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Engineering >> 2019, Volume 5, Issue 6 doi: 10.1016/j.eng.2019.10.003

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms

Department of Chemical Engineering, University College London, London WC1E 7JE, UK

Received:2018-11-04 Revised:2018-12-19 Accepted: 2019-03-06 Available online:2019-10-15

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Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many manufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of multiple objective functions related to process performance and cost is necessary. In this work, a multiobjective optimal experimental design framework is proposed to enhance the efficiency of online model-identification platforms. The proposed framework permits flexibility in the choice of trade-off experimental design solutions, which are calculated online—that is, during the execution of experiments. The application of this framework to improve the online identification of kinetic models in flow reactors is illustrated using a case study in which a kinetic model is identified for the esterification of benzoic acid and ethanol in a microreactor.


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[1]  Reitze A, Jürgensmeyer N, Lier S, Kohnke M, Riese J, Grünewald M. Roadmap for a smart factory: a modular, intelligent concept for the production of specialty chemicals. Angew Chem Int Ed Engl 2018;57(16):4242–7. link1

[2]  Zhong RY, Xu X, Klotz E, Newman ST. Intelligent manufacturing in the context of industry 4.0: a review. Engineering 2017;3(5):616–30. link1

[3]  Bédard AC, Adamo A, Aroh KC, Russell MG, Bedermann AA, Torosian J, et al. Reconfigurable system for automated optimization of diverse chemical reactions. Science 2018;361(6408):1220–5. link1

[4]  Schweidtmann AM, Clayton AD, Holmes N, Bradford E, Bourne RA, Lapkin AA. Machine learning meets continuous flow chemistry: automated optimization towards the Pareto front of multiple objectives. Chem Eng J 2018;352:277–82. link1

[5]  Cherkasov N, Bai Y, Expósito AJ, Rebrov EV. OpenFlowChem—a platform for quick, robust and flexible automation and self-optimisation of flow chemistry. React Chem Eng 2018;3(5):769–80. link1

[6]  Holmes N, Akien GR, Savage RJD, Stanetty C, Baxendale IR, Blacker AJ, et al. Online quantitative mass spectrometry for the rapid adaptive optimisation of automated flow reactors. React Chem Eng 2016;1(1):96–100. link1

[7]  Echtermeyer A, Amar Y, Zakrzewski J, Lapkin A. Self-optimisation and modelbased design of experiments for developing a C-H activation flow process. Beilstein J Org Chem 2017;13:150–63. link1

[8]  Schaber SD, Born SC, Jensen KF, Barton PI. Design, execution, and analysis of time-varying experiments for model discrimination and parameter estimation in microreactors. Org Process Res Dev 2014;18(11):1461–7. link1

[9]  Hone CA, Holmes N, Akien GR, Bourne RA, Muller FL. Rapid multistep kinetic model generation from transient flow data. React Chem Eng 2017;2(2):103–8. link1

[10]  Reizman BJ, Jensen KF. An automated continuous-flow platform for the estimation of multistep reaction kinetics. Org Process Res Dev 2012;16 (11):1770–82. link1

[11]  McMullen JP, Jensen KF. Rapid determination of reaction kinetics with an automated microfluidic system. Org Process Res Dev 2011;15(2):398–407. link1

[12]  Robbins H. Some aspects of the sequential design of experiments. Bull Am Math Soc 1952;58(5):527–35. link1

[13]  Burke EK, Kendall G, editors. Search methodologies: introductory tutorials in optimization and decision support techniques. Boston: Springer; 2014. link1

[14]  Marler RT, Arora JS. The weighted sum method for multi-objective optimization: new insights. Struct Multidiscipl Optim 2010;41(6):853–62. link1

[15]  Mavrotas G. Effective implementation of the e-constraint method in multiobjective mathematical programming problems. Appl Math Comput 2009;213 (2):455–65. link1

[16]  Zitzler E. Evolutionary algorithms for multiobjective optimization: methods and applications. Zurich: Swiss Federal Institute of Technology; 1999. link1

[17]  Hwang CL, Masud ASM. Multiple objective decision making—methods and applications. Berlin: Springer-Verlag; 1979. link1

[18]  Telen D, Logist F, Van Derlinden E, Tack I, Van Impe J. Optimal experiment design for dynamic bioprocesses: a multi-objective approach. Chem Eng Sci 2012;78:82–97. link1

[19]  Galvanin F, Cao E, Al-Rifai N, Gavriilidis A, Dua V. A joint model-based experimental design approach for the identification of kinetic models in continuous flow laboratory reactors. Comput Chem Eng 2016;95:202–15. link1

[20]  Maheshwari V, Rangaiah GP, Samavedham L. Multiobjective framework for model-based design of experiments to improve parameter precision and minimize parameter correlation. Ind Eng Chem Res 2013;52(24):8289–304. link1

[21]  Manesso E, Sridharan S, Gunawan R. Multi-objective optimization of experiments using curvature and fisher information matrix. Processes 2017;5(4):63. link1

[22]  Burger J, Asprion N, Blagov S, Böttcher R, Nowak U, Bortz M, et al. Multiobjective optimization and decision support in process engineering— implementation and application. Chem Ing Tech (Weinh) 2014;86 (7):1065–72. link1

[23]  Forte E, von Harbou E, Burger J, Asprion N, Bortz M. Optimal design of laboratory and pilot-plant experiments using multiobjective optimization. Chem Ing Tech (Weinh) 2017;89(5):645–54. link1

[24]  Nöh K, Niedenführ S, Beyß M, Wiechert W. A Pareto approach to resolve the conflict between information gain and experimental costs: multiple-criteria design of carbon labeling experiments. PLoS Comput Biol 2018;14(10): e1006533. link1

[25]  Quaglio M, Waldron C, Pankajakshan A, Cao E, Gavriilidis A, Fraga ES, et al. An online reparametrisation approach for robust parameter estimation in automated model identification platforms. Comput Chem Eng 2019;124:270–84. link1

[26]  Valera FE, Quaranta M, Moran A, Blacker J, Armstrong A, Cabral JT, et al. The flow’s the thing...or is it? Assessing the merits of homogeneous reactions in flask and flow. Angew Chem Int Ed Engl 2010;49(14):2478–85. link1

[27]  Mozharov S, Nordon A, Littlejohn D, Wiles C, Watts P, Dallin P, et al. Improved method for kinetic studies in microreactors using flow manipulation and noninvasive Raman spectrometry. J Am Chem Soc 2011;133 (10):3601–8. link1

[28]  McMullen JP, Stone MT, Buchwald SL, Jensen KF. An integrated microreactor system for self-optimization of a Heck reaction: from micro- to mesoscale flow systems. Angew Chem Int Ed Engl 2010;49(39):7076–80. link1

[29]  McMullen JP, Jensen KF. An automated microfluidic system for online optimization in chemical synthesis. Org Process Res Dev 2010;14(5):1169–76. link1

[30]  Audoly S, Bellu G, D’Angiò L, Saccomani MP, Cobelli C. Global identifiability of nonlinear models of biological systems. IEEE Trans Biomed Eng 2001;48 (1):55–65. link1

[31]  Montgomery DC. Design and analysis of experiments. 8th ed. Hoboken: John Wiley & Sons; 2012. link1

[32]  van Rossum G, Drake FL. Python language reference manual. Bristol: Network Theory Limited; 2003. link1

[33]  Cover TM, Thomas JA. Elements of information theory. 2nd ed. Hoboken: John Wiley & Sons; 2006. link1

[34]  Bard Y. Nonlinear parameter estimation. New York: Academic Press; 1974. link1

[35]  Franceschini G, Macchietto S. Model-based design of experiments for parameter precision: state of the art. Chem Eng Sci 2008;63(19):4846–72. link1

[36]  Pukelsheim F. Optimal design of experiments. New York: John Wiley & Sons; 1993. link1

[37]  Pipus G, Plazl I, Koloini T. Esterification of benzoic acid inmicrowave tubular flow reactor. Chem Eng J 2000;76(3):239–45. link1

[38]  Buzzi-Ferraris G, Manenti F. Kinetic models analysis. Chem Eng Sci 2009;64 (5):1061–74. link1

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