Modeling and optimization of biodiesel engine performance using advanced machine learning methods

• Published in: Energy (Oxford) Vol. 55; pp. 519 - 528
• Main Authors: Wong, Ka In, Wong, Pak Kin, Cheung, Chun Shun, Vong, Chi Man
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.06.2013; Elsevier
• Subjects: Applied sciences; Biodiesel; case studies; Elm; emissions; Energy; Energy. Thermal use of fuels; Engine modeling; Engine optimization; Engine performance; Engines; Engines and turbines; Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc; Exact sciences and technology; least squares; Machine learning; Mathematical analysis; Mathematical models; Neural networks; Optimization; support vector machines; Swarm intelligence; system optimization; Transformations; ANN; NSGA-II; Engine optimization; Engine performance; CO2; ELM; SLFN; RMSPE; LOOCV; SVM; AFR; SA; BSFC; HCLA; QNM; LS-SVM; MAPEtrain; NOx; GA; RMSPEtrain; TEOM; MAPE; CO; Biodiesel; PSO; MAPEtest; RMSPEtest; rpm; RBFNN; Machine learning; SPEA-2; HC; NDIR; Engine modeling; PM; Diesel fuel; Biofuel; Performance; Modeling; Engine; Optimization

This study aims to determine optimal biodiesel ratio that can achieve the goals of fewer emissions, reasonable fuel economy and wide engine operating range. Different advanced machine learning techniques, namely ELM (extreme learning machine), LS-SVM (least-squares support vector machine) and RBFNN...