Suchergebnisse

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG ; [Abstract] To assess the knocking properties of natural gas (NG) when it is used as fuel for vehicles is vital to optimize the design and functioning of their motors. Analytical efforts in this field are needed as the engines used to define it empirically are not available anymore, and existent mathematical algorithms yield different accuracy. The hybridization of gas-phase infrared spectrometry and partial least squares multivariate regression is presented first time to address the determination of the methane number (MN) of NG samples. It circumvents the need for the previous knowledge of the NG composition required to apply dedicated equations. The use of true NG samples to develop the models is also quite new in the field. Proof-of-concept studies were made with synthetic spectra and, then, a collection of liquefied NG samples for which MN values were computed by the National Physics Laboratory algorithm (NPL) from their sample composition were used to develop operative models. Additional validation was made with a collection of synthetic standard mixtures prepared for two European projects (EMRP LNG II and EMPIR LNG III) whose service methane numbers (SMN) were measured with an engine. The FTIR-PLS approach yielded statistically unbiased predictions with average standard errors around 0.4% MN when compared to the NPL-MN and SMN values, and standard deviations of the means ca. 1% MN. The approach is fast, cost effective as it involves standard instrumentation, and can be considered compliant with the green chemistry principles. ; This work is part of the EMPIR 16ENG09 project 'Metrological support for LNG and LBG as transport fuel (LNG III)'. This project has received funding from the EMPIR programme co-financed by the Participant States and from the European Union's Horizon 2020 Research and Innovation programme. The authors from TU Braunschweig would like to thank IAV, Mahle, MAN Truck & Bus and Motortech for their support in ...