Christina Schenk

Christina Schenk
Madrid Institute for Advanced Studies | IMDEA

Dr. rer. nat.

About

14
Publications
2,391
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
40
Citations
Introduction
I am an Applied Mathematician who is currently working at the intersection of Applied Mathematics and Mechanics. My main research interests lie in the following topics: mathematical modeling, optimization, nonlinear differential equations, numerical analysis and methods, control, data analytics, Bayesian and statistical inference, uncertainty quantification, applied analysis, machine learning, scientific computing, software development, energy and healthcare
Additional affiliations
April 2020 - present
Lawrence Berkeley National Laboratory
Position
  • Affilitate PostDoc Position
January 2020 - May 2022
Basque Center for Applied Mathematics
Position
  • PostDoc and Visiting Researcher
March 2018 - January 2020
Carnegie Mellon University
Position
  • PostDoc Position
Education
June 2013 - February 2018
Universität Trier
Field of study
  • Mathematics
April 2011 - April 2013
Universität Trier
Field of study
  • Applied Mathematics
October 2007 - August 2011
Universität Trier
Field of study
  • Applied Mathematics

Publications

Publications (14)
Conference Paper
We study energy-optimal control of the cooling process during wine fermentation. The process of wine fermentation is described by a novel model including a death phase for yeast and the influence of oxygen on the process. The parameters determining the fermentation dynamics are estimated from measurements and the optimal cooling profile is computed...
Article
Full-text available
This paper presents a novel model for wine fermentation including a death phase for yeast and the influence of oxygen on the process. A model for the inclusion of the yeast dying phase is derived and compared to a model taken from the literature. The modeling ability of the several models is analyzed by comparing their simulation results.
Preprint
Full-text available
Predictive modeling is the key factor for saving time and resources with respect to manufacturing processes such as fermentation processes arising e.g.\ in food and chemical manufacturing processes. According to Zhang et al. (2002), the open-loop dynamics of yeast are highly dependent on the initial cell mass distribution. This can be modeled via p...
Chapter
Full-text available
A large amount of energy is used in the production of wine, which has to be cooled during fermentation in order to produce a multiplicity of aromas. If not cooled, the added yeast transforms the sugar content of the raw material, the must, into alcohol without much of a taste, in very short time. Thus the cooling during the fermentation process is...
Article
Laboratory and process measurements from spectroscopic instruments are ubiquitous in pharma processes, and directly using the data can pose a number of challenges for kinetic model building. Moreover, scaling up from laboratory to industrial level requires predictive models with accurate parameter values. This means that process identification does...
Article
Biogas plants have to be continuously or periodically mixed to ensure the homogenization of fermenting and fresh substrate. Externally installed mixers provide easier access than submerged mixers but concerns of insufficient mixing deter many operators from using this technology. In this paper, a new approach to improve homogenization of the substr...
Article
This paper presents KIPET (Kinetic Parameter Estimation Toolkit) an open-source toolbox for the determination of kinetic parameters from a variety of experimental datasets including spectra and concentrations. KIPET seeks to overcome limitations of standard parameter estimation packages by applying a unified optimization framework based on maximum...
Technical Report
Published in SIAM Review Vol 61, Issue 1, pp 223-225
Preprint
Full-text available
The art of viticulture and the quest for making wines has a long tradition and it just started recently that mathematicians entered this field with their main contribution of modelling alcoholic fermentation. These models consist of systems of ordinary differential equations that describe the kinetics of the bio-chemical reactions occurring in the...
Thesis
Industrial companies mainly aim for increasing their profit. That is why they intend to reduce production costs without sacrificing the quality. Furthermore, in the context of the 2020 energy targets, energy efficiency plays a crucial role. Mathematical modeling, simulation and optimization tools can contribute to the achievement of these industria...
Article
In this paper, we study model predictive control (MPC) of the cooling process during wine fermentation. A strategy to solve nonlinear control problems with changing model parameters and changes in the states is presented. The parameters and states determining the fermentation dynamics are regularly estimated from measurements and the optimal coolin...

Network

Cited By

Projects

Projects (7)
Project
Mathematical modeling and the development of machine learning algorithms for the control of complex diffusion models applied to disease transmission dynamics.
Project
Development of algorithms and software to come up with better predictive models for applications in metabolic engineering like microbiomes and the human metabolism through Monte Carlo sampling techniques.
Project
Investigation of nonlinear mixed-effects models for kinetic parameter estimation for multiresponse cases in collaboration with The Dow Chemical Company