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Prediction of the WPPO Biodiesel-Fuelled HCCI Engine Using Artificial Neural Networks

  • Ramavathu Jyothu NaikEmail author
  • Kota Thirupathi Reddy
Conference paper
  • 16 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 169)

Abstract

This paper presents experimental study efforts to explore the performance and emission characteristics of an existing single-cylinder, four-stroke, water-cooled, direct injection Kirloskar diesel engine was converted into HCCI engine. From the investigation, it was stated that WPPO with diesel results increased the brake thermal efficiency by 42.12% at 413 K inlet air temperature and full load condition. Formerly NOx were decreased for all blends and later slightly increases but smoke is negligible. However, the CO and UHC emissions are first increased and then decreased for the HCCI operation. The ANN was trained, validated and tested with experimental data sets. The artificial neural network system was created to predict the performance and emission parameters of the engine. A multi-layer discernment network was utilized for non-straight mapping among input and output parameters. Six objectives—BTE, EGT, NOx, Smoke, CO and UHC were considered. The performance of the ANN model is determined also illustrations the efficiency of the model to predict the performance and emission with a determination coefficient of 0.999.

Keywords

HCCI WPPO ANN Emission 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringJawaharlal Nehru Technological University AnantapurAnanthapuramuIndia
  2. 2.Department of Mechanical EngineeringRajeev Gandhi Memorial College of Engineering and TechnologyNandyal, KurnoolIndia

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