Abstract
A next-generation algorithm to calculate the PKI methane number is reported. The algorithm is suitable for a wide range of fuel compositions encountered in natural gas pipelines, including admixture of hydrogen and carbon monoxide from renewable sources. Comparison with measurements of knock in a commercial engine shows that the algorithm allows sharp distinction between fuel compositions that do or do not cause engine knock under given operating conditions. Moreover, the algorithm presented here demonstrates superior performance as compared to the existing methods from MWM and AVL. The methane numbers calculated using the PKI MN algorithm for a wide range of fuel compositions are within the uncertainty of the experimental knock measurements. In contrast, methods that are currently used do not predict the knock behavior of the measured gas compositions reliably. A major benefit of the algorithm presented here is that it consists of a simple polynomial equation that can be easily integrated into real-time gas-quality sensing equipment to calculate the PKI MN for assessment of pipeline gas quality or into engine management systems to allow next-generation feed-forward, fuel-adaptive control. In contrast, the current methods such as AVL and MWM need dedicated (and for AVL, proprietary) solvers that iteratively calculate the methane number. Furthermore, given the experimentally verified reliability and ease of implementation of the PKI MN algorithm, we assert that it is an excellent, open-source candidate for international standards for specifying the knock resistance of gaseous fuels.
Highlights
The globalization of the natural gas market and the drive towards sustainability are increasing the diversity of the supply of gases to the natural gas infrastructure
This paper describes the development of a next-generation algorithm to calculate the knock resistance of gaseous fuels using the Propane Knock Index Methane Number (PKI MN) for current- and future fuel compositions transported in natural gas grids
The algorithm has been derived using a polynomial regression analyses applied to thousands of knock simulations using a dedicated engine knock model developed for a lean-burn, medium-BMEP gas engine
Summary
The globalization of the natural gas market and the drive towards sustainability are increasing the diversity of the supply of gases to the natural gas infrastructure. The majority of the MN methods, including AVL and MWM do not differentiate between isomers of higher hydrocarbons while these isomers have demonstrably different knock resistance [17, 18] Another shortcoming is that most of the existing methane number methods are based on complex iterative relations to find the methane number for a given gas composition, which complicates the integration of these methods into gas-analysis equipment, such as real-time gas sensors. The model uses as input all relevant engine parameters such as geometry, operating conditions, combustion air humidity and gas composition and calculates the knock resistance of a given gaseous fuel.
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