Zobrazeno 1 - 10
of 1 226
pro vyhledávání: '"Neubauer, Peter"'
Silicon-based photonic biosensors, such as microring resonators and Mach-Zehnder interferometers, offer significant potential for the detection of analytes at low concentrations. To enhance response time and improve the limit of detection within prac
Externí odkaz:
http://arxiv.org/abs/2406.19534
Autor:
Lange, Christoph, Thiele, Isabel, Santolin, Lara, Riedel, Sebastian L., Borisyak, Maxim, Neubauer, Peter, Bournazou, M. Nicolas Cruz
In biotechnology Raman Spectroscopy is rapidly gaining popularity as a process analytical technology (PAT) that measures cell densities, substrate- and product concentrations. As it records vibrational modes of molecules it provides that information
Externí odkaz:
http://arxiv.org/abs/2402.00851
Mechanistic growth models play a major role in bioprocess engineering, design, and control. Their reasonable predictive power and their high level of interpretability make them an essential tool for computer aided engineering methods. Additionally, s
Externí odkaz:
http://arxiv.org/abs/2312.03427
Inferring parameters of macro-kinetic growth models, typically represented by Ordinary Differential Equations (ODE), from the experimental data is a crucial step in bioprocess engineering. Conventionally, estimates of the parameters are obtained by f
Externí odkaz:
http://arxiv.org/abs/2312.03166
Cultivation experiments often produce sparse and irregular time series. Classical approaches based on mechanistic models, like Maximum Likelihood fitting or Monte-Carlo Markov chain sampling, can easily account for sparsity and time-grid irregulariti
Externí odkaz:
http://arxiv.org/abs/2312.02079
Autor:
Krausch, Niels, Doff-Sotta, Martin, Canon, Mark, Neubauer, Peter, Bournazou, Mariano Nicolas Cruz
Bioprocesses are often characterized by nonlinear and uncertain dynamics. This poses particular challenges in the context of model predictive control (MPC). Several approaches have been proposed to solve this problem, such as robust or stochastic MPC
Externí odkaz:
http://arxiv.org/abs/2312.00847
Autor:
Duong-Trung, Nghia, Born, Stefan, Kim, Jong Woo, Schermeyer, Marie-Therese, Paulick, Katharina, Borisyak, Maxim, Cruz-Bournazou, Mariano Nicolas, Werner, Thorben, Scholz, Randolf, Schmidt-Thieme, Lars, Neubauer, Peter, Martinez, Ernesto
Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimen
Externí odkaz:
http://arxiv.org/abs/2209.01083
Autor:
Kim, Jong Woo, Krausch, Niels, Aizpuru, Judit, Barz, Tilman, Lucia, Sergio, Neubauer, Peter, Bournazou, Mariano Nicolas Cruz
We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of \textit{E. coli} fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were co
Externí odkaz:
http://arxiv.org/abs/2203.07211
Autor:
Aizpuru, Judit, Kemmer, Annina Karolin, Kim, Jong Woo, Born, Stefan, Neubauer, Peter, Bournazou, Mariano N. Cruz, Barz, Tilman
High throughput experimental systems play an important role in bioprocess development, as they provide an efficient way of analysing different experimental conditions and perform strain discrimination in previous phases to the industrial scale produc
Externí odkaz:
http://arxiv.org/abs/2112.13283
Autor:
Kim, Jong Woo, Krausch, Niels, Aizpuru, Judit, Barz, Tilman, Lucia, Sergio, Martínez, Ernesto C., Neubauer, Peter, Bournazou, Mariano Nicolas Cruz
Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model. With the recent developments in miniaturization and parallelization of cult
Externí odkaz:
http://arxiv.org/abs/2112.10548