The reutilization of coke oven gas (COG), a byproduct of steel manufacturing, is increasingly emphasized as a strategy to improve energy efficiency and reduce emissions. However, raw COG contains hydrogen sulfide (H2S) and ammonia (NH3) impurities, which can lead to equipment corrosion, catalyst deactivation, and environmental pollution, thereby limiting its direct reuse. Limited research has quantitatively compared the performance of conventional amine-based purification systems with emerging integrated-column configurations for simultaneous removal of H2S and NH3. In this study, two COG purification configurations were modeled using Aspen Plus and systematically compared from 2ES (Energy, Economic, and Safety) perspectives. Case 1 represents a conventional absorption-regeneration process employing a methyldiethanolamine (MDEA)-based amine solvent, whereas Case 2 integrates the H2S and NH3 scrubbers into a single column. Both cases were modeled under identical inlet conditions to achieve a 95% H2S removal efficiency. The comparative analysis reveals a clear trade-off between energy efficiency and safety. While the economic performance of the two cases was nearly identical, with only similar to 1% difference in gas processing cost (GPC), Case 2 exhibited an energy-saving potential of about 65% compared to Case 1. However, under a large accidental release scenario, Case 2 indicated a leakage rate about 3.6 times higher, leading to a hazard radius approximately 1.7 times larger than that of Case 1. These findings highlight that the MDEA-based absorption process is more favorable from a safety perspective, whereas the integrated NH3/H2S column process offers a substantial advantage in energy efficiency.
The efficient, economical, and sustainable generation and distribution of power represents a major global challenge with significant technological and social implications. A common approach to solving this complex problem involves formulating it within a mathematical optimization framework, which typically incorporates both discrete and continuous decision variables. Due to the inherent complexity of these optimization problems, conventional practice often necessitates the use of a problem decomposition scheme to enable solution derivation. While this decoupling procedure facilitates the computation of solutions, the removal of intrinsic problem interactions inherently leads to a solution that is, at best, suboptimal. To address this limitation and seek optimal solutions, this work investigates the application of the Quantum Computing paradigm for solving a realistic, albeit reduced, version of the Mexican energy system. In particular, we address the QUBO-QAOA solution of a deterministic UC/ED instance under fixed hourly demand and deterministic generator parameters. The primary objectives of this work are to identify suitable optimization formulations amenable to quantum algorithms, determine the potential advantages and disadvantages of using Quantum Computing for this specific domain and examine the scalability and characteristic requirements imposed by power generation and distribution systems. As the availability of quantum processors and robust, noise-tolerant quantum systems increases, quantum computing is anticipated to become capable of providing optimal solutions to some of the most challenging scientific and technological problems across diverse fields, including materials design, computational fluid dynamics, and logistics.
To clarify the oxidation kinetics and controlling mechanism of iron vanadium spinel (FeV2O4) in air at elevated temperatures, high purity FeV2O4 samples were subjected to isothermal thermogravimetric oxidation at 450-700 degrees C. The thermogravimetric data were used to establish the conversion versus time and reaction rate versus conversion relationships, on the basis of which the oxidation process was divided into two kinetic stages. Solidstate kinetic models for each stage were then fitted and evaluated using both the linear integral method and the nonlinear least squares method, and their suitability was further examined by residual analysis. The results indicate that the first stage is best described by the A3 model, whereas the second stage is more consistent with the D3 model. This result suggests that the rate-controlling step gradually changes from an initial nucleation and growth process to a diffusion controlled process in the product layer. Iso-conversional analysis further shows that the apparent activation energy increases from 50 to 174 kJ & sdot;mol- 1 as conversion increases, indicating that the reaction becomes progressively governed by steps associated with higher energy barriers. This trend is consistent with the change in controlling mechanism identified for the two stages. In addition, XRD and XPS analyses reveal the coupled evolution of phase reconstruction and valence states during FeV2O4 oxidation. These findings provide useful guidance for understanding oxidation behavior in vanadium-bearing systems and for optimizing hightemperature oxidative vanadium extraction processes.
The escalation in annual oilfield production and water content in produced fluids has drawn increased attention towards downhole oil-water separation technology. Among various solutions, hydrocyclone-based downhole separation devices are being extensively studied. This work proposes an axial inlet hydrocyclone incorporating guide spiral vanes for swirl generation to overcome limitations of conventional hydrocyclones, such as restricted operating windows and inadequate separation performance. Laboratory experiments were first conducted to evaluate the separation performance, demonstrating that the modified hydrocyclone significantly expands the operating window. With split ratios ranging from 20% to 50%, it achieves separation efficiency exceeding 99% and maintains underflow oil concentration below 150 mg/L. Under high water-cut conditions, this value can be further reduced to below 60 mg/L. Furthermore, Computational Fluid Dynamics (CFD) simulations were performed to investigate the effects of spiral vane outlet angle and guide section length on internal flow field characteristics. Numerical results indicate that an optimal conversion efficiency between inlet momentum and separation force is achieved at a vane outlet angle of 27.75 degrees. A guide section length of 9 mm provides an optimal balance between turbulence suppression, droplet size control, and energy dissipation, thereby stabilizing the flow field and enhancing reverse oil discharge efficiency. These findings, integrating experimental validation with mechanistic analysis, suggest significant potential for practical application of this separator in downhole oilwater separation.
Turbulent emulsification devices are used in many applications of chemical engineering. There is large need to predict the drop sizes resulting from such devices, for example in the context of designing new devices, modifying current design, or adapting designs to new products. The Kolmogorov-Hinze framework is the most used approach, much due to its simplicity and flexibility. However, there is not only one Kolmogorov-Hinze model but several, differing in modelling assumptions and on model complexity. This contribution provides a review focusing on how reliable the framework is in its current form for making predictions on new conditions, on when the different extended forms of the framework are needed to obtain a high predictive power, and on what are the limitations or missing pieces of understanding in contemporary literature. The perspective is that of a researcher or engineer interested in using Kolmogorov-Hinze theory to make predictions in an applied setting.
In powder near-net-shape manufacturing, powder segregation significantly compromises the forming quality of final products. However, the mechanisms by which different die-filling methods influence the segregation behavior of multi-component mixtures remain largely unclear. To enhance the uniformity of the powder filling process, a coupled computational fluid dynamics-discrete element method (CFD-DEM) was employed in this study to quantitatively compare the segregation characteristics of linear and rotary die-filling systems under suction-assisted conditions, utilizing an improved local segregation index (SI) and the volume fraction ratio of light to heavy particles (k). The results indicate that the linear die-filling system, driven by a higher initial horizontal velocity, effectively suppresses vertical segregation. Conversely, the rotary system excels in mitigating horizontal segregation by leveraging the lateral diffusion effect of the powder. Furthermore, optimizing suction parameters to establish a stable negative-pressure environment within the die cavity significantly balances the momentum discrepancies between light and heavy particles. Compared to the non-suction condition, the application of suction reduces the maximum segregation index of high-density-gradient mixtures by approximately 50%. This study elucidates the underlying mechanisms by which flow field modulation mitigates mixture segregation, providing a robust theoretical foundation for the selection of complex forming equipment and process optimization in powder metallurgy.
This review highlights the transformative role of computational fluid dynamics (CFD) in modern rhinology, emphasizing its impact on objectively managing complex nasal pathologies and optimizing targeted nano-delivery. Moving beyond traditional subjective methods, CFD enables detailed insights into airway parameters like pressure gradients and wall shear stress. Quantitatively, CFD has revealed critical aerodynamic thresholds: studies indicate that minimal cross-sectional area (mCSA) may cease to be the primary determinant of nasal resistance when exceeding approximately 0.4 cm2, while airflow alterations from adenoid obstruction become significant only above a 50% threshold. Furthermore, virtual surgical planning allows for preoperative optimization; for instance, LeFort I advancement can yield a 27% increase in cross-sectional area and a 6% decrease in nasal resistance. The understanding of targeted nano-delivery is enhanced by modeling particle dynamics, with studies identifying ideal size ranges of 10-16 & micro;m for inhaled particles and providing a framework for sub-micron aerosol filtration. The nasal cavity's air conditioning functions are similarly optimized through these simulations. Despite these advancements, challenges remain in correlating objective findings with subjective patient experiences. To address this, future research directions in computational nanotechnology are proposed, including advancing multiphase flow models for nano-aerosol interactions (e.g., evaporation, mucus rheology), applying reaction engineering to biofilm formation, and integrating heat and mass transport. Ultimately, developing patient-specific digital twins and computationally efficient models will bridge the gap between simulation and clinical accessibility, ushering in a new era of predictive, personalized rhinologic care.
Steam methane reforming (SMR) coupled with the water-gas shift (WGS) reaction and pressure swing adsorption (PSA) remains a widely used route for hydrogen production. However, its representation in open-source simulation environments is often challenged by inconsistencies in transferring kinetic models and reactor assumptions from commercial platforms. In this work, a process-level SMR-WGS flowsheet with simplified downstream separation was developed and analyzed in DWSIM. A packed-bed reactor configuration, referred to as the DoanXu configuration, was implemented to incorporate Xu-Froment kinetics within a reactor-scale framework that accounts for catalyst properties and pressure drop effects, while avoiding idealized reactor assumptions. Process optimization was conducted using a combination of Response Surface Methodology (RSM), Taguchi analysis, and subset-based screening to evaluate the influence of operating variables on methane conversion, energy demand, and hydrodynamic constraints. Downstream units were represented using simplified but physically consistent models, including air-cooled condensation, compression, and tail-gas combustion, to enable improved energy accounting. The optimized configuration increased hydrogen yield from 1.19 to 1.31 kmol H2 per kmol CH4 and reduced total energy demand from 20.29 to 17.09 GJ & sdot;h- 1 relative to the baseline case. These results highlight the importance of process-level parameter selection and system integration in influencing overall performance. The proposed framework provides a transparent and reproducible approach for comparative analysis, sensitivity evaluation, and preliminary process design using open-source simulation tools. The results should be interpreted within the scope of the modeling assumptions, particularly with respect to simplified treatment of downstream separation and heat transfer.
As a potential medium for hydrogen storage, the desorption characteristics of coal seams directly determine the storage and release efficiency of hydrogen. Accurate prediction of coal seam gas desorption volume is the core premise for optimizing hydrogen energy extraction efficiency, reducing engineering costs, and supporting technological innovation of coal seam hydrogen storage. This study clarifies the adaptability of different optimization algorithms to machine learning (ML) models to improve gas desorption prediction accuracy. We constructed 10 hybrid models by combining two mainstream ML models (multi-layer perceptron, MLP; extreme gradient boosting, XGBoost) with four state-of-the-art swarm intelligence optimization algorithms (sparrow search algorithm, SSA; pelican optimization algorithm, POA; northern goshawk optimizer, NGO; African vulture optimization algorithm, AVOA), and conducted a full-factor systematic comparison of all models. The core methodological contributions of this study include: (1) For the first time, we quantified the performance difference and adaptability boundary of different algorithm-model combinations in gas desorption prediction; (2) We combined multi-index comprehensive evaluation with physical mechanism interpretation, realizing the unification of high-precision prediction and interpretable analysis of gas desorption. The results show that optimization algorithms can significantly improve the prediction accuracy and generalization performance of the MLP model, while the prediction performance of the XGBoost model decreases after being optimized by the same algorithms, revealing a significant difference in adaptability between different types of ML models and swarm intelligence optimization algorithms. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) comprehensive evaluation shows that the optimized MLP models have better overall prediction performance than XGBoost models; among them, the AVOA-MLP model has the optimal comprehensive performance, with a Mean Absolute Error (MAE) of 6.5775, Mean Absolute Percentage Error (MAPE) of 0.010199, Root Mean Square Error (RMSE) of 8.3352, and Coefficient of Determination (R2) of 0.99783. The Shapley additive explanation (SHAP) analysis indicates that the gas desorption volume at 90-meter depth has the highest global importance for the prediction of desorption volume at 99-meter depth, revealing that in-situ stress and mining disturbance are the key controlling factors affecting gas desorption. The main limitation of this study is that the dataset comes from a single working face of a single coal mine, and the cross-site generalization ability of the model needs to be further verified in coal seams with different geological conditions and mining intensities. This study provides a methodological basis for the selection and optimization of gas desorption prediction models, and can provide scientific support for the feasibility evaluation and engineering design of coal seam hydrogen storage projects, so as to promote the sustainable development of hydrogen energy industry.
This work demonstrates that thermal aging, i.e., establishing oil-water-rock equilibrium, significantly impacts recovery mechanisms compared to non-equilibrated systems with similar initial oil saturations. Under identical injection conditions, the thermal maturation of the system lowers the interfacial tension and modifies the partitioning of the surface-active molecules within the crude oil, resulting in a rather distinct oil distribution compared to that of a non-heated oil. We have shown that in the thermally matured case, trapping is primarily governed by wettability, whereas in the non-heated case, the hydrocarbon is significantly less wetting and becomes capillary-trapped by topological constrictions. These findings highlight the critical role of geochemical equilibrium in hydrocarbon recovery.
Biodiesel production from waste cooking oil (WCO) is fundamentally constrained by complex, nonlinear interactions among transesterification parameters, which conventional linear optimization fails to capture. This study addresses this knowledge gap by developing an automated ensemble machine learning (AutoML) framework within the PyCaret environment. Utilizing 49 in-house experimental data points derived from a previously established two-step acid-base catalysis process, 25 machine learning (ML) regression algorithms were systematically benchmarked. The top 15 models were optimized via Optuna's Tree-structured Parzen Estimator (TPE) Bayesian method to construct bagging, boosting, and stacking ensemble strategies. The boosting ensemble model served as a process interaction mapping framework, achieving superior performance under 3-fold cross-validation (R2 = 0.987, MAE = 0.599, and RMSE = 0.836), significantly outperforming individual learners as well as bagging and stacking ensemble architectures. Shapley Additive exPlanations (SHAP) analysis served as a sophisticated tool for operational boundary identification, deconstructing model sensitivity regarding reaction temperature and time. Furthermore, external validation using 123 literature-derived points demonstrated significant predictive capability (R2 = 0.973), proving the boosting ensemble's promising generalizability across related WCO-based transesterification datasets. This framework establishes an operational space mapping protocol, identifying biodiesel yield and optimal transesterification conditions through data-driven modeling.
Vapor-liquid-solid (V-L-S) three-phase fluidized bed evaporators are widely used in industrial processes, where complex multiphase interactions often induce strong vibration behaviors that affect operational stability. In this work, a unified data-driven prediction framework is employed to evaluate the short-term predictability of vibration acceleration signals in a V-L-S fluidized bed evaporator. Experiments were conducted on a real system sampled at 20 kHz, covering heating steam pressures from 70 to 150 kPa and solid holdup ranging from 0% to 3.5%. The results show that vibration signal predictability is strongly dependent on operating conditions. Under epsilon= 1% and p = 110-130 kPa, relatively stable short-term temporal structures are observed, with MSE as low as 0.4933 and R2 reaching 0.2665, whereas increasing solid holdup (epsilon >= 2.5%) leads to a marked deterioration of predictability, with MSE rising to 1.1658 at epsilon= 3.5%.These findings indicate that operating regimes and noise intensity jointly govern the short-term predictability of vibration signals, providing practical guidance for vibration-based condition monitoring of V-L-S fluidized bed evaporators.
In powder forming processes, the powder filling stage is a critical step, where powder properties and die filling methods significantly influence the filling uniformity and density distribution. To investigate the effects of suction on powder filling characteristics, this study employed a suction-assisted linear die filling system to conduct comparative experiments with and without suction assistance, using two distinct powder types differentiated by particle shape and density. Experimental results demonstrated that for two iron powder types with distinct morphologies, the filling rate curves under non-suction conditions converged with each other when the filling velocity exceeded 200 mm/s, while the curves under suction-assisted filling remained consistently and significantly higher across the entire velocity range. The critical filling velocity during suction-assisted die filling showed significant enhancement compared to non-suction scenarios across all tested materials. Among five materials with varying densities, lower-density materials exhibited greater percentage increases in critical filling velocity. Specifically, polypropylene powder, glass powder, and white corundum powder exhibited 90-100% increase in critical filling velocity under suction-assisted conditions. Furthermore, the application of upper-side suction ports demonstrated superior filling efficiency compared to other suction port configurations.
Membrane fouling remains the major operational challenge across several industries, including water, food and bioprocess industries. Various cleaning approaches have been developed to partially restore membrane performance and maximize their long-term application. For instance, chemical cleaning experimental findings revealed restoration of permeability. However, it progressively degrades the polymeric membranes, thus altering structural integrity, mechanical strength, and solute rejection, consequently shortening the membrane lifespan. Therefore, this study reviewed physical, chemical, biological and advanced oxidation processes (AOPs) cleaning strategies and provided evidence on how cleaning agents (acids, bases, oxidants and surfactants) and cleaning conditions (exposure duration, concentrations, temperature, and pH) could alter the physicochemical properties of the membrane. Unlike previous reviews, this study proposes the decision-making framework that balances the trade-off between cleaning, risk of membrane damage and environmental footprint. Furthermore, the study provided standardized cleaning experiments including test metrics, FTIR, tensile, FRR and irreversible resistance analysis. Also, engineered membranes with self-cleaning/healing and chemical-resistance properties are reported. Critical considerations include comparative risk and benefit analysis for industrial-scale adoption. This study presents a framework towards priority research focusing on (i) robust and fouling-resistant membranes, (ii) lifecycle assessment of nanoparticle modified membranes and (iii) industrial adoption of AOP-based cleaning strategies. The review provides a guide for industrial operators and researchers towards increased membrane robustness while protecting the environment.
A two-dimensional mathematical model was developed to describe the pyrolysis of a cylindrical sawdust bed subjected to surface heating. The model incorporates mass and energy conservation with a sequential two-stage boundary condition for capturing the radial bed shrinkage effect. Initially, heat transfer occurs by conduction through direct contact with the reactor wall. As shrinkage progresses, the boundary transitions to a free-surface condition, enabling radial volatile release and shifting the dominant heat transfer mechanism to radiation. After validation, the model was applied to examine the influence of heating rate and particle size on pyrolysis behavior. The results indicate that heating rate has a limited effect on final char yield but strongly affects temperature evolution and internal pressure. Higher heating rates produce steeper temperature gradients, leading to a faster pressure rise and earlier pressure peak, while lower heating rates yield slower evolution and clearer separation of endothermic and exothermic stages. Particle size plays a significant role: larger particles increase bed permeability, facilitating gas release and reducing pressure buildup, whereas smaller particles generate higher internal pressures, extending tar residence time and enhancing secondary reactions that convert tar to gas and char, with minimal impact on final char yield. A key finding of the proposed model is the prediction of a transient pressure peak prior to major shrinkage, followed by a sharp pressure drop as radial escape paths form. Overall, the model reliably captures the thermochemical behavior of surface-heated cylindrical sawdust beds and provides valuable insights to support the design and optimization of industrial-scale pyrolysis systems.
Building sector is responsible for a huge amount of greenhouse gas emissions with most of them related to concrete industry, mostly accounting for Portland cement's production. The strategy was therefore to select geopolymers which are known to release up to 60% less CO2 than concrete during its production (Mushtaq, 2025) (Mushtaq, 2025). On the other hand, phase change materials (PCM) can store energy within their physical structure thanks to the latent heat they release during phase change. Phase change materials represent a wide family of components (Pielichowska, 2014) (Pielichowska, 2014), fatty acids were chosen as they are biosourced organic PCM. When incorporated in building walls, PCM can reduce daytime temperature peaks and increase nighttime temperature by up to 3 degrees C (Yu, 2022) (Yu, 2022). In this research, a biobased material with a solid/liquid phase change range of temperature going from 15 degrees C to 20 degrees C was used. Thus, enabling the application of thermal comfort inside houses. This approach enables contributing to ecological and financial reduction in the energy impact of homes. This study combining several concepts tend to reduce gas emissions and energy efficiency during and after construction. In this work, one of the main issues was the unwanted chemical reaction during the incorporation of the biobased PCM (octanoic acid was used as a model compound), inside standard alkaline-activated geopolymers due to the carboxylic acid groups reacting with the alkaline solution. Alternatively, acid-activated geopolymers, which represent a relatively new approach (Alvi, 2024) (Alvi, 2024), were chosen to overcome this issue. The molten PCM was then incorporated into the geopolymer paste before curing. Surface characterization techniques, including Fourier-Transform infrared spectroscopy and microscopy analyses (optical and electron microscopy) confirmed the incorporation of the PCM within the geopolymer in the form of droplets with an average diameter of similar to 6 & micro;m. To quantify the amount of biobased PCM truly incorporated inside the geopolymer, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were performed, confirming the full functionality of the 15 wt% content of PCM. Additionally, durability tests were conducted by DSC and demonstrated no degradation after 200 heat-cooling cycles. Finally, the macroscopic response of the composite was evaluated using T-history method, which demonstrated the influence of the PCM's melting behavior on temperature profile of the samples reacting to a temperature change. Notably, the composite highlighted a cooling delay of 13 min for a sample being only 25 mm thick and containing 15 wt%, indicating outstanding thermal regulation potential.
The discrete element method (DEM) is widely used to analyze wet powder behavior. Although several models have been proposed to describe the interparticle forces in wet powders, such as capillary and viscous forces, they do not capture the reduction in friction force caused by liquid addition. To address this limitation, we propose a new model, called the Mixed Contact Friction Force (MCFF) model, based on hydrodynamic lubrication in tribology. In hydrodynamic lubrication, the tangential motion of wet particles induces liquid entrainment into the particle contact surface. The resulting thickening of the liquid film at the contact surface hinders solid-solid contacts and reduces the friction force. The MCFF model accounts for this mixed contact state, in which direct solid-solid and liquid film-mediated contacts coexist. The performance of the model was evaluated by comparing experimental results with the MCFF model and conventional DEM simulation results of wet powders in dynamic and quasi-static systems. The MCFF model yielded results closer to the experimental results, particularly in a dynamic system with high liquid volume fraction, where conventional DEM significantly overestimated particle velocity. These results demonstrate the importance of hydrodynamic lubrication-induced friction reduction in the DEM simulations of wet powders.
Conventional heat transfer fluids such as water, ethylene glycol (EG), and mineral oils exhibit inherently low thermal conductivities (0.25-0.60 W.m-1.K-1) and poor thermal stability. This creates critical performance bottlenecks in next-generation energy systems, where over half of all primary energy is lost as waste. This review systematically examines MXene-based nanofluids as an advanced heat transfer fluids (AHTF) across five major industrial domains such as solar thermal systems, hybrid photovoltaic-thermal modules, battery cooling, carbon dioxide capture, and electromagnetic interference shielding (EMIS). Synthesis pathways include hydrofluoric acid etching (HF), mild lithium fluoride/hydrochloric acid etching (LiF/HCl), and molten salt etching. They are evaluated for their influence on surface termination, interlayer spacing, colloidal stability, and thermophysical performances. Titanium carbide-based MXene nanofluids have achieved thermal conductivity enhancements of 25-65% at below 0.1% weight loadings. These values are substantially outperforming alumina and copper oxidebased nanofluids at equivalent concentrations. In battery thermal management, thermal conductivity enhancement of 20-50%, reduce inter cell temperature gradients, supporting within 20-40 degrees C stable operating window. In carbon dioxide capture, MXene/amine hybrid nanofluids achieved a capture capacity of 2.6 mol/kg, with regeneration efficiencies of 92-98%, and energy penalties as low as 1.90 MJ/kg, which outperforming current conventional amine solvents. Electromagnetic interference shielding effectiveness reaches 30-68 dB with simultaneous thermal management, a multifunctionality unavailable in conventional metallic shields. This study integrates synthesis, structure, and performance relationships across all domains within a unified framework. Fluoride-free and molten salt routes are identified as the most scalable and oxide-resistant synthesis pathways. To the best of the author knowledge, this is the first review systematically analysis, surface termination-driven thermophysical performance, and multi-domain application benchmarking of MXene-based nanofluids within a single coherent framework covering solar thermal, battery cooling, CO2 capture, HVAC, and EMI shielding applications. Based on these findings, a roadmap is presented prioritizing green synthesis, hybrid nanocomposite design, and artificial intelligence-driven optimization for next-generation HTF development.
The design of absorption columns requires reliable hydraulic data as well as a good understanding of local hydrodynamic phenomena such as flow redistribution and wall flow. However, experimental validation with laboratory- or pilot-scale columns is costly and provides only limited insight into these local effects. In this study, a miniaturized measuring cell designed to reduce setup size and costs while minimizing wall effects is characterized based on the fluid dynamics of structured packings under air/water conditions. For this setup, pressure drop and liquid hold up are measured up to liquid loads of < 90 m & sup3;m(-)& sup2;h(-1) and compared with literature data as well as established correlations. In addition, the influence of different hydrodynamic phenomena, including initial liquid maldistribution, wall flow, and packing interfaces, is analyzed. The results show good agreement with characteristic hydraulic trends reported for conventional columns, while deviations occur mainly in the loading region. The study demonstrates that miniaturized measuring cells provide a suitable experimental platform for comparative hydraulic investigations and for analyzing local flow effects relevant to absorption column design.
Microbial fuel cells (MFCs) are a creative form of bio-electrochemical technology where the simultaneous treatment of wastewater and renewable power generation takes place, utilising biodegradable substrates (often waste material) as the source of electron donors, in contrast to traditional fuel cells that require refined energy sources. In this review, the fundamental design and working mechanism of MFC, along with the electrocatalysis happening in the cell, have been discussed. How electrocatalytic modifications of anode and cathode materials affect the power output of cells will be discussed with other factors as well. Furthermore, recent developments of MFCs and limitations that restrict their use at an industrial scale are also highlighted. This review correlates electrode biochemistry with electrocatalytic material design. It bridges the gap between microbial activity, surface chemistry of electrodes, and electrochemical output of MFCs. This review provides a consolidated framework that explains how electrocatalytic modifications impact both power generation and wastewater treatment, which will assist the scientific community in rationally designing high-performance MFCs and accelerating their transition from laboratory research to practical applications.
