Object-oriented modeling of distillation processes

Tränkle, Frank; Kienle, Achim; Mohl, K. D.; Zeitz, Michael; Gilles, Ernst Dieter

doi:10.1016/s0098-1354(99)80182-3

Cited by 9 publications

(8 citation statements)

References 4 publications

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“…The same applies to N NS1,NS2 = 1=8 j NS1,NS2 . Numerical algorithms to solve the given system of ODE were provided by DIVA (Kroener et al, 1990) and ProMoT (Traenkle et al, 1999), a software package to build structured dynamic simulation models.…”

Section: Initial Conditions Kinetic Parameters and Modeling Assumptmentioning

confidence: 99%

Structured model of influenza virus replication in MDCK cells

Sidorenko

Reichl

2004

Biotech & Bioengineering

110

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Intracellular events that take place during influenza virus replication in animal cells are well understood qualitatively. However, to better understand the complex interaction of the virus with its host cell and to quantitatively analyze the use of cellular resources for virion formation or the overall dynamic for the entire infection cycle, a mathematical model for influenza virus replication has to be formulated. Here, we present a structured model for the single-cell reproductive cycle of influenza A virus in animal cells that accounts for the individual steps of the process such as attachment, internalization, genome replication and translation, and progeny virion assembly. The model describes an average cell surrounded by a small quantity of medium and infected by a low number of virus particles. The model allows estimation of the cellular resources consumed by virus replication. Simulation results show that the number of cellular surface receptors and endosomes, as well as other resources, such as the number of free nucleotides or amino acids, is not significantly influenced by influenza virus propagation. A factor that limits the growth rate of progeny viruses and their release is the total amount of matrix proteins (M1) in the nucleus while other newly synthesized viral proteins (e.g., nucleoprotein NP) and viral RNAs accumulate. During budding, synthesis of vRNPs (viral ribonucleoprotein complexes) represents another limiting factor. Based on this model it is also possible to analyze effects of parameter changes on the dynamics of virus replication, to identify possible targets for molecular engineering, or to develop strategies for improving yields in vaccine production processes. Furthermore, a better insight into the interactions of viruses and host cells might help to improve our understanding of virus-related diseases and to develop therapies.

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Section: Initial Conditions Kinetic Parameters and Modeling Assumptmentioning

confidence: 99%

Structured model of influenza virus replication in MDCK cells

Sidorenko

Reichl

2004

Biotech & Bioengineering

110

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“…They can derive their own modeling entities from the pre-defined modeling entities by aggregation and inheritance. The design and application of a knowledge base for modeling distillation processes are presented in [25,24].…”

Section: Basic Modeling Entitiesmentioning

confidence: 99%

“…Existing modeling entities can be modified or extended in subclasses without modifying themselves. Thus, inheritance next to aggregation is the fundamental of the design of knowledge bases that contain reusable modeling entities [25,24].…”

Section: Coupling Relationshipsmentioning

confidence: 99%

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PROMOT: A Modeling Tool for Chemical Processes

Tränkle¹,

Zeitz²,

Ginkel³

et al. 2000

Mathematical and Computer Modelling of Dynamical Systems

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The novel process modeling tool PROMOTsupports the object-oriented modeling of chemical processes for the simulation environment DIVA. In PROMOT, differential-algebraic process models can be built by aggregating structural and behavioral modeling entities that represent the topological structure or the dynamic and steady-state behavior, respectively, of the investigated chemical processes. Process models and their modeling entities may be defined either in an object-oriented modeling language or with a graphical user interface. This paper discusses the modeling concept, the modeling language, the knowledge representation aspects, and the implementation of PROMOT.

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“…As this concept was already used for the structuring of various other chemical engineering [5,6] and even biological applications [7], the special focus of this paper will be on the hierarchical structuring of a fuel cell as an electrochemical system.…”

Section: Introductionmentioning

confidence: 99%

Application of Hierarchical Process Modelling Strategies to Fuel Cell Systems – Towards a Virtual Fuel Cell Laboratory

Hanke¹,

Mangold²,

Sundmacher³

2005

Fuel Cells

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There is an increasing need for adequate modelling and simulation tools for the design and analysis of fuel cell systems. In the present contribution, a modular modelling strategy is proposed, which is based on network theory for chemical engineering processes.According to this network theory, a fuel cell system is decomposed into elementary units on several hierarchical levels (process unit level, phase level, storage level). After decomposition, the model formulation starts on the storage level: electrochemical source terms were combined with the diffusive and convective transport phenomena and state equations, forming an elementary unit of the phase level. On the phase level several thermodynamic phases (e.g. fluid compartments, electrode backings, catalyst layers, and the membrane electrolyte) are aggregated to a single fuel cell unit. Finally on the top level, the process unit level, single cells or fuel cell stacks are combined with other process units to form a complete process model.This modelling procedure is demonstrated with a simple proton exchange membrane fuel cell system operated with hydrogen and oxygen.

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Object-oriented modeling of distillation processes

Cited by 9 publications

References 4 publications

Structured model of influenza virus replication in MDCK cells

Structured model of influenza virus replication in MDCK cells

PROMOT: A Modeling Tool for Chemical Processes

Application of Hierarchical Process Modelling Strategies to Fuel Cell Systems – Towards a Virtual Fuel Cell Laboratory

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