Christoph Kirse

Affiliations: 
TUM - TU München 
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"Christoph Kirse"
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Foerst P, Carvalho TMd, Lechner M, et al. (2019) Estimation of mass transfer rate and primary drying times during freeze-drying of frozen maltodextrin solutions based on x-ray μ-computed tomography measurements of pore size distributions Journal of Food Engineering. 260: 50-57
Kirse C, Kindlein M, Luxenburger F, et al. (2019) Analysis of two common algorithms to compute self-diffusion coefficients in infinite dilution from molecular dynamics simulations and application to n-alkanes (C1 to C35) in water Fluid Phase Equilibria. 485: 211-219
Schmideder S, Kirse C, Hofinger J, et al. (2018) Modeling the Separation of Microorganisms in Bioprocesses by Flotation Processes. 6: 184
Kuhn M, Kirse C, Briesen H. (2018) Population Balance Modeling and Opinion Dynamics—A Mutually Beneficial Liaison? Processes. 6: 164
Ho YK, Kirse C, Briesen H, et al. (2018) Towards improved predictions for the enzymatic chain-end scission of natural polymers by population balances: the need for a non-classical rate kernel Chemical Engineering Science. 176: 329-342
Shamshuddin Z, Kirse C, Briesen H, et al. (2018) Mathematical modelling of AHL production in Exiguobacterium MPO strain Biochemical Engineering Journal. 138: 54-62
Kuhn M, Kirse C, Briesen H. (2018) Improving the design of depth filters: A model‐based method using optimal control theory Aiche Journal. 64: 68-76
Kirse C, Briesen H. (2017) Temperature profile optimization: potential for multi-enzymatic biopolymer depolymerization processes. Bioprocess and Biosystems Engineering. 40: 867-876
Kirse C, Briesen H. (2015) Numerical solution of mixed continuous–discrete population balance models for depolymerization of branched polymers Computers & Chemical Engineering. 73: 154-171
Kirse C, Briesen H. (2015) Population balance model for enzymatic depolymerization of branched starch Computer-Aided Chemical Engineering. 37: 221-226
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