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Using CFD Simulation as a Tool to Identify Optimal Operating Conditions for Regeneration of a Catalytic Diesel Particulate Filter

Using CFD Simulation as a Tool to Identify Optimal Operating Conditions for Regeneration of a... Article Using CFD Simulation as a Tool to Identify Optimal Operating Conditions for Regeneration of a Catalytic Diesel Particulate Filter 1, 2 Valeria Di Sarli * and Almerinda Di Benedetto Istituto di Ricerche sulla Combustione, Consiglio Nazionale delle Ricerche (CNR), Piazzale V. Tecchio 80, 80125 Napoli, Italy Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli Federico II, Piazzale V. Tecchio 80, 80125 Napoli, Italy * Correspondence: valeria.disarli@irc.cnr.it; Tel.: +39-081-762-2673 Received: 10 August 2019; Accepted: 16 August 2019; Published: 21 August 2019 Featured Application: The results obtained in this work show that, in order to conduct a safe and effective regeneration of catalytic (i.e., catalyst-coated) diesel particulate filters, it is essential not only to design increasingly active catalysts and to maximize the contact between soot and catalyst, but to also choose operating conditions that allow the prevention of kinetic and oxygen- transport limitations. Abstract: In the work presented in this paper, CFD-based simulations of the regeneration process of a catalytic diesel particulate filter were performed with the aim of identifying optimal operating conditions in terms of trade-off between time for regeneration and peak temperature. In the model, all the soot trapped inside the filter was assumed to be in contact with the catalyst. Numerical results have revealed that optimization can be achieved at low inlet gas velocity by taking advantage of the high sensitivity of the soot combustion dynamics to the availability of oxygen. In particular, optimal conditions have been identified when operating with highly active catalysts at sufficiently low inlet gas temperatures, so as to lie on the boundary between kinetics-limited regeneration and oxygen transport-limited regeneration. As catalyst activity is increased, this boundary progressively shifts towards lower inlet gas temperatures, resulting in lower peak temperatures and shorter times for filter regeneration. Under such conditions, in order to further speed up the process while still ensuring temperature control, it is essential to keep the filter adequately hot, thus minimizing the time required for the preheating phase, which may be a significant part (up to 65%) of the total time required for regeneration (preheating plus soot consumption). Keywords: catalytic diesel particulate filter; regeneration dynamics; soot-catalyst contact; catalyst activity; operating conditions; kinetic limitations; oxygen-transport limitations; computational fluid dynamics 1. Introduction Catalytic (i.e., catalyst-coated) diesel particulate filters (DPFs) have been proposed to allow regeneration (i.e., oxidation of soot) at low temperatures [1], thus overcoming the drawbacks of the thermal process, including the formation of excessively hot regions that may cause irreversible damage to the filter [2]. The regeneration performance of catalytic DPFs is dependent not only on catalyst activity [3,4], but also on the quality of the contact established between soot and catalyst particles [3–6]. As observed for DPFs wash-coated with nano-metric ceria particles, the soot trapped inside the walls of the filter can come into intimate contact with a well-dispersed catalyst [5,7], whereas this contact Appl. Sci. 2019, 9, 3453; doi:10.3390/app9173453 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3453 2 of 20 remains rather poor for the soot cake layer accumulated on top of the walls [5], which represents most of the soot retained in the filter. Although some soot particles in the cake layer may relocate during the regeneration process, possibly coming into contact with the catalyst, most of the soot accumulated as cake remains far from the catalytic walls, thus burning via the thermal path [5]. This may lead to severe temperature excursions also when regenerating catalytic filters. Spatio-temporal infrared measurements of temperature taken during the combustion process of the cake layer accumulated on planar catalytic single-layer filters have shown that, depending on the operating conditions, two different regimes of regeneration may be established, slow regeneration and fast regeneration [8–10]. In the regime of slow regeneration, combustion of soot occurs uniformly all over the filter, leading to moderate temperature rises. Conversely, in the regime of fast regeneration, combustion of soot occurs via propagation of sharp reaction fronts, resulting in high peak temperatures. Regeneration dynamics evolves from the slow regime to the fast regime as a function of increasing oxygen concentration [8], exhaust gas temperature [9], and soot load [10]. The transition from slow to fast regeneration is rather abrupt [8,10]. Studies on filter regeneration have also been performed by means of models of different complexity. Yu et al. [11] used a one-dimensional model of a single-channel filter to develop simple criteria for predicting the maximum temperature under both the regimes of slow and fast regeneration. Results of two-dimensional simulations by Mizutani et al. [12] have highlighted the sensitivity of the filter temperature profile to changes in soot load, inlet gas temperature, and, at low flow rate, oxygen concentration. In work by Bensaid et al. [13], the transition from the slow regime to the fast regime with increasing inlet gas temperature was simulated through a three-dimensional CFD-based model of thermal regeneration. More recently, we proposed a two-dimensional CFD-based model of regeneration of a single- channel catalytic DPF [14], and performed simulations varying the catalyst activity, the inlet gas velocity, and the amount of soot loaded as cake [14–16]. Our attention was mainly focused on the interaction between combustion of the soot trapped inside the catalytic wall of the filter and combustion of the cake. Preliminary results were also obtained in the absence of cake [16]. An abrupt transition from slow to fast regeneration was predicted to occur with increasing catalyst activity [14,15] and soot load [16]. This transition progressively shifts towards higher catalyst activity with increasing inlet gas velocity [15]. In the slow regime, combustion of the cake proceeds independently of combustion of the soot trapped inside the catalytic wall. Regeneration is a “safe” process, but it takes a long time to complete. In the fast regime, the catalytic wall behaves as a “pilot” for the cake, causing its violent ignition. Under such conditions, the time for regeneration is suitable for practical applications. However, the cake burns via a propagating front in an uncontrolled fashion, leading to “runway” of the catalytic filter. Overall, our model results have shown that, in the presence of cake, it is possible to conduct fast regeneration of the catalytic filter, but not to prevent high temperatures [14–16]. Interestingly, preliminary results have shown that, in the absence of cake, regeneration of the catalytic filter may occur as a low-temperature uniform process taking a time to complete that, even when normalized with respect to the soot load, is lower than the time required for slow regeneration in the presence of cake [16], thus suggesting the chance of identifying optimal operating conditions for a fast but at the same time “safe” regeneration when avoiding the formation of the cake layer. This possibility is also supported by the outcomes of heating-ramp experiments showing that the temperatures required for regenerating catalytic filters decrease with decreasing soot load as a result of improved conditions of soot-catalyst contact [3,5]. Indeed, in the absence of cake, the contact between soot and catalyst can be maximized, thus resulting in a truly catalytic regeneration [5,7]. From this perspective, the development of substrates [17] and procedures for their catalyst coating [18] that ensure high filtration efficiencies even at low soot loads, when the mechanism of cake filtration is ruled out and the soot particles are trapped only via deep-bed filtration, becomes a crucial issue. In the work presented in this paper, the regeneration dynamics of a catalytic DPF has been deeply investigated with the aim of identifying, in the absence of cake, optimal operating conditions in terms of trade-off between time for regeneration and peak temperature. To this end, CFD-based Appl. Sci. 2019, 9, 3453 3 of 20 simulations of soot combustion in a single-channel configuration were performed, varying the catalyst activity and the inlet gas velocity and temperature. The effect of the preheating phase on the regeneration dynamics of the filter has also been assessed. In the model, all the soot trapped inside the wall of the filter was assumed to be in contact with the catalyst. 2. Mathematical Model The CFD-based model developed in Reference [14] was used to run simulations of the regeneration process for a single-channel catalytic DPF. Figure 1 shows the two-dimensional computational domain. The soot was assumed to be trapped only inside the porous wall of the filter, and not even in the form of a cake layer deposited on top of the wall. Figure 1. Two-dimensional computational domain (not to scale): single-channel filter (length of the inlet and outlet channels = 45 mm; thickness of the wall = 0.38 mm). The model is described in detail in Reference [14] and here is only briefly presented. The governing fluid flow equations are the mass, momentum, species, and energy conservation equations. In the porous wall, these equations were coupled to the soot conservation equation, and local thermal equilibrium was assumed between the fluid and solid phases (in other words, it was assumed that fluid and solid are at the same temperature). The model equations were solved in “time- dependent” form. The consumption rate of soot was expressed according to the kinetics obtained by Darcy et al. [19] from experiments of non-catalytic (i.e., thermal) and catalytic oxidation of real diesel soot with oxygen: rr=+r (1) total slow _ oxidation fast _ oxidation with contribution of slow (i.e., thermal) oxidation: slow_ oxidation r  a m slow _ oxidation (-E RT ) total , t = Ae x (2) () Oxygen  mm total , t== 00 total ,t  and r contribution of fast (i.e., catalytic) oxidation—this contribution involves only the fast _oxidation soot in contact with the catalyst: b' rm a' fast _ oxidation catalyzed , t (-E' RT ) (3) = A' e x  () Oxygen  mm catalyzed , t== 00 catalyzed ,t  (Equations (2) and (3)) is the oxygen mole fraction in the gas phase, (Equation x m Oxygen total (2)) is the local concentration of soot (all the soot present in the system), and m (Equation catalyzed (3)) is the local concentration of soot in contact with the catalyst. The values of the kinetic parameters for Equations (2) and (3) are given in Table 1. Table 1. Kinetic parameters for Equations (2) and (3) (from Reference [19]). Appl. Sci. 2019, 9, 3453 4 of 20 Equation (2) Equation (3) “Slow” (i.e., Thermal) Oxidation “Fast” (i.e., Catalytic) Oxidation 7 −1 5 −1 A 6.05 × 10 s A’ (= A’Darcy) 1.19 × 10 s Ea 161 kJ/mol E’a 114 kJ/mol a 0.7 a’ 0.3 b 0.8 b’ 0.8 In the model, all the soot was assumed to be in contact with the catalyst (mm = ) catalyzed total and, thus, both contributions (Equations (2) and (3)) were considered for the regeneration kinetics. The (intrinsic) catalyst activity was varied by changing the pre-exponential factor, A’, in Equation (3). In particular, the ratio, k, between A’ and the pre-exponential factor of the kinetics by Darcy et al. [19], A’Darcy, was increased from 1 to 200. The model was developed in the frame of the porous medium approach of the CFD code ANSYS Fluent 15.0 [20]. A user-defined scalar was used to specify the local soot concentration, whereas the regeneration kinetics was implemented via user-defined subroutines. The model equations were discretized through the finite-volume method on a grid having about 2.2 × 10 square cells with size equal to 0.025 mm. The spatial discretization and the time integration were performed by using second-order schemes (also for convective terms) and the second-order implicit Crank-Nicholson scheme, respectively. Fixed flat profiles were assumed as inlet boundary conditions for velocity, species concentration, and temperature. At the outlet boundary, a condition of fixed static pressure was assigned. All the remaining boundaries were assumed to be adiabatic walls. If not otherwise specified, the simulation (inlet and initial) conditions are those listed in Table 2. Table 2. Simulation conditions. Parameter Inlet Value Initial Value Velocity [m/s] 1 0 O2 concentration [% mol] 15 15 Temperature [K] 813 523 Soot concentration [kg/m] - 15 1 3 2 Superficial filtration velocity: 0.047 m /m s. Table 3 gives the values of the properties of the support material (Silicon Carbide, SiC) [21], along with the values of porosity and permeability of the (clean) wall of the filter. A similar value of porosity is reported in Reference [21] for SiC DPFs. The permeability was calculated using the formula reported in Reference [22] for a porous wall made of spherical “grains”, the size of which was assumed equal to 24 μm. Table 3. Properties of the support material (Silicon Carbide, SiC), and porosity and permeability of the (clean) wall of the filter. Parameter (Silicon Carbide, SiC) Value Intrinsic density [kg/m] 3240 Specific heat capacity [J/kg K] 1120 Thermal conductivity [W/m K] 18 Parameter (Wall of the Filter) Value Porosity [-] 0.5 2 −13 Permeability [m] 5 × 10 3 3 The initial soot concentration (15 kg/m ) corresponds to a specific soot load of around 1.8 kg/m (of filter). Experimental results have shown that the walls of SiC filters wash-coated with a proper amount of nano-metric ceria, so as to preserve the filtering properties of the bare support, allow even Appl. Sci. 2019, 9, 3453 5 of 20 higher specific soot loads (~2.5 kg/m ), while still enabling intimate contact between soot and a highly dispersed catalyst [5,23]. During the computations, the time histories of different variables (temperature, soot concentration and combustion rate, and oxygen mole fraction) were recorded in the monitor points shown in Figure 2. Figure 2. Monitor points. The highest value of peak temperature recorded in the monitor points of Figure 2 was assumed as the maximum temperature attained in the filter during the regeneration process, Tmax. The time for filter regeneration, trig, was estimated by post-processing the solution data (saved at every 2.5 s). In particular, trig was assumed as the time at which the volume-averaged conversion of the soot trapped inside the wall of the filter reaches 95%. 3. Results and Discussion 3.1. Effect of the Catalyst Activity 3.1.1. Low Inlet Gas Velocity (Vin = 1 m/s) Figures 3–7 show the time histories of temperature and soot concentration as recorded in the monitor points of Figure 2 during simulations run at different values of k (i.e., catalyst activity). In these computations, the inlet gas velocity, Vin, was set equal to 1 m/s. Appl. Sci. 2019, 9, 3453 6 of 20 Figure 3. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 1 (from Reference [16]). Appl. Sci. 2019, 9, 3453 7 of 20 Figure 4. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 5. The arrows indicate the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm); u = upstream propagation (from downstream—29 mm- to upstream—1 mm). Appl. Sci. 2019, 9, 3453 8 of 20 Figure 5. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 10. The arrow indicates the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm). Appl. Sci. 2019, 9, 3453 9 of 20 Figure 6. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 100. The arrow indicates the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm). Appl. Sci. 2019, 9, 3453 10 of 20 Figure 7. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 200. The arrow indicates the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm). The transition from slow to fast regeneration with increasing catalyst activity has already been simulated in the presence of cake [14,15]. Figures 3–7 show that this transition occurs even in the absence of cake. At k = 1, a regime of slow regeneration is established, according to which combustion of soot is a substantially uniform process all over the filter leading to moderate temperature rises (Figure 3). Conversely, in all the other cases, a regime of fast regeneration is established, with combustion of soot occurring via propagation of the reaction front, thus resulting in high temperature rises (Figures 4–7). In particular, starting from k = 10, all the soot is burned by a single reaction front moving from upstream to downstream (d wave). At k = 100 and k = 200, the regeneration dynamics exhibits the typical features of a process limited by the availability of oxygen [16]. This is further Appl. Sci. 2019, 9, 3453 11 of 20 confirmed by Figure 8, showing the time histories of oxygen concentration and soot combustion rate as recorded in the monitor points of Figure 2 at k = 200. Figure 8. Time histories of oxygen mole fraction (solid lines) and soot combustion rate (dashed lines) (monitor points of Figure 2): k = 200 (Vin = 1 m/s). In most of the filter (x ≥ 10 mm), combustion of soot stops when oxygen is depleted. It restarts only when oxygen becomes available again. 3.1.2. Different Inlet Gas Velocities In Figure 9, the maximum temperature attained in the filter during the regeneration process, Tmax, and the time for filter regeneration, trig, are plotted versus k as obtained from simulations run at different inlet gas velocities (Vin = 1 m/s; 3 m/s; 5 m/s). Appl. Sci. 2019, 9, 3453 12 of 20 Figure 9. Maximum temperature, Tmax, and time for filter regeneration, trig, versus k at different inlet gas velocities. Regardless of the inlet velocity, the transition from slow to fast regeneration is gradual. Conversely, in the presence of cake, this transition is rather abrupt [14,15]. At Vin = 1 m/s, Tmax/trig monotonically increases/decreases with increasing k from 1 to 10. When k is further increased (k > 10), the opposite trends have been simulated, and overall the quantitative features of the regeneration dynamics become much less sensitive to variations in catalyst activity. This behavior is due to the fact that, at low catalyst activity, regeneration takes place according to a kinetics-limited mode, whereas at high catalyst activity, it takes place according to an oxygen transport-limited mode. In the kinetics-limited mode, regeneration is sensitive to convective heat removal by the gas flowing through the filter. The heat removal increases with increasing inlet gas velocity, thus explaining the trends of decreasing Tmax and increasing trig at low catalyst activity. Appl. Sci. 2019, 9, 3453 13 of 20 As the inlet gas velocity is increased, the transition from the kinetics-limited mode to the oxygen transport-limited mode shifts towards higher values of k (in other words, the oxygen-transport limitations start playing a role at higher catalyst activity). This explains the inversion in the trends of Tmax and trig with the inlet gas velocity in going from low to high catalyst activity. In Figure 10, the results of Figure 9 are shown in the operating map of the filter built in the plane Tmax versus trig. Figure 10. Operating map of the filter in the plane Tmax versus trig: simulations run by varying the catalyst activity, k, at different inlet gas velocities. At Vin = 1 m/s, two branches – upper and lower – are well distinguished. In moving from the upper branch (1 ≤ k ≤ 10) to the lower branch (10 < k ≤ 200), we go from the kinetics-limited mode to the oxygen transport-limited mode. Under the conditions of this latter mode, regeneration can be conducted at lower temperatures and/or over shorter times than under the conditions of the kinetics- limited mode. As the inlet gas velocity is increased (from 1 m/s to 5 m/s), the distance between the upper branch and the lower branch decreases, meaning that the potential gain from working at high catalyst activity under the conditions of the oxygen transport-limited mode decreases as well. At Vin = 5 m/s, the two branches almost overlap and, thus, there is no substantial gain. 3.2. Effect of the Inlet Gas Temperature From this point on, our attention will be focused on the curve at Vin = 1 m/s of Figure 10 and, in particular, on its lower branch. In order to identify operating conditions that allow for the further decrease of the maximum temperature and/or the time for filter regeneration, simulations were run at lower values of inlet gas temperature (Tin < 813 K). In Figure 11, the results in terms of Tmax and trig are plotted versus Tin, as obtained at k = 35, 50, 100, and 200. Appl. Sci. 2019, 9, 3453 14 of 20 Figure 11. Maximum temperature, Tmax, and time for filter regeneration, trig, versus inlet gas temperature, Tin, at different values of k. There are several interesting features shown in this figure. First of all, at each value of k, a transition from fast regeneration to slow regeneration is predicted with decreasing Tin. As the value of k is increased, this transition shifts towards lower values of Tin. Tmax/trig monotonically increases/decreases with increasing Tin until a nearly constant plateau is reached. In addition, at high inlet temperatures, Tmax decreases with increasing k, whereas trig slightly increases. Conversely, at low inlet temperatures, Tmax/trig increases/decreases with increasing k. These trends suggest that, at high inlet temperatures, regeneration takes place according to the oxygen transport-limited mode, whereas at low inlet temperatures, it takes place according to the kinetics- limited mode. Once a maximum allowable catalyst temperature is assumed, Tmax_allowable, of 1023 K (i.e., 750 °C) [24], the optimal inlet gas temperature, Tin_optimal, can be identified as the temperature that minimizes Appl. Sci. 2019, 9, 3453 15 of 20 regeneration time while still ensuring temperature control (i.e., Tmax ≤ Tmax_allowable). As an example, Figure 12 shows this criterion applied to the case of k = 100. Figure 12. Criterion for the identification of Tin_optimal applied to the case of k = 100: Tin_optimal lies on the boundary between the oxygen transport-limited mode and the kinetics-limited mode. It can be seen that Tin_optimal lies on the boundary between the oxygen transport-limited mode and the kinetics-limited mode. This also occurs in the cases of k = 35, 50, and 200 (not shown). Under such optimal conditions, the regeneration process is not controlled either by mass transport or by kinetics. The results of Figure 11 were also rearranged in terms of the objective function, φ = trig Tmax. Interestingly, it has been found that, at each value of k, φ exhibits a minimum at Tin = Tin_optimal. Figure 13 shows the plot of Tin_optimal versus k. Appl. Sci. 2019, 9, 3453 16 of 20 Figure 13. Optimal inlet gas temperature, Tin_optimal, versus k. In Figure 14, Tmax and trig at Tin = Tin_optimal are shown as a function of k. Figure 14. Tmax and trig at Tin = Tin_optimal versus k. Appl. Sci. 2019, 9, 3453 17 of 20 Global assessment of Figures 13 and 14 demonstrates that, as the catalyst activity is increased, the optimal operating point progressively shifts towards lower inlet gas temperatures, resulting in lower peak temperatures and shorter times for filter regeneration. In Figure 15, the results of Figure 14 are shown in the plane Tmax versus trig (circles). In this map, the lower branch of the curve at Vin = 1 m/s of Figure 10 is also shown. In these computations, Tin was set equal to 813 K. Figure 15. Operating map of the filter in the plane Tmax versus trig: results at Tin = Tin_optimal for different values of k (circles). The lower branch of the curve at Vin = 1 m/s of Figure 10 is also shown (Tin = 813 K). At k = 35 and 50, the main effect of decreasing the inlet gas temperature from 813 K to the optimal value is lowering Tmax. Conversely, at k = 100 and 200, the main effect is lowering trig. It is worth remembering that our simulations were run assuming the cake to be absent, and that all the soot trapped inside the wall of the filter is in contact with the catalyst. In real-world applications, the accumulation of ash, which is considered as one of the greatest challenges [25,26], will tend to progressively complicate the conditions of soot-catalyst contact and, although the ash itself may have a catalytic effect on soot [27], this will impact on the regeneration performance of the catalytic filter, even when starting with high catalyst activity (i.e., high values of k). 3.3. Effect of the Preheating Phase The regeneration process consists of two phases in series. The first phase is that of preheating, the initial temperature of the filter (523 K in our simulations) being lower than the inlet gas temperature. The second phase is that of true soot consumption. Therefore, the (total) time for filter regeneration, trig, is the sum of two contributions, the time for preheating and the time for soot consumption. In Table 4, the time for preheating, tpreheat, is given, along with trig and the tpreheat/trig ratio, as calculated at Tin = Tin_optimal for different values of k. tpreheat was assumed as the time at which the volume-averaged conversion of the soot trapped inside the wall of the filter reaches 10% (whereas, trig was assumed as the time at which the volume-averaged soot conversion reaches 95%). Appl. Sci. 2019, 9, 3453 18 of 20 Table 4. Time for preheating, tpreheat, time for filter regeneration, trig, and tpreheat/trig ratio, as calculated at Tin = Tin_optimal for different values of k. k [-] tpreheat [s] trig [s] tpreheat/trig [-] 35 37.5 60.0 0.625 50 37.5 57.5 0.652 100 35.0 52.5 0.667 200 32.5 52.5 0.619 tpreheat is a significant part (up to~65%) of trig. In order to assess the effect of the preheating phase on the regeneration dynamics of the filter, computations were run at higher initial temperatures (> 523 K). In particular, at each value of k, the initial temperature was set equal to the inlet gas temperature (= Tin_optimal), thus avoiding the phase of preheating. The operating map of Figure 16 shows the comparison between the results obtained with (full circles) and without (empty circles) preheating. Figure 16. Operating map of the filter in the plane Tmax versus trig: results at Tin = Tin_optimal with (full circles) and without (empty circles) preheating for different values of k. The lower branch of the curve at Vin = 1 m/s of Figure 10 is also shown. At each value of k, when avoiding the preheating of the filter, regeneration becomes much faster, but the peak temperature is slightly affected. 4. Conclusions CFD-based simulations of the regeneration process of a single-channel catalytic DPF were performed assuming the soot cake layer to be absent, and that all the soot trapped inside the wall of the filter is in contact with the catalyst. The obtained results have shown that it is possible to identify optimal operating conditions in terms of trade-off between time for regeneration and peak temperature. This optimization can be achieved at low inlet gas velocity by taking advantage of the high sensitivity of the regeneration dynamics to the availability of oxygen. In particular, optimal Appl. Sci. 2019, 9, 3453 19 of 20 conditions have been identified when operating with highly active catalysts at sufficiently low inlet gas temperatures, so as to lie on the boundary between kinetics-limited regeneration and oxygen transport-limited regeneration. As catalyst activity is increased, this boundary progressively shifts towards lower inlet gas temperatures, resulting in lower peak temperatures and shorter times for filter regeneration. Under such conditions, in order to further speed up the process while still ensuring temperature control, it is essential to keep the filter adequately hot, thus minimizing the time required for the preheating phase, which may be a significant part (up to 65%) of the total time required for regeneration (preheating plus soot consumption). On the basis of these results, it can be concluded that, in order to conduct a safe and effective regeneration of catalytic DPFs, it is essential not only to design increasingly active catalysts and to maximize the soot-catalyst contact inside the walls of the filter, but also to properly choose the operating conditions. Author Contributions: Conceptualization, V.D.S. and A.D.B.; Methodology, V.D.S. and A.D.B.; Software, V.D.S.; Validation, V.D.S.; Investigation, V.D.S.; Resources, V.D.S. and A.D.B.; Data curation, V.D.S.; Visualization, V.D.S. and A.D.B.; Writing—original draft, V.D.S.; Writing—review & editing, V.D.S. and A.D.B. Funding: This research received no external funding. Acknowledgments: Vincenzo Smiglio and Luigi Muriello are gratefully acknowledged for their technical assistance in the computing activity. Conflicts of Interest: The authors declare no conflict of interest. References 1. Fino, D.; Bensaid, S.; Piumetti, M.; Russo, N. A review on the catalytic combustion of soot in diesel particulate filters for automotive applications: From powder catalysts to structured reactors. Appl. Catal. A Gen. 2016, 509, 75–96. 2. Yang, K.; Fox, J.T.; Hunsicker, R. Characterizing diesel particulate filter failure during commercial fleet use due to pinholes, melting, cracking, and fouling. Emiss. 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Highly dispersed ceria for catalytic regeneration of diesel particulate filter. Adv. Sci. Lett. 2017, 23, 5909–5911. 24. Piumetti, M.; Bensaid, S.; Russo, N.; Fino, D. Nanostructured ceria-based catalysts for soot combustion: Investigations on the surface sensitivity. Appl. Catal. B Environ. 2015, 165, 742–751. 25. Liu, Y.; Su, C.; Clerc, J.; Harinath, A.; Rogoski, L. Experimental and Modeling Study of Ash Impact on DPF Backpressure and Regeneration Behaviors; SAE Technical Paper; 2015-01-1063; SAE International: Warrendale, PA, USA, 2015. 26. Su, C.; Brault, J.; Munnannur, A.; Liu, Z.G.; Milloy, S.; Harinath, A.; Dunnuck, D.; Federle, K. Model-Based Approaches in Developing an Advanced Aftertreatment System: An Overview; SAE Technical Paper; 2019-01- 0026; SAE International: Warrendale, PA, USA, 2019. 27. Du, Y.; Meng, Z.; Fang, J.; Qin, Y.; Jiang, Y.; Li, S.; Li, J.; Chen, C.; Bai, W. Characterization of soot deposition and oxidation process on catalytic diesel particulate filter with ash loading through an optimized visualized method. Fuel 2019, 243, 251–261. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute

Using CFD Simulation as a Tool to Identify Optimal Operating Conditions for Regeneration of a Catalytic Diesel Particulate Filter

Applied Sciences , Volume 9 (17) – Aug 21, 2019

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Article Using CFD Simulation as a Tool to Identify Optimal Operating Conditions for Regeneration of a Catalytic Diesel Particulate Filter 1, 2 Valeria Di Sarli * and Almerinda Di Benedetto Istituto di Ricerche sulla Combustione, Consiglio Nazionale delle Ricerche (CNR), Piazzale V. Tecchio 80, 80125 Napoli, Italy Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli Federico II, Piazzale V. Tecchio 80, 80125 Napoli, Italy * Correspondence: valeria.disarli@irc.cnr.it; Tel.: +39-081-762-2673 Received: 10 August 2019; Accepted: 16 August 2019; Published: 21 August 2019 Featured Application: The results obtained in this work show that, in order to conduct a safe and effective regeneration of catalytic (i.e., catalyst-coated) diesel particulate filters, it is essential not only to design increasingly active catalysts and to maximize the contact between soot and catalyst, but to also choose operating conditions that allow the prevention of kinetic and oxygen- transport limitations. Abstract: In the work presented in this paper, CFD-based simulations of the regeneration process of a catalytic diesel particulate filter were performed with the aim of identifying optimal operating conditions in terms of trade-off between time for regeneration and peak temperature. In the model, all the soot trapped inside the filter was assumed to be in contact with the catalyst. Numerical results have revealed that optimization can be achieved at low inlet gas velocity by taking advantage of the high sensitivity of the soot combustion dynamics to the availability of oxygen. In particular, optimal conditions have been identified when operating with highly active catalysts at sufficiently low inlet gas temperatures, so as to lie on the boundary between kinetics-limited regeneration and oxygen transport-limited regeneration. As catalyst activity is increased, this boundary progressively shifts towards lower inlet gas temperatures, resulting in lower peak temperatures and shorter times for filter regeneration. Under such conditions, in order to further speed up the process while still ensuring temperature control, it is essential to keep the filter adequately hot, thus minimizing the time required for the preheating phase, which may be a significant part (up to 65%) of the total time required for regeneration (preheating plus soot consumption). Keywords: catalytic diesel particulate filter; regeneration dynamics; soot-catalyst contact; catalyst activity; operating conditions; kinetic limitations; oxygen-transport limitations; computational fluid dynamics 1. Introduction Catalytic (i.e., catalyst-coated) diesel particulate filters (DPFs) have been proposed to allow regeneration (i.e., oxidation of soot) at low temperatures [1], thus overcoming the drawbacks of the thermal process, including the formation of excessively hot regions that may cause irreversible damage to the filter [2]. The regeneration performance of catalytic DPFs is dependent not only on catalyst activity [3,4], but also on the quality of the contact established between soot and catalyst particles [3–6]. As observed for DPFs wash-coated with nano-metric ceria particles, the soot trapped inside the walls of the filter can come into intimate contact with a well-dispersed catalyst [5,7], whereas this contact Appl. Sci. 2019, 9, 3453; doi:10.3390/app9173453 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3453 2 of 20 remains rather poor for the soot cake layer accumulated on top of the walls [5], which represents most of the soot retained in the filter. Although some soot particles in the cake layer may relocate during the regeneration process, possibly coming into contact with the catalyst, most of the soot accumulated as cake remains far from the catalytic walls, thus burning via the thermal path [5]. This may lead to severe temperature excursions also when regenerating catalytic filters. Spatio-temporal infrared measurements of temperature taken during the combustion process of the cake layer accumulated on planar catalytic single-layer filters have shown that, depending on the operating conditions, two different regimes of regeneration may be established, slow regeneration and fast regeneration [8–10]. In the regime of slow regeneration, combustion of soot occurs uniformly all over the filter, leading to moderate temperature rises. Conversely, in the regime of fast regeneration, combustion of soot occurs via propagation of sharp reaction fronts, resulting in high peak temperatures. Regeneration dynamics evolves from the slow regime to the fast regime as a function of increasing oxygen concentration [8], exhaust gas temperature [9], and soot load [10]. The transition from slow to fast regeneration is rather abrupt [8,10]. Studies on filter regeneration have also been performed by means of models of different complexity. Yu et al. [11] used a one-dimensional model of a single-channel filter to develop simple criteria for predicting the maximum temperature under both the regimes of slow and fast regeneration. Results of two-dimensional simulations by Mizutani et al. [12] have highlighted the sensitivity of the filter temperature profile to changes in soot load, inlet gas temperature, and, at low flow rate, oxygen concentration. In work by Bensaid et al. [13], the transition from the slow regime to the fast regime with increasing inlet gas temperature was simulated through a three-dimensional CFD-based model of thermal regeneration. More recently, we proposed a two-dimensional CFD-based model of regeneration of a single- channel catalytic DPF [14], and performed simulations varying the catalyst activity, the inlet gas velocity, and the amount of soot loaded as cake [14–16]. Our attention was mainly focused on the interaction between combustion of the soot trapped inside the catalytic wall of the filter and combustion of the cake. Preliminary results were also obtained in the absence of cake [16]. An abrupt transition from slow to fast regeneration was predicted to occur with increasing catalyst activity [14,15] and soot load [16]. This transition progressively shifts towards higher catalyst activity with increasing inlet gas velocity [15]. In the slow regime, combustion of the cake proceeds independently of combustion of the soot trapped inside the catalytic wall. Regeneration is a “safe” process, but it takes a long time to complete. In the fast regime, the catalytic wall behaves as a “pilot” for the cake, causing its violent ignition. Under such conditions, the time for regeneration is suitable for practical applications. However, the cake burns via a propagating front in an uncontrolled fashion, leading to “runway” of the catalytic filter. Overall, our model results have shown that, in the presence of cake, it is possible to conduct fast regeneration of the catalytic filter, but not to prevent high temperatures [14–16]. Interestingly, preliminary results have shown that, in the absence of cake, regeneration of the catalytic filter may occur as a low-temperature uniform process taking a time to complete that, even when normalized with respect to the soot load, is lower than the time required for slow regeneration in the presence of cake [16], thus suggesting the chance of identifying optimal operating conditions for a fast but at the same time “safe” regeneration when avoiding the formation of the cake layer. This possibility is also supported by the outcomes of heating-ramp experiments showing that the temperatures required for regenerating catalytic filters decrease with decreasing soot load as a result of improved conditions of soot-catalyst contact [3,5]. Indeed, in the absence of cake, the contact between soot and catalyst can be maximized, thus resulting in a truly catalytic regeneration [5,7]. From this perspective, the development of substrates [17] and procedures for their catalyst coating [18] that ensure high filtration efficiencies even at low soot loads, when the mechanism of cake filtration is ruled out and the soot particles are trapped only via deep-bed filtration, becomes a crucial issue. In the work presented in this paper, the regeneration dynamics of a catalytic DPF has been deeply investigated with the aim of identifying, in the absence of cake, optimal operating conditions in terms of trade-off between time for regeneration and peak temperature. To this end, CFD-based Appl. Sci. 2019, 9, 3453 3 of 20 simulations of soot combustion in a single-channel configuration were performed, varying the catalyst activity and the inlet gas velocity and temperature. The effect of the preheating phase on the regeneration dynamics of the filter has also been assessed. In the model, all the soot trapped inside the wall of the filter was assumed to be in contact with the catalyst. 2. Mathematical Model The CFD-based model developed in Reference [14] was used to run simulations of the regeneration process for a single-channel catalytic DPF. Figure 1 shows the two-dimensional computational domain. The soot was assumed to be trapped only inside the porous wall of the filter, and not even in the form of a cake layer deposited on top of the wall. Figure 1. Two-dimensional computational domain (not to scale): single-channel filter (length of the inlet and outlet channels = 45 mm; thickness of the wall = 0.38 mm). The model is described in detail in Reference [14] and here is only briefly presented. The governing fluid flow equations are the mass, momentum, species, and energy conservation equations. In the porous wall, these equations were coupled to the soot conservation equation, and local thermal equilibrium was assumed between the fluid and solid phases (in other words, it was assumed that fluid and solid are at the same temperature). The model equations were solved in “time- dependent” form. The consumption rate of soot was expressed according to the kinetics obtained by Darcy et al. [19] from experiments of non-catalytic (i.e., thermal) and catalytic oxidation of real diesel soot with oxygen: rr=+r (1) total slow _ oxidation fast _ oxidation with contribution of slow (i.e., thermal) oxidation: slow_ oxidation r  a m slow _ oxidation (-E RT ) total , t = Ae x (2) () Oxygen  mm total , t== 00 total ,t  and r contribution of fast (i.e., catalytic) oxidation—this contribution involves only the fast _oxidation soot in contact with the catalyst: b' rm a' fast _ oxidation catalyzed , t (-E' RT ) (3) = A' e x  () Oxygen  mm catalyzed , t== 00 catalyzed ,t  (Equations (2) and (3)) is the oxygen mole fraction in the gas phase, (Equation x m Oxygen total (2)) is the local concentration of soot (all the soot present in the system), and m (Equation catalyzed (3)) is the local concentration of soot in contact with the catalyst. The values of the kinetic parameters for Equations (2) and (3) are given in Table 1. Table 1. Kinetic parameters for Equations (2) and (3) (from Reference [19]). Appl. Sci. 2019, 9, 3453 4 of 20 Equation (2) Equation (3) “Slow” (i.e., Thermal) Oxidation “Fast” (i.e., Catalytic) Oxidation 7 −1 5 −1 A 6.05 × 10 s A’ (= A’Darcy) 1.19 × 10 s Ea 161 kJ/mol E’a 114 kJ/mol a 0.7 a’ 0.3 b 0.8 b’ 0.8 In the model, all the soot was assumed to be in contact with the catalyst (mm = ) catalyzed total and, thus, both contributions (Equations (2) and (3)) were considered for the regeneration kinetics. The (intrinsic) catalyst activity was varied by changing the pre-exponential factor, A’, in Equation (3). In particular, the ratio, k, between A’ and the pre-exponential factor of the kinetics by Darcy et al. [19], A’Darcy, was increased from 1 to 200. The model was developed in the frame of the porous medium approach of the CFD code ANSYS Fluent 15.0 [20]. A user-defined scalar was used to specify the local soot concentration, whereas the regeneration kinetics was implemented via user-defined subroutines. The model equations were discretized through the finite-volume method on a grid having about 2.2 × 10 square cells with size equal to 0.025 mm. The spatial discretization and the time integration were performed by using second-order schemes (also for convective terms) and the second-order implicit Crank-Nicholson scheme, respectively. Fixed flat profiles were assumed as inlet boundary conditions for velocity, species concentration, and temperature. At the outlet boundary, a condition of fixed static pressure was assigned. All the remaining boundaries were assumed to be adiabatic walls. If not otherwise specified, the simulation (inlet and initial) conditions are those listed in Table 2. Table 2. Simulation conditions. Parameter Inlet Value Initial Value Velocity [m/s] 1 0 O2 concentration [% mol] 15 15 Temperature [K] 813 523 Soot concentration [kg/m] - 15 1 3 2 Superficial filtration velocity: 0.047 m /m s. Table 3 gives the values of the properties of the support material (Silicon Carbide, SiC) [21], along with the values of porosity and permeability of the (clean) wall of the filter. A similar value of porosity is reported in Reference [21] for SiC DPFs. The permeability was calculated using the formula reported in Reference [22] for a porous wall made of spherical “grains”, the size of which was assumed equal to 24 μm. Table 3. Properties of the support material (Silicon Carbide, SiC), and porosity and permeability of the (clean) wall of the filter. Parameter (Silicon Carbide, SiC) Value Intrinsic density [kg/m] 3240 Specific heat capacity [J/kg K] 1120 Thermal conductivity [W/m K] 18 Parameter (Wall of the Filter) Value Porosity [-] 0.5 2 −13 Permeability [m] 5 × 10 3 3 The initial soot concentration (15 kg/m ) corresponds to a specific soot load of around 1.8 kg/m (of filter). Experimental results have shown that the walls of SiC filters wash-coated with a proper amount of nano-metric ceria, so as to preserve the filtering properties of the bare support, allow even Appl. Sci. 2019, 9, 3453 5 of 20 higher specific soot loads (~2.5 kg/m ), while still enabling intimate contact between soot and a highly dispersed catalyst [5,23]. During the computations, the time histories of different variables (temperature, soot concentration and combustion rate, and oxygen mole fraction) were recorded in the monitor points shown in Figure 2. Figure 2. Monitor points. The highest value of peak temperature recorded in the monitor points of Figure 2 was assumed as the maximum temperature attained in the filter during the regeneration process, Tmax. The time for filter regeneration, trig, was estimated by post-processing the solution data (saved at every 2.5 s). In particular, trig was assumed as the time at which the volume-averaged conversion of the soot trapped inside the wall of the filter reaches 95%. 3. Results and Discussion 3.1. Effect of the Catalyst Activity 3.1.1. Low Inlet Gas Velocity (Vin = 1 m/s) Figures 3–7 show the time histories of temperature and soot concentration as recorded in the monitor points of Figure 2 during simulations run at different values of k (i.e., catalyst activity). In these computations, the inlet gas velocity, Vin, was set equal to 1 m/s. Appl. Sci. 2019, 9, 3453 6 of 20 Figure 3. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 1 (from Reference [16]). Appl. Sci. 2019, 9, 3453 7 of 20 Figure 4. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 5. The arrows indicate the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm); u = upstream propagation (from downstream—29 mm- to upstream—1 mm). Appl. Sci. 2019, 9, 3453 8 of 20 Figure 5. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 10. The arrow indicates the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm). Appl. Sci. 2019, 9, 3453 9 of 20 Figure 6. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 100. The arrow indicates the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm). Appl. Sci. 2019, 9, 3453 10 of 20 Figure 7. Time histories of temperature and soot concentration (monitor points of Figure 2): k = 200. The arrow indicates the direction of propagation of the reaction front: d = downstream propagation (from upstream—1 mm-to downstream—29 mm). The transition from slow to fast regeneration with increasing catalyst activity has already been simulated in the presence of cake [14,15]. Figures 3–7 show that this transition occurs even in the absence of cake. At k = 1, a regime of slow regeneration is established, according to which combustion of soot is a substantially uniform process all over the filter leading to moderate temperature rises (Figure 3). Conversely, in all the other cases, a regime of fast regeneration is established, with combustion of soot occurring via propagation of the reaction front, thus resulting in high temperature rises (Figures 4–7). In particular, starting from k = 10, all the soot is burned by a single reaction front moving from upstream to downstream (d wave). At k = 100 and k = 200, the regeneration dynamics exhibits the typical features of a process limited by the availability of oxygen [16]. This is further Appl. Sci. 2019, 9, 3453 11 of 20 confirmed by Figure 8, showing the time histories of oxygen concentration and soot combustion rate as recorded in the monitor points of Figure 2 at k = 200. Figure 8. Time histories of oxygen mole fraction (solid lines) and soot combustion rate (dashed lines) (monitor points of Figure 2): k = 200 (Vin = 1 m/s). In most of the filter (x ≥ 10 mm), combustion of soot stops when oxygen is depleted. It restarts only when oxygen becomes available again. 3.1.2. Different Inlet Gas Velocities In Figure 9, the maximum temperature attained in the filter during the regeneration process, Tmax, and the time for filter regeneration, trig, are plotted versus k as obtained from simulations run at different inlet gas velocities (Vin = 1 m/s; 3 m/s; 5 m/s). Appl. Sci. 2019, 9, 3453 12 of 20 Figure 9. Maximum temperature, Tmax, and time for filter regeneration, trig, versus k at different inlet gas velocities. Regardless of the inlet velocity, the transition from slow to fast regeneration is gradual. Conversely, in the presence of cake, this transition is rather abrupt [14,15]. At Vin = 1 m/s, Tmax/trig monotonically increases/decreases with increasing k from 1 to 10. When k is further increased (k > 10), the opposite trends have been simulated, and overall the quantitative features of the regeneration dynamics become much less sensitive to variations in catalyst activity. This behavior is due to the fact that, at low catalyst activity, regeneration takes place according to a kinetics-limited mode, whereas at high catalyst activity, it takes place according to an oxygen transport-limited mode. In the kinetics-limited mode, regeneration is sensitive to convective heat removal by the gas flowing through the filter. The heat removal increases with increasing inlet gas velocity, thus explaining the trends of decreasing Tmax and increasing trig at low catalyst activity. Appl. Sci. 2019, 9, 3453 13 of 20 As the inlet gas velocity is increased, the transition from the kinetics-limited mode to the oxygen transport-limited mode shifts towards higher values of k (in other words, the oxygen-transport limitations start playing a role at higher catalyst activity). This explains the inversion in the trends of Tmax and trig with the inlet gas velocity in going from low to high catalyst activity. In Figure 10, the results of Figure 9 are shown in the operating map of the filter built in the plane Tmax versus trig. Figure 10. Operating map of the filter in the plane Tmax versus trig: simulations run by varying the catalyst activity, k, at different inlet gas velocities. At Vin = 1 m/s, two branches – upper and lower – are well distinguished. In moving from the upper branch (1 ≤ k ≤ 10) to the lower branch (10 < k ≤ 200), we go from the kinetics-limited mode to the oxygen transport-limited mode. Under the conditions of this latter mode, regeneration can be conducted at lower temperatures and/or over shorter times than under the conditions of the kinetics- limited mode. As the inlet gas velocity is increased (from 1 m/s to 5 m/s), the distance between the upper branch and the lower branch decreases, meaning that the potential gain from working at high catalyst activity under the conditions of the oxygen transport-limited mode decreases as well. At Vin = 5 m/s, the two branches almost overlap and, thus, there is no substantial gain. 3.2. Effect of the Inlet Gas Temperature From this point on, our attention will be focused on the curve at Vin = 1 m/s of Figure 10 and, in particular, on its lower branch. In order to identify operating conditions that allow for the further decrease of the maximum temperature and/or the time for filter regeneration, simulations were run at lower values of inlet gas temperature (Tin < 813 K). In Figure 11, the results in terms of Tmax and trig are plotted versus Tin, as obtained at k = 35, 50, 100, and 200. Appl. Sci. 2019, 9, 3453 14 of 20 Figure 11. Maximum temperature, Tmax, and time for filter regeneration, trig, versus inlet gas temperature, Tin, at different values of k. There are several interesting features shown in this figure. First of all, at each value of k, a transition from fast regeneration to slow regeneration is predicted with decreasing Tin. As the value of k is increased, this transition shifts towards lower values of Tin. Tmax/trig monotonically increases/decreases with increasing Tin until a nearly constant plateau is reached. In addition, at high inlet temperatures, Tmax decreases with increasing k, whereas trig slightly increases. Conversely, at low inlet temperatures, Tmax/trig increases/decreases with increasing k. These trends suggest that, at high inlet temperatures, regeneration takes place according to the oxygen transport-limited mode, whereas at low inlet temperatures, it takes place according to the kinetics- limited mode. Once a maximum allowable catalyst temperature is assumed, Tmax_allowable, of 1023 K (i.e., 750 °C) [24], the optimal inlet gas temperature, Tin_optimal, can be identified as the temperature that minimizes Appl. Sci. 2019, 9, 3453 15 of 20 regeneration time while still ensuring temperature control (i.e., Tmax ≤ Tmax_allowable). As an example, Figure 12 shows this criterion applied to the case of k = 100. Figure 12. Criterion for the identification of Tin_optimal applied to the case of k = 100: Tin_optimal lies on the boundary between the oxygen transport-limited mode and the kinetics-limited mode. It can be seen that Tin_optimal lies on the boundary between the oxygen transport-limited mode and the kinetics-limited mode. This also occurs in the cases of k = 35, 50, and 200 (not shown). Under such optimal conditions, the regeneration process is not controlled either by mass transport or by kinetics. The results of Figure 11 were also rearranged in terms of the objective function, φ = trig Tmax. Interestingly, it has been found that, at each value of k, φ exhibits a minimum at Tin = Tin_optimal. Figure 13 shows the plot of Tin_optimal versus k. Appl. Sci. 2019, 9, 3453 16 of 20 Figure 13. Optimal inlet gas temperature, Tin_optimal, versus k. In Figure 14, Tmax and trig at Tin = Tin_optimal are shown as a function of k. Figure 14. Tmax and trig at Tin = Tin_optimal versus k. Appl. Sci. 2019, 9, 3453 17 of 20 Global assessment of Figures 13 and 14 demonstrates that, as the catalyst activity is increased, the optimal operating point progressively shifts towards lower inlet gas temperatures, resulting in lower peak temperatures and shorter times for filter regeneration. In Figure 15, the results of Figure 14 are shown in the plane Tmax versus trig (circles). In this map, the lower branch of the curve at Vin = 1 m/s of Figure 10 is also shown. In these computations, Tin was set equal to 813 K. Figure 15. Operating map of the filter in the plane Tmax versus trig: results at Tin = Tin_optimal for different values of k (circles). The lower branch of the curve at Vin = 1 m/s of Figure 10 is also shown (Tin = 813 K). At k = 35 and 50, the main effect of decreasing the inlet gas temperature from 813 K to the optimal value is lowering Tmax. Conversely, at k = 100 and 200, the main effect is lowering trig. It is worth remembering that our simulations were run assuming the cake to be absent, and that all the soot trapped inside the wall of the filter is in contact with the catalyst. In real-world applications, the accumulation of ash, which is considered as one of the greatest challenges [25,26], will tend to progressively complicate the conditions of soot-catalyst contact and, although the ash itself may have a catalytic effect on soot [27], this will impact on the regeneration performance of the catalytic filter, even when starting with high catalyst activity (i.e., high values of k). 3.3. Effect of the Preheating Phase The regeneration process consists of two phases in series. The first phase is that of preheating, the initial temperature of the filter (523 K in our simulations) being lower than the inlet gas temperature. The second phase is that of true soot consumption. Therefore, the (total) time for filter regeneration, trig, is the sum of two contributions, the time for preheating and the time for soot consumption. In Table 4, the time for preheating, tpreheat, is given, along with trig and the tpreheat/trig ratio, as calculated at Tin = Tin_optimal for different values of k. tpreheat was assumed as the time at which the volume-averaged conversion of the soot trapped inside the wall of the filter reaches 10% (whereas, trig was assumed as the time at which the volume-averaged soot conversion reaches 95%). Appl. Sci. 2019, 9, 3453 18 of 20 Table 4. Time for preheating, tpreheat, time for filter regeneration, trig, and tpreheat/trig ratio, as calculated at Tin = Tin_optimal for different values of k. k [-] tpreheat [s] trig [s] tpreheat/trig [-] 35 37.5 60.0 0.625 50 37.5 57.5 0.652 100 35.0 52.5 0.667 200 32.5 52.5 0.619 tpreheat is a significant part (up to~65%) of trig. In order to assess the effect of the preheating phase on the regeneration dynamics of the filter, computations were run at higher initial temperatures (> 523 K). In particular, at each value of k, the initial temperature was set equal to the inlet gas temperature (= Tin_optimal), thus avoiding the phase of preheating. The operating map of Figure 16 shows the comparison between the results obtained with (full circles) and without (empty circles) preheating. Figure 16. Operating map of the filter in the plane Tmax versus trig: results at Tin = Tin_optimal with (full circles) and without (empty circles) preheating for different values of k. The lower branch of the curve at Vin = 1 m/s of Figure 10 is also shown. At each value of k, when avoiding the preheating of the filter, regeneration becomes much faster, but the peak temperature is slightly affected. 4. Conclusions CFD-based simulations of the regeneration process of a single-channel catalytic DPF were performed assuming the soot cake layer to be absent, and that all the soot trapped inside the wall of the filter is in contact with the catalyst. The obtained results have shown that it is possible to identify optimal operating conditions in terms of trade-off between time for regeneration and peak temperature. This optimization can be achieved at low inlet gas velocity by taking advantage of the high sensitivity of the regeneration dynamics to the availability of oxygen. In particular, optimal Appl. Sci. 2019, 9, 3453 19 of 20 conditions have been identified when operating with highly active catalysts at sufficiently low inlet gas temperatures, so as to lie on the boundary between kinetics-limited regeneration and oxygen transport-limited regeneration. As catalyst activity is increased, this boundary progressively shifts towards lower inlet gas temperatures, resulting in lower peak temperatures and shorter times for filter regeneration. Under such conditions, in order to further speed up the process while still ensuring temperature control, it is essential to keep the filter adequately hot, thus minimizing the time required for the preheating phase, which may be a significant part (up to 65%) of the total time required for regeneration (preheating plus soot consumption). On the basis of these results, it can be concluded that, in order to conduct a safe and effective regeneration of catalytic DPFs, it is essential not only to design increasingly active catalysts and to maximize the soot-catalyst contact inside the walls of the filter, but also to properly choose the operating conditions. Author Contributions: Conceptualization, V.D.S. and A.D.B.; Methodology, V.D.S. and A.D.B.; Software, V.D.S.; Validation, V.D.S.; Investigation, V.D.S.; Resources, V.D.S. and A.D.B.; Data curation, V.D.S.; Visualization, V.D.S. and A.D.B.; Writing—original draft, V.D.S.; Writing—review & editing, V.D.S. and A.D.B. Funding: This research received no external funding. Acknowledgments: Vincenzo Smiglio and Luigi Muriello are gratefully acknowledged for their technical assistance in the computing activity. Conflicts of Interest: The authors declare no conflict of interest. References 1. Fino, D.; Bensaid, S.; Piumetti, M.; Russo, N. A review on the catalytic combustion of soot in diesel particulate filters for automotive applications: From powder catalysts to structured reactors. Appl. Catal. A Gen. 2016, 509, 75–96. 2. Yang, K.; Fox, J.T.; Hunsicker, R. Characterizing diesel particulate filter failure during commercial fleet use due to pinholes, melting, cracking, and fouling. Emiss. 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Characterization of soot deposition and oxidation process on catalytic diesel particulate filter with ash loading through an optimized visualized method. Fuel 2019, 243, 251–261. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Applied SciencesMultidisciplinary Digital Publishing Institute

Published: Aug 21, 2019

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