Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings

Applied Energy

Volumn 88, Issue 7, 2011, Pages 2344-2354

  Shivakumar, ^a   Srinivasa Pai, P ^b   Shrinivasa Rao, B R ^b  

^a MANIPAL INSTITUTE OF TECHNOLOGY   (India)

^b NMAM INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Artificial Neural Network; Injection timing; Transesterification; Waste cooking oil

Indexed keywords

BIODIESEL; BRAKES; COMPRESSION RATIO (MACHINERY); DIESEL ENGINES; ENERGY UTILIZATION; ENGINE CYLINDERS; EXHAUST SYSTEMS (ENGINE); FOSSIL FUELS; HYDROCARBONS; MACHINE DESIGN; RENEWABLE ENERGY RESOURCES;

ARTIFICIAL NEURAL NETWORK; BRAKE THERMAL EFFICIENCY; CI ENGINE; COMPRESSION RATIOS; EMISSION CHARACTERISTICS; EMISSION MODEL; ENGINE EXHAUST; ENGINE PERFORMANCE; ENVIRONMENTAL THREATS; EXHAUST EMISSION; EXHAUST GAS TEMPERATURES; EXPERIMENTAL VALUES; FOUR STROKES; GOOD CORRELATIONS; INJECTION TIMING; INPUT PARAMETER; MEAN ERRORS; OUTPUT PARAMETERS; PERFORMANCE AND EMISSIONS; PERFORMANCE MODEL; PERFORMANCE PARAMETERS; RENEWABLE SOURCES; SPECIFIC ENERGY CONSUMPTION; VARIABLE COMPRESSION RATIO; WASTE COOKING OIL;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; BIOENERGY; BIOFUEL; CHLORINE; COMPRESSION; DIESEL; ENGINE; ENVIRONMENTAL RISK; EXHAUST EMISSION; EXPERIMENTAL STUDY; FOSSIL FUEL; HYDROCARBON; MODEL TEST; NITROGEN OXIDES; PERFORMANCE ASSESSMENT; PREDICTION; RENEWABLE RESOURCE; SMOKE; VEGETABLE OIL;

EID: 79952446940     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2010.12.030     Document Type: Article

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