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Polymerization Reaction Engineering
Published in F. Joseph Schurk, Pradeep B. Deshpande, Kenneth W. leffew, Vikas M. Nadkarni, Control of Polymerization Reactors, 2017
Schurk F. Joseph, Deshpande Pradeep B.
In a plug flow reactor, each element of the reaction mixture can be viewed as an individual batch reactor. The batch time is the residence time in the tubular reactor, which is easily calculated as the total volume of the tube divided by the volumetric flow rate. Because no material enters or leaves the fluid element during the reaction time, all of the kinetic relationships derived thus far for the batch reactor are directly applicable to the plug flow reactor. The plug flow reactor, then, becomes the reactor of choice if it is desired to exploit the kinetic advantages of the batch reactor (high conversion, etc.) while enjoying the operational advantages of continuous processing (ease of operation, lack of batch-to-batch variability). This, in fact, has been done extensively in the production of high-volume s~growth polymers such as polyester and the nylons where the require-ment for a high extent of reaction to obtain a high molecular weight is coupled with the requirement of continuous processing to obtain product uniformity and reduce production costs.
Use of Residence- and Contact-Time Distributions in Reactor Design
Published in James J. Carberry, Arvind Varma, Chemical Reaction and Reactor Engineering, 2020
One must always remember that transport processes are nonlinear and cannot be described adequately by simplified models. F(t) is a linear property of such a model and the only time one can use it to predict reactor performance is in first-order reactions in homogeneous systems, a problem that is not often encountered by the reaction engineer. However, simplified models are a very valuable tool. In Shinnar (1978) the term learning models was introduced to distinguish them from models used in actual design predictions. They provide an understanding of how the transport processes might affect the chemical reactions and give some guidance for designing the scaleup. Fortunately, many reactions are not very sensitive to scaleup. Nevertheless, it is important to be able to recognize those cases where significant scaleup problems may be expected. There is one important case where models derived from tracer experiments are directly useful in reactor design. In many reactor problems, a plug-flow reactor is the optimal configuration or if not optimal, is the only design that is safe for scaleup. As real reactors are seldom true plug-flow reactors, one wants to know how closely the design approaches plug-flow and how the deviations could affect reactor performance. Here one utilizes the fact that if deviations from plug flow are small, one should get a reasonable estimate about their impact from any model that has a similar residence-time distribution. This is equivalent to an asymptotic expansion around a solution retaining only the first-order terms. In such a case, one could use either a model based on one-dimensional diffusion or a stirred tank followed by plug flow or a series of stirred tanks. The latter is preferred as it is easier to compute, and the additional complexity of a diffusion model is not justified for cases where the real physical transport processes are not molecular diffusion. It is also more similar in its form to actual measure tracer responses, as compared to a single stirred tank followed by a plug-flow reactor.
Discussion: “On Danckwerts’ boundary conditions for the plug-flow with dispersion/reaction model”
Published in Chemical Engineering Communications, 2022
At the two extremes of straight-through flow reactor behavior we observe, (A) the ideal continuous process, best represented by the so-called plug-flow reactor (PFR) model, where the isotropic axial dispersion coefficient (D) is zero, and (B) a reactor with such significant levels of axial dispersion that its behavior becomes very similar to that of a continuously stirred tank reactor (CSTR). At some intermediate levels of axial dispersion, both diffusion and reaction impact the axial concentration gradient of reactants and products, and the governing differential equation for a reactant species whose concentration is represented by C (assuming no radial gradients, isothermal first- or pseudo-first order kinetics, and no flow expansivity corrections) becomes:
A Theoretical Study on Cool Flame Oxidation as an Effective Way for Fuel Reforming: Emphasis on Ignition Characteristics and Chemical Analysis
Published in Combustion Science and Technology, 2023
Jiaying Pan, Ruoyue Tang, Jian Gao, Zhandong Wang, Haiqiao Wei, Gequn Shu
For the Plug Flow Reactor, reactants are introduced into the inlet, and products are released from the outlet. There are no radial variations in velocity, concentration, temperature, and reaction rates. The geometry configuration and boundary conditions are consistent with the previous experiments (Geng et al. 2018). Meanwhile, an additional Plug Flow Reactor with enhanced heat transfer is also employed to allow for the cooling effect of reforming products in connection pipes. It shows that the mole fraction of the components contained in reforming products almost remains unchanged during cooling processes. The variations in mole fraction of typical reforming products before and after cooling treatment can be found in the Supplementary Materials.
Residence time distribution studies using radiotracers in chemical industry—A review
Published in Chemical Engineering Communications, 2018
Meenakshi Sheoran, Avinash Chandra, Haripada Bhunia, Pramod K. Bajpai, Harish J. Pant
Most of the large-scale chemical processes are based on continuous flow reactors including plug flow reactor (PFR) or continuous stirred tank reactor (CSTR). In practice, no system behaves ideally and always shows deviation from the ideal state. Hence, the real systems behave in between plug flow reactor (PFR) and continuous stirred tank reactor (CSTR). There are numerous reasons for the deviation from the actual behavior which includes improper design, fluctuating operating conditions, scale-up effects, different raw material sources, nonuniform heating/cooling, etc. These deviations may cause malfunctioning or failure of the process and may lead to inferior/nonuniform quality product (Hweij and Azizi, 2015).