Artificial intelligence-based super learner approach for prediction and optimization of biodiesel synthesis—A case of waste utilization

S. M. Zakir Hossain, Nahid Sultana, Muhammad Faisal Irfan, S. Manirul Haque, Nawaf Nasr, Shaikh Abdur Razzak*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In this article, super learner approaches such as hybrid Bayesian Optimization Algorithm-Support Vector Regression (BOA-SVR), Bayesian Optimization Algorithm-Boosted Regression Tree (BOA-BRT), along with a statistical method (response surface methodology, RSM) were utilized as potential tools for predicting biodiesel synthesis using waste date seed oil as feedstock. Novelties of this investigation comprise (a) hybridization of BOA with each artificial intelligence (AI) approach resulting in the formation of BOA-SVR and BOA-BRT super learner models, (b) the model performance was compared using several performance indicators including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), etc., (c) validation of the model was confirmed using extra simulated data, (d) the crow search algorithm (CSA) was integrated with the BOA-SVR resulting advanced super learner model (BOA-SVR-CSA) for finding the global optimal point. BOA-BRT model provided relatively low R2 (0.81) and high errors (MAE of 8.5159, RSME of 12.4674, MAPE of 106.0391). RSM model was statistically significant (P-value <.05) with relatively high R2 (0.95) and moderate errors (MAE of 4.8886, RSME of 5.5964, MAPE of 22.1574). The BOA-SVR model provided low errors (MAE of 3.8342, RSME of 3.8884, MAPE of 18.91) with a high R2 of 0.98. The overall results suggested that the BOA-SVR model performs better with increased accuracy than other models. The extra simulated data further confirmed the prediction capability of the developed super learner model (BOA-SVR). The maximum biodiesel yield of 91.35% was achieved with a KOH dose of 0.6 wt%, M:O of 7:1 at a reaction time of 2 hours using the advanced super learner model (BOA-SVR-CSA). Overall, this novel platform could be of considerable promise in other process modeling and multiobjective optimization applications.

Original languageEnglish
Pages (from-to)20519-20534
Number of pages16
JournalInternational Journal of Energy Research
Issue number14
StatePublished - Nov 2022

Bibliographical note

Funding Information:
All authors would like to thank the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, for funding this research work through project number RDO‐2019‐001‐CSIT.

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.


  • biodiesel
  • boosted regression tree
  • crow search algorithm
  • response surface methodology
  • support vector regression

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology


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