A Model Development for Evaluating Soot-NOxInteractions in a Blended 2-Way Diesel Particulate Filter/Selective Catalytic Reduction

2012 ◽  
Vol 51 (48) ◽  
pp. 15582-15592 ◽  
Author(s):  
Soo-Youl Park ◽  
Kushal Narayanaswamy ◽  
Steven J. Schmieg ◽  
Christopher J. Rutland
Keyword(s):  
Selective Catalytic Reduction ◽  
Diesel Particulate Filter ◽  
Catalytic Reduction ◽  
Model Development ◽  
Particulate Filter ◽  
Diesel Particulate

Comparative analysis on the effects of diesel particulate filter and selective catalytic reduction systems on a wide spectrum of chemical species emissions

2018 ◽  
Author(s):  
Z. Gerald Liu ◽  
Devin R. Berg ◽  
Thaddeus A. Swor ◽  
James J. Schauer‡
Keyword(s):  
Diesel Engine ◽  
Selective Catalytic Reduction ◽  
Diesel Particulate Filter ◽  
Catalytic Reduction ◽  
Wide Spectrum ◽  
Chemical Species ◽  
Pollutant Emissions ◽  
Particulate Filter ◽  
Diesel Particulate ◽  
Aftertreatment Systems

Two methods, diesel particulate filter (DPF) and selective catalytic reduction (SCR) systems, for controlling diesel emissions have become widely used, either independently or together, for meeting increasingly stringent emissions regulations world-wide. Each of these systems is designed for the reduction of primary pollutant emissions including particulate matter (PM) for the DPF and nitrogen oxides (NOx) for the SCR. However, there have been growing concerns regarding the secondary reactions that these aftertreatment systems may promote involving unregulated species emissions. This study was performed to gain an understanding of the effects that these aftertreatment systems may have on the emission levels of a wide spectrum of chemical species found in diesel engine exhaust. Samples were extracted using a source dilution sampling system designed to collect exhaust samples representative of real-world emissions. Testing was conducted on a heavy-duty diesel engine with no aftertreatment devices to establish a baseline measurement and also on the same engine equipped first with a DPF system and then a SCR system. Each of the samples was analyzed for a wide variety of chemical species, including elemental and organic carbon, metals, ions, n-alkanes, aldehydes, and polycyclic aromatic hydrocarbons, in addition to the primary pollutants, due to the potential risks they pose to the environment and public health. The results show that the DPF and SCR systems were capable of substantially reducing PM and NOx emissions, respectively. Further, each of the systems significantly reduced the emission levels of the unregulated chemical species, while the notable formation of new chemical species was not observed. It is expected that a combination of the two systems in some future engine applications would reduce both primary and secondary emissions significantly.

A Physically-Based, Control-Oriented Diesel Particulate Filter (DPF) Model for the Applications of NO/NO2 Ratio Estimation Using a NOx Sensor

2010 ◽  
Author(s):  
Ming-Feng Hsieh ◽  
Junmin Wang
Keyword(s):  
Diesel Particulate Filter ◽  
Catalytic Reduction ◽  
Nox Reduction ◽  
Diesel Oxidation Catalyst ◽  
Oxidation Catalyst ◽  
Particulate Filter ◽  
Exhaust Gas ◽  
Diesel Particulate ◽  
Physically Based ◽  
Aftertreatment Systems

This paper presents a physically-based, control-oriented Diesel particulate filter (DPF) model for the purposes of NO and NO2 concentration estimations in Diesel engine aftertreatment systems. The presence of NO2 in exhaust gas plays an important role in selective catalytic reduction (SCR) NOx reduction efficiency. However, current NOx cannot differentiate NO and NO2 from the total NOx concentration. A model which can be used to estimate NO and NO2concentrations in exhaust gas flowing into the SCR catalyst is thus necessary. Current aftertreatment systems for light-, medium-, and heavy-duty Diesel engines generally include Diesel oxidation catalyst (DOC), DPF, and SCR. The DPF related NO/NO2 dynamics was investigated in this study, and a control-oriented model was developed and validated with experimental data.

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