An integrated framework of data-driven, metaheuristic, and mechanistic modeling approach for biomass pyrolysis

Zahid Ullah, Muzammil Khan, Salman Raza Naqvi*, Muhammad Nouman Aslam Khan, Wasif Farooq, Muhammad Waqas Anjum, Muhammad Waqas Yaqub, Hamad AlMohamadi, Fares Almomani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


This study presents an integrated hybrid framework of data-driven (cascade forward neural network (CFNN)), metaheuristic (artificial bee colony (ABC)), and a mechanistic modeling (Aspen simulation) approach for the biomass pyrolysis process for bio-oil production. We applied CFNN and an ABC to predict and optimize bio-oil yield. The CFNN model achieved high prediction performance with a correlation coefficient value of 0.95 and a root mean squared error value of 0.39. Furthermore, the CFNN-ABC derived optimum parameters were then validated using a mechanistic model of the pyrolysis process. The CFNN and Aspen simulation results were following the experimental results, with an average deviation of 5%. The feature importance showed that the internal information about biomass was more relevant than external factors for bio-oil yield. The partial dependence plots were developed to know the insights into the biomass pyrolysis process. This study presents a modeling and simulation platform for bio-oil production that can increase the waste-to-energy process and can be helpful for academia.

Original languageEnglish
Pages (from-to)337-345
Number of pages9
JournalProcess Safety and Environmental Protection
StatePublished - Jun 2022

Bibliographical note

Funding Information:
The corresponding author would like to acknowledge Pakistan Science Foundation (grant number: PSF/CRP/C-NUST/T-Helix (47) ) for financial support and National University of Sciences & Technology for technical support.

Publisher Copyright:
© 2022 The Institution of Chemical Engineers


  • Artificial bee colony
  • Aspen plus
  • Bioenergy
  • Biomass
  • Cascade neural network
  • Machine learning

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Chemical Engineering (all)
  • Safety, Risk, Reliability and Quality


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