Ting Han

Agro‐industrial residues can supply dietary fiber together with extractable or cell‐wall‐associated phenolics, but coexistence does not establish phenolic‐bound dietary fiber (PBDF). This structured narrative review evaluates 95 peer‐reviewed articles published from 1993 to 2026 and four official regulatory sources across ingredient identity, processing, food functionality, biological evidence, safety, and regulation. It proposes operational terms for phenolic‐rich, phenolic‐associated, phenolic‐bound, processing‐modified, and food‐system phenolic–fiber states as a working, not regulatory, framework. Processing is treated as a sequence from stabilization and fiber extraction through matrix modification, food manufacture, and storage. Concentration, extractability, release, redistribution, and degradation are distinguished from true retention, which requires common‐basis mass balance across recovered streams. Food studies indicate combined fiber and phenolic value, although texture, color, bitterness, astringency, and stability often limit inclusion. Incremental advantage over conventional fiber remains uncertain because phenolic‐depleted matched controls and equal‐dose free‐phenolic controls are uncommon. Bioaccessibility, bioavailability, metabolism, and physiological activity are separated as successive evidence domains. Translation requires source and process specifications, contaminant and exposure assessment, regulatory evaluation, and final‐food validation. The field should move toward processing‐aware identity, quantitative phenolic fate, claim‐matched comparators, and food‐grade evidence.

Kapok is a lightweight hollow tropical fiber with potential as a renewable feedstock, but its waxy surface and lignin-containing matrix complicate aqueous processing. This study evaluated whether limited-data machine learning could predict kapok fiber composition after aqueous chemical pretreatment using only three process descriptors. Kapok fibers were pretreated using dilute H2SO4, hydrothermal water, and NaOH across a shared window of residence time, temperature and pH-associated pretreatment environment. A data set of 33 pretreatment runs was constructed, with cellulose, hemicellulose and lignin contents used as composition targets after correction with solid yield. Single-output and wrapper-based multitarget regression models were screened and then evaluated using leave-one-out cross-validation to reduce dependence on a single train–test split. The LOOCV results showed target-dependent predictability. In single-output modeling, cellulose was the most predictable component, with GBDT giving the strongest performance (R2 = 0.782; RMSE = 4.185 percentage points), whereas hemicellulose and lignin showed weaker prediction. In wrapper-based multitarget modeling, GBDT improved simultaneous prediction of cellulose, hemicellulose and lignin, giving R2 values of 0.909, 0.653, and 0.672, respectively. Feature-importance analysis indicated that temperature was the dominant descriptor for polysaccharide-related composition changes, whereas the pH-associated pretreatment environment contributed more strongly to lignin-related variation. These findings position limited-data machine learning as a useful exploratory tool for modeling pretreatment–composition relationships in kapok fiber, while also highlighting the need for cautious validation when sample size is small.

Machine learning (ML) is increasingly used to support biomass valorization for energy and fuels by linking feedstock properties, process conditions, and product outcomes. However, current studies differ substantially in feature definition, algorithm selection, validation design, uncertainty treatment, and the use of predictions for engineering decisions. This Review synthesizes representative ML studies across thermochemical, hydrothermal, biochemical, pretreatment, and porous-carbon production pathways, with an emphasis on feature representation, model–task alignment, and decision-ready process modeling. The reviewed literature shows that hybrid feedstock-process descriptors often improve predictive performance because they represent both biomass identity and conversion environment, but they also increase the risks of overfitting, inconsistent reporting, multicollinearity, and leakage when feature timing is unclear. Model selection should therefore be evaluated according to data set size, descriptor structure, target definition, output coupling, validation strength, uncertainty requirement, and intended decision use, rather than by algorithm ranking alone. Beyond response prediction, ML can support feedstock screening, operating-window optimization, pathway comparison, and multiobjective trade-off analysis only when predictions are checked for physical feasibility, applicability domain, uncertainty, and process-level relevance. This Review highlights the need to move biomass valorization ML from accuracy-centered prediction toward transparent, transferable, uncertainty-aware, and decision-ready modeling.

High-solids processing is increasingly viewed as a prerequisite for industrial lignocellulosic biorefineries because it can reduce dilution, increase sugar and product concentrations, decrease reactor volume, and lower downstream liquid throughput. However, increasing solids loading also shifts lignocellulosic conversion from a dilute biochemical reaction system to a slurry-based engineering problem. Reduced mobile water, stronger particle–particle interactions, higher viscosity and yield stress, non-uniform reagent or enzyme distribution, mass-transfer limitations, inhibitor accumulation, and difficult solid–liquid separation can offset the apparent benefits of a high product concentration. This review examines high-solids lignocellulosic biorefineries through the coupled effects of solids-loading regimes, water demand, energy input, particle architecture, slurry rheology, mixing power, hydrolysis and fermentation performance, downstream recovery, wastewater quality, and scale-up. Rather than cataloging pretreatment or conversion methods, it evaluates how increasing solids loading transfers burdens across pretreatment, particle engineering, enzymatic hydrolysis, SSF/SSCF, product recovery, wastewater management, and process integration. Representative studies are synthesized to show that high sugar or ethanol titers do not necessarily imply efficient carbohydrate conversion, manageable inhibition, complete sugar utilization, or scalable operation. The review further identifies reporting priorities for high-solids studies, including solids-loading basis, total water input, washing and detoxification steps, reactor geometry, mixing or power data, rheological behavior, residual sugars, inhibitor profiles, recovery metrics, wastewater characteristics, and stage-level mass, water, and energy balances. Scalable high-solids biorefineries require balanced operating ranges that convert concentration gains into recoverable, energy-aware, water-conscious, and scalable process performance.

Energy‐performance results for waste‐derived ethanol systems remain difficult to compare because reported indicators often quantify different services, boundaries, and energy carriers. This problem is amplified for municipal wastes, agricultural and forest residues, and agro‐industrial by‐products, where feedstock heterogeneity, moisture and ash burdens, collection or sorting boundaries, pretreatment severity, inhibitor management, low ethanol titres, and separation duties reshape the energy balance. Apparent disagreements therefore often reflect inconsistent functional units, system boundaries, electricity‐to‐primary conversion factors, fuel heating‐value bases, internal energy‐recovery assumptions, and co‐product or avoided‐disposal crediting rather than conversion performance alone. This review uses waste‐derived ethanol as the anchor case to organize energy‐performance metrics into a question‐driven framework linking analytical purpose, system scale, and indicator choice. Carrier‐resolved energy‐intensity metrics are positioned as the primary tools for hotspot attribution because they separate thermal duties for pretreatment and separation from electrical demands for size reduction, pumping, mixing, and auxiliary operations. Net‐return indicators are more appropriate for net‐gain claims but are highly sensitive to boundary breadth and crediting choices, whereas life‐cycle primary energy indicators expose upstream burdens from electricity supply, chemicals, enzymes, logistics, and waste preprocessing. Examples from electricity‐derived fuels and polygeneration systems are used only as methodological comparators for electricity accounting, heat integration, and multi‐output crediting. The review consolidates a minimum reporting set covering functional units, boundaries, thermal–electrical separation, grid context, fuel heating‐value basis, internal energy recovery, embedded inputs, and co‐product handling to support auditable comparison and defensible technology selection.

Bioethanol production from lignocellulosic biomass remains energy-intensive, and its energy performance can be affected by equipment degradation, utility disturbances, and operating variability. This study developed a degradation-aware mechanistic–AI framework for energy forecasting, anomaly detection, maintenance-oriented interpretation, and multi-objective optimization in bioethanol production under limited-data conditions. Reduced-order energy models were formulated for pretreatment, hydrolysis–fermentation, and ethanol purification. Equipment deterioration was represented through heat-transfer fouling, column-efficiency decline, and pump-efficiency decay. Condition-dependent modifiers were introduced to account for load-related degradation and intervention-related partial recovery. Benchmark-constrained synthetic time-series datasets were generated under baseline, accelerated-degradation, condition-dependent, stress, and data-quality perturbation scenarios. Empirical baselines and machine-learning models were compared for specific energy consumption prediction, with uncertainty reported using confidence intervals. The long short-term memory model achieved the lowest prediction errors under both baseline and stress conditions. Robustness testing showed that sensor drift, missing values, and outliers increased forecasting and anomaly-detection uncertainty. Sensitivity analysis identified degradation coefficients, seasonal disturbance, and anomaly-threshold selection as influential factors. Multi-objective optimization revealed trade-offs among specific energy consumption, ethanol purity, and equipment-health penalty. The proposed framework should be interpreted as a benchmarked methodological platform rather than a plant-validated maintenance or control system. Plant-specific deployment requires calibration with operating records, maintenance logs, cleaning records, and sensor-quality assessment.

Second-generation bioethanol from food industry lignocellulosic residues offers a promising route toward low-carbon, circular bioenergy systems. However, the reported environmental impacts differ markedly across studies, challenging efforts to assess the true sustainability of these waste-derived bioethanol routes. This review synthesizes current knowledge on the production of bioethanol from key agro-industrial wastes including oil palm empty fruit bunches, sugarcane bagasse, brewers’ spent grain, spent coffee grounds, tea waste, citrus residues, and potato peel waste. We outline feedstock characteristics, availability, and prevailing management practices, and map the principal biochemical conversion routes to identify process steps that drive environmental performance. A systematic comparison of life cycle assessments reveals substantial methodological heterogeneity across functional units, system boundaries, allocation procedures, and impact assessment methods. Nonetheless, consistent hotspots emerge, particularly associated with pretreatment severity, enzyme production, thermal energy demand, and co-product handling. The review highlights robust cross-study trends, pinpoints methodological gaps, and proposes recommendations for harmonized LCA practice. By integrating technological and methodological perspectives, this work aims to support the development and policy uptake of sustainable, waste-based bioethanol within circular bioeconomies.

Porous porphyrin-based photocatalysts combine strong light harvesting with tuneable frameworks. Advances in their design and synthesis enhance pollutant degradation and clarify structure activity trends for sustainable remediation.

Pollutant transformation in water is governed not only by catalyst composition but also by the applied interface where adsorption, light absorption, charge transfer, oxygen activation and product evolution occur.

Porphyrins are highly conjugated macrocyclic compounds that play vital roles in both natural and synthetic systems, with broad applications in catalysis, energy conversion, environmental remediation, and biomedicine. However, conventional synthetic approaches are often associated with harsh reaction conditions, toxic solvents, and low overall sustainability. In response, green chemistry principles have increasingly guided the development of more environmentally benign strategies for porphyrin synthesis. This review systematically examines recent progress (primarily from 2015 to 2025 in the development of green and sustainable synthetic methodologies for porphyrins, including solvent‐free techniques, the use of green solvents, microwave‐ and ultrasound‐assisted approaches, mechanochemical processes, and emerging biocatalytic and electrochemical methods. Representative studies are analyzed with respect to yield, scalability, and alignment with green chemistry metrics such as atom economy, energy efficiency, and waste reduction. By highlighting both achievements and current challenges, this review aims to guide future efforts toward more sustainable and industrially viable porphyrin syntheses.