Artificial neural network based determination of the performance and emissions of a Diesel engine using ethanol-diesel fuel blends

Can, Özer; Öztürk, Erkan; Arcaklıoğlu, Erol

doi:10.30939/ijastech..805531

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…In this context, studies such as the use of alternative fuels, exhaust after treatment systems and advanced combustion concepts have been carried out. Among these, alternative fuels especially bio fuels have been extensively researched and applied in internal combustion engines with promising results in emissions and a high amount of literature presented the significant benefits for using biodiesel and alcohol fuels in diesel engines [1][2][3]. On the other hand, the importance of studies on the reduction of emissions with exhaust after treatment systems has increased due to the increasingly tighter emission standards [4].…”

Section: Introductionmentioning

confidence: 99%

Experimental study on reduction of pollutant emissions in reactivity controlled compression ignition (RCCI) engine fueled with diesel/gasoline fuels

Fırat

Altun

Okcu

et al. 2022

International Journal of Automotive Engineering and Technologies

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Reactivity Controlled Compression-Ignition (RCCI) concept presents a great potential to reduce both NOx and soot emissions from conventional diesel engines with improved thermal efficiency. Therefore, in this work, a single-cylinder diesel engine with CRDI was operated on RCCI mode. To investigate the effect of RCCI mode on engine performance and emissions, gasoline was injected into the port as LRF, while diesel was injected directly into the cylinder as HRF. Premixed ratio of low reactivity fuel was varied from 0% (conventional diesel mode, CDM) to 60% with 15% intervals as energy ratio given to engine per cycle. Engine load was also studied from 20% to 60% of max. engine torque with 20% intervals to stimulate low, mid and mid-high load conditions. Experimental results showed that with increase of Rp, unburned HC and CO emissions increased while smoke opacity decreased significantly (up to about 95% in case of 0.60 Rp and 60% engine load) in gasoline/diesel RCCI compared to CDM. Though NOx emissions decreased at low engine loads with RCCI strategy, they started to increase with increase of Rp at high loads.

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Section: Introductionmentioning

confidence: 99%

Experimental study on reduction of pollutant emissions in reactivity controlled compression ignition (RCCI) engine fueled with diesel/gasoline fuels

Fırat

Altun

Okcu

et al. 2022

International Journal of Automotive Engineering and Technologies

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Experimental and artificial neural network (ANN) study on the impact of three different metal oxide nanoparticle combustion enhancers enriched on mahua biodiesel-diesel fuelled CI engine

Muniyappan,

Krishnaiah

2024

Eng. Res. Express

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The prediction of CI engine parameters has acquired significant attention and is regarded as a crucial tool for engine research and diagnosis studies. This contribution compares two different approaches for diesel engine viz. experimental and artificial neural networks (ANNs) predictions of performance and emission outputs. The base fuel M30 blend consist of mahua biodiesel 30 (% v) and 70 (% v) of diesel. The M30 blend and 50 ppm concentration of NPs (CeO2, CuO, and TiO2) were chosen for basic experimentation. The findings indicate an increase in BTE by 9.1%, peak CP by 11.3%, and HRR by 10.2%, with a decrease in BSFC by 13.7%, CO by 30.4%, HC by 30.1%, smoke by 34.7%, and NOx by 7.1%, resulting from the addition of 50 ppm CeO2 NP to D70M30 blend. The widely used backpropagation technique for ANN is implemented in multilayered feedforward design. To forecast the engine characteristics of a CI engine, a network structures including two inputs and one output is used. The given ANN model examined D100, M30, M30CeNP50, M30CuNP50, and M30TNP50 blends, using engine load and biodiesel with nanoparticle blend as the two input factors. A data-driven ANN model was created to forecast the optimised engine characteristics. The lowest and highest value of correlation coefficient (R2) and mean square errors (MSE), mean relative error (MRE) were found to for peak CP, HRR, BTE, BSFC, CO, CO2, HC, NOx and smoke. Using ANN one can choose right blend ratio among the variety of fuels blends for an appropriate requirement without much experimentation.

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Investigation of the Effect of CeO2 Nanoparticle Addition in Diesel Fuel on Engine Performance and Emissions

Arslan¹,

Çelik²

2022

JEMS

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Artificial neural network based determination of the performance and emissions of a Diesel engine using ethanol-diesel fuel blends

Cited by 3 publications

References 29 publications

Experimental study on reduction of pollutant emissions in reactivity controlled compression ignition (RCCI) engine fueled with diesel/gasoline fuels

Experimental study on reduction of pollutant emissions in reactivity controlled compression ignition (RCCI) engine fueled with diesel/gasoline fuels

Experimental and artificial neural network (ANN) study on the impact of three different metal oxide nanoparticle combustion enhancers enriched on mahua biodiesel-diesel fuelled CI engine

Investigation of the Effect of CeO2 Nanoparticle Addition in Diesel Fuel on Engine Performance and Emissions

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