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Performance and NOx Emissions Contro...

Sui, Wenbo.

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Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems./
作者:	Sui, Wenbo.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	154 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
Contained By:	Dissertations Abstracts International80-09B.
標題:	Automotive engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10975591
ISBN:	9780438906099

Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems.
Sui, Wenbo.

Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 154 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: B.

Thesis (Ph.D.)--Illinois Institute of Technology, 2018.

This item must not be sold to any third party vendors.

High combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control systems. In this work, different control strategies are investigated to improve the combustion efficiency of engines and to reduce the nitrogen oxide (NO x) emissions of vehicles. There are three main contributions of this work. First, to address emissions concerns, neural network based control algorithms were applied to selective catalyst reduction (SCR) systems. Compared with conventional model-based control, the control strategy based on neural networks can reduce the amount of time and cost required for model identification for these complex systems. The neural network controllers are developed and tested in simulations at different operating conditions for the Fe-zeolite SCR system first. In addition, methods for Jacobian information prediction are also discussed. According to the simulation results, the control strategy based on neural networks can track the desired reference and have reasonable NOx reduction efficiencies in most operating conditions. However, the NOx reduction efficiencies are poor at the low temperature situations in Fe-zeolite SCR systems. To improve this issue, the neural network control strategy was applied to a Cu-zeolite SCR and an improvement in the NOx reduction efficiencies was observed with reductions over 98% at different operating conditions. Second, to address efficiency concerns, a nonlinear model-based combustion control approach was investigated. This control approach aims to track a desired optimal combustion timing and leverages a combustion phasing model for a diesel engine that was developed and validated as part of this work. An intake gas properties model is also developed to capture the cylinder-to-cylinder difference of the temperature and pressure at intake valve closing (IVC). An adaptive controller and model-based controller were then designed for the diesel engine. These control strategies are evaluated in simulations and results show that the combustion phasing control system can track the optimal CA50 (crank angle at 50% mass of fuel burned). The combustion phasing control strategies were also expanded for use on dual-fuel compression ignition engines. The dual-fuel compression ignition engine is being considered as one of the candidates for the next generation of the modern diesel engines due to its ability to achieve high combustion efficiency and low emissions. To track the optimal combustion phasing in a dual-fuel engine, a non-linear combustion phasing model for this application was also developed and calibrated based on simulations. With the control-oriented model, controllers based on an adaptive control strategy and a feedforward control strategy are designed. The controllers are evaluated and shown to track the reference CA50s at varied operating conditions.

ISBN: 9780438906099Subjects--Topical Terms:

2181195
Automotive engineering.

Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems.
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520 $a High combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control systems. In this work, different control strategies are investigated to improve the combustion efficiency of engines and to reduce the nitrogen oxide (NO x) emissions of vehicles. There are three main contributions of this work. First, to address emissions concerns, neural network based control algorithms were applied to selective catalyst reduction (SCR) systems. Compared with conventional model-based control, the control strategy based on neural networks can reduce the amount of time and cost required for model identification for these complex systems. The neural network controllers are developed and tested in simulations at different operating conditions for the Fe-zeolite SCR system first. In addition, methods for Jacobian information prediction are also discussed. According to the simulation results, the control strategy based on neural networks can track the desired reference and have reasonable NOx reduction efficiencies in most operating conditions. However, the NOx reduction efficiencies are poor at the low temperature situations in Fe-zeolite SCR systems. To improve this issue, the neural network control strategy was applied to a Cu-zeolite SCR and an improvement in the NOx reduction efficiencies was observed with reductions over 98% at different operating conditions. Second, to address efficiency concerns, a nonlinear model-based combustion control approach was investigated. This control approach aims to track a desired optimal combustion timing and leverages a combustion phasing model for a diesel engine that was developed and validated as part of this work. An intake gas properties model is also developed to capture the cylinder-to-cylinder difference of the temperature and pressure at intake valve closing (IVC). An adaptive controller and model-based controller were then designed for the diesel engine. These control strategies are evaluated in simulations and results show that the combustion phasing control system can track the optimal CA50 (crank angle at 50% mass of fuel burned). The combustion phasing control strategies were also expanded for use on dual-fuel compression ignition engines. The dual-fuel compression ignition engine is being considered as one of the candidates for the next generation of the modern diesel engines due to its ability to achieve high combustion efficiency and low emissions. To track the optimal combustion phasing in a dual-fuel engine, a non-linear combustion phasing model for this application was also developed and calibrated based on simulations. With the control-oriented model, controllers based on an adaptive control strategy and a feedforward control strategy are designed. The controllers are evaluated and shown to track the reference CA50s at varied operating conditions.
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