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.
Bibliographical noteFunding 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.
© 2022 The Institution of Chemical Engineers
- Artificial bee colony
- Aspen plus
- Cascade neural network
- Machine learning
ASJC Scopus subject areas
- Environmental Engineering
- Environmental Chemistry
- Chemical Engineering (all)
- Safety, Risk, Reliability and Quality