Kim Y.J.,Pohang University of Science and Technology |
Kwon H.J.,Pohang University of Science and Technology |
Heo I.,Pohang University of Science and Technology |
Nam I.-S.,Pohang University of Science and Technology |
And 4 more authors.
Applied Catalysis B: Environmental | Year: 2012
A Mn-Fe/ZSM5 catalyst has been developed for removing NOx from diesel engine exhausts and its excellent low-temperature SCR activity and N 2 selectivity demonstrated in comparison with other representative SCR catalysts including CuZSM5 and a Cu-based commercial catalyst (COM). The well-dispersed MnO 2 and the high NH 3 adsorption capacity of the Mn-Fe/ZSM5 catalyst have been identified as the primary sources for its high deNOx activity for NH 3/SCR. Hydrothermal stability and durability of the Mn-Fe/ZSM5 catalyst have been examined and compared to those of the CuZSM5 and COM catalysts. The hydrothermal stability of the catalyst improved upon the increase of Mn content and/or the addition of Er, the latter of which helps to stabilize the dispersion of MnOx on the catalyst surface during hydrothermal aging. The deNOx activity of the Mn-Fe/ZSM5 and its Er-promoted counterpart was less affected by HC poisoning, C 3H 6 poisoning in particular, compared to the CuZSM5 and COM catalysts, mainly due to the excellent C 3H 6 oxidation activity of MnO 2. No poisoning of the Mn-based ZSM5 and CuZSM5 catalysts has been observed upon the addition of 2wt.% of K + and Ca 2+ to their surface, primarily due to the high NH 3 adsorption capacity of the ZSM5 support, whereas the COM catalyst has been severely deactivated by the deposition of K + and Ca 2+. The deNOx activity of the Mn-based ZSM5 catalyst, particularly the Er-promoted one, was less affected by SO 2 compared to the CuZSM5 and COM catalysts, although it was hardly regenerated at 500°C. Formation of MnSO 4 on the catalytic surface appears to be the primary cause for the deactivation of the Mn-based ZSM5 catalysts in the presence of SO 2 in the feed gas stream. © 2012 Elsevier B.V.
Park W.,Seoul National University |
Lee J.,Seoul National University |
Min K.,Seoul National University |
Yu J.,Power Train R and nter |
And 2 more authors.
Proceedings of the Combustion Institute | Year: 2013
Nitric oxides are one of major limiting factors in developing Diesel engines because of emission regulations. There are two methods to reduce tailpipe NOx emissions: reducing the engine-out NOx emissions and using after-treatment systems. Therefore, the control of both in-cylinder combustion and after-treatment systems are important in reducing the NOx emissions; to accomplish this goal, prediction of engine-out NOx is essential. In this study, a real-time nitric oxide prediction model was developed based on the in-cylinder pressure and on data available from the ECU. As computational fluid dynamics can describe the process of NO formation which is not directly obtainable from experiments on a physical basis, the NO formation model was developed based on both the analysis of CFD results as well as on a physical model. Furthermore, the in-cylinder pressure is used to predict the amount of NO formation under various engine operating conditions as the pressure reflects the change in the combustion characteristics. The prediction model consisted of a simple calculation process; therefore, the model could predict the cycle-by-cycle NO in real-time. The validation results show that the model presented can predict engine-out NO well; thus, this model can be applied to engines and after-treatment systems as a useful tool to control the engine-out NO without the use of an NOx sensor. In addition to being a virtual NO sensor, the prediction model can be applied to 1-D simulations, such as GT-SUITE and AMESIM, and demonstrate improved NO prediction results as the model is able to predict the NO level as same standard as the 3-D CFD simulation. © 2012 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
Choi J.-S.,Hanyang University |
Lee H.-A.,Hanyang University |
Lee J.-Y.,Hanyang University |
Park G.-J.,Hanyang University |
And 3 more authors.
Journal of Mechanical Science and Technology | Year: 2011
Vehicles should provide a comfortable environment for passengers. The noise from the transmission case is one of the causes of an uncomfortable environment. The transmission is composed of gears, shafts, bearing and cases. When transmission activity occurs, noise is transferred to the passengers through the transmission case. Design of the transmission case is performed in order to reduce the transmission noise. Acoustic analysis is carried out and structural optimization is utilized for the design to reduce the noise. Generally, the boundary element method (BEM) has been utilized for acoustic analysis. However, it is difficult to utilize the boundary element method in structural optimization because the cost to calculate the sensitivity information is fairly expensive. Instead, the finite element method (FEM) is employed for calculating the radiation noise of the transmission. Radiation noise is considered as the total noise from the transmission. Radiation noise is calculated at the outside of the transmission case and it can be expressed indirectly by multiplication of the velocity in the normal direction of the finite elements, the radiation efficiency and the characteristic acoustic impedance. The high frequencies are dominant for the transmission noise and the radiation efficiency is 1 at the high frequency range. Since the characteristic acoustic impedance has a constant value, the noise is the same as the norm of the velocity. The velocity of each finite element is calculated from modal analysis and the noise is expressed based on the average velocity of the vibrating structure. However, a long computation time is required to calculate the noise in a large scale structure such as a transmission. Thus, the entire model of transmission is condensed into the reduced model by the model reduction technique. The component mode synthesis (CMS) method is employed for the model reduction technique. The CMS method is an effective method for dynamic analysis of large and/or complex structures. The reduced model keeps the dynamic characteristics of the entire structure and it is used for structural optimization. In structural optimization, the design variables are the thicknesses of the groups of the transmission cases, the objective function is the mass of the structure and a constraint is imposed on the noise. An alternative formulation is made by exchanging the objective and constraint functions. The optimization results are discussed in terms of practical application. © 2011 The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg.