ICE Relevant Physical-chemical Properties and Air Pollutant Emission of Renewable Transport Fuels from Different Generations – An Overview

Szabados, György; Knaup, Jan; Bereczky, Ákos

doi:10.3311/pptr.14925

Cited by 5 publications

(1 citation statement)

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“…4,2023 Requirements for engine emissions are becoming increasingly stringent for environmental benefits [47]. Decarbonization programs have been implemented for using clean, low-carbon fuels [37]. Ambitious goals to reduce the concentrations of hazardous compounds in the exhaust gases were supplemented by strict requirements for the reduction of the concentration and volume of solid particles [16], which have already been transferred to the legal basis (requirements of Euro Standard 6) [15].…”

Section: Introductionmentioning

confidence: 99%

Improving Diesel Engine Reliability Using an Optimal Prognostic Model to Predict Diesel Engine Emissions and Performance Using Pure Diesel and Hydrogenated Vegetable Oil

Žvirblis,

Hunicz,

Matijošius

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The reliability of internal combustion engines becomes an important aspect when traditional fuels with biofuels. Therefore, the development of prognostic models becomes very important for evaluating and predicting the replacement of traditional fuels with biofuels in internal combustion engines. The models have been made to model AVL 5402 engine emission, vibration, and sound pressure parameters using a three-stage statistical regression models. The fifteen parameters might be accurately predicted by a single statistic presented here. Both fuel type (diesel fuel and HVO) and engine parameters that can be adjusted were considered, since this analysis followed the symmetry of the methods. The data analysis process included three distinct steps and symmetric statistical regression testing was performed. The algorithm examined the effectiveness of various engine settings. Finally, the optimal fixed engine parameter and the optimal statistic were used to construct an ANCOVA model. The ANCOVA model improved the accuracy of prediction for all fifteen missing parameters.

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Section: Introductionmentioning

confidence: 99%