Volume 23, Issue 11

Biomass conversion technologies are integral to the realization of sustainable, low-carbon energy systems; however, their scalability is significantly hampered by pronounced sensitivity to the composition of feedstock and the temperature of the processes employed. This review synthesizes insights on how temperature regimes and lignocellulosic composition interact to influence energy yields and product quality across various methodologies, including torrefaction, pyrolysis, gasification, and hydrothermal liquefaction. Furthermore, it elucidates how machine learning (ML) presents revolutionary prospects for mitigating variability, facilitating feedstock-agnostic forecasting of higher heating value, yields of bio-oil/char/biogas, syngas H 2 /CO ratios, and tar propensity; enabling adaptive closed-loop control of operational parameters; and promoting multi-objective optimization that incorporates techno-economic and life cycle considerations. A comprehensive, data-driven roadmap is proposed to expedite deployment, comprising: (i) process matching and operational set-points that are cognizant of composition; (ii) hybrid models informed by physics for enhanced interpretability; (iii) frameworks for federated and active learning to bolster generalization across diverse regions and feedstocks; and (iv) optimization integrated with techno-economic analysis (TEA) and life cycle assessment (LCA) to guarantee economic feasibility and environmental sustainability. This roadmap not only amalgamates disparate insights into a cohesive strategy but also furnishes practical guidance for stabilizing the quality of outputs, minimizing operational expenses, and promoting decentralized, intelligent bioenergy infrastructures. Subsequent research endeavors should focus on establishing standardized biomass datasets, integrating robust sensors, and developing explainable artificial intelligence frameworks to ensure the scalable, reliable, and sustainable deployment of these systems.

Water scarcity and the increasing levels of sewage effluent are intensifying the demand for innovative, sustainable wastewater treatment technologies. Among various alternatives, lignocellulose-based nanomaterials (LCNMs) have emerged as promising candidates due to their unique structural properties and natural abundance. This work offers a comprehensive exploration of LCNMs, covering their synthesis, characterization, and diverse applications in wastewater treatment. It begins with an overview of the fundamental properties of lignocellulose and introduces wood and forest-based biomass as resources for nano-bio photoelectron sensor interfacing. The practical utility of LCNMs is highlighted through their applications in heavy metal adsorption, organic pollutant removal, and the degradation of pharmaceuticals and personal care products, demonstrating high efficiency at a real-world scale. Case studies further underscore the potential of LCNMs in practical scenarios, supported by discussions on environmental and health outcomes, emphasizing biological safety within toxicity limits. Additionally, the work explores into innovative approaches for the synthesis of LCNMs, their integration with other treatment technologies, and their economic and environmental sustainability. By leveraging lignocellulose atoms and mesoporous materials, this work presents a novel and sustainable approach to wastewater purification processes, paving the way for future advancements in the field.

This study presents a numerical investigation into the effect of magnetic field strength and sheath flow rate on the performance of a magnetophoretic Lab-on-a-Chip (LOC) device for cell separation. Simulations were conducted using a two-dimensional model based on the principle of negative magnetophoresis to achieve label-free separation of particles with diameters of 15.8 and 5.8 µm. The results revealed that separation distance exhibited a non-linear dependence on sheath flow rate for a given magnetic field, with a distinct peak observed at optimal conditions. At a magnetic field strength of 350 mT and a sheath flow rate of 20 μL/min, the device achieved a maximum separation distance of 1.023 mm and a 100 % recovery rate. Furthermore, the separation distance increased at a diminishing rate with rising magnetic field strength and buffer flow rate, following an exponential profile. The optimized LOC design, based on computational fluid dynamics (CFD) simulation, demonstrates that precise control of flow and magnetic parameters can significantly enhance the separation efficiency and recovery rate of microfluidic devices.

In this study, a simulated model was developed using Aspen Plus software to investigate the pyrolysis of four different biomasses and waste plastics: Coconut shell (CS), Granular bacteria (GB), Azolla, and High-density polyethylene (HDPE). The primary goal was to produce valuable syngas and biofuel and analyze the impact of key operating conditions, temperature and pressure, on product distribution (bio oil, biogas, biochar), gaseous composition (H 2 , CO, CO 2 , CH 4 ), and the H 2 /CO ratio. The simulation results revealed that the highest bio oil yield (54 wt%) was obtained from High-density polyethylene at a temperature of 525 °C and pressure of 1 bar, while the maximum biogas yield (46 wt%) belonged to Azolla at a temperature of 600 °C and a pressure of 1 bar. The H 2 /CO ratio for all feeds decreased with increasing temperature but increased with rising pressure. High-density polyethylene consistently showed the highest H 2 /CO ratio, confirming its potential as an ideal feedstock for hydrogen-rich syngas production. These findings provide critical insights for optimizing the pyrolysis process and selecting suitable feedstocks for high-value biofuel production. This research offers a fundamental understanding for the design and optimization of industrial-scale pyrolysis reactors and contributes to the broader framework of the circular economy by demonstrating the feasibility of converting specific waste streams into valuable energy products.

This study presents an intensified degradation strategy for azo dyes using a flow microreactor (6 m-length/1 mm-diameter)-integrated sulfate and hydroxyl radicals advanced oxidation processes (SO 4 •− / • OH-AOPs) platform. Three systems were assessed: thermally activated persulfate (KPS), Fe(II)/KPS, and Fe(II)/sodium percarbonate (SPC), focusing on key parameters – bath temperature (20–70 °C), inlet dye concentration (5–20 µM), Fe(II) dosage (50–100 µM), pH (3–7), and oxidant flowrate (20–120 μL/s). Experiments were conducted with Basic Fuchsin (BF), a persistent dye of mutagenic and carcinogenic properties. Thermal KPS led to full dye conversion at 70 °C but negligible TOC removal at lower temperatures. Fe(II)/KPS and Fe(II)/SPC improved degradation across all temperatures. Fe(II)/KPS led to substantial mineralization (63 % TOC removal), while Fe(II)/SPC achieved only 33 %, and KPS alone 54 %. At 20 °C, Fe(II)/SPC showed slightly higher TOC removal than Fe(II)/KPS (18 % vs. 15 %). Performance was strongly influenced by Fe(II) speciation (pH-dependent) and radical scavenging by intermediates. Removal ratio analyses (Fe(II)/KPS to KPS: up to 5.0; Fe(II)/SPC to Fe(II)/KPS: up to 1.97) highlighted strong catalytic synergy, especially at low pH and low dye concentration. These findings demonstrate the potential of the microreactor-based SO 4 •− / • OH-AOPs platform for scalable, energy-efficient, and high-throughput water treatment applications.

Cavitating jet nozzles play a crucial role in various industries, including oil and gas exploration, cleaning and cutting, and the automotive sector. This study introduces a Venturi cavitation nozzle with two auxiliary flow channels (VWC), derived from the traditional Venturi cavitation nozzle (VN). The flow field of the nozzle is numerically analyzed using the large eddy simulation (LES) and the Zwart–Gerbera–Belamri (ZGB) cavitation model, and the results are compared with those of the traditional VN. This study examines the transient steam distribution and its variation within the two nozzles in the low-pressure range, assessing the influence of minor inlet pressure fluctuations on the internal cavitation flow. Under high-pressure conditions, the jet length and the velocity at the end of the external domain were compared, and the velocity of each section and the velocity change trend of the nozzle outlet and the end of the external flow field in the high-pressure range were analyzed. The numerical results demonstrate that the VWC exhibits enhanced vapor generation and jet performance under identical boundary conditions. In terms of steam generation, the vapor content of the VWC is consistently higher than that of the VN, with a maximum difference of 25 %. Regarding jet strength, under the same pressure, the VWC exhibits a lower axial jet velocity decay rate than the VN, which can be reduced by up to 52.8 %. Additionally, the longitudinal jet velocity of the VWC is slightly higher, surpassing that of the VN by up to 52 %. This research offers valuable insights and a novel reference for advancing the structural design of cavitation nozzles.

This research paper delves into designing heat exchangers using a user-friendly and robust GUI-based program developed using Python as a computational tool. In addition, various design methods such as LMTD and EDTR have been used, including the Kern and Bell-Delaware methods. The results showed that the EDTR method is a promising alternative design tool compared to traditional ones. This comparative analysis highlights the accuracy, efficiency, and potential advantages of the EDTR method in contrast to the LMTD approach. Complex, iterative, and time-intensive manual calculation challenges were mitigated using Python programming. This computational approach significantly streamlined the calculation process and reduced the required time and effort, ensuring precise and reliable results. The study demonstrated the potential of the EDTR method as an efficient and accurate alternative to the traditional LMTD approach.

In the hydrometallurgical processing of high-grade nickel matte, oxygen pressure sulfuric acid leaching produces substantial residues containing hematite precipitates. In this study, high-grade nickel matte oxygen pressure leaching residue, generated during the hydrometallurgical processing of high-grade nickel, was used as the research subject. The investigation focused on the effects of various factors in the reduction leaching process on the behavior of copper (Cu) and iron (Fe), as well as on the enrichment of precious metals. A comprehensive recovery process was designed for Cu and Fe recovery. Under the conditions of an initial sulfuric acid (H 2 SO 4 ) concentration of 100 g/L, a liquid–solid ratio of 6 mL/g, a reaction time of 3 h, a temperature of 90 °C, and a sulfur dioxide (SO 2 ) partial pressure of 0.15 MPa, high leaching efficiencies were achieved: 99.35 % for Fe and 77.46 % for Cu. Concurrently, silver content was enriched from 1,200.1 g/t to 36273.5 g/t (a 30.2-fold increase). Notably, for Cu recovery, when the H 2 SO 4 concentration was 20 g/L–30 g/L at 70 °C, with 5.7 g/L of Fe powder and a reaction time of 40 min, displacement precipitation achieved 99.70 % Cu recovery, with the precipitate containing 67.91 % Cu. Subsequently, freeze crystallization of the post-Cu solution at −10 °C, with a holding time of 30 min and an initial H 2 SO 4 concentration of 150 g/L, yielded ferrous sulfate heptahydrate with 72.6 % Fe precipitation and > 99 % purity. The crystallization mother liquor was recycled into the reduction leaching process for recovery. This integrated process exhibited high metal recovery efficiency, significant precious metal enrichment, and zero wastewater discharge, representing an environmentally friendly and sustainable approach to nickel matte residue processing.

The main objective of this study was to investigate the potential use of concrete debris generated from construction and demolition waste at the Technical Landfill Centre (TLC) for eliminating copper and lead ions from synthetic solutions. To assess its suitability, the physicochemical characteristics of the debris were thoroughly examined, including its cation exchange capacity (CEC) and pH at the zero-charge point (pH PZC). Various characterization methods were employed, such as Fourier-transform infrared spectroscopy (FTIR) to predict the functional groups. X-ray diffraction (XRD) was applied to determine the crystalline structure of the materials and identify the phases present, while BET surface area analysis and scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM/EDX) were also used to gain a comprehensive understanding of the concrete debris. Key parameters evaluated included solution pH, initial metal ion concentration, adsorbent mass, stirring speed, and temperature, all analyzed to determine their influence on adsorption capacity. Optimal removal conditions were identified as pH 5.5 for lead and pH 5 for copper, with agitation speeds of 300 and 400 rpm, contact times of 20 and 15 min, and adsorbent masses of 0.3 and 0.4 g for copper and lead, respectively. Furthermore, A kinetic analysis of adsorption was conducted to identify the mechanisms of the process by fitting experimental data to various models. Additionally, different isotherms were utilized to accurately fit the equilibrium adsorption. The results indicate that adsorption is best represented by the Langmuir model. The use of thermodynamics in the removal process revealed that this procedure was endothermic and spontaneous.