Skip to main content
SpringerLink
Log in

Towards the Development of Digital Twins for the Bio-manufacturing Industry

  • Chapter
  • First Online:
Digital Twins

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 176))

Abstract

The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.

Graphical Abstract

Carina L. Gargalo and Simoneta Caþo de las Heras are both first authors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

AI:

Artificial intelligence

AIC:

Akaike’s information criterion

ANN:

Artificial neural networks

BCO:

BioCompute Object

BIC:

Bayesian information criterion

BSM:

Benchmark simulation models

CAPE-OPEN:

Next-generation computer-aided process engineering open simulation environment

CFD:

Computational fluid dynamics

CM:

Compartmental models

CQA:

Critical quality attributes

DCS:

Distributed control systems

DEXPI:

Data exchange in the process industry

DT:

Digital twins

FPM:

First-principles models

GC/MS:

Gas chromatography/mass spectrometry

GLP:

Good laboratory practice

GMP:

Good modelling practices

HPC:

High-performance computing

IoT:

Internet of Things

ISO:

International Organization for Standardization

LCA:

Multilinear regression

MIR:

Mid infrared

OPC:

Open platform communications

ML:

Machine learning

MPC:

Model predictive control

MVA:

Multivariate data analysis

PAT:

Process analytical technology

PCA:

Principal component analysis

PID:

Proportional-integral-derivative

PLC:

Programmable logic controllers

PLS:

Partial least squares regression

QbD:

Quality by design

RTU:

Remote terminal units

SAX:

Symbolic aggregate proximity

SCADA:

Supervisory control and data acquisition

SSE:

Sum of squared errors

SVM:

Support vector machines

UV:

Ultraviolet

WWTP:

Wastewater treatment plants

XML:

Extensible markup language

References

  1. Sniderman B, Mahto M, Cotteleer MJ (2016) Industry 4.0 and manufacturing ecosystems: exploring the world of connected enterprises. Deloitte Consulting

    Google Scholar 

  2. Kagermann H, Wahlster W, Helbig J (2013) Securing the future of german manufacturing industry: recommendations for implementing the strategic initiative industrie 4.0. Technical report 0

    Google Scholar 

  3. Oliveira AL (2019) Biotechnology, big data and artificial intelligence. Biotechnol J 14(8):1800613

    Article  CAS  Google Scholar 

  4. Deloitte. Digital Thread for Additive Manufacturing (DTAM)

    Google Scholar 

  5. Fraunhofer Austria Research GmbH. Innovative Solutions for the Today of Tomorrow

    Google Scholar 

  6. Zhang M, Nee Fei Tao AYC (2019) Background and concept of digital twin. In: Digital twin driven smart manufacturing. Academic Press, pp 3–28

    Google Scholar 

  7. Garetti M, Rosa P, Terzi S (2012) Life cycle simulation for the design of product-service systems. Comput Indust 63(4):361–369

    Article  Google Scholar 

  8. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11):1016–1022

    Article  Google Scholar 

  9. Negri E, Fumagalli L, Macchi M (2017) A review of the roles of digital twin in CPS-based production systems. Proc Manuf 11:939–948

    Google Scholar 

  10. Hermann M, Pentek T, Otto B (2016) Design principles for industrie 4.0 scenarios. In: 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, pp 3928–3937

    Google Scholar 

  11. Tao F, Qi Q, Wang L, Nee AYC (2019) Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering 5(4):653–661

    Article  Google Scholar 

  12. O’Donovan P, Leahy K, Bruton K, O’Sullivan DTJ (2015) An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J Big Data 2(1):25

    Article  Google Scholar 

  13. Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–169

    Article  Google Scholar 

  14. Rosen R, VON Wichert G, Lo G, Bettenhausen KD (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3):567–572

    Article  Google Scholar 

  15. Wright L, Davidson S (2020) How to tell the difference between a model and a digital twin. Adv Model Simulat Eng Sci 7(1):13

    Article  Google Scholar 

  16. Baur D, Angelo J, Chollangi Ss, Müller-Späth T, Xu X, Ghose S, Li ZJ, Morbidelli M (2019) Model-assisted process characterization and validation for a continuous two-column protein A capture process. Biotechnol Bioeng 116(1):87–98

    Article  CAS  PubMed  Google Scholar 

  17. Möller J, Kuchemüller KB, Steinmetz T, Koopmann KS, Pörtner R (2019) Model-assisted design of experiments as a concept for knowledge-based bioprocess development. Bioprocess Biosyst Eng 42(5):867–882

    Article  PubMed  CAS  Google Scholar 

  18. Warshaw L, Cotteleer M (2017) Industry 4.0 and the digital twin. Deloite University Press

    Google Scholar 

  19. Holdowsky J, Mahto M, Raynor ME, Cotteleer M (2015) Inside the internet of things (iot). Retrieved 5 Apr 2016

    Google Scholar 

  20. Madni AM, Madni CC, Lucero SD (2019) Leveraging digital twin technology in model-based systems engineering. Systems 7(1):7

    Article  Google Scholar 

  21. Madni AM, Sievers M (2018) Model-based systems engineering: motivation, current status, and research opportunities. Syst Eng 21(3):172–190

    Article  Google Scholar 

  22. Madni AM, Sievers M (2017) Model-based systems engineering: motivation, current status, and needed advances. Disciplin Converg Syst Eng Res:311–325

    Google Scholar 

  23. Lim KYH, Zheng P, Chen CH (2020) A state-of-the-art survey of digital twin: techniques, engineering product lifecycle management and business innovation perspectives. J Intell Manuf 31(6):1313–1337

    Article  Google Scholar 

  24. Boschert S, Rosen R (2016) Digital twin-the simulation aspect. Mechatronic futures: challenges and solutions for mechatronic systems and their designers, pp 59–74

    Google Scholar 

  25. Swedberg C (2018) Digital twins bring value to big RFID and IoT data. https://www.rfidjournal.com/digital-twins-bring-value-to-big-rfid-and-iot-data-2

  26. Menard S (2017) 3 ways digital twins are going to help improve oil and gas maintenance and operations. https://www.linkedin.com/pulse/3-ways-digitaltwins-going-help-improve-oil-gas-sophie-menard

  27. Science Service Dr (2017) Hempel Digital Health Network. Healthcare solution testing for future|Digital Twins in healthcare. https://www.dr-hempel-network.com/digital-health-technolgy/digital-twins-in-healthcare/

  28. DNV.GL. WINDGEMINI DIGITAL TWIN: Data driven insights to reduce costs, extend life and maximise production. https://www.dnvgl.com/power-renewables/services/data-analytics/windgemini/?utm_campaign=wind&utm_source=google&utm_medium=cpc&utm_content=250560941230&utm_term=wind%20turbine%20digital%20twin&gclid=Cj0KCQjw3ZX4BRDmARIsAFYh7ZKDzHi57l1WoqfTZDz6VL6yfICGSef_mvHLkZOl90uzgcSgPvSxVMYaAoT1EALw_wcB

  29. Dassault Systèmes (2018) Meet virtual Singapore, the city’s 3D digital twin. https://govinsider.asia/digitalgov/meet-virtual-singapore-citys-3d-digital-twin/

  30. Narayanan H, Luna MF, von Stosch M, Cruz Bournazou MN, Polotti G, Morbidelli M, Butté A, Sokolov M (2019) Bioprocessing in the digital age: the role of process models. Biotechnol J:1900172

    Google Scholar 

  31. Endo I, Nagamune T (1987) A database system for fermentation processes. Bioprocess Eng 2(3):111–114

    Article  CAS  Google Scholar 

  32. FDA. FDA’s regulation of plant and animal biotechnology products

    Google Scholar 

  33. Markarian J (2018) Modernizing pharma manufacturing. Pharm Technol 42(4):20–25

    Google Scholar 

  34. Mercier SM, Diepenbroek B, Wijffels RH, Streefland M (2014) Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations. Trends Biotechnol 32(6):329–336

    Article  CAS  PubMed  Google Scholar 

  35. Simon LL, Pataki H, Marosi G, Meemken F, Hungerbühler K, Baiker A, Tummala S, Glennon B, Kuentz M, Steele G et al (2015) Assessment of recent process analytical technology (pat) trends: a multiauthor review. Org Process Res Dev 19(1):3–62

    Article  CAS  Google Scholar 

  36. Teixeira AP, Oliveira R, Alves PM, Carrondo MJT (2009) Advances in on-line monitoring and control of mammalian cell cultures: supporting the PAT initiative. Biotechnol Adv 27(6):726–732

    Article  CAS  PubMed  Google Scholar 

  37. Kroll P, Hofer A, Ulonska S, Kager J, Herwig C (2017) Model-based methods in the biopharmaceutical process lifecycle. Pharm Res 34(12):2596–2613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Venkatasubramanian V (2019) The promise of artificial intelligence in chemical engineering: is it here, finally. AICHE J 65(2):466–478

    Article  CAS  Google Scholar 

  39. Glassey J, Von Stosch M (2018) Hybrid modeling in process industries. CRC Press

    Google Scholar 

  40. von Stosch M, Davy S, Francois K, Galvanauskas V, Hamelink J, Luebbert A, Mayer M, Oliveira R, O’Kennedy R, Rice P, Glassey JA (2014) Hybrid modeling for quality by design and PAT – benefits and challenges of applications in biopharmaceutical industry. Biotechnol J 9:719–726

    Article  CAS  Google Scholar 

  41. Reis MS, Gins G (2017) Industrial process monitoring in the big data/industry 4.0 era: from detection, to diagnosis, to prognosis. PRO 5(3):35

    Google Scholar 

  42. Steinwandter V, Borchert D, Herwig C (2019) Data science tools and applications on the way to Pharma 4.0. Drug Discov Today 24(9):1795–1805

    Article  PubMed  Google Scholar 

  43. Freesense. https://www.freesense.dk

  44. Particletech solutions. https://particletech.dk/particletechsolution/

  45. Lawton JR, Martinez FA, Burks C (1989) Overview of the limb database. Nucleic Acids Res 17(15):5885–5889

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Biotrack product database. https://biotrackproductdatabase.oecd.org

  47. Biechele P, Busse C, Solle D, Scheper T, Reardon K (2015) Sensor systems for bioprocess monitoring. Eng Life Sci 15(5):469–488

    Article  CAS  Google Scholar 

  48. Zimmermann R, Fiabane L, Gasteuil Y, Volk R, Pinton J (2013) Measuring lagrangian accelerations using an instrumented particle. Phys Scr 2013(T155):014063

    Article  CAS  Google Scholar 

  49. Landgrebe D, Haake C, Höpfner T, Beutel S, Hitzmann B, Scheper T, Rhiel M, Reardon KF (2010) On-line infrared spectroscopy for bioprocess monitoring. Appl Microbiol Biotechnol 88(1):11–22

    Article  CAS  PubMed  Google Scholar 

  50. Kadlec P, Gabrys B, Strandt S (2009) Data-driven soft sensors in the process industry. Comput Chem Eng 33(4):795–814

    Article  CAS  Google Scholar 

  51. Pohlscheidt M, Charaniya S, Bork C, Jenzsch M, Noetzel TL, Luebbert A (2009) Bioprocess and fermentation monitoring. Encycl Indust Biotechnol Bioprocess Biosep Cell Technol:1469–1491

    Google Scholar 

  52. Gopakumar V, Tiwari S, Rahman I (2018) A deep learning based data driven soft sensor for bioprocesses. Biochem Eng J 136:28–39

    Article  CAS  Google Scholar 

  53. Spann R, Roca C, Kold D, Lantz AE, Gernaey KV, Sin G (2018) A probabilistic model-based soft sensor to monitor lactic acid bacteria fermentations. Biochem Eng J 135:49–60

    Article  CAS  Google Scholar 

  54. Thürlimann CM, Dürrenmatt DJ, Villez K (2018) Soft-sensing with qualitative trend analysis for wastewater treatment plant control. Control Eng Pract 70:121–133

    Article  Google Scholar 

  55. Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6:3585–3593

    Article  Google Scholar 

  56. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Networks Appl 19(2):171–209

    Article  Google Scholar 

  57. Boiarkina I, Depree N, Prince-Pike A, Yu W, Wilson DI, Young BR (2018) Using big data in industrial milk powder process systems. In: Computer aided chemical engineering, vol 44. Elsevier, pp 2293–2298

    Google Scholar 

  58. Data warehouse, data lake and database definition. https://blogs.oracle.com/bigdata/data-lake-database-data-warehouse-difference

  59. Charaniya S, Hu W, Karypis G (2008) Mining bioprocess data: opportunities and challenges. Trends Biotechnol 26(12):690–699

    Article  CAS  PubMed  Google Scholar 

  60. Mercier SM, Diepenbroek B, Dalm M, Wijffels RH, Streefland M (2013) Multivariate data analysis as a pat tool for early bioprocess development data. J Biotechnol 167(3):262–270

    Article  CAS  PubMed  Google Scholar 

  61. Al-Maskari S, Li X, Liu Q (2014) An effective approach to handling noise and drift in electronic noses. In: Australasian database conference. Springer, Berlin, pp 223–230

    Google Scholar 

  62. Goodner KL, Dreher JG, Rouseff RL (2001) The dangers of creating false classifications due to noise in electronic nose and similar multivariate analyses. Sensors Actuators B Chem 80(3):261–266

    Article  CAS  Google Scholar 

  63. Xie W, Li C, Wu Y, Zhang P (2019) A bayesian nonparametric framework for uncertainty quantification in simulation. arXiv preprint arXiv:1910.03766

    Google Scholar 

  64. Gupta SK (2012) Use of Bayesian statistics in drug development: advantages and challenges. Int J Appl Basic Med Res 2(1):3–6

    Article  PubMed  PubMed Central  Google Scholar 

  65. Tabora JE, Gonzalez FL, Tom JW (2019) Bayesian probabilistic modeling in pharmaceutical process development. AICHE J 65(11):e16744

    Article  CAS  Google Scholar 

  66. García-Muñoz S, Luciani CV, Vaidyaraman S, Seibert KD (2015) Definition of design spaces using mechanistic models and geometric projections of probability maps. Org Process Res Dev 19(8):1012–1023

    Article  CAS  Google Scholar 

  67. Richard X, Laird C, Vaidyaraman S, García-Muñoz S (2017) An optimization-based framework to define the probabilistic design space of pharmaceutical processes with model uncertainty. In: Computing and systems technology division 2017 – Core programming area at the 2017 aiche annual meeting, vol 2017. AIChE, pp 610–622

    Google Scholar 

  68. Albrecht J (2013) Estimating reaction model parameter uncertainty with Markov chain Monte Carlo. Comput Chem Eng 48:14–28

    Article  CAS  Google Scholar 

  69. Rathore AS, Bhushan A, Hadpe S (2011) Chemometrics applications in biotech processes: a review. Biotechnol Prog 27(2):307–315

    Article  CAS  PubMed  Google Scholar 

  70. Turitsyn SK, Prilepsky JE, Le ST, Wahls S, Frumin LL, Kamalian M, Derevyanko SA (2017) Nonlinear fourier transform for optical data processing and transmission: advances and perspectives. Optica 4(3):307–322

    Article  Google Scholar 

  71. Notaristefano A, Chicco G, Piglione F (2013) Data size reduction with symbolic aggregate approximation for electrical load pattern grouping. IET Gen Trans Distrib 7(2):108–117

    Article  Google Scholar 

  72. Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of the 2001 ACM SIGMOD international conference on Management of data, pp 151–162

    Google Scholar 

  73. Cordella CB (2012) Pca: the basic building block of chemometrics. Anal Chem 154

    Google Scholar 

  74. Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N (2007) A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharm Biomed Anal 44(3):683–700

    Article  CAS  PubMed  Google Scholar 

  75. Desai K, Badhe Y, Tambe SS, Kulkarni BD (2006) Soft-sensor development for fed-batch bioreactors using support vector regression. Biochem Eng J 27(3):225–239

    Article  CAS  Google Scholar 

  76. Dieterle F, Busche S, Gauglitz G (2004) Different approaches to multivariate calibration of nonlinear sensor data. Anal Bioanal Chem 380(3):383–396

    Article  CAS  PubMed  Google Scholar 

  77. Taib MN, Andres R, Narayanaswamy R (1996) Extending the response range of an optical fibre ph sensor using an artificial neural network. Anal Chim Acta 330(1):31–40

    Article  CAS  Google Scholar 

  78. Corominas L, Garrido-Baserba M, Villez K, Olsson G, Cortés U, Poch M (2018) Transforming data into knowledge for improved wastewater treatment operation: a critical review of techniques. Environ Model Softw 106:89–103

    Article  Google Scholar 

  79. Lopez PC, Udugama IA, Thomsen ST, Roslander C, Junicke H, Mauricio-Iglesias M, Gernaey KV (2020) Towards a digital twin: a hybrid data-driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation. Biofuels Bioprod Biorefin

    Google Scholar 

  80. Elsevier’s scopus, the largest abstract and citation database of peer-reviewed literature. https://www.scopus.com/

  81. Kell DB, Sonnleitner B (1995) Gmp – good modelling practice: an essential component of good manufacturing practice. Trends Biotechnol 13(11):481–492

    Article  CAS  Google Scholar 

  82. Mears L, Stocks SM, Albaek MO, Sin G, Gernaey KV (2017) Mechanistic fermentation models for process design, monitoring, and control. Trends Biotechnol 35(10):914–924

    Article  CAS  PubMed  Google Scholar 

  83. Kroll P, Hofer A, Stelzer IV, Herwig C (2017) Workflow to set up substantial target-oriented mechanistic process models in bioprocess engineering. Process Biochem 62:24–36

    Article  CAS  Google Scholar 

  84. Gernaey KV, Lantz AE, Tufvesson P, Woodley JM, Sin G (2010) Application of mechanistic models to fermentation and biocatalysis for next-generation processes. Trends Biotechnol 28(7):346–354

    Article  CAS  PubMed  Google Scholar 

  85. Sonnleitner B, Käppeli O (1986) Growth of saccharomyces cerevisiae is controlled by its limited respiratory capacity: formulation and verification of a hypothesis. Biotechnol Bioeng 28(6):927–937

    Article  CAS  PubMed  Google Scholar 

  86. Song H, Jang SH, Park JM, Lee SY (2008) Modeling of batch fermentation kinetics for succinic acid production by mannheimia succiniciproducens. Biochem Eng J 40(1):107–115

    Article  CAS  Google Scholar 

  87. Sin G, Ödman P, Petersen N, Lantz AE, Gernaey KV (2008) Matrix notation for efficient development of first-principles models within pat applications: integrated modeling of antibiotic production with streptomyces coelicolor. Biotechnol Bioeng 101(1):153–171

    Article  CAS  PubMed  Google Scholar 

  88. Abt V, Barz T, Cruz-Bournazou MN, Herwig C, Kroll P, Möller J, Pörtner R, Schenkendorf R (2018) Model-based tools for optimal experiments in bioprocess engineering. Curr Opin Chem Eng 22:244–252

    Article  Google Scholar 

  89. Lübbert A, Simutis R (1994) Using measurement data in bioprocess modelling and control. Trends Biotechnol 12(8):304–311

    Article  Google Scholar 

  90. Boyd S, Vandenberghe L (2018) Introduction to applied linear algebra: vectors, matrices, and least squares. Cambridge University Press

    Google Scholar 

  91. Burnham KP, Anderson DR (2002) A practical information-theoretic approach. Model selection and multimodel inference, 2nd edn. Springer, New York

    Google Scholar 

  92. Cozad A, Sahinidis NV, Miller DC (2014) Learning surrogate models for simulation-based optimization. AICHE J 60(6):2211–2227

    Article  CAS  Google Scholar 

  93. Ghojogh B, Crowley M (2019) The theory behind overfitting, cross validation, regularization, bagging, and boosting: Tutorial. p 23

    Google Scholar 

  94. Williams BA, Cremaschi S (2019) Surrogate model selection for design space approximation and surrogatebased optimization. In: Proceedings of the 9th international conference on foundations of computer-aided process design, vol 47 of Computer Aided Chemical Engineering, pp 353–358

    Google Scholar 

  95. Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, Berlin

    Google Scholar 

  96. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  Google Scholar 

  97. Mallows CL (1973) Some comments on cp. Technometrics 15(4):661–675

    Google Scholar 

  98. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464

    Article  Google Scholar 

  99. Hannan EJ, Quinn BG (1979) The determination of the order of an autoregression. J R Stat Soc Ser B (Methodol) 41(2):190–195

    Google Scholar 

  100. Jones M, Forero-Hernandez H, Zubov A, Sarup B, Sin G (2018) Superstructure optimization of oleochemical processes with surrogate models. In Proceedings of the 13th international symposium on process systems engineering – PSE 2018, volume 44 of Computer Aided Chemical Engineering, pp 277–282

    Google Scholar 

  101. Al R, Behera CR, Zubov A, Gernaey KV, Sin G (2019) Meta-modeling based efficient global sensitivity analysis for wastewater treatment plants – an application to the bsm2 model. Comput Chem Eng 127:233–246

    Article  CAS  Google Scholar 

  102. Davis SE, Cremaschi S, Eden MR (2018) Efficient surrogate model development: impact of sample size and underlying model dimensions, vol 44, pp 979–984

    Google Scholar 

  103. Garud SS, Karimi IA, Kraft M (2017) Design of computer experiments: a review. Comput Chem Eng 106:71–95

    Article  CAS  Google Scholar 

  104. Wilson ZT, Sahinidis NV (2017) The Alamo approach to machine learning. Comput Chem Eng 106:785–795

    Article  Google Scholar 

  105. Alizadeh R, Jia L, Nellippallil AB, Wang G, Hao J, Allen JK, Mistree F (2019) Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets. Artif Intell Eng Des Anal Manuf 33(4):484–501

    Article  Google Scholar 

  106. Esche E, Weigert J, Budiarto T, Hoffmann C, Repke J-U (2019) Optimization under uncertainty based on a data-driven model for a chloralkali electrolyzer cell. In: 29th European symposium on computer aided process engineering, volume 46 of Computer Aided Chemical Engineering. Elsevier, pp 577–582

    Google Scholar 

  107. Viana FAC, Haftka RT, Steffen V (2009) Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. Struct Multidiscip Optim 39(4):439–457

    Article  Google Scholar 

  108. McBride K, Sundmacher K (2019) Overview of surrogate modeling in chemical process engineering. Chem Ingen Tech 91(3):228–239

    Article  CAS  Google Scholar 

  109. Tajsoleiman T (2018) Automating experimentation in miniaturized reactors

    Google Scholar 

  110. Nauha EK, Kálal Z, Ali JM, Alopaeus V (2018) Compartmental modeling of large stirred tank bioreactors with high gas volume fractions. Chem Eng J 334:2319–2334

    Article  CAS  Google Scholar 

  111. Spann R, Gernaey KV, Sin G (2019) A compartment model for risk-based monitoring of lactic acid bacteria cultivations. Biochem Eng J 151:107293

    Article  CAS  Google Scholar 

  112. Öner M, Stocks SM, Sin G (2020) Comprehensive sensitivity analysis and process risk assessment of large scale pharmaceutical crystallization processes. Comput Chem Eng 135:106746

    Article  CAS  Google Scholar 

  113. Noorman HJ, Heijnen JJ (2017) Biochemical engineering’s grand adventure. Chem Eng Sci 170:677–693

    Article  CAS  Google Scholar 

  114. Nielsen RF, Kermani NA, la Cour Freiesleben L, Gernaey KV, Mansouri SS (2019) Novel strategies for predictive particle monitoring and control using advanced image analysis. In: 29th European Symposium on Computer Aided Process Engineering, 46:1435–1440

    Google Scholar 

  115. Nielsen RF, Nazemzadeh N, Sillesen LW, Andersson MP, Gernaey KV, Mansouri SS (2020) Hybrid machine learning assisted modelling framework for particle processes. Comput Chem Eng 140:106916

    Article  CAS  Google Scholar 

  116. Eikens B, Karim MN, Simon L (1999) Neural networks and first principle models for bioprocesses. IFAC Proc 32(2):6974–6979

    Google Scholar 

  117. Gao Y, Kipling K, Glassey J, Willis M, Montague G, Zhou Y, Titchener-Hooker NJ (2010) Application of agent-based system for bioprocess description and process improvement. Biotechnol Prog 26(3):706–716

    Article  CAS  PubMed  Google Scholar 

  118. Downs JJ, Vogel EF (1993) A plant-wide industrial process control problem. Comput Chem Eng 17(3):245–255

    Article  CAS  Google Scholar 

  119. Gernaey KV, Jeppsson U, Vanrolleghem PA, Copp JB (2014) Benchmarking of control strategies for wastewater treatment plants. IWA Publishing

    Google Scholar 

  120. Jeppsson U, Pons M-N, Nopens I, Alex J, Copp JB, Gernaey KV, Rosén C, Steyer J-P, Vanrolleghem PA (2007) Benchmark simulation model no 2: general protocol and exploratory case studies. Water Sci Technol 56(8):67–78

    Article  CAS  PubMed  Google Scholar 

  121. Nopens I, Benedetti L, Jeppsson U, Pons M-N, Alex J, Copp JB, Gernaey KV, Rosen C, Steyer J-P, Vanrolleghem PA (2010) Benchmark simulation model no 2: finalisation of plant layout and default control strategy. Water Sci Technol 62(9):1967–1974

    Article  CAS  PubMed  Google Scholar 

  122. Flores-Alsina X, Corominas L, Snip L, Vanrolleghem PA (2011) Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies. Water Res 45(16):4700–4710

    Article  CAS  PubMed  Google Scholar 

  123. Ochoa S, Wozny G, Repke J-U (2010) Plantwide optimizing control of a continuous bioethanol production process. J Process Control 20(9):983–998

    Article  CAS  Google Scholar 

  124. Feldman H, Flores-Alsina X, Ramin P, Kjellberg K, Jeppsson U, Batstone DJ, Gernaey KV (2017) Modelling an industrial anaerobic granular reactor using a multi-scale approach. Water Res 126:488–500

    Article  CAS  PubMed  Google Scholar 

  125. Lopez PC, Feldman H, Mauricio-Iglesias M, Junicke H, Huusom JK, Gernaey KV (2019) Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass. Biomass Bioenergy 127:105296. 73–86

    Article  CAS  Google Scholar 

  126. Udugama IA, Gernaey KV, Taube MA, Bayer C (2020) A novel use for an old problem: the Tennessee Eastman challenge process as an activating teaching tool. Educ Chem Eng 30:20–31

    Article  Google Scholar 

  127. Ricardez-Sandoval LA, Douglas PL, Budman HM (2011) A methodology for the simultaneous design and control of large-scale systems under process parameter uncertainty. Comput Chem Eng 35(2):307–318

    Article  CAS  Google Scholar 

  128. Montes F, Gernaey KV, Sin G (2018) Dynamic plantwide modeling, uncertainty, and sensitivity analysis of a pharmaceutical upstream synthesis: ibuprofen case study. Ind Eng Chem Res 57(30):10026–10037

    Article  CAS  Google Scholar 

  129. Ricker NL (1996) Decentralized control of the Tennessee Eastman challenge process. J Process Control 6(4):205–221

    Article  CAS  Google Scholar 

  130. Kulkarni A, Jayaraman VK, Kulkarni BD (2005) Knowledge incorporated support vector machines to detect faults in Tennessee Eastman process. Comput Chem Eng 29(10):2128–2133

    Article  CAS  Google Scholar 

  131. Mears L, Stocks SM, Sin G, Gernaey KV (2017) A review of control strategies for manipulating the feed rate in fed-batch fermentation processes. J Biotechnol 245:34–46

    Article  CAS  PubMed  Google Scholar 

  132. A. Udugama I, Munir MT, Kirkpatrick R, Young BR, Yu W (2018) Side draw control design for a high purity multi-component distillation column. ISA Trans 76:167–177

    Article  PubMed  Google Scholar 

  133. Bähner FD, Santacoloma PA, Huusom JK (2019) Assessment of the plantwide control structure in a pectin production plant. IFAC-PapersOnLine 52(1):251–256

    Article  Google Scholar 

  134. Lopez PC, Udugama IA, Thomsen ST, Roslander C, Junicke H, Mauricio-Iglesias M, Gernaey KV (2020) Towards a digital twin: a hybrid data-driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation. In: Biofuels, bioproducts and biorefining. Wiley

    Google Scholar 

  135. Zhang H (2009) Software sensors and their applications in bioprocess. pp 25–56

    Google Scholar 

  136. Nakhaeinejad M, Bryant MD (2011) Observability analysis for model-based fault detection and sensor selection in induction motors. Meas Sci Technol 22(7):075202

    Article  CAS  Google Scholar 

  137. Gernaey KV, Cervera-Padrell AE, Woodley JM (2012) A perspective on PSE in pharmaceutical process development and innovation. Comput Chem Eng 42:15–29

    Article  CAS  Google Scholar 

  138. Rathore AS, Bhambure R, Ghare V (2010) Process analytical technology (PAT) for biopharmaceutical products. Anal Bioanal Chem 398(1):137–154

    Article  CAS  PubMed  Google Scholar 

  139. Lopez PC, Feldman H, Mauricio-Iglesias M, Junicke H, Huusom JK, Gernaey KV (2019) Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass. Biomass Bioenergy 127:73–86. 105296

    Google Scholar 

  140. Tobyn M, Ferreira AP, Morris C, Menezes JC (2018) The preeminence of multivariate data analysis as a statistical data analysis technique in Pharmaceutical R&D and manufacturing. In: Multivariate analysis in the pharmaceutical industry. Elsevier, pp 3–12

    Google Scholar 

  141. Kourti T, MacGregor JF (1995) Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemom Intell Lab Syst 28(1):3–21

    Article  CAS  Google Scholar 

  142. Udugama I, Gargalo L, Yamashita Y, Taube MA, Palazoglu A, Young BR, Gernaey KV, Kulahci M, Bayer C (2020) The role of big data in industrial (bio)chemical process operations. Ind Eng Chem Res

    Google Scholar 

  143. Morari M, H. Lee J (1999) Model predictive control: past, present and future. Comput Chem Eng 23(4–5):667–682

    Article  CAS  Google Scholar 

  144. Liu C, Gong Z, Shen B, Feng E (2013) Modelling and optimal control for a fed-batch fermentation process. Appl Math Model 37(3):695–706

    Article  Google Scholar 

  145. Gomes J, Chopda VR, Rathore AS (2015) Integrating systems analysis and control for implementing process analytical technology in bioprocess development. J Chem Technol Biotechnol 90(4):583–589

    Article  CAS  Google Scholar 

  146. Sommeregger W, Sissolak B, Kandra K, von Stosch M, Mayer M, Striedner G (2017) Quality by control: towards model predictive control of mammalian cell culture bioprocesses. Biotechnol J 12(7):1600546

    Article  CAS  Google Scholar 

  147. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, Van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, Van Der Lei J, Van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Comment BM (2016) The fair guiding principles for scientific data management and stewardship. Sci Data 3(1):160018

    Article  PubMed  PubMed Central  Google Scholar 

  148. Gernaey KV, Rosen C, Batstone DJ, Alex J (2006) Efficient modelling necessitates standards for model documentation and exchange. Water Sci Technol 53(1):277–285

    Article  CAS  PubMed  Google Scholar 

  149. Simonyan V, Goecks J, Mazumder R (2017) Biocompute objects – a step towards evaluation and validation of biomedical scientific computations. PDA J Pharm Sci Technol 71(2):136–146

    Article  PubMed  Google Scholar 

  150. Home page – opc foundation. https://opcfoundation.org/. Accessed 28 Apr 2020

  151. The Cape-Open Laboratories Network. http://www.colan.org/. Accessed 28 Apr 2020

  152. Dexpi – data exchange in the process industry. https://dexpi.org/. Accessed 28 Apr 2020

  153. Protocol buffers. https://developers.google.com/protocol-buffers. Accessed 28 Apr 2020

  154. gRPC – a high-performance, open source universal rpc framework. https://grpc.io/. Accessed 28 Apr 2020

  155. Ladner RE (1975) On the structure of polynomial time reducibility. JACM 22(1):155–171

    Article  Google Scholar 

Download references

Acknowledgments

Financial support of the Novo Nordisk Foundation in the frame of the “Accelerated Innovation in Manufacturing Biologics” (AIMBio) project (grant number NNF19SA0035474) is gratefully acknowledged. In addition, the Technical University of Denmark (DTU) is acknowledged for the financial support of the PhD project of Simoneta Caño de las Heras.

Greater Copenhagen Food Innovation project (CPH-FOOD) is acknowledged for financially supporting the postdoc position of Mark Jones.

Author information

Authors and Affiliations

  1. Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark

    Carina L. Gargalo, Simoneta Caño de las Heras, Mark Nicholas Jones, Isuru Udugama, Seyed Soheil Mansouri, Ulrich Krühne & Krist V. Gernaey

  2. Molecular Quantum Solutions ApS, Copenhagen, Denmark

    Mark Nicholas Jones

Authors
  1. Carina L. Gargalo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simoneta Caño de las Heras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark Nicholas Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Isuru Udugama
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seyed Soheil Mansouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ulrich Krühne
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Krist V. Gernaey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krist V. Gernaey .

Editor information

Editors and Affiliations

  1. Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Wien, Austria

    Christoph Herwig

  2. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Ralf Pörtner

  3. Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany

    Johannes Möller

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gargalo, C.L. et al. (2020). Towards the Development of Digital Twins for the Bio-manufacturing Industry. In: Herwig, C., Pörtner, R., Möller, J. (eds) Digital Twins. Advances in Biochemical Engineering/Biotechnology, vol 176. Springer, Cham. https://doi.org/10.1007/10_2020_142

Download citation

Publish with us

Policies and ethics

Search

Navigation