[{"title":"View on a mechanistic model of Chlorella vulgaris in incubated shake flasks","publication_identifier":{"eissn":["1615-7605"],"issn":["1615-7591 "]},"volume":45,"intvolume":"        45","publisher":"Springer","author":[{"full_name":"Kuhfuß, Fabian","first_name":"Fabian","last_name":"Kuhfuß"},{"id":"74048","first_name":"Veronika","full_name":"Gassenmeier, Veronika","last_name":"Gassenmeier"},{"id":"52121","last_name":"Deppe","first_name":"Sahar","full_name":"Deppe, Sahar"},{"id":"73814","full_name":"Ifrim, George Adrian","first_name":"George Adrian","last_name":"Ifrim"},{"id":"52466","last_name":"Hernández Rodriguez","full_name":"Hernández Rodriguez, Tanja","first_name":"Tanja"},{"last_name":"Frahm","first_name":"Björn","full_name":"Frahm, Björn","id":"45666"}],"publication_status":"published","abstract":[{"lang":"eng","text":"Kinetic growth models are a useful tool for a better understanding of microalgal cultivation and for optimizing cultivation conditions. The evaluation of such models requires experimental data that is laborious to generate in bioreactor settings. The experimental shake flask setting used in this study allows to run 12 experiments at the same time, with 6 individual light intensities and light durations. This way, 54 biomass data sets were generated for the cultivation of the microalgae Chlorella vulgaris. To identify the model parameters, a stepwise parameter estimation procedure was applied. First, light-associated model parameters were estimated using additional measurements of local light intensities at differ heights within medium at different biomass concentrations. Next, substrate related model parameters were estimated, using experiments for which biomass and nitrate data were provided. Afterwards, growth-related model parameters were estimated by application of an extensive cross validation procedure."}],"status":"public","publication":"Bioprocess and Biosystems Engineering","user_id":"83781","date_updated":"2024-08-05T07:07:37Z","department":[{"_id":"DEP4021"}],"date_created":"2022-05-05T11:28:56Z","doi":"10.1007/s00449-021-02627-2","year":"2022","type":"scientific_journal_article","citation":{"apa":"Kuhfuß, F., Gassenmeier, V., Deppe, S., Ifrim, G. A., Hernández Rodriguez, T., &#38; Frahm, B. (2022). View on a mechanistic model of Chlorella vulgaris in incubated shake flasks. <i>Bioprocess and Biosystems Engineering</i>, <i>45</i>, 15–30. <a href=\"https://doi.org/10.1007/s00449-021-02627-2\">https://doi.org/10.1007/s00449-021-02627-2</a>","ufg":"<b>Kuhfuß, Fabian u. a.</b>: View on a mechanistic model of Chlorella vulgaris in incubated shake flasks, in: <i>Bioprocess and Biosystems Engineering</i> 45 (2022),  S. 15–30.","chicago-de":"Kuhfuß, Fabian, Veronika Gassenmeier, Sahar Deppe, George Adrian Ifrim, Tanja Hernández Rodriguez und Björn Frahm. 2022. View on a mechanistic model of Chlorella vulgaris in incubated shake flasks. <i>Bioprocess and Biosystems Engineering</i> 45: 15–30. doi:<a href=\"https://doi.org/10.1007/s00449-021-02627-2\">10.1007/s00449-021-02627-2</a>, .","bjps":"<b>Kuhfuß F <i>et al.</i></b> (2022) View on a Mechanistic Model of Chlorella Vulgaris in Incubated Shake Flasks. <i>Bioprocess and Biosystems Engineering</i> <b>45</b>, 15–30.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Kuhfuß, Fabian</span> ; <span style=\"font-variant:small-caps;\">Gassenmeier, Veronika</span> ; <span style=\"font-variant:small-caps;\">Deppe, Sahar</span> ; <span style=\"font-variant:small-caps;\">Ifrim, George Adrian</span> ; <span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: View on a mechanistic model of Chlorella vulgaris in incubated shake flasks. In: <i>Bioprocess and Biosystems Engineering</i> Bd. 45. Berlin, Springer (2022), S. 15–30","havard":"F. Kuhfuß, V. Gassenmeier, S. Deppe, G.A. Ifrim, T. Hernández Rodriguez, B. Frahm, View on a mechanistic model of Chlorella vulgaris in incubated shake flasks, Bioprocess and Biosystems Engineering. 45 (2022) 15–30.","short":"F. Kuhfuß, V. Gassenmeier, S. Deppe, G.A. Ifrim, T. Hernández Rodriguez, B. Frahm, Bioprocess and Biosystems Engineering 45 (2022) 15–30.","ieee":"F. Kuhfuß, V. Gassenmeier, S. Deppe, G. A. Ifrim, T. Hernández Rodriguez, and B. Frahm, “View on a mechanistic model of Chlorella vulgaris in incubated shake flasks,” <i>Bioprocess and Biosystems Engineering</i>, vol. 45, pp. 15–30, 2022, doi: <a href=\"https://doi.org/10.1007/s00449-021-02627-2\">10.1007/s00449-021-02627-2</a>.","van":"Kuhfuß F, Gassenmeier V, Deppe S, Ifrim GA, Hernández Rodriguez T, Frahm B. View on a mechanistic model of Chlorella vulgaris in incubated shake flasks. Bioprocess and Biosystems Engineering. 2022;45:15–30.","ama":"Kuhfuß F, Gassenmeier V, Deppe S, Ifrim GA, Hernández Rodriguez T, Frahm B. View on a mechanistic model of Chlorella vulgaris in incubated shake flasks. <i>Bioprocess and Biosystems Engineering</i>. 2022;45:15-30. doi:<a href=\"https://doi.org/10.1007/s00449-021-02627-2\">10.1007/s00449-021-02627-2</a>","chicago":"Kuhfuß, Fabian, Veronika Gassenmeier, Sahar Deppe, George Adrian Ifrim, Tanja Hernández Rodriguez, and Björn Frahm. “View on a Mechanistic Model of Chlorella Vulgaris in Incubated Shake Flasks.” <i>Bioprocess and Biosystems Engineering</i> 45 (2022): 15–30. <a href=\"https://doi.org/10.1007/s00449-021-02627-2\">https://doi.org/10.1007/s00449-021-02627-2</a>.","mla":"Kuhfuß, Fabian, et al. “View on a Mechanistic Model of Chlorella Vulgaris in Incubated Shake Flasks.” <i>Bioprocess and Biosystems Engineering</i>, vol. 45, 2022, pp. 15–30, <a href=\"https://doi.org/10.1007/s00449-021-02627-2\">https://doi.org/10.1007/s00449-021-02627-2</a>."},"place":"Berlin","quality_controlled":"1","_id":"7977","language":[{"iso":"eng"}],"page":"15-30"},{"issue":"4","publication":"Bioprocess and Biosystems Engineering","status":"public","abstract":[{"lang":"eng","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."}],"publication_status":"published","publisher":"Springer","author":[{"id":"52466","first_name":"Tanja","full_name":"Hernández Rodriguez, 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"},{"full_name":"Frahm, Björn","first_name":"Björn","last_name":"Frahm","id":"45666"}],"intvolume":"        44","volume":44,"publication_identifier":{"issn":["1615-7591"],"eissn":["1615-7605"]},"title":"Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train","keyword":["Dynamic parameter estimation","Bioprocess","Cell cultures","Moving horizon estimation","Prior knowledge"],"page":"793-808","_id":"7985","language":[{"iso":"eng"}],"quality_controlled":"1","place":"Berlin ; Heidelberg [u.a.]","citation":{"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.","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>.","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>.","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>","short":"T. Hernández Rodriguez, C. Posch, R. Pörtner, B. Frahm, Bioprocess and Biosystems Engineering 44 (2021) 793–808.","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","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.","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>","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>, .","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."},"type":"scientific_journal_article","year":"2021","doi":"10.1007/s00449-020-02488-1 ","date_created":"2022-05-05T13:06:12Z","department":[{"_id":"DEP4021"}],"date_updated":"2023-08-21T07:51:38Z","user_id":"83781"},{"title":"Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses","volume":44,"intvolume":"        44","publication_identifier":{"eissn":["1615-7605"],"isbn":["1615-7591"]},"publication_status":"published","abstract":[{"lang":"eng","text":"Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.org/10.1007/s00449-019-02089-7, 2019) was extended and implemented into a software (“mDoE-toolbox”) to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration."}],"publisher":"Springer","author":[{"full_name":"Moser, André","first_name":"André","last_name":"Moser"},{"full_name":"Kuchemüller, Kim B.","first_name":"Kim B.","last_name":"Kuchemüller"},{"full_name":"Deppe, Sahar","first_name":"Sahar","last_name":"Deppe"},{"last_name":"Hernández Rodriguez","first_name":"Tanja","full_name":"Hernández Rodriguez, Tanja","id":"52466"},{"id":"45666","first_name":"Björn","full_name":"Frahm, Björn","last_name":"Frahm"},{"first_name":"Ralf","full_name":"Pörtner, Ralf","last_name":"Pörtner"},{"last_name":"Hass","full_name":"Hass, Volker C.","first_name":"Volker C."},{"full_name":"Möller, Johannes","first_name":"Johannes","last_name":"Möller"}],"publication":"Bioprocess and Biosystems Engineering","issue":"4","status":"public","doi":"10.1007/s00449-020-02478-3","user_id":"83781","date_updated":"2023-08-21T07:55:05Z","date_created":"2022-05-05T13:10:11Z","department":[{"_id":"DEP4021"}],"type":"scientific_journal_article","citation":{"van":"Moser A, Kuchemüller KB, Deppe S, Hernández Rodriguez T, Frahm B, Pörtner R, et al. Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses. Bioprocess and Biosystems Engineering. 2021;44(4):683–700.","ieee":"A. Moser <i>et al.</i>, “Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses,” <i>Bioprocess and Biosystems Engineering</i>, vol. 44, no. 4, pp. 683–700, 2021, doi: <a href=\"https://doi.org/10.1007/s00449-020-02478-3\">10.1007/s00449-020-02478-3</a>.","chicago":"Moser, André, Kim B. Kuchemüller, Sahar Deppe, Tanja Hernández Rodriguez, Björn Frahm, Ralf Pörtner, Volker C. Hass, and Johannes Möller. “Model-Assisted DoE Software: Optimization of Growth and Biocatalysis in Saccharomyces Cerevisiae Bioprocesses.” <i>Bioprocess and Biosystems Engineering</i> 44, no. 4 (2021): 683–700. <a href=\"https://doi.org/10.1007/s00449-020-02478-3\">https://doi.org/10.1007/s00449-020-02478-3</a>.","mla":"Moser, André, et al. “Model-Assisted DoE Software: Optimization of Growth and Biocatalysis in Saccharomyces Cerevisiae Bioprocesses.” <i>Bioprocess and Biosystems Engineering</i>, vol. 44, no. 4, 2021, pp. 683–700, <a href=\"https://doi.org/10.1007/s00449-020-02478-3\">https://doi.org/10.1007/s00449-020-02478-3</a>.","ama":"Moser A, Kuchemüller KB, Deppe S, et al. Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses. <i>Bioprocess and Biosystems Engineering</i>. 2021;44(4):683-700. doi:<a href=\"https://doi.org/10.1007/s00449-020-02478-3\">10.1007/s00449-020-02478-3</a>","short":"A. Moser, K.B. Kuchemüller, S. Deppe, T. Hernández Rodriguez, B. Frahm, R. Pörtner, V.C. Hass, J. Möller, Bioprocess and Biosystems Engineering 44 (2021) 683–700.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Moser, André</span> ; <span style=\"font-variant:small-caps;\">Kuchemüller, Kim B.</span> ; <span style=\"font-variant:small-caps;\">Deppe, Sahar</span> ; <span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span> ; <span style=\"font-variant:small-caps;\">Pörtner, Ralf</span> ; <span style=\"font-variant:small-caps;\">Hass, Volker C.</span> ; <span style=\"font-variant:small-caps;\">Möller, Johannes</span>: Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses. In: <i>Bioprocess and Biosystems Engineering</i> Bd. 44. Berlin ; Heidelberg [u.a.], Springer (2021), Nr. 4, S. 683–700","havard":"A. Moser, K.B. Kuchemüller, S. Deppe, T. Hernández Rodriguez, B. Frahm, R. Pörtner, V.C. Hass, J. Möller, Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses, Bioprocess and Biosystems Engineering. 44 (2021) 683–700.","apa":"Moser, A., Kuchemüller, K. B., Deppe, S., Hernández Rodriguez, T., Frahm, B., Pörtner, R., Hass, V. C., &#38; Möller, J. (2021). Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses. <i>Bioprocess and Biosystems Engineering</i>, <i>44</i>(4), 683–700. <a href=\"https://doi.org/10.1007/s00449-020-02478-3\">https://doi.org/10.1007/s00449-020-02478-3</a>","chicago-de":"Moser, André, Kim B. Kuchemüller, Sahar Deppe, Tanja Hernández Rodriguez, Björn Frahm, Ralf Pörtner, Volker C. Hass und Johannes Möller. 2021. Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses. <i>Bioprocess and Biosystems Engineering</i> 44, Nr. 4: 683–700. doi:<a href=\"https://doi.org/10.1007/s00449-020-02478-3\">10.1007/s00449-020-02478-3</a>, .","ufg":"<b>Moser, André u. a.</b>: Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses, in: <i>Bioprocess and Biosystems Engineering</i> 44 (2021), H. 4,  S. 683–700.","bjps":"<b>Moser A <i>et al.</i></b> (2021) Model-Assisted DoE Software: Optimization of Growth and Biocatalysis in Saccharomyces Cerevisiae Bioprocesses. <i>Bioprocess and Biosystems Engineering</i> <b>44</b>, 683–700."},"year":"2021","_id":"7986","language":[{"iso":"eng"}],"place":"Berlin ; Heidelberg [u.a.]","quality_controlled":"1","page":"683-700","keyword":["Biocatalysis","Monte Carlo methods","Fed-batch strategy","Model-assisted design of experiments","Quality by design"]}]