Experimental investigation on engine performance, emission, and combustion characteristics of a DI CI engine using tyre pyrolysis oil and diesel blends doped with nanoparticles

S, Kumaravel Thangavelu; Murugesan, A.

doi:10.1002/ep.13321

Cited by 28 publications

(13 citation statements)

References 25 publications

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“…The addition of Al 2 O 3 nanoparticles to the diesel fuel provided a large surface area for interaction between water molecules and [80] Ginger grass oil CeO 2 30 ppm Engine performance: Higher BTE and lower BSFC Harmful emission: Lower CO 2 , HC, and NO x emission. All samples have lower CO emission except for B10, which was higher at lower load and then decreased at maximum load Thangavelu et al [81] Waste tyre oil CeO 2 100 ppm Engine performance: Improvement in BTE fuel particles. Due to the high surface activity of the water molecules, hydrogen atom decomposes from the water during combustion which promotes complete combustion.…”

Section: Al 2 Omentioning

confidence: 97%

“…Thangavelu et al [81] explored the potential of CeO 2 nanoparticles as a fuel additive in a four-stroke singlecylinder water-cooled compression ignition engine with the volumetric proportions of 5, 10, 15, and 20%. From the study, a significant reduction in the emission of HC by 3%, smoke by 7.7%, and CO by 1.33% was found as compared to that of the conventional diesel.…”

Section: Ceomentioning

confidence: 99%

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A comprehensive review of the influences of nanoparticles as a fuel additive in an internal combustion engine (ICE)

Yusof¹,

Sidik²,

Asako³

et al. 2020

Nanotechnology Reviews

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Nanofluid is a colloidal mixture consisting of nano-sized particles dispersed in a liquid medium. It improves heat transfer properties and promotes high energy efficiency in a wide spectrum of engineering applications. In recent years, particularly in the automotive industry, the addition of nanofluid in diesel/biodiesel as an additive for ICE has become an attractive approach to promote enhanced combustion efficiency and emission reduction due to their superior thermophysical properties. Many researchers have previously demonstrated that the addition of nanoparticles in diesel/biodiesel fuel improved the overall engine combustion characteristics. As a whole, this study aims to summarize the recent research findings related to the effect of nanoparticles on the fuel properties and engine combustion efficiency. Furthermore, different types of additive blended with varying fuel properties are also compared and discussed. Lastly, the advantages and prospects of using nanofluid as an additive fuel are summarized for future research opportunities.

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Section: Al 2 Omentioning

confidence: 97%

Section: Ceomentioning

confidence: 99%

A comprehensive review of the influences of nanoparticles as a fuel additive in an internal combustion engine (ICE)

Yusof¹,

Sidik²,

Asako³

et al. 2020

Nanotechnology Reviews

View full text Add to dashboard Cite

show abstract

“…As mentioned before, the sigmoid function has been used in the proposed code as a hidden layer activation function. Equation (6) shows the mathematical expression of the sigmoid function [22]:…”

Section: The Narx Model Setupmentioning

confidence: 99%

“…Modeling gas turbines has many generalized aims, which represent the core objective of our research, such as control system orientations, performance monitoring, control, prediction, reducing emissions and grid code compliance. Furthermore, the trustable modeling of gas turbines or engines can lead to significant sustainable development in these generation systems in terms of using other fuels that are formed from other materials and waste, which can be fired in the same device or by a similar principle, such as biogas, leading to promising, sustainable and flexible power generation scenarios [6][7][8][9][10]. The topic of GT power generation modeling is apparently an interdisciplinary research field that can be deeply related to many disciplines during the research phase, but their research outcomes are extremely useful, so they are therefore released beyond the boundaries of every discipline alone.…”

Section: Introduction 1aims and Motivationsmentioning

confidence: 99%

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

2022

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Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network (CNN). The outputs selected for simulations are: the output power, the exhausted temperature and the turbine speed or system frequency, whereas the inputs are the natural gas (NG) control valve, the pilot gas control valve and the compressor variables. The data-sets have been prepared in three essential formats for the training and validation of the networks: normalized data, standardized data and SI units’ data. Rigorous effort has been carried out for wide-range trials regarding tweaking the network structures and hyper-parameters, which leads to highly satisfactory results for both models (overall, the minimum recorded MSE in the training of the MISO NARX was 6.2626 × 10−9 and the maximum MSE that was recorded for the MISO CNN was 2.9210 × 10−4, for more than 15 h of GT operation). The results have shown a comparable satisfactory performance for both dynamic NARX ANN and the CNN with a slight superiority of NARX. It can be newly argued that the dynamic ANN is better than the deep learning ANN for the time-based performance simulation of gas turbines (GTs).

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“…Consequently, TPO has gained popularity as a supplement for diesel, and the current demand to conduct research related to the wear and friction characteristics of engine fuel are imperative. To our understanding, there is a great deal of research available on tire oils' combustion, emission properties, and performance in diesel engines [22][23][24][25][26][42][43][44][45], but there is no technical study investigating the tribological characteristics of TPO.…”

Section: Introductionmentioning

confidence: 99%

Tribological Behaviour and Lubricating Mechanism of Tire Pyrolysis Oil

et al. 2021

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The four-ball tester was used in this analysis to demonstrate the lubricity of tire pyrolysis oil (TPO). The tribological performance of the tire pyrolysis oil was compared with diesel fuel (DF) and their blends, DT10 (TPO 10%, Diesel 90%) and DT20 (TPO 20%, Diesel 80%). A scanning electron microscope (SEM) was used to investigate the wear scar. In contrast to diesel fuel, TPO demonstrated better antiwear behaviour in terms of higher load-carrying capacity. DT10, DT20, and TPO’s wear scar diameter (WSD) was 22.35%, 16.01%, and 31.99% smaller than that of diesel at 80 kg load, respectively. The scanning electron microscope micrographs showed that the TPO and DT10 had less wear than their counterparts.

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Experimental investigation on engine performance, emission, and combustion characteristics of a DI CI engine using tyre pyrolysis oil and diesel blends doped with nanoparticles

Cited by 28 publications

References 25 publications

A comprehensive review of the influences of nanoparticles as a fuel additive in an internal combustion engine (ICE)

A comprehensive review of the influences of nanoparticles as a fuel additive in an internal combustion engine (ICE)

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Tribological Behaviour and Lubricating Mechanism of Tire Pyrolysis Oil

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