Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model

Javed, Syed; Baig, Rahmath Ulla; Murthy, Y.V.V. Satyanarayana

doi:10.1016/j.energy.2018.07.041

Cited by 24 publications

(10 citation statements)

References 33 publications

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“…Furthermore, the engine with 17% hydrogen content, the hydrogen-diesel-air mixture was stoichiometric and provided favorable conditions for generating combustion knock [12]. The other study by Calik [13] and Syed et al [14] showed that the addition of hydrogen gas further managed the engine vibration both with a conventional diesel engine and biodiesel blend.…”

Section: Introductionmentioning

confidence: 95%

Thermal efficiency and emission characteristics of a diesel-hydrogen dual fuel CI engine at various loads condition

Putrasari

Praptijanto

Nur

et al. 2018

J. Mechatron. Electr. Power Veh. Technol.

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Efforts to find alternative fuels and reduce emissions of CI engines have been conducted, one of which is the use of dieselhydrogen dual fuel. One of the goals of using hydrogen in dual-fuel combustion systems is to reduce particulate emissions and increase engine power. This study investigates the thermal efficiency and emission characteristics of a diesel-hydrogen dual fuel CI engine at the various loads condition. The hydrogen was used as a secondary fuel in a single cylinder 667 cm 3 diesel engine. The hydrogen was supplied to intake manifold by fumigation method, and diesel was injected directly into the combustion chamber. The results show that the performance test yielding an increase around 10% for the thermal efficiency value of diesel engines with the addition of hydrogen either at 2000 or 2500 rpm. Meanwhile, emission analyses show that the addition of hydrogen at 2000 and 2500 rpm lead to the decrease of NOx value up to 43%. Furthermore, the smokeless emissions around 0% per kWh were occurred by hydrogen addition at 2000 and 2500 rpm of engine speeds with load operation under 20 Nm.

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Section: Introductionmentioning

confidence: 95%

Thermal efficiency and emission characteristics of a diesel-hydrogen dual fuel CI engine at various loads condition

Putrasari

Praptijanto

Nur

et al. 2018

J. Mechatron. Electr. Power Veh. Technol.

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“…For engine performance, most studies used AI to predict indicators related to either energy or emissions (as shown in Table 4), which can reduce the cost and time for engine testing and calibration. Several studies investigated the sound level (Ghaderi et al, 2019; Javed et al, 2018) and vibration of engines (Çelebi et al, 2017), both of which are essential parameters of vehicle comfort. Most studies used FNN, while several studies used ELM, a learning scheme of FNN (Huang et al, 2004), and concluded that ELM‐based methods show better performance in predicting biofuel engine performance and emissions than other methods such as SVM and ANN (Aghbashlo et al, 2016; Sebayang et al, 2017b; Wong, Wong, et al, 2015).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

“…Most AI studies investigated biodiesel blended with fossil‐based diesel as engine fuel, but several studies investigated blending biodiesel‐diesel complex with other fuels, including ethanol (Ghaderi et al, 2019; Oǧuz et al, 2010; Silitonga et al, 2018), H 2 (Javed et al, 2015), zinc oxide nanoparticles (Javed et al, 2018), and natural gas (Çelebi et al, 2017). These studies used similar input–output variables as the AI models for biodiesel–diesel blends.…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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“…During the experiment, uncertainty contributed by several factors such as setup, environment, method of measuring and type of instruments [27]. During this experiment, the measurement of engine performance and emission were recorded 5 minutes after the engine was set at the desired condition to ensure no changes in parameters.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Effect of Al2O3 and SiO2 Metal Oxide Nanoparticles Blended with POME on Combustion, Performance and Emissions Characteristics of a Diesel Engine

Adzmi

Adam

Abdullah

et al. 2019

IJAME

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Evaluation of combustion characteristic, engine performances and exhaust emissions of nanoparticles blended in palm oil methyl ester (POME) was conducted in this experiment using a single-cylinder diesel engine. Nanoparticles used was aluminium oxide (Al2O3) and silicon dioxide (SiO2) with a portion of 50 ppm and 100 ppm. SiO2 and Al2O3 were blended in POME and labelled as PS50, PS100 and PA50, PA100, respectively. The data results for PS and PA fuel were compared to POME test fuel. Single cylinder diesel engine YANMAR TF120M attached with DEWESoft data acquisition module (DAQ) model SIRIUSi-HS was used in this experiment. Various engine loads of zero, 7 N.m, 14 Nm, 21 N.m and 28 N.m at a constant engine speed of 1800 rpm were applied during engine testing. Results for each fuel were obtained by calculating the average three times repetition of engine testing. Findings show that the highest maximum pressure of nanoparticles fuel increase by 16.3% compared to POME test fuel. Other than that, the engine peak torque and engine power show a significant increase by 43% and 44%, respectively, recorded during the PS50 fuel test. Meanwhile, emissions of nanoparticles fuel show a large decrease by 10% of oxide of nitrogen (NOx), 6.3% reduction of carbon dioxide (CO2) and a slight decrease of 0.02% on carbon monoxide (CO). Addition of nanoparticles in biodiesel show positive improvements when used in diesel engines and further details were discussed.

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Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model

Cited by 24 publications

References 33 publications

Thermal efficiency and emission characteristics of a diesel-hydrogen dual fuel CI engine at various loads condition

Thermal efficiency and emission characteristics of a diesel-hydrogen dual fuel CI engine at various loads condition

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Effect of Al2O3 and SiO2 Metal Oxide Nanoparticles Blended with POME on Combustion, Performance and Emissions Characteristics of a Diesel Engine

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