An important application concerns residencetimedistribution measurements of reactors in continuous flow processes. In these systems the reactor is defined as the primary system volume in which the desired chemical transformation takes place. Often there will be more than one chemical reaction occurring at the required conditions, such as competing reactions or decomposition of the product. The various reaction pathways are dependent on the reaction parameters, which include residencetime in the reactor; based on an understanding of the various pathway kinetics, optimal processing conditions usually requires a specific residencetime with minimal distribution. Jumbam (Jumbam, Skilton et al. 2012) have demonstrated the ability to optimise the methylation of alcohols in continuous flow supercritical carbon dioxide; the yield and selectivity are shown to depend on residencetime among other reaction parameters. Another practical example is the continuous hydrothermal synthesis of nanomaterials in supercritical fluids; Adschiri (Adschiri, Hakuta et al. 2001) first proposed a mechanism for the generation of metal oxides using this technique. With countless applications, the properties of nanomaterials are greatly influenced by particle size, which is often in turn dictated by residencetime in the system reactor. For many nanomaterial species where nucleation is virtually instantaneous, particle growth can be directly related to time spent in the reactor, as studied by various groups including Norby (Norby, Jensen et al. 2013); thus the residencetimedistribution in the reactor will directly translate to particle size distribution in the product solution. The use of different reactor geometries by Blood (Blood, Denyer et al. 2004) for continuous hydrothermal nanomaterial synthesis have been investigated previously and it is well known that mixing dynamics has a significant impact on particle nucleation and growth. In addition to reactor design issues, other control variables for this continuous hydrothermal process were discussed by Lester (Lester, Aksomaityte et al. 2012), including temperature, flow rate, flow ratio, pipe diameter and pressure. A recent project scales this process from bench to pilot through to industrial scale, at over 100 tons per year capacity, with the FP7 Sustainable Hydrothermal Manufacturing of Nanomaterials (SHYMAN) project (Particles 2015, Shyman 2015). Better control of products from this process, and the inevitable changes in product quality (as a result of scaling the process) will become a focus for the industry and therefore understanding residencetime distributions and mixing dynamics is essential.
Abstract: The quantified residencetimedistribution (RTD) provides a numerical characterization of mixing in the continue casting tundish, thus allowing the engineer to better understand the metallurgical performance of the reactor. This paper describes a computational fluid dynamic (CFD) modelling study for analyzing the flow pattern and the residencetimedistribution in a five-strand tundish. Two passive scalar transport equations are applied to separately calculate the E-curve and F-curve in the tundish. The numerical modelling results are compared to the water modelling results for the validation of the mathematical model. The volume fraction of different flow regions (plug, mixed and dead) and the intermixing time during the ladle changeover are calculated to study the effects of the flow control device (FCD) on the tundish performance. The result shows that a combination of the U-baffle with deflector holes and the turbulence inhibitor has three major effects on the flow characteristics in the tundish: i) reduce the extent of the dead volume; ii) evenly distribute the liquid streams to each strand and iii) shorten the intermixing time during the ladle changeover operation.
flow rates by measuring the residencetimedistribution using two probes placed on the inner surface o f the outer cylinder. All o f their measurements were taken in the range o f 1 < Ta/Tac < 1 2 . And 0 < Re < 23. They showed that the toroidal vortices motion o f liquid elements caused highly effective radial mixing within cellular vortices, whereas the cell boundaries prevented liquid elements from being exchanged over the vortex inflow boundaries. Each pair o f vortices could be regarded as a well-mixed batch vessel, which moved axially at a constant velocity. The vortices, whose size was approximately equal to the annular gap, marched through the annulus at a constant velocity equal to the mean axial velocity. Therefore, it could be considered that all the liquid elements leaving the annulus had the same residencetime in the apparatus.
From the result, the jet clarifier can be used for removing turbidity in the water treatment. In fact, it is a conceptually simple process, but complex in practice. The process design is mainly based on empirical rules and experience rather than on general design criteria . As a result, the parameters affecting the performance should be thoroughly investigated. The hydrodynamic in the reactor, which relates to flow pattern in the reactor, is one among the important factors in a jet clarifier. Therefore, it was studied in detail by the residencetimedistribution (RTD) approach as in the next section.
Reaction conversion in riser and downer reactors is of primary interest in the design and operation of CFBs (Kunii & Levenspiel, 1997). A maximum yield of very specific products is desired; this yield depends on the conversion rate and the unit throughput. Since the reaction kinetics and conversion in CFB units are time-dependent phenomena, they are both therefore dependent on the residencetimedistribution and gas-solids contact time. This is especially important for catalytic cracking or fast pyrolysis reactors, in which the desired product is an intermediate product: in both types of reactor, it is important to keep the vapor residencetime within a narrow band, as longer residence times lead to undesired non-condensable gases. Hence, knowledge of the flow and mixing behavior is of critical importance in CFB design and operation. The flow patterns and mixing / contact experienced by each phase determine the amount of time spent in different zones of the reactor by each phase, which in turn impact on the conversion and product yield.
Abstract The residencetimedistribution (RTD) of mineral slurry and slurry holdup volume in an industrial ball mill has been successfully studies using tracer tests. Six different conditions of solids concentration and three levels of ball loading were assessed. The effects of the slurry solids concentration and the ball loading on the mean residencetime of slurry were clearly depicted in the results. Using the mass balance of salt tracer inside the mill, the slurry flow rate through the mill was estimated and the calculated values compared reasonably with the measured ones within the limits of experimental error. Further, based on the measured flow rates and residence times, it was possible to estimate the slurry holdup volume inside the mill. The correlation between slurry holdup volume with ball loading, slurry solids concentration and feed flow rate yielded plausible results. ANOVA statistical tests (F-test and t-test) were utilized to assess the adequacy of the correlation equation and the significance of the correlation coefficients.
number resulted in an increase in the Peclet number (ratio of convectional mass transfer to diffusion mass transfer). Experimental tests were conducted with 3, 9, 15, 27, and 33 bends. As the number of bends increased, a slight increase in axial dispersion coefficient resulted, which was attributed to the effect of the straight sections between the bends. In the helical tube, transition from laminar to turbulent regime occurred at Reynolds number values of 8,000 - 10,000. After this transition, the turbulent regime dominated the effect of secondary flow and dispersion increased with further increase in Reynolds number. The chaotic system was found to be similar to the helical system in terms of critical Reynolds number at which the transition occurs. For Re < 2,500, axial dispersions in the two systems were similar. However, as Reynolds number increased further, the residencetimedistribution narrowed in the chaotic configuration due to the chaotic advection effects. To compare the efficiency of these two systems as mixing devices, Peclet number was plotted against Reynolds number. Pressure drop or energy consumption were not taken into account. In addition, for Re < 2,500, the agreement with the plug flow axial dispersion model and residencetime data was less reliable for the chaotic system (tails of the residencetimedistribution curves were wider). This study revealed that the use of the chaotic system would result in narrow
Abstract––Residencetimedistribution (RTD) measurements were carried out when electrolyte flows through a tubular electrochemical reactor having parallel plate electrodes using impulse response experiments. The experiments were performed at flow rates varying from 10 – 40 lph. A two-parameter model has been developed to explain the flow characteristics of electrolyte in the reactor and the proposed model is validated with experimental observations.
We compared four different system identification methods to estimate the residencetimedistribution. The identification was surprisingly not straight- forward and the results we got did not always match our expectations. We found out that the identification in time-domain from impulse response is not possible with the parametric model structures, such as ARX. We did get good identification results using methods from realization theory, such as balanced truncation and the eigensystem realization algorithm. In this project we got the best estimation results by using the methods from the realization theory together with an estimated time-delay of the system. In the last section of Chapter 3 we tried, but did not succeed in finding a more realistic positive realization of low order of the system. From the literature we know that to find such a positive realization is a difficult task with a lot of not yet solved problems. Nevertheless, for further research it would be interesting to find a (minimal) positive realization of the system.
Radiotracer technology is a technique of radioactive injection into the system and the detection is done using radiation detector. It is also a tool for investigating and solving plant process problems namely process malfunctions and mechanical damages. Radiotracer is the most preferred stimulus response techniques in the industries due to its non-invasive application and on line monitoring capabilities, which avoid shut down of the plant. Radiotracer techniques have many advantages, such as high detection sensitivity, in-situ detection, availability of a wide range of compatible radiotracers for different phases, rapid response and high reliability and accuracy of the results. The residencetimedistribution (RTD) is one of the important parameters that can provide information on the characteristics or hydrodynamics of the nuclear reactor. In this paper, the overall review is presented in brief regarding radiotracer technology in plant operation.
reactor. The tracer must not disturb the flow pattern of the system. The analysis of the output concentration with time, gives the desired information about the system and helps to determine the residencetimedistribution function E (t) [15, 16]. The RTD curve can be used as a diagnostic tool for ascertaining features of flow patterns in reactors. These include the possibilities of bypassing and/or regions of stagnant fluid (i.e., dead space). Since these maldistributions can cause unpredictable conversions in reactors, they are usually detrimental to reactor operation. According to Levenspiel in , the application of the RTD to the prediction of reactor behavior is based on the assumption that each fluid (assume constant density) behaves as a batch reactor and that the total reactor conversion is then the average of the fluid elements, that is:
The mean residencetime and the variance of distribution which are RTD characterizing parameters for a stirred-tank reactor and 2 stirred-tank reactors in series were also evaluated empirically and tabulated in Table 6. However, Skewness and kurtosis (peakedness) of residencetimedistribution (i.e., third moment and fourth moment in that order though, not considered in this work) are also empirical parameters obtained from E(t).The choice of RTD characterizing parameters is a matter of balancing complexity against the required degree of precision . However, theoretical RTD (N-CSTR Theoretical Flow and Axial Dispersion) model parameters can as well be determined through E(t). This is discussed next.
Abstract. Artificial recharge of aquifers is a technique for improving water quality and increasing groundwater re- sources. Understanding the fate of a potential contaminant requires knowledge of the residencetimedistribution (RTD) of the recharged water in the aquifer beneath. A simple way to obtain the RTDs is to perform a tracer test. We performed a pulse injection tracer test in an artificial recharge sys- tem through an infiltration basin to obtain the breakthrough curves, which directly yield the RTDs. The RTDs turned out to be very broad and we used a numerical model to interpret them, to characterize heterogeneity, and to extend the model to other flow conditions. The model comprised nine layers at the site scaled to emulate the layering of aquifer deposits. Two types of hypotheses were considered: homogeneous (all flow and transport parameters identical for every layer) and heterogeneous (diverse parameters for each layer). The pa- rameters were calibrated against the head and concentration data in both model types, which were validated quite satisfac- torily against 1,1,2-Trichloroethane and electrical conductiv- ity data collected over a long period of time with highly vary- ing flow conditions. We found that the broad RTDs can be attributed to the complex flow structure generated under the basin due to three-dimensionality and time fluctuations (the homogeneous model produced broad RTDs) and the hetero- geneity of the media (the heterogeneous model yielded much
It is highly desirable to optimise the design parameters, such as the tower diameter and height, type of spray nozzle, and hot air injection nozzle angle and position, and operating conditions, such as the feed temperature, moisture, inject pressure, mass flow and droplet size, for the production of particles with required properties as well as stable and energy efficient operations. The optimisation is generally carried out using experimental trials which are expensive and time consuming. The use of computational models for optimization has been limited due to the complexity of spray drying process as it involves billions of poly-dispersed droplets/particles interacting with complex gas flow patterns that affect their trajectories and residence times. Additionally, there are interactions between the droplets and particles resulting in coalescence, agglomeration and breakage as well as interactions with the tower wall resulting in deposition, re-entrainment of deposited material, and breakage of particles. If the deposited material is exposed to a high temperature for a prolonged period of time, it may result in combustion, hence posing safety issues. Therefore, the spray drying tower needs to be inspected for material build-up on the wall and cleaned periodically, which requires shutting down the plant resulting in the loss of production (Masters, 1985).
factors on water flow patterns and then used computer simulations, (2D flow, transport model) to evaluate the relative importance of bottom topography, vegetation distribution, water exchange with stagnant zones and dispersion. The results concluded that the bottom topography of the pond decreased the variance in water residence times to a minor extent (only 10%), whereas, heterogeneity in vegetation significantly contributed (60-80%) to the spread in hydraulic residence times. Kjellin et al (2007) have indicated that there were uncertainties in the method used to calculate HRT, by the variation of inflow over time and in the ability to accurately estimate the volume of the ponds. Most other published tracer studies concluded that in the field it is hard to vary conditions to see how they influence the HRT, as the HRT can only be investigated under the vegetation present in the pond at the time of the study.
No. 2 and No. 3) and had the value of 266 s with the average quadratic deviation in the range of 34 to 40 s. This time complies with the requirements of the Decree of the Ministry of Agriculture of the Czech Republic No. 287/1999 of the Coll., Art. 51, Par. 3, that says that, during a continuous flow pasteurisation of yolk, it is necessary to maintain the temperature of 65°C for the time of 180 s. This requirement is also fulfilled by the minimum resi- dence time that can be estimated from Figs. 2 and 3 as the time from the first detection of salted yolk in Probe No. 2 to the first detection of salted yolk in Probe No. 3. For the first experiment, measurement No. 1, this time was 234 s, for the second experiment, measurement No. 1, this time was 221 s, and for the measurement No. 2 225 s. As a reserve of the inactivation effect, the temperature history in the heating part of the plate exchanger can, also be taken into account.
Danckwerts (1953) suggested an alternative approach to quantifying a system’s mixing characteristics by considering the system’s ResidenceTimeDistribution (RTD). The RTD shows the distribution of times taken by an idealised, instantaneous injection of tracer to exit a system, and thus quanti Þ es the fundamental mixing response of that system. As such, the RTD makes no assumptions about the mixing characteristics of the system. Figure 2 shows Cumulative ResidenceTime Distributions (CRTDs), the integral form of the RTD, for a variety of systems. The mixing characteristics of most real world systems can be explained as either one or a combination of the regimes proposed in Figure 2. Mixing case (b) is analogous to turbulent pipe flow.
that adding an appropriate thread configuration to the electrode can increase the residencetime of the exhaust passing through the reactor, due to the formation of recirculating flows inside the threads. Furthermore, adding thread to the electrode increased the sharp corners in the reactor, which produced a higher streamer and as a result a higher discharge current. The results showed that the screw thread electrode with a thread width of 1 mm had the best perfor- mance among the electrodes studied with respect to the residencetime and NO x removal effi-
The fundamental hypothesis behind the drug residencetime concept is very appealing: A detailed understanding of the kinetics of association and dissociation of a target-ligand complex can provide crucial insight into the molecular mechanism of action of a compound. This deeper understanding might help to improve decision making in drug discovery, thus leading to a better selection of interesting compounds to be profiled further. When an initial core group of scientists from pharmaceutical companies decided to further explore the concept, it was soon obvious that quite a number of open questions needed to be addressed. These comprise the important aspect of small molecule optimization by analyzing molecular aspects of drug binding kinetics, by providing data-driven guidelines for future drug discovery, and by enabling rapid and robust generation of structure-kinetic data in the design-make- test-analyze (DMTA) cycle. As these tasks go across all pharmaceutical companies which might consider the drug residencetime concept relevant for their daily work, it perfectly fits the precompetitive collaboration concept of the Innovative Medicines Initiative (IMI) . With this idea in mind, an IMI project was initiated: K4DD (Kinetics for Drug Discovery, www.k4dd.eu). The 5 year project with a budget of 21M€ started in November 2012. 20 partners (9 academic institutes, 7 large pharmaceutical companies and 4 SMEs) from 6 European countries work closely together on targets that have been selected by the consortium. The approach is truly collaborative: Several partners contribute to the work on each target and we share our results in regular meetings including bi-annual meetings of the entire consortium. K4DD focuses on how drug binding kinetics can be influenced and how therefore compounds can be optimized in terms of residencetime (Figure 1).