[{"title":"Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train","volume":44,"intvolume":"        44","publication_identifier":{"eissn":["1615-7605"],"issn":["1615-7591"]},"publication_status":"published","abstract":[{"text":"Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.","lang":"eng"}],"author":[{"id":"52466","full_name":"Hernández Rodriguez, Tanja","first_name":"Tanja","last_name":"Hernández Rodriguez"},{"last_name":"Posch","first_name":"Christoph","full_name":"Posch, Christoph"},{"first_name":"Ralf","full_name":"Pörtner, Ralf","last_name":"Pörtner"},{"id":"45666","last_name":"Frahm","full_name":"Frahm, Björn","first_name":"Björn"}],"publisher":"Springer","publication":"Bioprocess and Biosystems Engineering","issue":"4","status":"public","doi":"10.1007/s00449-020-02488-1 ","user_id":"83781","date_updated":"2023-08-21T07:51:38Z","date_created":"2022-05-05T13:06:12Z","department":[{"_id":"DEP4021"}],"type":"scientific_journal_article","citation":{"short":"T. Hernández Rodriguez, C. Posch, R. Pörtner, B. Frahm, Bioprocess and Biosystems Engineering 44 (2021) 793–808.","ama":"Hernández Rodriguez T, Posch C, Pörtner R, Frahm B. Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train. <i>Bioprocess and Biosystems Engineering</i>. 2021;44(4):793-808. doi:<a href=\"https://doi.org/10.1007/s00449-020-02488-1 \">10.1007/s00449-020-02488-1 </a>","bjps":"<b>Hernández Rodriguez T <i>et al.</i></b> (2021) Dynamic Parameter Estimation and Prediction over Consecutive Scales, Based on Moving Horizon Estimation - Applied to an Industrial Cell Culture Seed Train. <i>Bioprocess and Biosystems Engineering</i> <b>44</b>, 793–808.","ufg":"<b>Hernández Rodriguez, Tanja u. a.</b>: Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train, in: <i>Bioprocess and Biosystems Engineering</i> 44 (2021), H. 4,  S. 793–808.","chicago-de":"Hernández Rodriguez, Tanja, Christoph Posch, Ralf Pörtner und Björn Frahm. 2021. Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train. <i>Bioprocess and Biosystems Engineering</i> 44, Nr. 4: 793–808. doi:<a href=\"https://doi.org/10.1007/s00449-020-02488-1 \">10.1007/s00449-020-02488-1 </a>, .","havard":"T. Hernández Rodriguez, C. Posch, R. Pörtner, B. Frahm, Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train, Bioprocess and Biosystems Engineering. 44 (2021) 793–808.","ieee":"T. Hernández Rodriguez, C. Posch, R. Pörtner, and B. Frahm, “Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train,” <i>Bioprocess and Biosystems Engineering</i>, vol. 44, no. 4, pp. 793–808, 2021, doi: <a href=\"https://doi.org/10.1007/s00449-020-02488-1 \">10.1007/s00449-020-02488-1 </a>.","van":"Hernández Rodriguez T, Posch C, Pörtner R, Frahm B. Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train. Bioprocess and Biosystems Engineering. 2021;44(4):793–808.","mla":"Hernández Rodriguez, Tanja, et al. “Dynamic Parameter Estimation and Prediction over Consecutive Scales, Based on Moving Horizon Estimation - Applied to an Industrial Cell Culture Seed Train.” <i>Bioprocess and Biosystems Engineering</i>, vol. 44, no. 4, 2021, pp. 793–808, <a href=\"https://doi.org/10.1007/s00449-020-02488-1 \">https://doi.org/10.1007/s00449-020-02488-1 </a>.","chicago":"Hernández Rodriguez, Tanja, Christoph Posch, Ralf Pörtner, and Björn Frahm. “Dynamic Parameter Estimation and Prediction over Consecutive Scales, Based on Moving Horizon Estimation - Applied to an Industrial Cell Culture Seed Train.” <i>Bioprocess and Biosystems Engineering</i> 44, no. 4 (2021): 793–808. <a href=\"https://doi.org/10.1007/s00449-020-02488-1 \">https://doi.org/10.1007/s00449-020-02488-1 </a>.","apa":"Hernández Rodriguez, T., Posch, C., Pörtner, R., &#38; Frahm, B. (2021). Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train. <i>Bioprocess and Biosystems Engineering</i>, <i>44</i>(4), 793–808. <a href=\"https://doi.org/10.1007/s00449-020-02488-1 \">https://doi.org/10.1007/s00449-020-02488-1 </a>","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Posch, Christoph</span> ; <span style=\"font-variant:small-caps;\">Pörtner, Ralf</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train. In: <i>Bioprocess and Biosystems Engineering</i> Bd. 44. Berlin ; Heidelberg [u.a.], Springer (2021), Nr. 4, S. 793–808"},"year":"2021","language":[{"iso":"eng"}],"_id":"7985","place":"Berlin ; Heidelberg [u.a.]","quality_controlled":"1","keyword":["Dynamic parameter estimation","Bioprocess","Cell cultures","Moving horizon estimation","Prior knowledge"],"page":"793-808"},{"status":"public","publication":"Digital Twins Tools and Concepts for Smart Biomanufacturing","publisher":"Springer","author":[{"last_name":"Hernández Rodriguez","full_name":"Hernández Rodriguez, Tanja","first_name":"Tanja","id":"52466"},{"last_name":"Frahm","first_name":"Björn","full_name":"Frahm, Björn","id":"45666"}],"abstract":[{"text":"Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development and process steps. One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities in cell behavior, and influences of changes in cultivation conditions necessitate adequate digital solutions to provide information about the current and near future process state to derive correct process decisions.\r\nFor this purpose digital seed train twins have proved to be efficient, which digitally display the time-dependent behavior of important process variables based on mathematical models, strategies, and adaption procedures.\r\nThis chapter will outline the needs for digitalization of seed trains, the construction of a digital seed train twin, the role of parameter estimation, and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed train for seed train digitalization.","lang":"eng"}],"publication_status":"published","publication_identifier":{"eisbn":["978-3-030-71660-8"],"eissn":["1616-8542"],"isbn":["978-3-030-71659-2"],"issn":["0724-6145"]},"intvolume":"       176","volume":176,"title":"Digital Seed Train Twins and Statistical Methods","page":"97–131","keyword":["Bayes","Digital twin","Parameter estimation","Seed train","Uncertainty"],"quality_controlled":"1","place":"Berlin, Heidelberg","_id":"3349","language":[{"iso":"eng"}],"year":"2021","editor":[{"last_name":"Herwig","first_name":"Christoph ","full_name":"Herwig, Christoph "},{"first_name":"Ralf ","full_name":"Pörtner, Ralf ","last_name":"Pörtner"},{"first_name":"Johannes ","full_name":"Möller, Johannes ","last_name":"Möller"}],"type":"book_chapter","citation":{"van":"Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods. In: Herwig C, Pörtner R, Möller J, editors. Digital Twins Tools and Concepts for Smart Biomanufacturing. Berlin, Heidelberg: Springer; 2021. p. 97–131. (Advances in Biochemical Engineering/Biotechnology; vol. 176).","ieee":"T. Hernández Rodriguez and B. Frahm, “Digital Seed Train Twins and Statistical Methods,” in <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, vol. 176, C. Herwig, R. Pörtner, and J. Möller, Eds. Berlin, Heidelberg: Springer, 2021, pp. 97–131. doi: <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>.","chicago":"Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins and Statistical Methods.” In <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, edited by Christoph  Herwig, Ralf  Pörtner, and Johannes  Möller, 176:97–131. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer, 2021. <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>.","mla":"Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins and Statistical Methods.” <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, edited by Christoph  Herwig et al., vol. 176, Springer, 2021, pp. 97–131, <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>.","ama":"Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods. In: Herwig C, Pörtner R, Möller J, eds. <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>. Vol 176. Advances in Biochemical Engineering/Biotechnology. Springer; 2021:97-131. doi:<a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>","short":"T. Hernández Rodriguez, B. Frahm, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digital Twins Tools and Concepts for Smart Biomanufacturing, Springer, Berlin, Heidelberg, 2021, pp. 97–131.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Digital Seed Train Twins and Statistical Methods. In: <span style=\"font-variant:small-caps;\">Herwig, C.</span> ; <span style=\"font-variant:small-caps;\">Pörtner, R.</span> ; <span style=\"font-variant:small-caps;\">Möller, J.</span> (Hrsg.): <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, <i>Advances in Biochemical Engineering/Biotechnology</i>. Bd. 176. Berlin, Heidelberg : Springer, 2021, S. 97–131","havard":"T. Hernández Rodriguez, B. Frahm, Digital Seed Train Twins and Statistical Methods, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digital Twins Tools and Concepts for Smart Biomanufacturing, Springer, Berlin, Heidelberg, 2021: pp. 97–131.","apa":"Hernández Rodriguez, T., &#38; Frahm, B. (2021). Digital Seed Train Twins and Statistical Methods. In C. Herwig, R. Pörtner, &#38; J. Möller (Eds.), <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i> (Vol. 176, pp. 97–131). Springer. <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>","ufg":"<b>Hernández Rodriguez, Tanja/Frahm, Björn</b>: Digital Seed Train Twins and Statistical Methods, in: <i>Herwig, Christoph/Pörtner, Ralf/Möller, Johannes (Hgg.)</i>: Digital Twins Tools and Concepts for Smart Biomanufacturing, Bd. 176, Berlin, Heidelberg 2021 (Advances in Biochemical Engineering/Biotechnology),  S. 97–131.","bjps":"<b>Hernández Rodriguez T and Frahm B</b> (2021) Digital Seed Train Twins and Statistical Methods. In Herwig C, Pörtner R and Möller J (eds), <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, vol. 176. Berlin, Heidelberg: Springer, pp. 97–131.","chicago-de":"Hernández Rodriguez, Tanja und Björn Frahm. 2021. Digital Seed Train Twins and Statistical Methods. In: <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, hg. von Christoph  Herwig, Ralf  Pörtner, und Johannes  Möller, 176:97–131. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer. doi:<a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>, ."},"date_created":"2020-08-19T07:14:11Z","department":[{"_id":"DEP4021"}],"date_updated":"2023-08-16T06:48:35Z","user_id":"83781","doi":"https://doi.org/10.1007/10_2020_137","series_title":"Advances in Biochemical Engineering/Biotechnology"}]