Development of a 2D SCR Catalyst on a Diesel Particulate Filter Model for Design and Control Applications to a Ultra Low NOx Aftertreatment System

Chundru, Venkata Rajesh

doi:10.37099/mtu.dc.etdr/869

Cited by 2 publications

(4 citation statements)

References 44 publications

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“…The PM retained model was able to simulate the PM mass retained to within ±2 gm and the pressure drop model was able to simulate the pressure drop to within ±0.1 kPa of the experimental data. Detailed description of these sub models can be found in references 1,10,14 .…”

Section: Development Of Kalman Filter Based Scr-f State Estimatormentioning

confidence: 99%

Development of a Kalman filter estimator for simulation and controlof NO_x and PM in a SCR catalyston a DPF

Chundru

Parker

Johnson

2020

International Journal of Engine Research

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The goal of a control system for a SCR catalyst on a DPF (SCR-F) is to minimize the fuel consumption due to back pressure while maximizing the NOx conversion across the SCR-F. To meet these goals, prediction of the internal states of the 2D spatial distribution of temperature, PM mass retained and NH3 coverage fraction in an SCR-F is required. To predict these internal states, a state estimator capable of simulating the PM oxidation rate, SCR reactions, filtration efficiency and pressure drop across the SCR-F based on inlet conditions and outlet sensor data is required. A 2D model of the SCR-F capable of predicting the SCR-F internal states was developed and validated using experimental data from a Johnson Matthey SCRF® and Cummins 2013 ISB engine. To reduce the computational cost, an estimator model with a coarser mesh and a quasi-steady state solution for the chemical species and energy conservation equations was developed which was faster than real time. The model was combined with the outlet thermocouple, pressure drop and NOx sensor data from the engine control unit using an extended Kalman filter to create the SCR-F state estimator. The estimator was able to predict the target internal states to within 5% of the experimental data. The estimator was able to compensate for calibration parameter errors by up to 5% using the sensor data along with filtering of the sensor noise. A DOC estimator was used to obtain the SCR-F inlet NOx concentrations and temperatures.

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Section: Development Of Kalman Filter Based Scr-f State Estimatormentioning

confidence: 99%

Development of a Kalman filter estimator for simulation and controlof NO_x and PM in a SCR catalyston a DPF

Chundru

Parker

Johnson

2020

International Journal of Engine Research

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“…More detailed models also exist, such as [5] where washcoat diffusion is considered. With the introduction of stricter emission legislations, the usage of these types of models can be assumed to increase [6,7]. However, since one-dimensional models are already too complex for control purposes, more detailed models are out of the scope here.…”

mentioning

confidence: 99%

“…k i n e t i c s = @ ( T , C , t h e t a , par ) par ( 1 ) * exp ( − par ( 2 ) * ( 1 / T− 1/ 600) ) * t h e t a * C ( 4 ) ; 61 % HNCO H y d r a l i s a t i o n 62 r e a c t i o n s( 6 ) . name = 'HNCO H y d r a l i s a t i o n ' ; 63 r e a c t i o n s( 6 ) . s u b s c r i p t = 'Hyd ' ; 64 r e a c t i o n s( 6 ) .…”

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Selective Catalytic Reduction Catalyst Modeling for Control Purposes

Holmer

Eriksson

2022

Energies

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In markets with strict emission legislations Selective Catalytic Reduction (SCR) has become the industry standard for NOx abatement in heavy-duty vehicles, and therefore modeling and control of these systems are vital. Many SCR catalyst models are available in the literature and in this paper different models are discussed and classified into groups. Two models, based on the two most popular classes for control-oriented models, are implemented and compared with each other, one based on the continuously stirred-tank reactor approximation, and the other on a quasi-static behavior of the gas phase. The results show that assuming a quasi-static behavior of the gas phase in the catalyst gives better results in terms of accuracy and simulation time, especially when it comes to predictions of ammonia slip.

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Development of a 2D SCR Catalyst on a Diesel Particulate Filter Model for Design and Control Applications to a Ultra Low NOx Aftertreatment System

Cited by 2 publications

References 44 publications

Development of a Kalman filter estimator for simulation and controlof NO_x and PM in a SCR catalyston a DPF

Development of a Kalman filter estimator for simulation and controlof NO_x and PM in a SCR catalyston a DPF

Selective Catalytic Reduction Catalyst Modeling for Control Purposes

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Development of a 2D SCR Catalyst on a Diesel Particulate Filter Model for Design and Control Applications to a Ultra Low NOx Aftertreatment System

Cited by 2 publications

References 44 publications

Development of a Kalman filter estimator for simulation and controlof NOx and PM in a SCR catalyston a DPF

Development of a Kalman filter estimator for simulation and controlof NOx and PM in a SCR catalyston a DPF

Selective Catalytic Reduction Catalyst Modeling for Control Purposes

Contact Info

Product

Resources

About

Development of a Kalman filter estimator for simulation and controlof NO_x and PM in a SCR catalyston a DPF

Development of a Kalman filter estimator for simulation and controlof NO_x and PM in a SCR catalyston a DPF