Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine

Taghavifar, Hadi; Taghavifar, Hamid; Mardani, Aref; Mohebbi, Arash; Khalilarya, Shahram; Jafarmadar, Samad

doi:10.1016/j.jclepro.2015.03.035

Cited by 64 publications

(23 citation statements)

References 17 publications

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“…The SVM is the most appropriate method for obtaining the prediction model of a stochastic system from a small A good number of research activities have been conducted and have proposed a variety of ways for predicting NO x emissions [16][17][18][19]. A study [20] used artificial neural networks to build a prediction model for CO 2 , soot, and NO x . In references [19,21], an adaptive least squares support vector machine model was built for the prediction of NO x emissions with a novel update to tackle process variations.…”

Section: Experimental Setup and Methodsmentioning

confidence: 99%

Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

Liu

Yan

et al. 2016

Sustainability

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Abstract:The objective of the study is to present the modeling and multi-objective optimization of NO x conversion efficiency and NH 3 slip in the Selective Catalytic Reduction (SCR) catalytic converter for a diesel engine. A novel ensemble method based on a support vector machine (SVM) and genetic algorithm (GA) is proposed to establish the models for the prediction of upstream and downstream NO x emissions and NH 3 slip. The data for modeling were collected from a steady-state diesel engine bench calibration test. After obtaining the two conflicting objective functions concerned in this study, the non-dominated sorting genetic algorithm (NSGA-II) was implemented to solve the multi-objective optimization problem of maximizing NO x conversion efficiency while minimizing NH 3 slip under certain operating points. The optimized SVM models showed great accuracy for the estimation of actual outputs with the Root Mean Squared Error (RMSE) of upstream and downstream NO x emissions and NH 3 slip being 44.01ˆ10´6, 21.87ˆ10´6 and 2.22ˆ10´6, respectively. The multi-objective optimization and subsequent decisions for optimal performance have also been presented.

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Section: Experimental Setup and Methodsmentioning

confidence: 99%

Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

Liu

Yan

et al. 2016

Sustainability

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“…Step 1: Involved the use of grid search in the range of C 2 [2 À10 , 2 15 ] and g 2 [2 À5 , 2 10 ], according to the aforementioned exponential sequences. The cross-validated RMSE of the SVM models for all pairs of C and g parameters was evaluated to find the best pair.…”

Section: Rmesmentioning

confidence: 99%

“…This method was quite complicated and time-consuming, and may not be suitable for practical applications. Another study [10] networks to build the prediction models for CO 2 , soot, and NO X . The artificial neural network model was easy to apply and showed a good ability of nonlinear fitting, but it needed large numbers of data samples to train and was poor in robustness.…”

Section: Introductionmentioning

confidence: 99%

A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine

Liu

Yan

et al. 2016

Measurement

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“…Today, the development of power units with low environmental impact has become one of the most exciting challenges in automotive technology . In fact, advanced combustion strategies about the internal combustion engines (ICEs) are the most challenging approaches to reach better fuel economy and lower exhaust emissions . In recent decades, ecological concerns along with the rapid reduction of fossil fuel reserves have prompted many countries to pass legislation aimed at reducing vehicle pollution and consumption .…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of effects of magnetic field on performance, combustion, and emission characteristics of a spark ignition engine

Niaki

Zadeh

Niaki³

et al. 2019

Env Prog and Sustain Energy

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This article presents a comprehensive experimental work for the application of permanent magnets with the intensity of 9,000 G to a four‐stroke four cylinders gasoline engine on fuel lines at a location near to the combustion chamber. By using the permanent magnet, the liquid fuel disintegrates into small diameter, and de‐clustering of the fuel molecules of hydrocarbon has been proved to provide better atomization of the fuel, which makes sure that the fuel strenuously combines with oxygen and results in complete and more efficient burning process inside the combustion chamber. The experimental analysis revealed that magnetic treatment has improved performance and emission characteristics. Analysis over the engine test results with magnetic fuel conditioning showed that the reduction of 4–12% in fuel consumption and reduction in 11, 10, 18, and 10% for CO, CO2, HC, and NOx emissions, respectively, compared to gasoline fuel without magnetic condition. Further, we experimentally investigated the performance of petrol engine parameters such as in‐cylinder temperature and pressure, cylinder, and head cylinder temperature, the temperature of different parts of the piston. As a whole, magnetic fuel treatment has improved combustion and reduced the harmful pollutants of the compression ignition engine.

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Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine

Cited by 64 publications

References 17 publications

Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine

Experimental investigation of effects of magnetic field on performance, combustion, and emission characteristics of a spark ignition engine

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