Sustainable production process of mechanically prepared nanocellulose from hardwood and softwood: A comparative investigation of refining energy consumption at laboratory and pilot scale

In the drive towards sustainable development, the growing application of renewable, biodegradable, green materials to substitute non-renewable resources has roused substantial interest. However, the very high electrical energy consumption typical of mechanical nanocellulose production is a significant drawback, from the point of view of both environmental impact and production cost. The aim of this study was to investigate the effect of fiber source, mechanical treatment mode and scale on the energy consumption for nanocellulose production. Nanocellulose (mechanically prepared fibers), also known as cellulose nanofibers, or CNF) from hardwood eucalyptus (Eucalyptus grandis) and northern bleached softwood which is believed to be a mix of lodgepole pine (Pinus contorta) and white spruce (Picea glauca) pulp was prepared using lab scale and pilot scale mechanical refiners. The rate of increase in fiber aspect ratio confirms that disc (pilot scale) refining is seven and thirty-three times more energy efficient than lab scale PFI (Papir Forsknings Institutet) mill refining for processing softwood and hardwood, respectively. A novel quality index (Q) is proposed which considers the percentage of fines, fiber diameter and relative bonded area in the sheet to demonstrate the production of high quality nanocellulose. When the energy required to reach equivalent values of Q were compared, it was found that pilot disc refining of hardwood is approximately fifty-eight times more energy efficient than laboratory PFI mill refining of softwood. Fines generation for hardwood and softwood was fifteen and eight times more energy efficient by disc refining as compared with PFI mill refining, respectively. The results indicate that disc refining of hardwood is the most energy efficient method of nanocellulose production, and this outcome can be applied to predict and minimise mechanical energy consumption, and therefore cost and environmental impacts, for nanocellulose production.