0
Research Papers

NOx Sensor Ammonia-Cross-Sensitivity Factor Estimation in Diesel Engine Selective Catalytic Reduction Systems

[+] Author and Article Information
Hui Zhang

Merchant Marine College,
Shanghai Maritime University,
Shanghai 201306, China
e-mail: huizhang285@gmail.com

Junmin Wang

Department of Mechanical and
Aerospace Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: wang.1381@osu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 18, 2014; final manuscript received December 4, 2014; published online February 4, 2015. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 137(6), 061015 (Jun 01, 2015) (9 pages) Paper No: DS-14-1181; doi: 10.1115/1.4029347 History: Received April 18, 2014; Revised December 04, 2014; Online February 04, 2015

Since the NOx sensor is cross-sensitive to gaseous ammonia, the reading of NOx sensor may not be accurate when the gaseous ammonia is involved and it is a combination of the actual NOx concentration plus the ammonia concentration multiplied by a factor. The factor is named as NOx sensor cross-sensitivity factor. As the reading is inaccurate, this cross-sensitivity phenomenon restricts the application of NOx sensor in the selective catalytic reduction (SCR) system. A practical and economic approach is to design an observer to estimate the cross-sensitivity factor and correct the NOx sensor reading. In this work, the NOx sensor ammonia-cross-sensitivity factor observer design problem for diesel engine SCR systems is investigated. To achieve the objective, first, a three-state nonlinear model for the SCR system is adopted. To establish the model of the ammonia cross-sensitivity factor, it is assumed that the qth-order derivative of the factor is zero. Then, based on the nonlinear model, a proportional-multiple-integral (PMI) observer of the factor is proposed and a nonlinear estimation error system is obtained. With the linear-parameter-varying (LPV) technique, the stability, and the H performance of the nonlinear estimation error system are investigated. Based on the analysis results, the design methods of the stable and the H observer gains are developed. At the end, simulations and comparisons via an experimentally validated full vehicle simulator are carried out to illustrate the efficacy and the advantages of the proposed approach over the existing methods. Since the effect of disturbance to the estimation is considered and constrained in the H observer, the designed H observer has a better estimation performance when the system is subject to disturbance.

FIGURES IN THIS ARTICLE
<>
Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Johnson, T., 2008, “Diesel Engine Emissions and Their Control an Overview,” Platinum Met. Rev., 52(1), pp. 23–37. [CrossRef]
Chen, P., and Wang, J., 2014, “Air-Fraction Modeling for Simultaneous Diesel Engine NOx and PM Emissions Control During Active DPF Regenerations,” Appl. Energy, 122(1), pp. 310–320. [CrossRef]
Zhao, J., and Wang, J., 2013, “Control-Oriented Multi-Phase Combustion Model for Biodiesel Fueled Engines,” Appl. Energy, 108(8), pp. 92–99. [CrossRef]
Koebel, M., Elsener, M., and Kleemann, M., 2000, “Urea-SCR: A Promising Technique to Reduce NOx Emissions From Automotive Diesel Engines,” Catal. Today, 59(3–4), pp. 335–345. [CrossRef]
Zhang, H., Wang, J., and Wang, Y.-Y., 2013, “Robust Filtering for Ammonia Coverage Estimation in Diesel Engine Selective Catalytic Reduction (SCR) Systems,” ASME J. Dyn. Syst., Meas., Control, 135(6), p. 064504. [CrossRef]
Liang, Z., Ma, X., Lin, H., and Tang, Y., 2010, “The Energy Consumption and Environmental Impacts of SCR Technology in China,” Appl. Energy, 88(4), pp. 1120–1129. [CrossRef]
Zhang, H., Chen, Y., Wang, J., and Yang, S., 2015, “Cycle-Based Optimal NOx Emission Control of Selective Catalytic Reduction Systems With Dynamic Programming Algorithm,” Fuel, 141, pp. 200–206. [CrossRef]
Camarillo, M. K., Stringfellow, W. T., Hanlon, J. S., and Watson, K. A., 2013, “Investigation of Selective Catalytic Reduction for Control of Nitrogen Oxides in Full-Scale Dairy Energy Production,” Appl. Energy, 106(6), pp. 328–336. [CrossRef]
Dardiotis, C., Martini, G., Marotta, A., and Manfredi, U., 2013, “Low-Temperature Cold-Start Gaseous Emissions of Late Technology Passenger Cars,” Appl. Energy, 111(11), pp. 468–478. [CrossRef]
Koebel, M., Elsener, M., and Madia, G., 2001, “Reaction Pathways in the Selective Catalytic Reduction Process With NO and NO2 at Low Temperatures,” Ind. Eng. Chem. Res., 40(1), pp. 52–59. [CrossRef]
Zhang, H., Wang, J., and Wang, Y.-Y., 2014, “Nonlinear Observer Design of Diesel Engine Selective Catalytic Reduction Systems With NOx Sensor Measurements,” IEEE/ASME Trans. Mechatron., (in press).
Sasaki, S., Sarlashkar, J., Neely, G. D., Wang, J., Lu, Q., and Sono, H., 2008, “Investigation of Alternative Combustion, Airflow-Dominant Control and Aftertreatment System for Clean Diesel Vehicles,” SAE Int. J. Fuels Lubr., 116(4), pp. 486–495.
Ichi Shimizu, K., and Satsuma, A., 2007, “Hydrogen Assisted Urea-SCR and NH3-SCR With Silver-Alumina as Highly Active and SO2-Tolerant de-NOx Catalysis,” Appl. Catal., 77(1–2), pp. 202–205. [CrossRef]
Zhang, H., Wang, J., and Wang, Y.-Y., 2014, “Sensor Reduction in Diesel Engine Two-Cell Selective Catalytic Reduction (SCR) Systems for Automotive Applications,” IEEE/ASME Trans. Mechatron. (in press).
Ciardelli, C., Nova, I., Tronconi, E., Konrad, B., Chatterjee, D., Ecke, K., and Weibel, M., 2004, “SCR-DeNOx for Diesel Engine Exhaust Aftertreatment: Unsteady-State Kinetic Study and Monolith Reactor Modeling,” Chem. Eng. Sci., 59(22–23), pp. 5301–5309. [CrossRef]
Willems, F., Cloudt, R., van den Eijnden, E., van Genderen, M., Verbeek, R., de Jager, B., Boomsma, W., and van den Heuvel, I., 2007, “Is Closed-Loop SCR Control Required to Meet Future Emission Targets?,” Proceedings of the SAE 2007 World Congress, SAE Paper No. 2007-01-1574.
Hsieh, M.-F., and Wang, J., 2010, “An Extended Kalman Filter for NOx Sensor Ammonia Cross-Sensitivity Elimination in Selective Catalytic Reduction Applications,” 2010 American Control Conference, Baltimore, MD, pp. 3033–3038.
Zhang, H., Wang, J., and Wang, Y.-Y., 2013, “Robust Mixed H2/H∞ Gain-Scheduling Observer Design for Removal of NOx Sensor Ammonia Cross-Sensitivity in Selective Catalytic Reduction Systems,” American Control Conference, Washington, DC, pp. 2180–2185.
Fridman, E., Shaked, U., and Xie, L., 2003, “Robust H∞Filtering of Linear Systems With Time-Varying Delay,” IEEE Trans. Autom. Control, 48(1), pp. 159–165. [CrossRef]
Zhang, X.-M., and Han, Q.-L., 2009, “A Less Conservative Method for Designing H∞Filters for Linear Time-Delay Systems,” Int. J. Robust Nonlinear Control, 19(12), pp. 1376–1396. [CrossRef]
Qiu, J., and Feng, G., 2008, “Improved Delay-Dependent H∞Filtering Design for Discrete-Time Polytopic Linear Delay Systems,” IEEE Trans. Circuits Syst. II, 55(2), pp. 178–182. [CrossRef]
Zhang, J., Xia, Y., and Shi, P., 2009, “Parameter-Dependent Robust H∞Filtering for Uncertain Discrete-Time Systems,” Automatica, 45(2), pp. 560–565. [CrossRef]
Zhang, H., Shi, Y., and Wang, J., 2014, “On Energy-to-Peak Filtering for Nonuniformly Sampled Nonlinear Systems: A Markovian Jump System Approach,” IEEE Trans. Fuzzy Syst., 22(1), pp. 212–222. [CrossRef]
Zhang, H., Saadat Mehr, A., and Shi, Y., 2010, “Improved Robust Energy-to-Peak Filtering for Uncertain Linear Systems,” Sig. Process, 90(9), pp. 2667–2675. [CrossRef]
Zhang, H., Shi, Y., and Saadat Mehr, A., 2010, “Robust Energy-to-Peak Filtering for Networked Systems With Time-Varying Delays and Randomly Missing Data,” IET Control Theory Appl., 4(12), pp. 2921–2936. [CrossRef]
Upadhyay, D., and Nieuwstadt, M. V., 2006, “Model Based Analysis and Control Design of a Urea-SCR deNOx Aftertreatment System,” ASME J. Dyn. Sys., Meas., Control, 128(3), pp. 737–741. [CrossRef]
Hsieh, M.-F., and Wang, J., 2010, “Observer-Based Estimation of Selective Catalytic Reduction Catalyst Ammonia Storage,” Proc. Inst. Mech. Eng., Part D, 224(9), pp. 1199–1211. [CrossRef]
Hsieh, M.-F., and Wang, J., 2011, “Design and Experimental Validation of an Extended Kalman Filter-Based NOx Concentration Estimator in Selective Catalytic Reduction System Applications,” Control Eng. Pract., 19(4), pp. 346–353. [CrossRef]
White, A., Choi, J., Nagamune, R., and Zhu, G., 2011, “Gain-Scheduling Control of Port-Fuel-Injection Processes,” Control Eng. Pract., 19(4), pp. 380–394. [CrossRef]
Gahinet, P., and Apkarian, P., 1994, “A Linear Matrix Inequality Approach to H∞Control,” Int. J. Rob. Nonlinear Control, 4(4), pp. 421–448. [CrossRef]
Arnettm, M., Bayer, K., Coburn, C., Guezzenec, Y., Koprubasi, K., Mudlam-Mohler, S., Sevel, K., Shakiba-Herfeh, M., and Rizzoni, G., 2008, “Cleaner Diesel Using Model-Based Design and Advanced Aftertreatment in a Student Competition Vehicle,” Proceedings of the SAE 2008 World Congress, Detroit, MI, Paper No. 2008–01–0868.

Figures

Grahic Jump Location
Fig. 1

Schematic diagram of a typical SCR aftertreatment system and the corresponding sensors for the SCR system

Grahic Jump Location
Fig. 2

NOx concentration, exhaust temperature, and flow rate during an FTP75 test cycle

Grahic Jump Location
Fig. 3

Performance of the designed observer with the gains in Eq. (39) for a fixed cross-sensitivity factor

Grahic Jump Location
Fig. 4

NOx concentration comparison with the gains in Eq. (39) for a fixed cross-sensitivity factor

Grahic Jump Location
Fig. 5

NOx concentration error comparison with the gains in Eq. (39) for a fixed cross-sensitivity factor

Grahic Jump Location
Fig. 6

Performance comparison for a fast varying cross-sensitivity factor

Grahic Jump Location
Fig. 7

Performance of the observer with the gain in Eq. (43)

Grahic Jump Location
Fig. 8

Performance of the observer with the gain in Eq. (40)

Grahic Jump Location
Fig. 9

Estimation error comparison between different observers (43) and (40)

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In