TY - JOUR
T1 - Prospective life cycle assessment
T2 - Identifying the most promising methods for sustainable cellulose nanocrystal production
AU - Hoo, Do Yee
AU - Tang, Siah Ying
AU - Kikuchi, Yasunori
AU - Ng, Boon-Junn
AU - Foo, Chuan Yi
AU - Tan, Khang Wei
AU - Tan, Jully
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/10/15
Y1 - 2024/10/15
N2 - Concerted endeavors are presently in progress to discern eco-friendlier methods for cellulose nanocrystals (CNC) production, negating the conventional reliance on hazardous concentrated sulfuric acids. Despite the proliferation of methods purported to be environmentally sound, their assertions often lack rigorous scrutiny, relying on theoretical postulations. This study presents the first prospective glimpse into their environmental performance utilizing a life cycle assessment (LCA) methodology from cradle-to-gate. The results transpired that many of the vaunted environmentally-friendly methods resulted in higher environmental loads compared to the traditional method, due to lower conversion rates and higher reaction energy demand. Notably, only specific methods demonstrate lower endpoint impact scores, including deep eutectic solvent extraction (−48 %), ammonium persulfate-assisted oxidation (−62 %), sulfuric acid hydrolysis in glycerol medium (−56 %), and enzymatic hydrolysis (−40 %). Their primary green attributes associate with the utilization of solvents carrying lower climate impact and toxicity level. The reaction stage carries the heaviest burden due to the increased energy required to counteract modest reactivities of the milder solvents. As mitigation strategies, heat integration and by-product recovery for energy generation were explored, resulting in emission reductions of 11–26 % and 41–68 %, respectively, across the proven eco-friendlier methods. This study comprehensively validated a roster of genuinely green candidates, offering a more sustainable prospect than traditional methods and paving the way for large-scale green CNC production.
AB - Concerted endeavors are presently in progress to discern eco-friendlier methods for cellulose nanocrystals (CNC) production, negating the conventional reliance on hazardous concentrated sulfuric acids. Despite the proliferation of methods purported to be environmentally sound, their assertions often lack rigorous scrutiny, relying on theoretical postulations. This study presents the first prospective glimpse into their environmental performance utilizing a life cycle assessment (LCA) methodology from cradle-to-gate. The results transpired that many of the vaunted environmentally-friendly methods resulted in higher environmental loads compared to the traditional method, due to lower conversion rates and higher reaction energy demand. Notably, only specific methods demonstrate lower endpoint impact scores, including deep eutectic solvent extraction (−48 %), ammonium persulfate-assisted oxidation (−62 %), sulfuric acid hydrolysis in glycerol medium (−56 %), and enzymatic hydrolysis (−40 %). Their primary green attributes associate with the utilization of solvents carrying lower climate impact and toxicity level. The reaction stage carries the heaviest burden due to the increased energy required to counteract modest reactivities of the milder solvents. As mitigation strategies, heat integration and by-product recovery for energy generation were explored, resulting in emission reductions of 11–26 % and 41–68 %, respectively, across the proven eco-friendlier methods. This study comprehensively validated a roster of genuinely green candidates, offering a more sustainable prospect than traditional methods and paving the way for large-scale green CNC production.
KW - Cellulose nanocrystal
KW - Cotton fiber
KW - Environmental impact
KW - Green production
KW - Life cycle assessment
KW - Nanocellulose extraction
UR - http://www.scopus.com/inward/record.url?scp=85202012836&partnerID=8YFLogxK
U2 - 10.1016/j.cej.2024.154964
DO - 10.1016/j.cej.2024.154964
M3 - Article
AN - SCOPUS:85202012836
SN - 1873-3212
VL - 498
JO - Chemical Engineering Journal
JF - Chemical Engineering Journal
M1 - 154964
ER -