal design of chemical processes with chance constraints. Comput Chem Eng 2013; 59: 74–88.10.1016/B978-0-444-59519-5.50120-9Search in Google Scholar

Ouyang X, Ouyang J, Guo F. Development of a fuzzy analytical network process to evaluate alternatives on vitamin B12 adsorption from wastewater. Comput Chem Eng 2016; 95: 123–129.10.1016/j.compchemeng.2016.09.009Search in Google Scholar

Papadopoulos CE, Yeung H. Uncertainty estimation and Monte Carlo simulation method. Flow Meas Instrum 2001; 12: 291–298.10.1016/S0955-5986(01)00015-2Search in Google Scholar

Patil BP, Maia E, Ricardez-Sandoval LA. Integration of scheduling, design, and control of multiproduct chemical processes under uncertainty. AIChE J 2015; 61: 2456–2470.10.1002/aic.14833Search in Google Scholar

Paulson JA, Heirung TA, Braatz RD, Mesbah A, editors. Closed-loop active fault diagnosis for stochastic linear systems. Annual American control conference (ACC), 2018.10.23919/ACC.2018.8431031Search in Google Scholar

Pistikopoulos EN. Uncertainty in process design and operations. Comput Chem Eng 1995; 19: 553–563.10.1016/0098-1354(95)87094-6Search in Google Scholar

Puschke J, Zubov A, Kosek J, Mitsos A. Multi-model approach based on parametric sensitivities – a heuristic approximation for dynamic optimization of semi-batch processes with parametric uncertainties. Comput Chem Eng 2017; 98: 161–179.10.1016/j.compchemeng.2016.12.004Search in Google Scholar

Quaglia A, Sarup B, Sin G, Gani R. Synthesis and design of processing networks: stochastic formulation and solution. In: Bogle IDL, Fairweather M, editors. Computer aided chemical engineering: 22nd European symposium on computer aided process engineering. Elsevier, 2012: 467–471.10.1016/B978-0-444-59519-5.50094-0Search in Google Scholar

Quddus MA, Chowdhury S, Marufuzzaman M, Yu F, Bian L. A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network. Int J Prod Econ 2018; 195: 27–44.10.1016/j.ijpe.2017.09.019Search in Google Scholar

Rabiu Ado M, Greaves M, Rigby SP. Dynamic Simulation of the toe-to-heel air injection heavy oil recovery process. Energy Fuels 2017; 31: 1276–1284.10.1021/acs.energyfuels.6b02559Search in Google Scholar

Rahman S, Xu H. A univariate dimension-reduction method for multi-dimensional integration in stochastic mechanics. Probab Eng Mech 2004; 19: 393–408.10.1016/j.probengmech.2004.04.003Search in Google Scholar

Rahman S, Karanki DR, Epiney A, Wicaksono D, Zerkak O, Dang VN. Deterministic sampling for propagating epistemic and aleatory uncertainty in dynamic event tree analysis. Reliab Eng Syst Safe 2018; 175: 62–78.10.1016/j.ress.2018.03.009Search in Google Scholar

Rajaraman S, Hahn J, Mannan MS. Sensor fault diagnosis for nonlinear processes with parametric uncertainties. J Hazard Mater 2006; 130: 1–8.10.1016/j.jhazmat.2005.07.037Search in Google Scholar

Rasoulian S, Ricardez-Sandoval LA. Uncertainty analysis and robust optimization of multiscale process systems with application to epitaxial thin film growth. Chem Eng Sci 2014; 116: 590–600.10.1016/j.ces.2014.05.027Search in Google Scholar

Ratcliffe C, Ratcliffe B. Doubt-free uncertainty in measurement: an introduction for engineers and students. Switzerland: Springer, 2015.Search in Google Scholar

Refsgaard JC, van der Sluijs JP, Højberg AL, Vanrolleghem PA. Uncertainty in the environmental modelling process – a framework and guidance. Environ Model Softw 2007; 22: 1543–1556.10.1016/j.envsoft.2007.02.004Search in Google Scholar

Regan HM, Colyvan M, Burgman MA. A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 2002; 12: 618–628.10.1890/1051-0761(2002)012[0618:ATATOU]2.0.CO;2Search in Google Scholar

Reich S, Cotter C. Probabilistic forecasting and Bayesian data assimilation. Cambridge: Cambridge University Press, 2015. Retrieved from https://www.cambridge.org/core/books/probabilistic-forecasting-and-bayesian-data-assimilation/EE5E222D6EE56B4DC6522B22ABD8266A.10.1017/CBO9781107706804Search in Google Scholar

Rizwan M, Zaman M, Lee JH, Gani R. Optimal processing pathway selection for microalgae-based biorefinery under uncertainty. Comput Chem Eng 2015; 82: 362–373.10.1016/j.compchemeng.2015.08.002Search in Google Scholar

Ruediger P, Hagen H. Dealing with uncertainties in manufacturing process simulations. In: Applied mechanics and materials, physical modeling for virtual manufacturing systems and processes. Trans Tech Publications 2017: 226–233.10.4028/www.scientific.net/AMM.869.226Search in Google Scholar

Sahinidis NV. Optimization under uncertainty: state-of-the-art and opportunities. Comput Chem Eng 2004; 28: 971–983.10.1016/j.compchemeng.2003.09.017Search in Google Scholar

Sánchez-Sánchez K, Ricardez-Sandoval L. Simultaneous process synthesis and control design under uncertainty: a worst-case performance approach. AIChE J 2013; 59: 2497–2514.10.1002/aic.14040Search in Google Scholar

Santander O, Elkamel A, Budman H. Economic model predictive control of chemical processes with parameter uncertainty. Comput Chem Eng 2016; 95: 10–20.10.1016/j.compchemeng.2016.08.010Search in Google Scholar

Sargent RG, editor. Verification and validation of simulation models. Winter simulation conference, 2007.10.1109/WSC.2007.4419595Search in Google Scholar

Sathishkumar K, Kirubakaran V, Radhakrishnan TK. Real time modeling and control of three tank hybrid system. Chem Product Process Model 2018; 13: 20170016.10.1515/cppm-2017-0016Search in Google Scholar

Savage LJ. The foundations of statistics, Dover ed., Mineola: Dover Publication, 1972.Search in Google Scholar

Schuurmans J, Rossiter JA. Robust predictive control using tight sets of predicted states. IEE Proc Control Theory Appl 2000; 147: 13–18.10.1049/ip-cta:20000139Search in Google Scholar

Schwarm AT, Nikolaou M. Chance-constrained model predictive control. AIChE J 1999; 45: 1743–1752.10.1002/aic.690450811Search in Google Scholar

Seepana MM, Samudrala S, Suresh PV, Vooradi R. Unit cell modelling and simulation of all vanadium redox flow battery. Chem Product Process Model 2018; 13: 20170014.10.1515/cppm-2017-0014Search in Google Scholar

Séguin S, Fleten S-E, Côté P, Pichler A, Audet C. Stochastic short-term hydropower planning with inflow scenario trees. Eur J Oper Res 2017; 259: 1156–1168.10.1016/j.ejor.2016.11.028Search in Google Scholar

Separovic L, Saviano AM, Lourenço FR. Using measurement uncertainty to assess the fitness for purpose of an HPLC analytical method in the pharmaceutical industry. Measurement 2018; 119: 41–45.10.1016/j.measurement.2018.01.048Search in Google Scholar

Shabanzadeh M, Sheikh-El-Eslami M-K, Haghifam M-R. The design of a risk-hedging tool for virtual power plants via robust optimization approach. Appl Energy 2015; 155: 766–777.10.1016/j.apenergy.2015.06.059Search in Google Scholar

Shacham M, Brauner N. Considering parameter uncertainties in the design of safe processes. In: Eden MR, Siirola JD, Towler GP, editors. Computer aided chemical engineering: proceedings of the 8th international conference on foundations of computer-aided process design. Elsevier, 2014: 429–434.10.1016/B978-0-444-63433-7.50056-0Search in Google Scholar

Shapiro A, Wardi Y. Convergence analysis of gradient descent stochastic algorithms. J Opt Theory Appl 1996; 91: 439–454.10.1007/BF02190104Search in Google Scholar

Sharifian S, Harasek M, Haddadi B. Simulation of membrane gas separation process using Aspen Plus® V8. 6. Chem Product Process Model 2016; 11: 67–72.10.1515/cppm-2015-0067Search in Google Scholar

Sharifian S, Madadkhani M, Rahimi M, Mir M, Baghban A. QSPR based ANFIS model for predicting standard molar chemical exergy of organic materials. Petrol Sci Technol 2019; 37: 2174–2181.10.1080/10916466.2018.1496100Search in Google Scholar

Sharifzadeh M. Integration of process design and control: a review. Chem Eng Res Des 2013; 91: 2515–2549.10.1016/j.cherd.2013.05.007Search in Google Scholar

Shindin E, Boni O, Masin M. Robust optimization of system design. Proc Comput Sci 2014; 28: 489–496.10.1016/j.procs.2014.03.060Search in Google Scholar

Simic V, Dabic-Ostojic S. Interval-parameter chance-constrained programming model for uncertainty-based decision making in tire retreading industry. J Cleaner Prod 2017; 167: 1490–1498.10.1016/j.jclepro.2016.10.122Search in Google Scholar

Skinner DJC, Rocks SA, Pollard SJT, Drew GH. Identifying uncertainty in environmental risk assessments: the development of a novel typology and its implications for risk characterization. Human Ecol Risk Assess Int J 2014; 20: 607–640.10.1080/10807039.2013.779899Search in Google Scholar

Sørensen KT. Asasim: adaptive sampling for electromagnetic simulations. Comput Phys Commun 2019; 236: 268–273.10.1016/j.cpc.2018.10.017Search in Google Scholar

Stefanou G. The stochastic finite element method: past, present and future. Comput Methods Appl Mech Eng 2009; 198: 1031–1051.10.1016/j.cma.2008.11.007Search in Google Scholar

Steimel J, Engell S. Conceptual design and optimization of chemical processes under uncertainty by two-stage programming. Comput Chem Eng 2015; 81: 200–217.10.1016/j.compchemeng.2015.05.016Search in Google Scholar

Steimel J, Harrmann M, Schembecker G, Engell S. Model-based conceptual design and optimization tool support for the early stage development of chemical processes under uncertainty. Comput Chem Eng 2013; 59: 63–73.10.1016/j.compchemeng.2013.06.017Search in Google Scholar

Strano M. Optimization under uncertainty of sheet-metal-forming processes by the finite element method. Proc Inst Mech Eng Pt B J Eng Manuf 2006; 220: 1305–1315.10.1243/09544054JEM480Search in Google Scholar

Subramanian K, Maravelias CT, Rawlings JB. A state-space model for chemical production scheduling. Comput Chem Eng 2012; 47: 97–110.10.1016/j.compchemeng.2012.06.025Search in Google Scholar

Suchartsunthorn N, Siemanond K. Two-stage stochastic optimization of carbon dioxide supply chain and utilization model through carbon dioxide capturing process. In: Espuña A, Graells M, Puigjaner L, editors. Computer aided chemical engineering: 27th European symposium on computer aided process engineering, Elsevier, 2017: 727–732.Search in Google Scholar

Tang Y, Al-Shaer E, Joshi K. Reasoning under uncertainty for overlay fault diagnosis. IEEE Trans Netw Serv Manag 2012; 9: 34–47.10.1109/TNSM.2011.010312.110126Search in Google Scholar

Tarhan B, Grossmann IE. A multistage stochastic programming approach with strategies for uncertainty reduction in the synthesis of process networks with uncertain yields. Comput Chem Eng 2008; 32: 766–788.10.1016/S1570-7946(06)80331-7Search in Google Scholar

Teow L, Loe K. An uncertainty framework for classification, National University of Singapore, 2000.Search in Google Scholar

Tong K, Feng Y, Rong G. Planning under demand and yield uncertainties in an oil supply chain. Ind Eng Chem Res 2012; 51: 814–834.10.1021/ie200194wSearch in Google Scholar

Tong K, You F, Rong G. Robust design and operations of hydrocarbon biofuel supply chain integrating with existing petroleum refineries considering unit cost objective. Comput Chem Eng 2014; 68: 128–139.10.1016/j.compchemeng.2014.05.003Search in Google Scholar

Torres Zúñiga I, Vande Wouwer A. Optimization of VARICOL SMB processes using hybrid modeling and nonlinear programming. Comput Chem Eng 2014; 71: 1–10.10.1016/j.compchemeng.2014.07.009Search in Google Scholar

Tripathy R, Bilionis I, Gonzalez M. Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation. J Comput Phys 2016; 321: 191–223.10.1016/j.jcp.2016.05.039Search in Google Scholar

ur Rehman MH, Liew CS, Abbas A, Jayaraman PP, Wah TY, Khan SU. Big data reduction methods: a survey. Data Sci Eng 2016; 1: 265–284.10.1007/s41019-016-0022-0Search in Google Scholar

Uusitalo L, Lehikoinen A, Helle I, Myrberg K. An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ Model Softw 2015; 63: 24–31.10.1016/j.envsoft.2014.09.017Search in Google Scholar

Vassiliadis CG, Pistikopoulos EN. On the interactions of chemical-process design under uncertainty and maintenance-optimisation. Anaheim: IEEE, 1998: 302–307.10.1109/RAMS.1998.653796Search in Google Scholar

Vassiliadis CG, Pistikopoulos EN. Process design and maintenance optimization under uncertainty. Comput Chem Eng 1999; 23: S555–S558.10.1016/S0098-1354(99)80137-9Search in Google Scholar

Vega P, Lamanna de Rocco R, Revollar S, Francisco M. Integrated design and control of chemical processes – part I: revision and classification. Comput Chem Eng 2014; 71: 602–617.10.1016/j.compchemeng.2014.05.010Search in Google Scholar

Verderame PM, Floudas CA. Integration of operational planning and medium-term scheduling for large-scale industrial batch plants under demand and processing time uncertainty. Ind Eng Chem Res 2010; 49: 4948–4965.10.1021/ie901973eSearch in Google Scholar

Verderame PM, Floudas CA. Multisite planning under demand and transportation time uncertainty: robust optimization and conditional value-at-risk frameworks. Ind Eng Chem Res 2011; 50: 4959–4982.10.1021/ie101401kSearch in Google Scholar

Verderame PM, Elia JA, Li J, Floudas CA. Planning and scheduling under uncertainty: a review across multiple sectors. Ind Eng Chem Res 2010; 49: 3993–4017.10.1021/ie902009kSearch in Google Scholar

Vásquez VR, Whiting WB, Meerschaert MM. Confidence interval estimation under the presence of non-Gaussian random errors: applications to uncertainty analysis of chemical processes and simulation. Comput Chem Eng 2010; 34: 298–305.10.1016/j.compchemeng.2009.11.004Search in Google Scholar

Vázquez-Ojeda M, Segovia-Hernández JG, Hernández S, Hernández-Aguirre A, Kiss AA. Design and optimization of an ethanol dehydration process using stochastic methods. Sep Purif Technol 2013; 105: 90–97.10.1016/j.seppur.2012.12.002Search in Google Scholar

Walker WE, Harremoës P, Rotmans J, van der Sluijs JP, van Asselt MBA, Janssen P, Krayer von Krauss MP. Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integr Assess 2003; 4: 5–17.10.1076/iaij.4.1.5.16466Search in Google Scholar

Wan Y, Braatz R, editors. Mixed polynomial chaos and worst-case synthesis approach to robust observer based linear quadratic regulation. Annual American control conference (ACC), 2018.10.23919/ACC.2018.8431698Search in Google Scholar

Wang B, Sharifian S, Surendar A, Ilyashenko LK, Yaghoubi E. Analytical simulation of the cloud combustion of corn starch microparticles. Chem Eng Technol 2018. doi:10.1002/ceat.201800350.10.1002/ceat.201800350Search in Google Scholar

Wechsung A, Oldenburg J, Yu J, Polt A. Supporting chemical process design under uncertainty. Braz J Chem Eng 2010; 27: 451–460.10.1016/S1570-7946(09)70377-3Search in Google Scholar

Wen X-l, Zhao Y-b, Wang D-x, Pan J. Adaptive Monte Carlo and GUM methods for the evaluation of measurement uncertainty of cylindricity error. Precis Eng 2013; 37: 856–864.10.1016/j.precisioneng.2013.05.002Search in Google Scholar

Wendt M, Li P, Wozny G. Nonlinear chance-constrained process optimization under uncertainty. Ind Eng Chem Res 2002; 41: 3621–3629.10.1021/ie010649sSearch in Google Scholar

Whiting WB. Effects of uncertainties in thermodynamic data and models on process calculations. J Chem Eng Data 1996; 41: 935–941.10.1021/je9600764Search in Google Scholar

Witkovský V, Wimmer G, Ďurišová Z, Ďuriš S, Palenčár R, editors. Brief overview of methods for measurement uncertainty analysis: GUM uncertainty framework, Monte Carlo method, characteristic function approach. 11th international conference on measurement, 2017.10.23919/MEASUREMENT.2017.7983530Search in Google Scholar

Wittmann-Hohlbein M, Pistikopoulos EN. Proactive scheduling of batch processes by a combined robust optimization and multiparametric programming approach. AIChE J 2013; 59: 4184–4211.10.1002/aic.14140Search in Google Scholar

Wu S, Jin Q, Zhang R, Zhang J, Gao F. Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties. ISA Trans 2017; 69: 273–280.10.1016/j.isatra.2017.04.006Search in Google Scholar PubMed

Wu X, Kozlowski T, Meidani H, Shirvan K. Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, part 1: theory. Nucl Eng Des 2018; 335: 339–355.10.1016/j.nucengdes.2018.06.004Search in Google Scholar

Xia Z, Zhao J. Steady-state optimization of chemical processes with guaranteed robust stability and controllability under parametric uncertainty and disturbances. Comput Chem Eng 2015; 77: 116–134.10.1016/j.compchemeng.2015.04.001Search in Google Scholar

Xu B, Zhong P-A, Zambon RC, Zhao Y, Yeh WW-G. Scenario tree reduction in stochastic programming with recourse for hydropower operations. Water Resour Res 2015; 51: 6359–6380.10.1002/2014WR016828Search in Google Scholar

Xu S, Jiang X, Huang J, Yang S, Wang X. Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty. Mech Syst Signal Process 2016; 80: 1–18.10.1016/j.ymssp.2016.04.031Search in Google Scholar

Ye Y, Li J, Li Z, Tang Q, Xiao X, Floudas CA. Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty. Comput Chem Eng 2014; 66: 165–185.10.1016/j.compchemeng.2014.02.028Search in Google Scholar

Yuan J, Allegri G, Scarpa F, Patsias S, Rajasekaran R. A novel hybrid Neumann expansion method for stochastic analysis of mistuned bladed discs. Mech Syst Signal Process 2016; 72–73: 241–253.10.1016/j.ymssp.2015.11.011Search in Google Scholar

Yue D, You F. Optimal supply chain design and operations under multi-scale uncertainties: nested stochastic robust optimization modeling framework and solution algorithm. AIChE J 2016; 62: 3041–3055.10.1002/aic.15255Search in Google Scholar

Zhao C, Guan Y. Unified stochastic and robust unit commitment. IEEE Trans Power Syst 2013; 28: 3353–3361.10.1109/TPWRS.2013.2251916Search in Google Scholar

Zhao S, Huang B, Luan X, Yin Y, Liu F. Robust fault detection and diagnosis for multiple-model systems with uncertainties. IFAC-Papers OnLine 2015; 48: 137–142.10.1016/j.ifacol.2015.09.517Search in Google Scholar

Zhou T, Zhou Y, Sundmacher K. A hybrid stochastic–deterministic optimization approach for integrated solvent and process design. Chem Eng Sci 2017; 159: 207–216.10.1016/j.ces.2016.03.011Search in Google Scholar

Zhuge J, Ierapetritou MG. An integrated framework for scheduling and control using fast model predictive control. AIChE J 2015; 61: 3304–3319.10.1002/aic.14914Search in Google Scholar