An Investigation of Diesohol-Biodiesel Mixture in Performance-Emission Characteristics of a Single Cylinder Diesel Engine: A Trade-Off Benchmark

Dey, Suman; Deb, Madhujit; Das, Pankaj Kumar

doi:10.15282/ijame.16.4.2019.18.0552

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…The two input parameters, namely PJME to different temperatures of 50 °C to 100 °C with an increment of 10 °C and engine load (30%, 50%, and 100%) are considered as main design factors. Part load efficiency of the diesel engine is better and, however, the engine should not be run at lower loads because this may lead to engine overheating [25][26][27]. In this investigation, higher part loads have been taken into account.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Relative Testing of Neat Jatropha Methyl Ester by Preheating to Viscosity Saturation in IDI Engine - An Optimisation Approach

Kolakoti¹,

Rao

2020

IJAME

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This paper investigates the effect of preheated Jatropha Methyl Ester (PJME) on the performance and emission characteristics of an indirect injection (IDI) diesel engine using combined Taguchi and Grey optimisation technique. For this purpose, PJME with different temperatures (50 °C to 100 °C), engine loads are considered as input factors and fuel consumption, thermal efficiency and exhaust emissions are output factors. Based on Taguchi orthogonal array design matrix, L18 experiments were conducted. The obtained results were analysed in Grey analysis and found that optimum performance and emissions for PJME at 60 °C with 50% load. From the analysis of variance (ANOVA) PJME influence factor is observed as 71.57% and a confirmation test is performed to validate the optimum parameters and observed both statistical and experimental are in good agreement. Finally, a full range of experiments was conducted for PJME at 60 °C and compared the results with diesel fuel. From the results, an appreciable reduction in exhaust emissions like CO, HC, and NO were observed for comparison, smoke and CO2 have also decreased to a marginal extent indicating that PJME can be taken as a replacement to diesel fuel.

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Section: Design Of Experimentsmentioning

confidence: 99%

Relative Testing of Neat Jatropha Methyl Ester by Preheating to Viscosity Saturation in IDI Engine - An Optimisation Approach

Kolakoti¹,

Rao

2020

IJAME

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“…Their research underscores the cost-effectiveness and environmental benefits of utilizing alternative fuels, paving the way for cleaner combustion and emissions reduction. Moreover, Dey et al (2019) and Campli et al (2021) investigated the performance and emission characteristics of diesel-palm biodiesel-ethanol blends and neem biodiesel-diesel blends with nickel oxide nanoparticles, respectively. These studies offer valuable insights into optimizing the fuel composition and additive concentrations to enhance engine efficiency while mitigating harmful emissions.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

Modi,

Patel

2024

Eng. Res. Express

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The study utilized neural network modeling to forecast the fuel consumption in compression ignition engines fueled by diesel, HDPE PO, PP PO, and LDPE PO. Using empirical data, a Neural Network model was constructed and used to estimate specific fuel consumption (SFC). Employing orthogonal arrays and parameter adjustments ensured accurate prediction of SFC, which was validated through experimentation. The multilayer perception network coupled with traditional backpropagation facilitates the nonlinear mapping of inputs to outcomes.In the LM10TP architecture, the key metrics from the training set included an impressive R-squared value of 1, indicating a perfect fit with a root mean square error (RMSE) of 0.0012 and a mean square error (MSE) of 1.5143E-06. Similarly, the validation set exhibited robust performance metrics with an R-squared value of 0.9999, RMSE of 0.0011, and MSE of 1.2185E-06. These metrics underscore the efficacy of neural networks in both the training and validation phases, affirming their utility as reliable predictive tools for SFC.Overall, this study highlights the effectiveness of neural network modeling for accurately predicting fuel consumption in compression ignition engines across diesel and polymer-based fuels. By leveraging empirical data and sophisticated modeling techniques, this study contributes to advancing the predictive capabilities in the field, offering valuable insights for optimizing engine performance and fuel efficiency.

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“…Therefore, the increased awareness of potential environmental risks on human health has been focused on keeping engine emissions under control. Diesel fuel has a crucial problem related to exhaust, vibration, and acoustic emissions [7][8][9][10][11]. Biodiesel is considered an eco-friendly, bio-degradable, energy efficient, renewable, scientifically feasible, and non-toxic fuel alternative [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of a Compression Ignition Engine Fueled with Biodiesel Blends for Performance Improvement

et al. 2022

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Biodiesel is considered to be a promising alternative option to diesel fuel. The main contribution of the current work is to improve compression ignition engine performance, fueled by several biodiesel blends. Three metrics were used to evaluate the output performance of the compression ignition engine, as follows: brake torque (BT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE), by varying two input parameters (engine speed and fuel type). The engine speeds were in the 1200–2400 rpm range. Three biodiesel blends, containing 20 vol.% of vegetable oil and 80 vol.% of pure diesel fuel, were prepared and tested. In all the experiments, pure diesel fuel was employed as a reference for all biodiesel blends. The experimental results revealed the following findings: although all types of biodiesel blends have low calorific value and slightly high viscosity, as compared to pure diesel fuel, there was an improvement in both BT and brake power (BP) outputs. An increase in BSFC by 7.4%, 4.9%, and 2.5% was obtained for palm, sunflower, and corn biodiesel blends, respectively, as compared to that of pure diesel. The BTE of the palm oil biodiesel blend was the lowest among other biodiesel blends. The suggested work strategy includes two stages (modeling and parameter optimization). In the first stage, a robust fuzzy model is created, depending on the experimental results, to simulate the output performance of the compression ignition engine. The particle swarm optimization (PSO) algorithm is used in the second stage to determine the optimal operating parameters. To confirm the distinction of the proposed strategy, the obtained outcomes were compared to those attained by response surface methodology (RSM). The coefficient of determination (R2) and the root-mean-square-error (RMSE) were used as comparison metrics. The average R2 was increased by 27.7% and 29.3% for training and testing, respectively, based on the fuzzy model. Using the proposed strategy in this work (integration between fuzzy logic and PSO) may increase the overall performance of the compression ignition engine by 2.065% and 8.256%, as concluded from the experimental tests and RSM.

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An Investigation of Diesohol-Biodiesel Mixture in Performance-Emission Characteristics of a Single Cylinder Diesel Engine: A Trade-Off Benchmark

Cited by 6 publications

References 27 publications

Relative Testing of Neat Jatropha Methyl Ester by Preheating to Viscosity Saturation in IDI Engine - An Optimisation Approach

Relative Testing of Neat Jatropha Methyl Ester by Preheating to Viscosity Saturation in IDI Engine - An Optimisation Approach

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

Modeling and Optimization of a Compression Ignition Engine Fueled with Biodiesel Blends for Performance Improvement

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