Performance and Emission Measurement of a Single Cylinder Diesel Engine Fueled with Palm Oil Biodiesel Fuel Blends

Rusli, M. Q.; Said, Mazlan; Sulaiman, Adedoyin M; Roslan, Muhammad Faizullizam; Veza, Ibham; Perang, Mohd Rozi Mohd; Lau, Harrison Lik Nang; Wafti, Nur Sulihatimarsyila Abd

doi:10.1088/1757-899x/1068/1/012020

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…Despite the emerging trend of electric vehicles [1], [2], the internal combustion engine (ICE) remains the most dominant power engine due to its mature technology [3]- [6]. However, it is important to note that ICE needs fuel from fossil hydrocarbons that are rare and not available in large amounts, making it costly to produce [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network

Rahaju

Hananto

Paristiawan

et al. 2023

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Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME) composition by developing an ANN model that was trained with 10 different algorithms. To the best of our knowledge, this is the first study to predict the CN of biodiesels using numerous ANN training algorithms utilizing sizeable datasets. Results revealed that the ANN model trained with Levenberg-Marquardt gave the highest prediction accuracy. LM algorithm successfully predicted the CN of biodiesels with the highest correlation and determination coefficient (R = 0.9615, R2 = 0.9245) as well as the lowest errors (MAD = 2.0804, RMSE = 3.1541, and MAPE = 4.2971). Hence, the Cascade neural network trained with the LM algorithm could be considered a promising alternative to the empirical correlations for predicting biodiesel’s CN.

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Section: Introductionmentioning

confidence: 99%

Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network

Rahaju

Hananto

Paristiawan

et al. 2023

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“…The ICE is more likely to meet the emission target [6]. In a diesel or CI engine, in addition to NOx, the typical high PM emissions need to be reduced by several techniques, such as changing injection pressure [7], using alternative fuels [8] and adding nanoparticle additives [9].…”

Section: Introductionmentioning

confidence: 99%

Strategies to achieve controlled auto-ignition (CAI) combustion: A review

Veza

Setiawan²,

Firman³

et al. 2022

Mech. Eng. for Soc. and Ind.

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Conventional gasoline engines suffer from low performance and NOx emissions. Controlled auto-ignition (CAI), sometimes referred to as homogeneous charge compression ignition (HCCI), is a promising concept to solve such problems. CAI has the potential to improve spark ignition (SI) engine fuel economy while at the same time solving the trade-off of NOx-soot emissions found in compression ignition (CI) engines. The CAI engine can reach a fuel economy comparable to that of a conventional diesel engine with ultra-low NOx and negligible soot emissions. However, controlling auto-ignition remains the biggest difficulty that hinders the implementation of CAI as a commercial engine. Research towards a cleaner and more efficient engine is driven by the progressively stringent emission regulation imposed worldwide. Therefore, the CAI was developed to meet the emissions target while maintaining engine performance. CAI works on the principle of lean mixture and auto-ignition. To obtain CAI combustion, the temperatures in the cylinder must be sufficient to initiate auto-ignition. Without the use of a spark plug or injector, the CAI suffers from a direct control mechanism to start the combustion. The most practical approach to controlling the initiation of auto-ignition in CAI is diluting the intake charge by either trapping the residual gas or recirculating the exhaust gas. Both approaches enable the engine to achieve CAI combustion without requiring significant modifications to control the onset of CAI combustion phase.

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“…Fossil fuels are considered a non-renewable energy source [22]. Fossil fuel price changes according to world political and economic problems and may be used as a mean of political pressure on any country [23,24]. Emissions from fossil-fueled engine have a harmful environmental effect [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Effect of CuO Nanoparticles on Performance and Emissions Behaviors of CI Engine Fueled with Biodiesel-Diesel Fuel Blends

Elkelawy

elhamid

Bastawissi

et al. 2022

Journal of Engineering Research

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Recently the world has a very important need for replacement fossil fuel with renewable sources of energy. Greenhouse effect is considered one of bad effects of fossil fuels, which causes increase of global temperature. Biodiesel is one of fossil fuel alternatives, which can be produced from many organic sources. Scientists are searching for adding nanomaterials to fueling system, which can modify fuel characteristic in compression champers. In this study diesel fuel will be replaced with blends of diesel and biodiesel produced from waste cooking oil (WCO) is created using a catalytic transesterification reaction (CTR). With the addition of a low concentration of alcohol over the period of an hour at a reaction temperature of 65 °C, (CTR) converts (WCO) to methyl esters. Blends consisting of (40 % diesel, 60 % biodiesel and CuO nano-martial with different concentration) will be prepared for fueling direct injection engine four-stroke. The engine will be run at 1400 rpm with natural aspiration under various loads. Using blends of (pure diesel, B40 [consist of 60 % biodiesel and 40 % diesel], 50b40 [consist of 60 % biodiesel, 40 % diesel and 50 mg CuO], 100B40 [consist of 60 % biodiesel, 40 % diesel and 100 mg CuO], 150 [consist of 60 % biodiesel, 40 % diesel and 150 mg CuO] and pure diesel). On engine performance and emissions, the impact of using copper oxide has been studied. The results of the experiment demonstrate that diesel engines can run on various mixtures of fuel, biodiesel, and CuO nano-material under the same operating conditions. The obtained data indicates that a 10% increase in brake thermal efficiency was noted, decrease in exhaust temperature with 11.6 % and decrease in brake specific fuel consumption with 6.66 %.

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Performance and Emission Measurement of a Single Cylinder Diesel Engine Fueled with Palm Oil Biodiesel Fuel Blends

Cited by 15 publications

References 10 publications

Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network

Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network

Strategies to achieve controlled auto-ignition (CAI) combustion: A review

Effect of CuO Nanoparticles on Performance and Emissions Behaviors of CI Engine Fueled with Biodiesel-Diesel Fuel Blends

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