Techno Press
You logged in as Techno Press

Advances in Energy Research
  Volume 8, Number 3, September 2022 , pages 175-184
 open access

Adaptive Neuro-fuzzy-based modeling of exhaust emissions from dual-fuel engine using biodiesel and producer gas
Prabhakar Sharma and Avdhesh Kr Sharma

    The dual-fuel technology, which uses gaseous fuel as the main fuel and liquid as the pilot fuel, is an appealing technology for reducing the exhaust emissions. The current study proposes emission models based on ANFIS for a dual-fuel using producer gas (PG)-diesel engine. Emissions measurements were taken at different engine load levels and fuel injection timings. The proposed model predictions were examined using statistical methods. With R2 values in the range of 0.9903 to 0.9951, the established ANFIS model was found to be consistently robust in predicting emission characteristics. The mean absolute percentage deviate in range 1.9 to 4.6%, and mean squared error varies in range 0.0018 to 13.9%. The evaluation of the ANFIS model developed shows a reliable claim of intrinsic sensitivity, strength, and outstanding generalization. The presented meta-model can be used to simulate the engine's operation in order to create an efficient control tool.
Key Words
    alternative fuels; emission; machine learning; renewable energy sustainability
Prabhakar Sharma: School of Engineering Science, Delhi Skill and Entrepreneurship University, Delhi-110042, India
Avdhesh Kr Sharma: Mechanical Engineering Dept., D.C.R. University of Sci. & Technology,
Murthal (Sonepat)-131039, Haryana India

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2023 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: