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    Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles-diesel blends using different machine learning algorithms
    (Pergamon-Elsevier Science Ltd, 2021) Agbulut, Umit; Gurel, Ali Etem; Sandemir, Suat
    Deep learning (DL), Artificial Neural Network (ANN), Kernel Nearest Neighbor (k-NN), and Support Vector Machine (SVM) have been applied to numerous fields owing to their high-accuracy and ability to analyze the non-linear problems. In this study, these machine learning algorithms (MLAs) are used to predict emission and performance characteristics of a CI engine fuelled with various metal-oxide based nano particles (Al2O3, CuO, and TiO2) at a mass fractions of 200 ppm. Assessed parameters in the study are carbon dioxide (CO), nitrogen oxide (NOx), exhaust gas temperature (EGT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE). To evaluate the success of algorithms, four metrics (R-2, RMSE, rRMSE, and MBE) are discussed in detail. Tests performed at varying engine speeds from 1500 rpm to 3400 rpm with the intervals of 100 rpm. The addition of nanoparticles simultaneously reduced CO and NOx emissions because they ensured more complete combustion thanks to their inherent oxygen, the higher surface to volume ratio, superior thermal conductivities and their catalytic activity role. Further, the nano-sized particles ensured an accelerated heat transfer from the combustion chamber. In comparison with that of neat diesel fuel, the reduction in NOx is found to be 3.28, 7.53, and 10.05%, and the reduction in CO is found to be 8.3, 11.6, and 15.5% for TiO2, Al2O3, and CuO test fuels, respectively. Moreover, the presence of nanoparticles in test fuels has improved engine performance. As compared with those of neat diesel fuel, the doping of nanoparticles drops the BSFC value by 5.54, 7.89, and 9.96% for TiO2, CuO, and Al2O3, respectively, and enhanced BTE value to be 6.15, 8.87, and 11.23% for TiO2, CuO, and Al2O3, respectively. On the other hand, it can be said that all algorithms presented very satisfying results in the prediction of CI engine responses. All R-2 has changed between 0.901 and 0.994, and DL has given the highest R-2 value for each engine response. In terms of rRMSE, all results (except for one result in k-NN) are categorized as excellent according to the classification in the literature. Considering all metrics together, DL is giving the best results in the prediction of engine responses for the dataset used in this paper. Then it is closely followed by ANN, SVM, and k-NN algorithms, respectively. In conclusion, this paper is proving that the nanoparticle addition for ICEs is significantly dropping the exhaust pollutants, and improving the engine performance, and further the results can be successfully predicted with the machine learning algorithms. (c) 2020 Elsevier Ltd. All rights reserved.
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    Investigating the role of fuel injection pressure change on performance characteristics of a DI-CI engine fuelled with methyl ester
    (Elsevier Sci Ltd, 2020) Sandemir, Suat; Gurel, Ali Etem; Agbulut, Umit; Bakan, Faruk
    This paper intended to investigate the impact of corn oil methyl-ester and diesel blends on performance, cornbustion and emission characteristics at varying injection pressure (210 and 230 bar). The tests were performed at a constant engine speed of 2000 rpm, and at two different engine loads of 5 and 10 Nm. Corn oil methyl-ester was produced by transesterification method in the study and then blended at 10%, 20% and 50% by volume into neat diesel fuel. The results presented that corn oil methyl-ester could improve combustion process owing to its high oxygen content in comparison with that of B0 fuel. High-injection pressure reduced the droplet diameter and accelerated the combustion process. This case has generally caused to high cylinder pressures. With respect to emissions, it was observed that CO (down to 66.67% at 230 bar) and HC (down to 52.38% at 230 bar) were sharply reduced depending on the increment of the blending rates of biodiesel while NOx (up to 22.45% at 230 bar) increased significantly. Depending on the increasing rate of corn oil methyl-ester in the blends, more fuel mass was injected into the combustion chamber and specific fuel consumption in biodiesel content-fuels were, therefore, higher than that of low injection pressure. Additionally, thermal efficiency decreased with the increment of biodiesel content owing to the lower heating value of biodiesel.