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

Ka In Wong, Pak Kin Wong, Chun Shun Cheung, Chi Man Vong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

63 Citations (Scopus)

Abstract

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 (radial-basis function neural network), are used to create engine models based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. Based on the engine models, two optimization methods, namely SA (simulated annealing) and PSO (particle swarm optimization), are employed and a flexible objective function is designed to determine the optimal biodiesel ratio subject to various user-defined constraints. A case study is presented to verify the modeling and optimization framework. Moreover, two comparisons are conducted, where one is among the modeling techniques and the other is among the optimization techniques. Experimental results show that, in terms of the model accuracy and training time, ELM with the logarithmic transformation is better than LS-SVM and RBFNN with/without the logarithmic transformation. The results also show that PSO outperforms SA in terms of fitness and standard deviation, with an acceptable computational time.
Original languageEnglish
Pages (from-to)519-528
Number of pages10
JournalEnergy
Volume55
DOIs
Publication statusPublished - 15 Jun 2013

Keywords

  • Biodiesel
  • Engine modeling
  • Engine optimization
  • Engine performance
  • Machine learning

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Energy(all)
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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