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Research Papers: Internal Combustion Engines

Engine Capability Prediction for Spark Ignited Engine Fueled With Syngas

[+] Author and Article Information
Hui Xu

Cummins, Inc.,
1900 McKinley Avenue,
Mail Code 50114,
Columbus, IN 47201
e-mail: hui.xu@cummins.com

Leon A. LaPointe

Cummins, Inc.,
1900 McKinley Avenue,
Mail Code 50114,
Columbus, IN 47201
e-mail: leon.a.lapointe@cummins.com

Contributed by the IC Engine Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 11, 2016; final manuscript received March 1, 2016; published online April 26, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 138(10), 102812 (Apr 26, 2016) (6 pages) Paper No: GTP-16-1068; doi: 10.1115/1.4033183 History: Received February 11, 2016; Revised March 01, 2016

There are increasing interests in converting solid waste or lignocellulosic biomass into gaseous fuels and using reciprocating internal combustion engine to generate electricity. A widely used technique is gasification. Gasification is a process where the solid fuel and air are introduced to a partial oxidation environment, and generate combustible gaseous called synthesis gas or syngas. Converting solid waste into gaseous fuel can reduce landfill and create income for process owners. However, it can be very challenging to use syngas on a gaseous fueled spark ignited (SI) engine, such as a natural gas (NG) engine. NG engines are typically developed with pipeline quality natural gas (PQNG). NG engines can operate at lean burn spark ignited (LBSI), or stoichiometric with exhaust gas recirculation (EGR) spark ignited (SESI) conditions. This work discusses the LBSI engine condition. NG engines can perform very differently when fueled with nonstandard gaseous fuels such as syngas without appropriate tuning. It is necessary to evaluate engine performance in terms of combustion duration, relative knock propensity, and NOx emissions for such applications. Due to constraints in time and resources it is often not feasible to test such fuel blends in the laboratory. An analytical method is needed to predict engine performance in a timely manner. This study investigated the possibility of using syngas on an SI engine developed with PQNG. Engine performance was predicted using in house developed models and PQNG as the reference fuel. Laminar flame speed (LFS), adiabatic flame temperature (AFT), and auto-ignition interval (AI) are used to predict combustion duration, engine out NOx and engine knock propensity relative to NG at the target lambda values. Single cylinder research engine data obtained under lean burn conditions fueled with PQNG was selected as the baseline. LFS, AFT, and AI of syngas were computed at reference conditions. Lambda of operation was predicted for syngas to provide the same burn rate as NG at the reference lambda value for NG. Analysis shows that, using syngas at the selected lambda, the engine can have less engine out NOx emissions and less knock propensity relative to NG at the same speed and load. Modifications to fuel system components may be required to avoid engine derate.

Copyright © 2016 by ASME
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References

Figures

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Fig. 1

Critical engine combustion parameters

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Fig. 2

Measured combustion duration correlates well with LFS

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Fig. 3

Measured BSNOx correlates well with AFT

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Fig. 4

Normalized mean knock index (MKI) correlates well with AI

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Fig. 5

Work flow to investigate feasibility of operating NG LBSI engine using nonstandard gases

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Fig. 6

Normalized LFS as function of lambda

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Fig. 7

Exhaust oxygen concentration as a function of lambda

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Fig. 8

Normalized AFT as a function of lambda

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Fig. 9

Normalized AI as a function of lambda

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Fig. 10

Normalized burn fraction

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Fig. 11

Normalized SI engine model cylinder pressure output

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Fig. 12

Normalized SI engine model output—unburned zone temperature

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Fig. 13

Normalized SI engine model output—burned zone temperature

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Fig. 14

SI engine model predict similar PBZT with the AFT analysis

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Fig. 15

SI engine model predicted NOx

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Fig. 16

Normalized MKI correlates well with dt/AI integral

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Fig. 17

dt/AI integral results

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