Skip to main content

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.

This is a preview of subscription content, access via your institution.

References

  1. 1.

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

  2. 2.

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

    CAS  Article  Google Scholar 

  3. 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

    CAS  Article  Google Scholar 

  4. 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, et al. Building a global alliance of biofoundries. Nature Communications, 2019, 10 (1): 1038–1041

    Article  CAS  Google Scholar 

  5. 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

    CAS  Article  Google Scholar 

  6. 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

    CAS  Article  Google Scholar 

  7. 7.

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

    Article  CAS  Google Scholar 

  8. 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

    CAS  Article  Google Scholar 

  9. 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

    CAS  Article  Google Scholar 

  10. 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

    Google Scholar 

  11. 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

    CAS  Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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

    CAS  Article  Google Scholar 

  15. 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

    CAS  Article  Google Scholar 

  16. 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

    CAS  Article  Google Scholar 

  17. 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

    CAS  Article  Google Scholar 

  18. 18.

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

    Article  CAS  Google Scholar 

  19. 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

    CAS  Article  Google Scholar 

  20. 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

    CAS  Article  Google Scholar 

  21. 21.

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

    Google Scholar 

  22. 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

    CAS  Article  Google Scholar 

  23. 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

    Article  Google Scholar 

  24. 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

    CAS  Article  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 26.

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

    CAS  Article  Google Scholar 

  27. 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

    CAS  Article  Google Scholar 

  28. 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

    CAS  Article  Google Scholar 

  29. 29.

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

    CAS  Google Scholar 

  30. 30.

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

    Google Scholar 

  31. 31.

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

    Article  Google Scholar 

  32. 32.

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

    Article  Google Scholar 

  33. 33.

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

    Article  Google Scholar 

  34. 34.

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

    Book  Google Scholar 

  35. 35.

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

    Article  Google Scholar 

  36. 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

    CAS  Article  Google Scholar 

  37. 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

    CAS  Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. 39.

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

    Google Scholar 

  40. 40.

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

    Article  Google Scholar 

  41. 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

    Article  Google Scholar 

  42. 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

    CAS  Article  Google Scholar 

  43. 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

    Google Scholar 

  44. 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

    CAS  Article  Google Scholar 

  45. 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

    CAS  Article  Google Scholar 

  46. 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

    Article  Google Scholar 

  47. 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

    CAS  Article  Google Scholar 

  48. 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

    Article  Google Scholar 

  49. 49.

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

    CAS  Article  Google Scholar 

  50. 50.

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

    CAS  Article  Google Scholar 

  51. 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

    CAS  Article  Google Scholar 

  52. 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

    CAS  Article  Google Scholar 

  53. 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

    CAS  Article  Google Scholar 

  54. 54.

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

    Article  Google Scholar 

  55. 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

    CAS  Article  Google Scholar 

  56. 56.

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

    Article  Google Scholar 

  57. 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

    Article  Google Scholar 

  58. 58.

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

    Google Scholar 

  59. 59.

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

    Article  Google Scholar 

  60. 60.

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

    Article  Google Scholar 

  61. 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

    CAS  Article  Google Scholar 

  62. 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

    Article  CAS  Google Scholar 

  63. 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

    CAS  Article  Google Scholar 

  64. 64.

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

    Article  CAS  Google Scholar 

  65. 65.

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

    CAS  Article  Google Scholar 

  66. 66.

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

    Book  Google Scholar 

  67. 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

    CAS  Article  Google Scholar 

  68. 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

    Article  CAS  Google Scholar 

  69. 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

    CAS  Article  Google Scholar 

  70. 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

    CAS  Article  Google Scholar 

  71. 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

    Article  CAS  Google Scholar 

  72. 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

    Article  CAS  Google Scholar 

  73. 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

    CAS  Article  Google Scholar 

  74. 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

    CAS  Article  Google Scholar 

  75. 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

    CAS  Article  Google Scholar 

  76. 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

    CAS  Article  Google Scholar 

  77. 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

    Article  CAS  Google Scholar 

  78. 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

    CAS  Article  Google Scholar 

  79. 79.

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

    CAS  Article  Google Scholar 

  80. 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

    CAS  Article  Google Scholar 

  81. 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

    CAS  Article  Google Scholar 

  82. 82.

    S3O GitHub Repository. 2021, 10.5281/zenodo.5017353

  83. 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

    CAS  Article  Google Scholar 

  84. 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

    Article  Google Scholar 

  85. 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

    CAS  Article  Google Scholar 

  86. 86.

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

    CAS  Article  Google Scholar 

  87. 87.

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

    CAS  Article  Google Scholar 

  88. 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

    CAS  Article  Google Scholar 

  89. 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

    CAS  Article  Google Scholar 

  90. 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. 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

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Gürkan Sin.

Electronic Supplementary Material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vollmer, N.I., Al, R., Gernaey, K.V. et al. Synergistic optimization framework for the process synthesis and design of biorefineries. Front. Chem. Sci. Eng. (2021). https://doi.org/10.1007/s11705-021-2071-9

Download citation

Keywords

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