Combustion process optimization by genetic algorithms: Reduction of NO2 emission via optimal postflame process

Homma, Ryoji; Chen, Jyh-Yuan

doi:10.1016/s0082-0784(00)80663-3

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Controlling the combustion location is the second phase of modeling ( Figure 14). In previous studies [5,28], results of parametric investigations are presented from which the optimum engine operation point can be obtained through inspection. It can be easily understood that a parametric analysis of a large number of parameters to find the optimum conditions, even with a systematic approach takes a lot of time for a full investigation of the design space.…”

Section: Genetic Algorithm (Ga) For Engine Optimizationmentioning

confidence: 99%

HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

Validi

Chen

Ghafourian

2012

Journal of Combustion

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A Dynamic model of Homogeneous Charge Compression Ignition (HCCI), based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC). A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA), so that combustion initiates and SOC timing posits in the desired crank angle. The best SOC timing to achieve higher performance and efficiency in HCCI engines is between 5 and 15 degrees crank angle (CAD) after top dead center (TDC). To achieve this SOC timing, in the first case, the inlet temperature and equivalence ratio are optimized simultaneously and in the second case, compression ratio is optimized by GA. The model's results are validated with previous works. The SOC timing can be predicted in less than 0.01 second and the CPU time savings are encouraging. This model can successfully be used for real engine control applications.

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Section: Genetic Algorithm (Ga) For Engine Optimizationmentioning

confidence: 99%

HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

Validi

Chen

Ghafourian

2012

Journal of Combustion

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“…For instance, an application of GAs to optimize engines has been demonstrated by Senecal and Reitz (2000) to achieve the dual goals of low emissions and high thermal efficiency. In the past few years, we have developed GAs coupled with a WMR for optimizing postcombustion processes to minimize NO and NO 2 emissions (Homma and Chen, 2001).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Optimization of homogeneous charge compression ignition with genetic algorithms

Chen¹,

Dibble

Kolbu

et al. 2003

Combustion Science and Technology

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Homogeneous charge compression ignition (HCCI) engines promise high efficiency and low emissions, and thus these engines hold great potential for reducing pollution in electric power generation, trucks, marine vehicles, locomotives, and automobiles. Controlling the time of the HCCI combustion event remains a major technical difficulty yet to be overcome. An integration of genetic algorithms (GAs) with well-mixed reactor simulations is developed for better understanding HCCI combustion and for guiding the development of optimal controls. With GAs, the effects of engine intake charge on engine performance are explored with three fuels: methane, propane, and acetylene. Simulation results are compared to available experimental data showing that model predictions are consistent with known trends. As the first application, GAs are used for searching the optimal intake charge conditions of HCCI combustion for power generation with methane. Results suggest that the use of intake charges with high equivalence ratio and with large amounts of exhaust gas recirculation is optimal. Subsequently, parameter optimization for intake conditions with the stoichiometric mixture at various power demand is explored. The results are analyzed and insights on the optimal managing schemes are revealed.

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“…*Corresponding author. Email: ari.saario@tut.fi Homma and Chen (2000) minimized NO 2 emission in post-flame processes. Kalogirou (2003) and Ward et al (2006) provided a review of artificial intelligence techniques (including optimization) in combustion engineering.…”

Section: Introductionmentioning

confidence: 99%

Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers

Saario¹,

Oksanen²

2008

Engineering Optimization

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Combustion process optimization by genetic algorithms: Reduction of NO2 emission via optimal postflame process

Cited by 9 publications

References 9 publications

HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

Optimization of homogeneous charge compression ignition with genetic algorithms

Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers

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