Ready to Use Biomass
Ready-to-use biomass research focuses on efficiently converting various organic sources, including forest resources and agricultural waste, into usable forms for energy, materials, and other applications. Current research heavily utilizes machine learning, particularly deep learning architectures like U-Nets and convolutional neural networks, along with evolutionary algorithms, to optimize biomass production processes, predict product properties, and accurately estimate biomass quantities from remote sensing data. These advancements improve carbon accounting, enable precision management of biomass resources, and offer sustainable alternatives to traditional waste disposal and energy production.
Papers
Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes
Hossein Shahbeik, Alireza Shafizadeh, Mohammad Hossein Nadian, Dorsa Jeddi, Seyedali Mirjalili, Yadong Yang, Su Shiung Lam, Junting Pan, Meisam Tabatabaei, Mortaza Aghbashlo
Machine learning-based characterization of hydrochar from biomass: Implications for sustainable energy and material production
Alireza Shafizadeh, Hossein Shahbeik, Shahin Rafiee, Aysooda Moradi, Mohammadreza Shahbaz, Meysam Madadi, Cheng Li, Wanxi Peng, Meisam Tabatabaei, Mortaza Aghbashlo