Simulations hybrid

In industrial applications, pervaporation has to compete with conventional separation processes, such as distillation, liquid-liquid extraction, adsorption, and stripping. Pervaporation has attracted the interest of the chemical industry for separations that are difficult to achieve by distillation, for example, separations giving azeotropic mixtures and separations of components with a small difference in volatility. 

Several authors have already developed methodologies for the simulation of hybrid distillation-pervaporation processes. Short-cut methods were developed by Moganti et al. [95] and Stephan et al. [96]. Due to simplifications such as the use of constant relative volatility, one-phase sidestreams, perfect mixing on feed and permeate sides of the membrane, and simple membrane transport models, the results obtained should only be considered qualitative in nature. Verhoef et al. [97] used a quantitative approach for simulation, based on simplified calculations in Aspen Plus/Excel VBA. Hommerich and Rautenbach [98] describe the design and optimization of combined pervaporation-distillation processes, incorporating a user-written routine for pervaporation into the Aspen Plus simulation software. This is an improvement over most approaches with respect to accuracy, although the membrane model itself is still quite 

Solvent-resistant nanofiltration and pervaporation are undoubtedly the membrane processes needed for a totally new approach in the chemical process industry, the pharmaceutical industry and similar industrial activities. This is generally referred to as process intensification and should allow energy savings, safer production, improved cost efficiency, and allow new separations to be carried out. 

Problems to be solved are related to membrane stability (of polymeric membranes, but also the development of hydrophobic ceramic nanofiltration membranes and pervaporation membranes resistant to extreme conditions), to a lack of fundamental knowledge on transport mechanisms and models, and to the need for simulation tools to be able to predict the performance of solvent-resistant nanofiltration and pervaporation in a process environment. This will require an investment in basic and applied research, but will generate a breakthrough in important societal issues such as energy consumption, global warming and the development of a sustainable chemical industry. 

1 Seader, J.D. and Henley, E.J. (2006) Separation Process Principles, John Wiley Sons, Hoboken, NJ. 

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Muino PL, Callis PR (1994) Hybrid simulations of solvation effects on electronic-spectra -indoles in water. J Chem Phys 100(6) 4093 1109... 

Callis PR, Burgess BK (1997) Tryptophan fluorescence shifts in proteins from hybrid simulations an electrostatic approach. J Phys Chem 101 9429-9432... 

Hahn [47] developed a hybrid simulation based on BD and Monte Carlo methods. Incorporation of the statistical techniques of Monte Carlo methods relaxes the constraint that time steps must be sufficiently short such that external force fields can be considered constant, and the BD improves upon the Monte Carlo methods by allowing dynamic information to be collected. Hahn applied the model to the investigation of theoretical deposition by simulating a... 

Fig. 6. Schematic of multigrid-type hybrid simulation with two grids. At the coarse grid a macroscopic model is advanced over large length and time scales. Information is passed to the macroscopic grid/coarse model from a microscopic simulation executed on a fine grid over short length and time scales. The coarse model is advanced over macroscopic length and time scales and provides to the microscopic simulation a field for constraint fine scale simulation.

Hybrid multiscale simulation is the most developed branch of multiscale simulation and will be covered in this section. The onion-type hybrid simulation... 

Several hybrid simulations on crystal growth can be found in recent literature. Examples include dendritic solidification by coupling finite-different discretization of a phase field model to a MC simulation (Plapp and Karma, 2000), coupling a finite difference for the melt with a cellular automata for the solidification (Grujicic et al., 2001), a DSMC model for the fluid phase with a Metropolis-based MC for the surface to address cluster deposition onto substrates (Hongo et al., 2002 Mizuseki et al., 2002), a step model for the surface processes coupled with a CFD simulation of flow (Kwon and Derby, 2001) (two continuum but different feature scale models), an adaptive FEM CVD model coupled with a feature scale model (Merchant et al., 2000), and one-way coupled growth models in plasma systems (Hoekstra et al., 1997). Some specific applications are discussed in more detail below. 

In these hybrid simulations, coupling happened through the boundary condition. In particular, the fluid phase provided the concentration to the KMC method to update the adsorption transition probability, and the KMC model computed spatially averaged adsorption and desorption rates, which were supplied to the boundary condition of the continuum model, as depicted in Fig. 7. The models were solved fully coupled. Note that since surface processes relax much faster than gas-phase ones, the QSS assumption is typically fulfilled for the microscopic processes one could solve for the surface evolution using the KMC method alone, i.e., in an uncoupled manner, for a combination of fluid-phase continuum model parameter values to develop a reduced model (see solution strategies on the left of Fig. 4). Note again that the QSS approach does not hold at very short (induction) times where the microscopic model evolves considerably. 

The rationale of using hybrid simulation here is that a classic diffusion-adsorption type of model, Eq. (2), can efficiently handle large distances between steps by a finite difference coarse discretization in space. As often happens in hybrid simulations, an explicit, forward discretization in time was employed. On the other hand, KMC can properly handle thermal fluctuations at the steps, i.e., provide suitable boundary conditions to the continuum model. Initial simulations were done in (1 + 1) dimensions [a pseudo-2D KMC and a ID version of Eq. (2)] and subsequently extended to (2 + 1) dimensions [a pseudo-3D KMC and a 2D version of Eq. (2)] (Schulze, 2004 Schulze et al., 2003). Again, the term pseudo is used as above to imply the SOS approximation. Speedup up to a factor of 5 was reported in comparison with KMC (Schulze, 2004), which while important, is not as dramatic, at least for the conditions studied. As pointed out by Schulze, one would expect improved speedup, as the separation between steps increases while the KMC region remains relatively fixed in size. At the same time, implementation is definitely complex because it involves swapping a microscopic KMC cell with continuum model cells as the steps move on the surface of a growing film. 

The discussion above focused on onion-type hybrid multiscale simulation. Finally, even though there are a limited number of examples published, I expect that the multigrid-type hybrid simulations share the same problems with onion-type hybrid multiscale models. In addition, appropriate boundary conditions for the microscopic grid model need to be developed to increase the accuracy and robustness of the hybrid scheme. Furthermore, the inverse problem of mapping coarse-grid information into a microscopic grid is ill posed. Thus, it is... 

As another example of hybrid simulation touched upon above, Haseltine and Rawlings (2002) treated fast reactions either deterministically or with Langevin equations and slow reactions as stochastic events. Vasudeva and Bhalla (2004) presented an adaptive, hybrid, deterministic-stochastic simulation scheme of fixed time step. This scheme automatically switches reactions from one type to the other based on population size and magnitude of transition probability. 

In this section, the power and the current limitations of AIMD in studying enzyme function is illustrated by a survey of selected recent applications. First, we present calculations on two very-well known enzymes, which are meant as benchmark studies for subsequent applications. Then, we outline application to pharmaceutical research and finally, we conclude this section by presenting state-of-the-art, QM/MM Car-Parrinello hybrid simulations on an enzyme relevant for synthetic and biotechnological applications. 

In this paper, we present a method for the fault detection and isolation based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurement using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical process. 

Keywords Fault Detection and Isolation, Extended Kalman Filter, Dynamic Hybrid Simulation, Object Differential Petri nets. Distance. 

The research works performed for several years within the PSE research department (LGC) on process modelling and simulation have led to the development of PrODHyS. This environment provides a library of classes dedicated to the dynamic hybrid simulation of processes. Based on object concepts, PrODHyS offers extensible and reusable software components allowing a rigorous and systematic modelling of processes. The primal contribution of these works consisted in determining and designing the foundation buildings classes. 

The last important evolution of PrODHyS is the integration of a dynamic hybrid simulation kernel (Ferret et al., 2004 Olivier et al., 2006, 2007). Indeed, the nature of the studied phenomena involves a rigorous description of the continuous and discrete dynamic. The use of Differential and Algebraic Equations (DAE) systems seems obvious for the description of continuous aspects. Moreover the high sequential aspect of the considered systems justifies the use of Petri nets model. This is why the Object Differential Petri Nets (ODPN) formalism is used to describe the simulation model associated with each component. It combines in the same structure a set of DAE systems and high level Petri nets (defining the legal sequences of commutation between states) and has the ability to detect state and time events. More details about the formalism ODPN can be found in previous papers (Ferret et al., 2004). 

Olivier N., G. Hetreux and J.M. LeLann (2007). Eault detection using a hybrid dynamic simulator Application to a hydraulic system CMS 07 Buenos Aires, Argentina Olivier N., G. Hetreux, J.M. LeLann and M.V. LeLann (2006). Use of an Object Oriented Dynamic Hybrid Simulator for the Monitoring of Industrial Processes, ADHS 06, Alghero, Italia... 

C.P. Naveira-Cotta, M. Lachi, M. Rebay, and R.M. Cotta, Comparison of Experiments and Hybrid Simulations of Transient Conjugated Conduction-... 

The hybrid simulation approach used here for the swirl combustor configuration in Fig. 11.1 involves effective boundary conditions emulating the fuel nozzle and LES to study the flow within the combustor. Case studies ranging from single-swirler to more complex triple-swirler nozzles were investigated. The inlet boundary conditions used to initialize the combustor flow involve velocity... 

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