Process Intensification in Soybean Hull Fractionation: Linking Delignification, Structural Opening, and Fibrillation

IF 9 Q2 ENGINEERING, CHEMICAL
ACS Engineering Au Pub Date : 2026-10-21 Epub Date: 2026-08-10 DOI:10.1021/acsengineeringau.6c00057
Ederson Paulo Xavier Guilherme, Eric Keven Silva
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引用次数: 0

Abstract

Efficient fractionation of lignocellulosic biomass remains a major challenge due to the complex and recalcitrant architecture of plant cell walls, requiring process intensification strategies capable of selectively promoting delignification while preserving the cellulosic fraction. This study investigated the combined influence of chemical (NaOH concentration), thermochemical (temperature), mechanical (high-intensity ultrasound), electrical (pulsed electric field, PEF), and physical (particle size) process intensification strategies on soybean hull fractionation using a Plackett–Burman screening design. Cellulose yield, water retention value (WRV), and fibrillation yield were adopted as complementary indicators of delignification efficiency, fiber accessibility, and structural disintegration. Temperature emerged as the dominant factor governing cellulose recovery and fibrillation, whereas particle size and NaOH concentration primarily influenced fiber swelling and structural accessibility, as reflected by WRV. In contrast, PEF showed no measurable contribution to cellulose recovery, WRV, or fibrillation yield within the investigated experimental domain, demonstrating that its influence was negligible compared to the thermochemical variables. The integration of statistical screening with scanning electron microscopy, Fourier transform infrared spectroscopy, and sustainability assessment enabled the organization of representative processing conditions into four empirical structural transformation regimes, describing the progressive evolution of the lignocellulosic matrix from mild delignification to extensive fibrillation. Rather than representing mechanistic transitions, these regimes constitute an empirical process-structure–property framework linking processing conditions, structural responses, and fractionation performance. The findings demonstrate that thermochemical variables should be prioritized for soybean hull fractionation while establishing the operational domain in which PEF is unlikely to provide meaningful gains. This integrated framework contributes to the rational design of intensified lignocellulosic fractionation processes and supports more efficient and sustainable biomass valorization strategies.

大豆壳分馏过程强化:连接脱木质素、结构打开和纤颤
由于植物细胞壁的复杂和顽固的结构,木质纤维素生物质的有效分馏仍然是一个主要挑战,需要能够选择性地促进脱木质素同时保留纤维素部分的过程强化策略。本研究采用Plackett-Burman筛选设计,研究了化学(NaOH浓度)、热化学(温度)、机械(高强度超声)、电学(脉冲电场,PEF)和物理(粒度)过程强化策略对大豆壳分选的综合影响。纤维素产率、保水值(WRV)和纤颤产率作为脱木质素效率、纤维可及性和结构分解的补充指标。温度是控制纤维素恢复和纤颤的主要因素,而颗粒和NaOH浓度主要影响纤维膨胀和结构可及性,这反映在WRV上。相比之下,PEF对纤维素回收率、WRV或纤化率没有可测量的贡献,这表明与热化学变量相比,它的影响可以忽略不计。将统计筛选与扫描电子显微镜、傅里叶变换红外光谱和可持续性评估相结合,可以将具有代表性的加工条件组织为四个经验结构转变机制,描述木质纤维素基质从轻度脱木质素到广泛纤颤的逐步演变。这些制度不是代表机械转变,而是构成了一个经验的工艺-结构-性能框架,将工艺条件、结构响应和分馏性能联系起来。研究结果表明,在建立PEF不太可能提供有意义收益的操作域时,热化学变量应优先用于大豆壳分馏。这一综合框架有助于强化木质纤维素分馏过程的合理设计,并支持更有效和可持续的生物质增值策略。
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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
自引率
0.00%
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0
期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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