Synergistic optimization framework for the process synthesis and design of biorefineries

Abstract

The conceptual process design of novel bioprocesses in biorefinery setups is an important task, which remains yet challenging due to several limitations. We propose a novel framework incorporating superstructure optimization and simulation-based optimization synergistically. In this context, several approaches for superstructure optimization based on different surrogate models can be deployed. By means of a case study, the framework is introduced and validated, and the different superstructure optimization approaches are benchmarked. The results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues, there remains a potential issue regarding their validation. The development of appropriate surrogate models, comprising the selection of surrogate type, sampling type, and size for training and cross-validation sets, are essential factors. Regarding this aspect, satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization problem. Furthermore, the framework’s synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulation-based optimization to consolidate the process design under uncertainty offer an alternative and promising approach. These findings invite for a critical assessment of surrogate-based optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.

Graphical abstract

Keywords

biotechnology / surrogate modelling / superstructure optimization / simulation-based optimization / process design

References

[1] United Nations. Transforming our world: the 2030 agenda for sustainable development, 2015

[2] Ubando A T, Felix C B, Chen W H. Biorefineries in circular bioeconomy: a comprehensive review. Bioresource Technology, 2020, 299: 122585

[3] Straathof A J J, Wahl S A, Benjamin K R, Takors R, Wierckx N, Noorman H J. Grand research challenges for sustainable industrial biotechnology. Trends in Biotechnology, 2019, 37(10): 1042–1050

[4] Hillson N, Caddick M, Cai Y, Carrasco J A, Chang M W, Curach N C, Bell D J, Feuvre R L, Friedman D C, Fu X, . Building a global alliance of biofoundries. Nature Communications, 2019, 10(1): 1038–1041

[5] Hassan S S, Williams G A, Jaiswal A K. Lignocellulosic biorefineries in Europe: current state and prospects. Trends in Biotechnology, 2019, 37(3): 231–234

[6] Hassan S S, Williams G A, Jaiswal A K. Moving towards the second generation of lignocellulosic biorefineries in the EU: drivers, challenges, and opportunities. Renewable & Sustainable Energy Reviews, 2019, 101: 590–599

[7] Moncada B J, Aristizábal M V, Cardona A C A. Design strategies for sustainable biorefineries. Biochemical Engineering Journal, 2016, 116: 122–134

[8] Chaturvedi T, Torres A I, Stephanopoulos G, Thomsen M H, Schmidt J E. Developing process designs for biorefineries-definitions, categories, and unit operations. Energies, 2020, 13(6): 1493

[9] Kokossis A C, Yang A. On the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries. Computers & Chemical Engineering, 2010, 34(9): 1397–1405

[10] Chemmangattuvalappil N G, Ng D K S, Ng L Y, Ooi J, Chong J W, Eden M R. A review of process systems engineering (PSE) tools for the design of ionic liquids and integrated biorefineries. Processes (Basel, Switzerland), 2020, 8(12): 1–29

[11] Tey S Y, Wong S S, Lam J A, Ong N Q X, Foo D C Y, Ng D K S. Extended hierarchical decomposition approach for the synthesis of biorefinery processes. Chemical Engineering Research & Design, 2021, 166: 40–54

[12] Clauser N M, Felissia F E, Area M C, Vallejos M E. A framework for the design and analysis of integrated multi-product biorefineries from agricultural and forestry wastes. Renewable & Sustainable Energy Reviews, 2021, 139: 110687

[13] Mountraki A D, Benjelloun-Mlayah B, Kokossis A C. A surrogate modeling approach for the development of biorefineries. Frontiers in Chemical Engineering, 2020, 2: 12

[14] Pyrgakis K A, Kokossis A C. A total site synthesis approach for the selection, integration and planning of multiple-feedstock biorefineries. Computers & Chemical Engineering, 2019, 122: 326–355

[15] Meramo-Hurtado S I, González-Delgado Á D. Biorefinery synthesis and design using sustainability parameters and hierarchical/3D multi-objective optimization. Journal of Cleaner Production, 2019, 240: 118134

[16] Galanopoulos C, Giuliano A, Barletta D, Zondervan E. An integrated methodology for the economic and environmental assessment of a biorefinery supply chain. Chemical Engineering Research & Design, 2020, 160: 199–215

[17] Ulonska K, König A, Klatt M, Mitsos A, Viell J. Optimization of multiproduct biorefinery processes under consideration of biomass supply chain management and market developments. Industrial & Engineering Chemistry Research, 2018, 57(20): 6980–6991

[18] Aristizábal-Marulanda V, Cardona Alzate C A. Methods for designing and assessing biorefineries. Biofuels, Bioproducts & Biorefining, 2019, 13(3): 789–808

[19] Meramo-Hurtado S I, González-Delgado Á D. Process synthesis, analysis, and optimization methodologies toward chemical process sustainability. Industrial & Engineering Chemistry Research, 2021, 60(11): 4193–4217

[20] Darkwah K, Knutson B L, Seay J R. A Perspective on challenges and prospects for applying process systems engineering tools to fermentation-based biorefineries. ACS Sustainable Chemistry & Engineering, 2018, 6(3): 2829–2844

[21] Biegler L T, Grossmann I E, Westerberg A W. Systematic Methods for Chemical Process design. 1st ed. London: Pearson, 1997

[22] Yuan Z, Eden M R. Superstructure optimization of integrated fast pyrolysis-gasification for production of liquid fuels and propylene. AIChE Journal. American Institute of Chemical Engineers, 2016, 62(9): 3155–3176

[23] Chen Q, Grossmann I E. Recent developments and challenges in optimization-based process synthesis. Annual Review of Chemical and Biomolecular Engineering, 2017, 8(1): 249–283

[24] Grossmann I E, Apap R M, Calfa B A, García-Herreros P, Zhang Q. Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty. Computers & Chemical Engineering, 2016, 91: 3–14

[25] Koutinas M, Kiparissides A, Pistikopoulos E N, Mantalaris A. Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology. Computational and Structural Biotechnology Journal, 2012, 3(4): e201210022

[26] Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: a review. Computers & Chemical Engineering, 2018, 108: 250–267

[27] Al R, Behera C R, Gernaey K V, Sin G. Stochastic simulation-based superstructure optimization framework for process synthesis and design under uncertainty. Computers & Chemical Engineering, 2020, 143: 107118

[28] Wang Z, Ierapetritou M. Constrained optimization of black-box stochastic systems using a novel feasibility enhanced kriging-based method. Computers & Chemical Engineering, 2018, 118: 210–223

[29] McBride K, Sundmacher K. Overview of surrogate modeling in chemical process engineering. Chemieingenieurtechnik (Weinheim), 2019, 91(3): 228–239

[30] Friedman M. Multivariate adaptive regression splines. Annals of Statistics, 1991, 19(1): 1–67

[31] Sudret B. Global sensitivity analysis using polynomial chaos expansions. Reliability Engineering & System Safety, 2008, 93(7): 964–979

[32] Williams B A, Cremaschi S. Surrogate model selection for design space approximation and surrogatebased optimization. Computer-Aided Chemical Engineering, 2019, 47: 353–358

[33] Janssen H. Monte-Carlo based uncertainty analysis: sampling efficiency and sampling convergence. Reliability Engineering & System Safety, 2013, 109: 123–132

[34] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009

[35] Wilson Z T, Sahinidis N V. The ALAMO approach to machine learning. Computers & Chemical Engineering, 2017, 106: 785–795

[36] Cozad A, Sahinidis N V, Miller D C. Learning surrogate models for simulation-based optimization. AIChE Journal. American Institute of Chemical Engineers, 2014, 60(6): 2211–2227

[37] Eslick J C, Ng B, Gao Q, Tong C H, Sahinidis N V, Miller D C. A framework for optimization and quantification of uncertainty and sensitivity for developing carbon capture systems. Energy Procedia, 2014, 63: 1055–1063

[38] Miller D C, Siirola J D, Agarwal D, Burgard A P, Lee A, Eslick J C, Nicholson B, Laird C, Biegler L T, Bhattacharyya D, Sahinidis N V, Grossmann I E, Gounaris C E, Gunter D. Next generation multi-scale process systems engineering framework. Computer-Aided Chemical Engineering, 2018, 44: 2209–2214

[39] Delaunay B. On the empty sphere. Journal of Physics and Radium. 1934, 12(7): 793–800 (in French)

[40] Žalik B. An efficient sweep-line Delaunay triangulation algorithm. CAD Computer Aided Design, 2005, 37(10): 1027–1038

[41] Barber C B, Dobkin D P, Huhdanpaa H. The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 1996, 22(4): 469–483

[42] Al R, Behera C R, Zubov A, Gernaey K V, Sin G. Meta-modeling based efficient global sensitivity analysis for wastewater treatment plants—an application to the BSM2 model. Computers & Chemical Engineering, 2019, 127: 233–246

[43] Rasmussen C E. Gaussian processes in machine learning. In: Bousquet O, von Luxburg U, Rätsch G, eds. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Berlin, Heidelberg: Springer Verlag, 2004, 63–71

[44] Boukouvala F, Ierapetritou M G. Feasibility analysis of black-box processes using an adaptive sampling kriging-based method. Computers & Chemical Engineering, 2012, 36(1): 358–368

[45] Caballero J A, Grossmann I E. An algorithm for the use of surrogate models in modular flowsheet optimization. AIChE Journal. American Institute of Chemical Engineers, 2008, 54(10): 2633–2650

[46] Davis E, Ierapetritou M. A kriging based method for the solution of mixed-integer nonlinear programs containing black-box functions. Journal of Global Optimization, 2009, 43(2-3): 191–205

[47] Hwangbo S, Al R, Sin G. An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations. Computers & Chemical Engineering, 2020, 143: 107071

[48] Schweidtmann A M, Mitsos A. Deterministic global optimization with artificial neural networks embedded. Journal of Optimization Theory and Applications, 2019, 180(3): 925–948

[49] Henao C A, Maravelias C T. Surrogate-based superstructure optimization framework. AIChE Journal. American Institute of Chemical Engineers, 2011, 57(5): 1216–1232

[50] Yeomans H, Grossmann I E. A systematic modeling framework of superstructure optimization in process synthesis. Computers & Chemical Engineering, 1999, 23(6): 709–731

[51] Mencarelli L, Chen Q, Pagot A, Grossmann I E. A review on superstructure optimization approaches in process system engineering. Computers & Chemical Engineering, 2020, 136: 106808

[52] Huster W R, Schweidtmann A M, Lüthje J T, Mitsos A. Deterministic global superstructure-based optimization of an organic Rankine cycle. Computers & Chemical Engineering, 2020, 141: 106996

[53] Jones M, Forero-Hernandez H, Zubov A, Sarup B, Sin G. Superstructure optimization of oleochemical processes with surrogate models. Computer-Aided Chemical Engineering, 2018, 44: 277–282

[54] Misener R, Floudas C A. Piecewise-linear approximations of multidimensional functions. Journal of Optimization Theory and Applications, 2010, 145(1): 120–147

[55] Misener R, Gounaris C E, Floudas C A. Global optimization of gas lifting operations: a comparative study of piecewise linear formulations. Industrial & Engineering Chemistry Research, 2009, 48(13): 6098–6104

[56] Pistikopoulos E N. Uncertainty in process design and operations. Computers & Chemical Engineering, 1995, 19(Suppl 1): 553–563

[57] Amaran S, Sahinidis N V, Sharda B, Bury S J. Simulation optimization: a review of algorithms and applications. 4OR, 2014, 12(4): 301–333

[58] Fu M C, Price C C, Zhu J, Hillier F S. Handbook of Simulation Optimization Associate Series Editor. New York: Springer, 2015

[59] Ankenman B, Nelson B L, Staum J. Stochastic kriging for simulation metamodeling. Operations Research, 2010, 58(2): 371–382

[60] Bertsimas D, Sim M. The price of robustness. Operations Research, 2004, 52(1): 35–53

[61] Ning C, You F. Optimization under uncertainty in the era of big data and deep learning: when machine learning meets mathematical programming. Computers & Chemical Engineering, 2019, 125: 434–448

[62] Hüllen G, Zhai J, Kim S H, Sinha A, Realff M J, Boukouvala F. Managing uncertainty in data-driven simulation-based optimization. Computers & Chemical Engineering, 2020, 136: 106519

[63] Marques C M, Moniz S, de Sousa J P, Barbosa-Póvoa A P. A simulation-optimization approach to integrate process design and planning decisions under technical and market uncertainties: a case from the chemical-pharmaceutical industry. Computers & Chemical Engineering, 2017, 106: 796–813

[64] Crater J S, Lievense J C. Scale-up of industrial microbial processes. FEMS Microbiology Letters, 2018, 365(13): 138

[65] Noorman H J, Heijnen J J. Biochemical engineering’s grand adventure. Chemical Engineering Science, 2017, 170: 677–693

[66] Da Silva S S, Chandel A K. D-Xylitol: Fermentative Production, Application and Commercialization. Berlin Heidelberg: Springer-Verlag, 2012

[67] Choi S, Song C W, Shin J H, Lee S Y. Biorefineries for the production of top building block chemicals and their derivatives. Metabolic Engineering, 2015, 28: 223–239

[68] de Albuquerque T L, da Silva I J, de MacEdo G R, Rocha M V P. Biotechnological production of xylitol from lignocellulosic wastes: a review. Process Biochemistry, 2014, 49(11): 1779–1789

[69] Venkateswar Rao L, Goli J K, Gentela J, Koti S. Bioconversion of lignocellulosic biomass to xylitol: an overview. Bioresource Technology, 2016, 213: 299–310

[70] Dasgupta D, Bandhu S, Adhikari D K, Ghosh D. Challenges and prospects of xylitol production with whole cell bio-catalysis: a review. Microbiological Research, 2017, 197: 9–21

[71] Felipe Hernández-Pérez A, de Arruda P V, Sene L, da Silva S S, Kumar Chandel A, de Almeida Felipe M G. Xylitol bioproduction: state-of-the-art, industrial paradigm shift, and opportunities for integrated biorefineries. Critical Reviews in Biotechnology, 2019, 39(7): 924–943

[72] Delgado Arcaño Y, Valmaña García O D, Mandelli D, Carvalho W A, Magalhães Pontes L A. Xylitol: a review on the progress and challenges of its production by chemical route. Catalysis Today, 2020, 344: 2–14

[73] Mountraki A D, Koutsospyros K R, Mlayah B B, Kokossis A C. Selection of biorefinery routes: the case of xylitol and its integration with an organosolv process. Waste and Biomass Valorization, 2017, 8(7): 2283–2300

[74] Franceschin G, Sudiro M, Ingram T, Smirnova I, Brunner G, Bertucco A. Conversion of rye straw into fuel and xylitol: a technical and economical assessment based on experimental data. Chemical Engineering Research & Design, 2011, 89(6): 631–640

[75] Giuliano A, Barletta D, De Bari I, Poletto M. Techno-economic assessment of a lignocellulosic biorefinery co-producing ethanol and xylitol or furfural. Computer-Aided Chemical Engineering, 2018, 43: 585–590

[76] Mancini E, Mansouri S S, Gernaey K V, Luo J, Pinelo M. From second generation feed-stocks to innovative fermentation and downstream techniques for succinic acid production. Critical Reviews in Environmental Science and Technology, 2020, 50(18): 1829–1873

[77] Ragauskas A J, Beckham G T, Biddy M J, Chandra R, Chen F, Davis M F, Davison B H, Dixon R A, Gilna P, Keller M, Langan P, Naskar A K, Saddler J N, Tschaplinski T J, Tuskan G A, Wyman C E. Lignin valorization: improving lignin processing in the biorefinery. Science, 2014, 344(6185): 1246843

[78] Ponnusamy V K, Nguyen D D, Dharmaraja J, Shobana S, Banu J R, Saratale R G, Chang S W, Kumar G. A review on lignin structure, pretreatments, fermentation reactions and biorefinery potential. Bioresource Technology, 2019, 271: 462–472

[79] Wang W C, Tao L. Bio-jet fuel conversion technologies. Renewable & Sustainable Energy Reviews, 2016, 53: 801–822

[80] Prunescu R M, Blanke M, Jakobsen J G, Sin G. Dynamic modeling and validation of a biomass hydrothermal pretreatment process—a demonstration scale study. AIChE Journal. American Institute of Chemical Engineers, 2015, 61(12): 4235–4250

[81] Tochampa W, Sirisansaneeyakul S, Vanichsriratana W, Srinophakun P, Bakker H H C, Chisti Y. A model of xylitol production by the yeast Candida mogii. Bioprocess and Biosystems Engineering, 2005, 28(3): 175–183

[82] S3O GitHub Repository. 2021, 10.5281/zenodo.5017353

[83] Al R, Behera C R, Gernaey K V, Sin G. Towards development of a decision support tool for conceptual design of wastewater treatment plants using stochastic simulation optimization. Computer-Aided Chemical Engineering, 2019, 46: 325–330

[84] Kılınç M R, Sahinidis N V. Exploiting integrality in the global optimization of mixed-integer nonlinear programming problems with BARON. Optimization Methods & Software, 2018, 33(3): 540–562

[85] Vassilev S V, Baxter D, Andersen L K, Vassileva C G, Morgan T J. An overview of the organic and inorganic phase composition of biomass. Fuel, 2012, 94: 1–33

[86] Eason J, Cremaschi S. Adaptive sequential sampling for surrogate model generation with artificial neural networks. Computers & Chemical Engineering, 2014, 68: 220–232

[87] Garud S S, Karimi I A, Kraft M. Smart sampling algorithm for surrogate model development. Computers & Chemical Engineering, 2017, 96: 103–114

[88] Garud S S, Karimi I A, Brownbridge G P E, Kraft M. Evaluating smart sampling for constructing multidimensional surrogate models. Computers & Chemical Engineering, 2018, 108: 276–288

[89] Obermeier A, Vollmer N, Windmeier C, Esche E, Repke J U. Generation of linear-based surrogate models from non-linear functional relationships for use in scheduling formulation. Computers & Chemical Engineering, 2021, 146: 107203

[90] Chen Y, Goetsch P, Hoque M A, Lu J, Tarkoma S. d-Simplexed: adaptive delaunay triangulation for performance modeling and prediction on big data analytics. IEEE Transactions on Big Data, 2019, in press

[91] Jiang P, Zhang Y, Zhou Q, Shao X, Hu J, Shu L. An adaptive sampling strategy for kriging metamodel based on Delaunay triangulation and TOPSIS. Applied Intelligence, 2018, 48(6): 1644–1645

Acknowledgments

The authors would like to express their gratitude to the Novo Nordisk Foundation (Grant No. NNF17SA0031362) for funding the Fermentation-Based Biomanufacturing Initiative of which this project is a part.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://dx.doi.org/10.1007/s11705-021-2071-9 and is accessible for authorized users.

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