Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural network

Saraee, Hossein Soukht; Taghavifar, Hadi; Jafarmadar, Samad

doi:10.1016/j.applthermaleng.2016.11.044

Cited by 119 publications

(20 citation statements)

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“…Research interests on additives of the main bases have continued to increase owing to improved combustion in the combustion process [12,13]. The additives mainly include copper, cerium, and platinum.…”

Section: Introductionmentioning

confidence: 99%

Comparison of NOx and Smoke Characteristics of Water-in-Oil Emulsion and Marine Diesel Oil in 400-kW Marine Generator Engine

et al. 2019

Energies

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Currently, the exhaust gas of a ship is regulated for nitrogen oxides and sulphur compounds; however, there is no IMO regulation on smoke under discussion. This study investigated the reduction of exhaust gas through ship emulsion fuel, which can simultaneously reduce nitrogen oxides and smoke in ship engines before smoke regulations are established. The combustion and exhaust characteristics were investigated according to the moisture content of emulsion fuel using a 400-kW generator engine. As the water content of the emulsion and the temperature of the combustion chamber increase, micro explosion increases and the combustion period decreases. The nitrogen oxide and smoke from the emulsion fuel used in this study decreased by 7% and 75%, respectively. The nitrogen oxides and soot reductions obtained by the use of emulsion fuel were boosted by micro-explosion of water contained in the fuel during combustion.

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Section: Introductionmentioning

confidence: 99%

Comparison of NOx and Smoke Characteristics of Water-in-Oil Emulsion and Marine Diesel Oil in 400-kW Marine Generator Engine

et al. 2019

Energies

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“…The sensitivity analysis obtained the optimum value of PEDE’s. Similarly, the effects of CeO2 nano-particle addition and consequent ANN model with 3-12-4 architecture with trainlm training rule showed a correlation coefficient close to 1 [ 93 ].…”

Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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“…A common type of ANN is a multi-layer perceptron which consists of at least three layers of nodes. Backpropagation algorithm is a method used in ANN to calculate the error contribution of each neuron after processing a series of data [24]. ANN consists of three main layers.…”

Section: The Modeling With Annmentioning

confidence: 99%

Experimental Study and Prediction of Performance and Emission in an SI Engine Using Alternative Fuel with Artificial Neural Network

Balki¹,

Çavuş²,

Duran³

et al. 2018

International Journal of Automotive Engineering and Technologies

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In this study, the effect of using pure ethanol in different operating conditions of a spark ignition engine was experimentally investigated, and a backpropagation artificial neural network (ANN) model was developed to estimate the engine performance and exhaust emissions. For this purpose, the spark ignition (SI) engine having a compression ratio (CR) of 8.5:1, a single cylinder and air-cooled was used in the engine tests experiments, and the ANN model was created with using the C# programming language. The engine tests were, firstly, conducted for four varied CR, three types of air excess coefficient (AEC) and three different ignition timing (IGT) at 2400 rpm, and the performance and exhaust emission of the engine were recorded. Secondly, the performance and exhaust emission values of SI engine for the same test conditions were estimated with a backpropagation ANN model. The ANN model was trained with the data obtained from the experimental study. The engine torque, brake specific fuel consumption (BSFC), hydrocarbon (HC) emission and carbon dioxide (CO2) emission results obtained from the pure ethanol were compared with those of the gasoline. The comparison was made separately for the same test conditions, and the changes were given as a percentage into the paper. Furthermore, the estimated performance and emission values obtained from the ANN trained with experimental data are compared with verification experiments. According to the results, it can be seen that the percentage change in the difference between the values obtained from the verification experiments, and the estimated results is acceptable. When the results are generally evaluated, it is observed that is improved of the performance and exhaust emissions as using pure ethanol at lowering the IGT and increasing the CR. When all tests are evaluated according to the best results, it is shown that engine torque and BSFC increased respectively about 11% and 51% (20 CA) on average while CO2 and HC emissions reduced about 6% and 49.2% on average. In addition, it is seen that the ANN model can be used to estimate the performance and emissions of an SI engine using alternative fuels.

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Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural network

Cited by 119 publications

References 50 publications

Comparison of NOx and Smoke Characteristics of Water-in-Oil Emulsion and Marine Diesel Oil in 400-kW Marine Generator Engine

Comparison of NOx and Smoke Characteristics of Water-in-Oil Emulsion and Marine Diesel Oil in 400-kW Marine Generator Engine

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Experimental Study and Prediction of Performance and Emission in an SI Engine Using Alternative Fuel with Artificial Neural Network

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