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Modeling, control, and diagnosis of ...

Midlam-Mohler, Shawn Wesley.

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Modeling, control, and diagnosis of a diesel lean nitrogen oxide trap catalyst.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling, control, and diagnosis of a diesel lean nitrogen oxide trap catalyst./
作者:	Midlam-Mohler, Shawn Wesley.
面頁冊數:	379 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2691.
Contained By:	Dissertation Abstracts International66-05B.
標題:	Engineering, Automotive. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3176892
ISBN:	0542166887

Modeling, control, and diagnosis of a diesel lean nitrogen oxide trap catalyst.
Midlam-Mohler, Shawn Wesley.

Modeling, control, and diagnosis of a diesel lean nitrogen oxide trap catalyst. - 379 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2691.

Thesis (Ph.D.)--The Ohio State University, 2005.

Diesel engines require the use of alternative catalytic methods to meet future emissions standards.{09}One such alternative system is the bypass-regeneration, Lean NOx Trap (LNT), which is the focus of this work. A novel method of providing reductants for management of this system is presented, which is referred to as flame reforming. This method uses rich, premixed combustion of Diesel fuel to generate carbon monoxide, hydrogen, and light-chain hydrocarbons for LNT management. Through the development of a prototype flame reformer and experimental testing, this concept is demonstrated to offer advantages over traditional methods in cost and dynamic response.

ISBN: 0542166887Subjects--Topical Terms:

1018477
Engineering, Automotive.

Modeling, control, and diagnosis of a diesel lean nitrogen oxide trap catalyst.
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500 $a Adviser: Yann Guezennec.
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520 $a Diesel engines require the use of alternative catalytic methods to meet future emissions standards.{09}One such alternative system is the bypass-regeneration, Lean NOx Trap (LNT), which is the focus of this work. A novel method of providing reductants for management of this system is presented, which is referred to as flame reforming. This method uses rich, premixed combustion of Diesel fuel to generate carbon monoxide, hydrogen, and light-chain hydrocarbons for LNT management. Through the development of a prototype flame reformer and experimental testing, this concept is demonstrated to offer advantages over traditional methods in cost and dynamic response.
520 $a A technique, which is referred to as exotherm analysis, is developed which allows the observation of chemical phenomena inside the catalyst using substrate temperature measurements. Through the proper analytical methods, it is demonstrated experimentally that the temperature rise in the catalyst can be correlated to the rate that key reactions are taking place, as well as the mass of NOx stored and the effects of sulfur poisoning. These key reactions include the reduction of stored oxidizers as well as the readsorption of oxygen by the catalyst. This technique is exclusive to the bypass-regeneration system because of the low gas flaw rates involved.
520 $a A control-oriented model of the storage and regeneration process is also developed. This model is used to develop a complete LNT NOx management algorithm using the techniques of model-based control. This algorithm, which uses catalyst temperatures as the primary feedback signals, contains an adaptive engine-out NOx estimator as well as an adaptive catalyst-out NO x estimator. In this way, the algorithm automatically compensates for sulfur poisoning of the catalyst. The model also is used to develop a virtual LNT system simulator with all of associated control algorithms and adaptive estimators. With this tool, the LNT management algorithm is evaluated for an intended Tier 2, Bin 5 emissions application. In addition, experimental validation of key sub-algorithms is conducted on an engine dynamometer. The simulated performance of the system suggests that the system and control algorithm are capable of meeting Tier 2 standards with fuel economy penalties of less than 1%.
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