Multiscale dynamic modeling and simulation of a biorefinery

Ploch, Tobias; Zhao, Xiaohui; Hüser, Jonathan; Lieres, Eric von; Hannemann-Tamás, Ralf; Naumann, Uwe; Wiechert, Wolfgang; Mitsos, Alexander; Noack, Stephan

doi:10.1002/bit.27099

Cited by 12 publications

(10 citation statements)

References 42 publications

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“…Application of modeling to the results of bioconversion of RBY reported in this study helps to optimize the target products, as in previous studies with other brewery residues [67,68]. Bioprocessing of waste streams of the brewing industry is an attractive emerging sector, whose possibilities can be potentiated provided that the involved processes are properly optimized [69][70][71]. Accurate description and simulation of bioprocesses using mathematical models based on lab-scale results are crucial for their industrial-scale implementation with minimal economic affectations [56,72].…”

Section: Modeling As a Tool For Optimizing Bioconversion Of Brewery Residuesmentioning

confidence: 74%

Residual Brewing Yeast as Substrate for Co-Production of Cell Biomass and Biofilm Using Candida maltosa SM4

et al. 2021

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Candida maltosa was cultivated in the liquid phase of residual brewing yeast, a major brewery residue, to produce biomass and biofilm. Using response surface methodology, the effect of two variables at two different levels was investigated. The independent variables were agitation speed (at 100 and 200 rpm), and aeration (at 1 and 3 L min−1). Aeration was identified to be important for the production of both biomass and biofilm, while agitation was the only factor significantly affecting biofilm production. The maximal production of biofilm (2.33 g L−1) was achieved for agitation of 200 rpm and aeration of 1 L min−1, while the maximum for biomass (16.97 g L−1) was reached for 100 rpm agitation and 3 L min−1 air flow. A logistic model applied to predict the growth of C. maltosa in the exponential phase and the biofilm production, showed a high degree of agreement between the prediction and the actual biomass measured experimentally. The produced biofilms were further characterized using Fourier-transform infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Thermogravimetric Analysis (TGA). FTIR allowed the identification of methyl, carbonyl ester and sulfate groups, and revealed the presence of uronic acid moieties and glycosidic bonds. Water-retention ability up to relatively high temperatures was revealed by TGA, and that makes the produced biofilm suitable for production of hydrogels. SEM also gave indications on the hydrogel-forming potential of the biofilm.

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Section: Modeling As a Tool For Optimizing Bioconversion Of Brewery Residuesmentioning

confidence: 74%

Residual Brewing Yeast as Substrate for Co-Production of Cell Biomass and Biofilm Using Candida maltosa SM4

et al. 2021

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“…They have also been applied to model the cell metabolism under changes in the environment by using metabolic networks 17 . Models resulting from dynamic flux analysis are a special type of OCDE 18‐21 …”

Section: Introductionmentioning

confidence: 99%

“…In comparison to the work of Landary et al 14 and our previous work, 20,21 we do not target a specific engineering problem but rather aim to mathematically analyze the general form of OCDE. A CS‐based reformulation is presented along with rigorous formulations to construct local solutions of the original OCDE through the derived CS.…”

Section: Introductionmentioning

confidence: 99%

“…Theorem 3.4) are more general than condition (3.3) in the work 20 . Contrasting with the work, 21 we consider here OCDE with nonlinear optimization problems and the focus is on mathematical analysis and algorithm development.…”

Section: Introductionmentioning

confidence: 99%

“…Sufficient conditions are given to analyze the local existence and uniqueness of local solutions of OCDE. In particular, an OCDE is first reformulated into a CS by applying Karush‐Kuhn‐Tucker (KKT) conditions 14,20,21 . We show that (1) under local optimality conditions, namely linear independence constraint qualification (LICQ), strict complementarity condition (SCC) and second‐order sufficient condition (SOSC), the local solution of OCDE is well‐posed, which corresponds to a single mode of the derived CS, (2) under LICQ, SOSC and transversality conditions, a local solution of the original OCDE can be obtained by activating or deactivating an inequality constraint.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the local well‐posedness of optimization‐constrained differential equations by local optimality conditions

et al. 2020

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Optimization‐constrained differential equations (OCDE) are a class of mathematical problems where differential equations are constrained by an embedded algebraic optimization problem. We analyze the well‐posedness of the local solutions of OCDE based on local optimality. By assuming linear independence constraint qualification and applying the Karush‐Kuhn‐Tucker optimality conditions, an OCDE is transformed into a complementarity system (CS). Under second‐order sufficient condition we show that (a) if strict complementary condition (SCC) holds, the local solution of OCDE is well‐posed, which corresponds to a mode of the derived CS; (b) at points where SCC is violated, a local solution of OCDE exists by sequentially connecting the local solutions of two selected modes of the derived CS. We propose an event‐based algorithm to numerically solve OCDE. We illustrate the approach and algorithm for microbial cultivation, single flash unit and contrived numerical examples.

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Simultaneous design of fermentation and microbe

Ziegler,

Manchanda,

Stumm

et al. 2024

AIChE Journal

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Constraint‐based optimization of microbial strains and model‐based bioprocess design have been used extensively to enhance yields in biotechnological processes. However, strain and process optimization are usually carried out in sequential steps, causing underperformance of the biotechnological process when scaling up to industrial fermentation conditions. Herein, we propose the optimization formulation SimulKnock that combines the optimization of a fermentation process with metabolic network design in a bilevel optimization program. The upper level maximizes space‐time yield and includes mass balances of a continuous fermentation, while the lower level is based on flux balance analysis. SimulKnock predicts optimal gene deletions and finds the optimal trade‐off between growth rate and product yield. Results of a case study with a genome‐scale metabolic model of Escherichia coli indicate higher space‐time yields than a sequential approach using OptKnock for almost all target products considered. By leveraging SimulKnock, we reduce the gap between strain and process optimization.

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Multiscale dynamic modeling and simulation of a biorefinery

Cited by 12 publications

References 42 publications

Residual Brewing Yeast as Substrate for Co-Production of Cell Biomass and Biofilm Using Candida maltosa SM4

Residual Brewing Yeast as Substrate for Co-Production of Cell Biomass and Biofilm Using Candida maltosa SM4

Analysis of the local well‐posedness of optimization‐constrained differential equations by local optimality conditions

Simultaneous design of fermentation and microbe

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