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<p>Despite growing interest in enzymatic fiber modification, the impact of high-consistency enzymatic refining on the mechanical performance of paper remains unexplored. Unlike conventional low-consistency systems, high-consistency enzymatic refining offers a more energy-efficient and industrially scalable pathway for surface modification of cellulose fibers. This study investigates, under industrially relevant conditions, how high-consistency enzymatic refining of bleached kraft eucalyptus pulp with endoglucanases can support the rational design of fiber-based bioproducts. Pulp consistency (3–15 wt% ), enzyme dosage (0–300&#xa0;mg/kg), and treatment time (15–60&#xa0;min) were systematically varied. High-consistency enzymatic refining significantly enhanced mechanical performance: breaking length increased up to 89% and internal bonding up to 387%, without substantial freeness reduction. To enable predictive design and process optimization, machine-learning models were developed first based on process variables (consistency, time, dosage). LightGBM model achieved the best results with high predictive accuracy for property prediction (R<sup>2</sup> up to 0.955). To overcome data scarcity, a physics-informed generative augmentation strategy was implemented that incorporates freeness to generate 30 synthetic datapoints. The augmented dataset enhances predictive performance, validating the quality of the synthetic data. A physics-informed Gaussian process regression model was used to extrapolate performance at 400&#xa0;mg/kg enzyme dosage, and a targeted experiment confirmed its prediction. The results suggest that high-consistency enzymatic refining enhances interfiber bonding through the generation of nanoscale fibrillar elements at the surface, influencing the final bonding of the sheet. Altogether, high-consistency enzymatic refining coupled with predictive modeling shows as a viable pathway towards producing all-cellulose materials with improved mechanical properties, while reducing energy consumption.</p> Graphical Abstract <p></p>

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Enzyme-assisted high-consistency fiber refining: enhancing cellulose materials performance in the paper industry through process and physics-informed machine learning modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月10日

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  • Giovana Signori-Iamin,
  • Ronald Marquez,
  • Gabriela A. Bastida,
  • Quim Tarrés,
  • Alexandre F. Santos,
  • Marc Delgado-Aguilar

摘要

Despite growing interest in enzymatic fiber modification, the impact of high-consistency enzymatic refining on the mechanical performance of paper remains unexplored. Unlike conventional low-consistency systems, high-consistency enzymatic refining offers a more energy-efficient and industrially scalable pathway for surface modification of cellulose fibers. This study investigates, under industrially relevant conditions, how high-consistency enzymatic refining of bleached kraft eucalyptus pulp with endoglucanases can support the rational design of fiber-based bioproducts. Pulp consistency (3–15 wt% ), enzyme dosage (0–300 mg/kg), and treatment time (15–60 min) were systematically varied. High-consistency enzymatic refining significantly enhanced mechanical performance: breaking length increased up to 89% and internal bonding up to 387%, without substantial freeness reduction. To enable predictive design and process optimization, machine-learning models were developed first based on process variables (consistency, time, dosage). LightGBM model achieved the best results with high predictive accuracy for property prediction (R2 up to 0.955). To overcome data scarcity, a physics-informed generative augmentation strategy was implemented that incorporates freeness to generate 30 synthetic datapoints. The augmented dataset enhances predictive performance, validating the quality of the synthetic data. A physics-informed Gaussian process regression model was used to extrapolate performance at 400 mg/kg enzyme dosage, and a targeted experiment confirmed its prediction. The results suggest that high-consistency enzymatic refining enhances interfiber bonding through the generation of nanoscale fibrillar elements at the surface, influencing the final bonding of the sheet. Altogether, high-consistency enzymatic refining coupled with predictive modeling shows as a viable pathway towards producing all-cellulose materials with improved mechanical properties, while reducing energy consumption.

Graphical Abstract