An experimental investigation of performance and emission of thumba biodiesel using butanol as an additive in an IDI CI engine and analysis of results using multi-objective fuzzy-based genetic algorithm

Debnath, Rabisankar; Sastry, G. R. K.; Naresh, Ram

doi:10.1007/s11356-018-3699-y

Cited by 13 publications

(4 citation statements)

References 28 publications

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“…where y denotes the predictive value of performance measures, x i is the input variables, Trade-off analysis Trade-off analysis can be defined as a strategic move that involves lowering of one quality or quantity of a set in return for overall profit of the system. Some of the literature involves the application of trade-off analysis [45][46][47][48]. The main intent of trade-off analysis is to reach at the most significant result by minimizing and maximizing the multiple objective simultaneously by obeying the restrictions imposed by the physical nature of the problem.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: a novel MCDM amalgamated MORSM approach

Medhi

Hussain

Roy

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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The paper focuses on developing a novel decision-making model, to select the best welding parameters for friction stir welding of dissimilar aluminium to copper plates. A set of 21 experiments are carried out based on a d-optimal design matrix. Apart from considering the rotational speed and traverse speed as input variables, the position of aluminium and copper either in the advancing or retreating side is also considered as an input variable. On the successful fabrication of joints, the ultimate tensile strength, elongation, hardness and impact energy are estimated experimentally which are considered as performance parameters. The proposed decision-making process is categorized under multi-criteria decision-making (MCDM) methods. In the first phase, the novel MCDM model is applied to select the best welding parameters. The concept of the model is to minimize the risk of not attaining the global best result. The results obtained by the MCDM method is justified by carrying out a sensitivity analysis and finally validated with the results obtained from the fuzzy logic decision-making approach. The second phase is related to the computation of optimized values for the joining process that reports the development of a mathematical model by response surface methodology to predict different performance parameters. This is followed by conducting a trade-off analysis by developing a multi-objective response surface methodology (MORSM) that computes the optimal welding parameters. Finally, a confirmatory test is carried out for validation of the proposed MORSM model.

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Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: a novel MCDM amalgamated MORSM approach

Medhi

Hussain

Roy

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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“…The fuzzy approach had an edge over theoretical and empirical methods in terms of prediction accuracy. Debnath et al [132] used GA to predict BTE, CO, NO x of engine butanol-based biodiesel. Less biodiesel and higher butanol percentage yields a good impact on performance and emission.…”

Section: Neuro Fuzzy Inference Systemmentioning

confidence: 99%

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

et al. 2021

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Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

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Section: Neuro Fuzzy Inference Systemmentioning

confidence: 99%

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Ahmad¹,

Kano²,

Menezes³

et al. 2020

Preprint

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Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

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An experimental investigation of performance and emission of thumba biodiesel using butanol as an additive in an IDI CI engine and analysis of results using multi-objective fuzzy-based genetic algorithm

Cited by 13 publications

References 28 publications

Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: a novel MCDM amalgamated MORSM approach

Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: a novel MCDM amalgamated MORSM approach

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

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