Mixing and product distribution for a liquid‐phase, second‐order, competitive‐consecutive reaction

Paul, Edward L.; Treybal, Robert E.

doi:10.1002/aic.690170340

Cited by 89 publications

(49 citation statements)

References 16 publications

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“…If more than one reaction occurs simultaneously, then interactions between turbulence enhanced mixing and reaction become very important, since it is recognized that selectivity to specific products is a strong function of mixing rate (Patterson, 1985). That complex reactions are strongly affected by the mixing intensity has been noted by other investigators such as Paul and Treybal (1971) and Cheng and Tookey (1978).…”

Section: A + B + Rmentioning

confidence: 79%

“…November 1995 Paul and Treybal (1971) studied the product distribution for a homogenous, liquid phase, series-parallel reaction and found that the reaction rate and/or the product distribution may be influenced by mixing if the course of the reaction is influenced by concentration. Based on laboratory measurements of the yield of the diazo-coupling reactions in a CSTR, Bourne et al (1978Bourne et al ( , 1981 observed that the product distribution from fast, series-parallel reactions, depends upon the stoichiometric ratio, the volumetric ratio of the reagent solution, the location of the feed point, the backmixing into the feed pipe, the operating mode of the reactor, the viscosity of the solutions, the type, diameter and rotation speed of the impeller, and the concentrations of the feed solution and concentrations in the tank.…”

Section: Aiche Journalmentioning

confidence: 99%

See 1 more Smart Citation

Direct numerical simulation of chemical selectivity in homogenous turbulence

Chakrabarti¹,

Kerr²,

Hill³

1995

AIChE Journal

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(Nauman, 1975), and the nitration of aromatic hydrocarbons with nitronium salts (Pfister et al., 1975) are some examples of reaction schemes exhibiting seriesparallel behavior. The results of manufacturing the wrong distribution of products are a waste of raw materials and also an increased load on the separation stages to extract the desired product in pure form. Such considerations are also important in air-pollution, propulsion, and combustion problems, which involve a wide range of complex multiple reactions.Except for combustion studies, the principal fundamental experimental work on turbulent reacting flows with complex chemistry has been carried out by Bourne (1981) who studied a particular series-parallel reaction system-the diazo-coupling reaction, now referred to as Bourne's reaction. Several 2356November 1995 subsequent experimental studies (Li and Toor, 1986;Mehta and Tarbell, 1987) have made use of Bourne's reaction. It should be noted that in Bourne's reaction, the first reaction is very fast compared to the second. Since most experimental studies were limited to just this particular reaction system, knowledge about turbulent flow with multiple reactions has been extremely limited. Advances in the power and speed of computers have now made it possible to directly solve the equations for turbulent reacting flows at low to moderate Reynolds numbers. In direct numerical simulations (DNS), the nonlinear dynamical equations are solved with no turbulence modeling and with all time and length scales well-resolved. Direct numerical simulations of three-dimensional chemically reacting homogenous turbulent flow for unmixed reactants in a single step, finite rate reaction have been made by Givi and McMurtry (1988), Leonard and Hill (1988, 1989, 1991 and Me11 et al. (1993). Some preliminary investigations on the effect of various physical and chemical parameters for the seriesparallel reaction case have been made by Chakrabarti and Hill (1990) for decaying homogenous turbulence and by Gao and O'Brien (1991) for a forced turbulent flow.In the present study, described in more detail by Chakrabarti (19911, DNS of a series-parallel reaction pair in a decaying, homogenous turbulent flow was used to predict the product distribution in the problem of chemical selectivity and to help explain the interaction of turbulence, mixing, Vol. 41, No. 11AIChE Journal and complex chemistry in this problem. The effect of turbulence, compared to the case with no motion, was studied by examining the variations of product distributions with turbulence intensity and viscosity, that is, by varying the turbulence Reynolds number. The effects of other physical parameters such as Schmidt numbers, diffusivities, reaction rate constants, -initial stoichiometric ratios, and initial scalar field conditions were also studied. Comparisons between the single reaction and the series-parallel reaction systems were made to better understand the complexity introduced by a two-step chemical reaction. These results are used in a forthcoming arti...

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Section: A + B + Rmentioning

confidence: 79%

Section: Aiche Journalmentioning

confidence: 99%

Direct numerical simulation of chemical selectivity in homogenous turbulence

Chakrabarti¹,

Kerr²,

Hill³

1995

AIChE Journal

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“…Higher concentrations of R occur in the contact region between reactants A and B, with regions rich in R delimiting the B-rich regions. When spatial inhomogeneity of A and B concentrations occur, excess of B in the presence of A yields R that is later consumed into S. This is clearly the case, with the local maximum regions of B overlapping for the three segregated injection schemes with local deficit regions of R. Such general behavior for the case of a fast consecutive competitive reaction system was noted and qualitatively discussed by Levenspiel 2 and Bourne and Toor, with subsequent experimental studies 51,52 and DNS simulations confirming the general validity of this observation. 53 The experimental selectivity in S profiles, shown in Figure 10, presents maximum values at the central exit channel.…”

Section: Comparison With the Netmix V R Modelmentioning

confidence: 81%

NETmix®, a new type of static mixer: Experimental characterization and model validation

et al. 2011

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in Wiley Online Library (wileyonlinelibrary.com).In a previous article (Laranjeira et al.), a network model was developed to describe and predict the behavior and performance of NETmix V R , a new type of static mixer consisting of a network of interconnected chambers and channels. This work reports experimental results on a transparent prototype NETmix V R unit constructed to enable the characterization of the mixing mechanisms at the local scale. Tracer flow visualization experiments show that the mixing characteristics in the NETmix V R unit depend on the Reynolds number both for macromixing and micromixing. A critical Reynolds number for the onset of mixing was obtained where oscillating flow in the chambers is observed. Chemical reaction experiments were done using test reactions systems that exhibit mixing effects in the final product distribution and selectivity. Reaction selectivity was shown to depend on the reactants injection scheme. Theoretical predictions obtained with the network model were in agreement with experimental data for high Reynolds numbers.

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“…This was first reported in the 1950s. [6][7][8] Since then much research has been devoted to the subject of mixing in different systems, [9][10][11] including multiphase flows 2,3,12,13 that are the focus of this work.…”

Section: Introductionmentioning

confidence: 99%