Optimization of homogeneous charge compression ignition combustion in a light-duty diesel engine operated using ethyl acetate-gasoline blends

被引:5
作者
Kale, Aneesh Vijay [1 ]
Krishnasamy, Anand [1 ,2 ]
机构
[1] Indian Inst Technol Madras, Chennai, Tamil Nadu, India
[2] Indian Inst Technol Madras, IC Engines Lab 201, Chennai 600036, Tamil Nadu, India
关键词
HCCI; ethyl acetate; 2-ethylhexyl nitrate; artificial neural network; genetic algorithm; HYBRID ELECTRIC VEHICLES; HCCI ENGINES; EXHAUST EMISSIONS; NEURAL-NETWORKS; PERFORMANCE; ETHANOL; REACTIVITY; IMPROVERS; ACID;
D O I
10.1177/14680874221138126
中图分类号
O414.1 [热力学];
学科分类号
摘要
The low temperature combustion mode of homogeneous charge compression ignition eliminates particulate matter and oxides of nitrogen (NO x ) emissions trade-off that prevails in high-temperature, diffusion-controlled conventional diesel combustion (CDC). In the present research, the significant challenge of narrow operating load range that hinders the commercial implementation of light-duty HCCI engines was overcome by employing ethyl acetate-gasoline blends. The gasoline concentration in test fuels was reduced in 10% decrements, from 84% to 24%, to replace it with ethyl acetate. The use of ethyl acetate, a renewable fuel, can help solve the energy crisis due to the rapid depletion of fossil fuels. An ignition improver was blended in the test fuels in a predetermined amount of 6% so that combustion stability was not hampered at lower loads. Parametric investigations were conducted to study the effect of progressively increasing ethyl acetate in test fuels on HCCI combustion, performance, and emissions. The machine learning tool of artificial neural network was implemented to learn the behavior of the test engine, considering load and fuel composition as input variables. The feedforward artificial neural network models were developed to predict the start of combustion, combustion phasing, indicated thermal efficiency, and emissions of carbon monoxide, soot, NO x , and unburned hydrocarbon (HC). A multi-objective optimization was performed to arrive at the best operating condition by integrating artificial neural network models with the genetic algorithm. All the developed artificial neural network models could predict responses with acceptable accuracy. The genetic algorithm indicated that the optimum point of the operation was at 80% load and 65% ethyl acetate in the test fuels. Experiments were conducted to validate the optimal HCCI conditions that resulted in 27% higher indicated thermal efficiency, 54% lower HC+NOx, and 99% lower soot emissions than CDC. Overall, the present study demonstrated the benefits of considering ethyl acetate as a fuel to improve HCCI engine metrics of off-road diesel engines.
引用
收藏
页码:3000 / 3016
页数:17
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