![]() ![]() It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data. The graph-ML CAMD framework successfully identifies well-established high-octane components. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). These mean that the proposed gasoline composition was more efficient when operating gasoline engine at this condition.įuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. The results reported that calculated values obtained for developed compositions turned out to be slightly higher than for baseline gasolines. The key hypothesis of the merit function was improved gasoline efficiency and reduce exhaust emissions when operating gasoline engines at various conditions. The vast expertise of this laboratory work has obviously reported that fuel compositions samples contained the following concentrations by weight percentage, i.e., light condensate naphtha – 46–56, isopentane fraction – up to 4, aromatic components – up to 20, MTBE –14–15, isoolefins hydrocarbons – 15–16, and isooctane – up to 20. Physical and chemical characteristics of these innovative gasoline recipes were investigated in accordance with standard test methods regulations. In addition, preliminary mathematical digital model for evaluating a comprehensive merit function of automotive gasoline for newly proposed fuel motor compositions was developed to maximize gasoline performance and decrease emissions when operating gasoline engines at various conditions. This paper addresses producing new recipes of environmentally friendly high octane gasoline fuel grades RONs 92 and 95. One of these requirements is introducing merit function to assess the advantages of innovative motor gasoline formulations, regarding their anticipated influence on performance for future engines. New fuels composition should be corresponded with modern engine technologies requirements. Gasoline engines have an even greater potential for optimization. It is discovered that even with the same octane number, deviations in the end-gas auto-ignition and flame characteristics of the three fuel blends lead to different knock onsets, which in turn influences their knock intensity. ![]() The methanol/PRF blend shows an advanced knock onset and higher knock intensity compared to the ethanol/PRF and iso-propanol/PRF blends at the RON test condition, while the iso-propanol/PRF blend is observed to have the earliest knock onset at the MON test condition. It is observed that the three fuel blends with the same octane rating present different knocking combustion characteristics. In this work, the knocking combustion characteristics of methanol/PRF (Primary Reference Fuels), ethanol/PRF, and iso-propanol/PRF fuel blends with either the same research octane number (RON) or the motor octane number (MON) were studied using a three-dimensional numerical model of a Cooperative Fuel Research (CFR) engine. However, whether octane number could capture the real knocking combustion process in practical engines remains unknown. Fuel octane number has long been used as an indicator for anti-knock capability of gasolines. ![]()
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