Abstract Machine learning holds significant research potential in the field of nanotechnology,
enabling nanomaterial structure and property predictions, facilitating materials design and …

This study compares the performance of artificial neural networks (ANN) trained by grey wolf
optimization (GWO), biogeography-based optimization (BBO), and Levenberg–Marquardt …

Considerable efforts have been made to replace formaldehyde-containing adhesives in the
manufacturing of wood products, particularly particleboard, with natural alternatives. One …

Biomass fuels are a promising renewable energy source for both heat and electricity
generation. When weighing the economics of biomass energy, the higher heating value …

The research conducted focused on the capabilities of various non-linear and machine
learning (ML) models in estimating the higher heating value (HHV) of biomass using …

This study reports a novel hybrid model for the prediction of six critical process variables of
importance in an industrial-scale FCC (fluid catalytic cracking) riser reactor: vacuum gas oil …

Biomass fuels offer a viable answer as a sustainable energy source that can significantly
decrease reliance on fossil fuels in the future. Biomass higher heating value (HHV) is a key …