Review on nanocellulose production from agricultural residue through response surface methodology and its applications

• Nanocellulose offers broad industrial applications and is extracted from agricultural residues, with acid concentration, reaction time, and temperature being crucial parameters for optimization using RSM. • Although RSM has been effective in optimizing NC extraction, its limitations in handling complex, nonlinear relationships suggest that advanced techniques like ANN could provide improvements. • Relying on literature-based experimental ranges may overlook the optimal conditions for nanocellulose extraction, highlighting a limitation of RSM and the need for exploring broader ranges. Nanocellulose (NC) shows great potential across industries like food, pharmaceuticals, cosmetics, textiles, electronics, and construction. It can be sustainably extracted from agricultural residues using methods such as mechanical processes, acid hydrolysis, and bacterial biosynthesis. This review emphasizes the use of Response Surface Methodology (RSM) in optimizing NC extraction by examining variables like acid concentration, reaction time, and temperature. While RSM is effective, its assumptions of linear and quadratic relationships limit its accuracy in complex systems. Advanced techniques like artificial neural networks (ANN) offer a better alternative, capturing nonlinear relationships more effectively. However, ANN's application in NC extraction is underexplored, calling for future research to improve model precision. Expanding optimization to include response variables like thermal stability and surface charge is also essential for enhancing NC's industrial applications.