Integrated techno-economic and structure-property assessment of continuous TEMPO-mediated oxidation for cellulose nanofiber production

The transition towards sustainable materials manufacturing demands scalable and economically viable processes for producing cellulose nanofibers (CNFs). TEMPO-mediated oxidation is among the most efficient routes for generating carboxylated fibers, yet its industrial application remains constrained by chemical costs and the challenge of shifting from batch to continuous operation. In this work, TEMPO-mediated oxidation of bleached eucalyptus kraft pulp for CNF production was performed in a continuous stirred-tank reactor (CSTR) to assess how residence time affects CNF properties and process economics, through integrated experimentation, reaction-kinetics modeling and techno-economic assessment (TEA). Short residence times generated under-oxidized fibers, whereas excessive exposure led to partial degradation, indicating an optimal residence time of 90 min. TEA under two scenarios, fixed production rate and fixed reactor volume, showed that operating at 90 min offers the most cost-effective balance between CNF quality and processing costs. Finally, an AI-assisted TEA framework (predictive R 2 > 0. 99) allowed a 10, 000-run Monte Carlo Simulation, yielding a 90% confidence interval for the Minimum Selling Price of 4. 46/kg - 9. 78/kg, considering catalyst recovery. Overall, this study establishes a process-level framework by providing an understanding of the relation between the residence times in a CSTR with final CNF properties, indispensable for implementing industrial production and application of these nanomaterials. By integrating the experimental structure-property maps with the TEA, an ideal operating window was identified, that balances CNF performance with reaction cost, and quantitatively identified production scale (≈90% importance) and catalyst cost (≈5% importance) as the dominant drivers of economic viability. • Continuous TEMPO-oxidation of fibers proof of concept for scale up • Best conditions for optimal residence time were identified. • ML-augmented TEA identifies feature importance. • Monte Carlo simulation showed economic potential for speciality applications.