Evaluation of alternative fuels in spark ignition engines using normalized data and multicriteria decision analysis

The use of alternative fuels in spark ignition internal combustion engines (ICEs) has attracted growing interest in recent years due to their potential to reduce greenhouse gas emissions and decrease dependence on fossil fuels. However, differences in measurement conditions across previous studies make the consistent evaluation of available data challenging. The aim of this study is to establish and analyze a normalized database that enables the comparison of ethanol and selected synthetic fuels in terms of engine performance and regulated emissions. Based on this database, a meta-analysis is conducted to identify the optimal ethanol blending ratio using a Multi-Criteria Decision Analysis (MCDA) approach. In addition, the roles of exhaust gas recirculation (EGR) and exhaust aftertreatment systems are examined to support the interpretation of the results. The study also reviews potential feedstocks for alternative fuels, outlining their advantages, disadvantages, and the technological and environmental challenges associated with their production, which may hinder widespread adoption. Particular emphasis is placed on exhaust emissions from alternative fuels, including ethanol and synthetic (E-Fuel) blends, with a focus on key regulated species such as CO, CO 2 , and NO x . Experimental engine parameters reported in the reviewed studies are also considered, including engine displacement, fuel injection systems, and exhaust aftertreatment technologies. An MCDA-based analysis applied to the normalized cross-study dataset identifies an optimal ethanol blending ratio relative to RON95E10. The results indicate that E25 ethanol blends achieve substantial reductions in emission levels, particularly for CO (− 29%), HC (− 4%), and NO x (− 11%), with average trends showing consistent reductions across the reviewed studies. At the same time, emission responses remain comparatively balanced within the E20–E40 range, without a systematic deterioration that would offset these benefits. Overall, the results demonstrate that standardized data normalization combined with MCDA improves the comparability of experimental studies and provides a structured basis for future research on ethanol and synthetic fuel blends, taking into account the influence of modern exhaust aftertreatment systems such as EGR, three-way catalysts (TWC), and gasoline particulate filters (GPF).