Search Results (90)

Adsorption is a widely employed separation technique valued for its low energy requirements and its applicability to diverse processes, including air separation, water purification, chromatographic analysis, wastewater treatment, and protein immobilization on biomaterials. Industrial adsorption–desorption cycles are typically carried out in parallel packed-bed columns. The accurate design and optimization of these columns rely on experimental breakthrough curves. These curves provide essential information on adsorption capacity and mass-transfer kinetics. In this study, five modelling approaches, based on instantaneous adsorption, non-instantaneous adsorption, Fickian diffusion, and anomalous diffusion, were evaluated for their ability to predict breakthrough behaviour during the adsorption of butanol on activated carbon. The first four models were formulated using conventional partial differential equations of varying complexity, whereas the fifth model incorporated anomalous diffusion through fractional-order differential equations. The results indicate that model performance depended strongly on the adsorbent type: certain models provided superior predictions for one activated carbon, while different models were more accurate for the other. Full article

This study was aimed at producing galactooligosaccharides (GOS) from Porungo cheese whey in immobilized enzyme bioreactors. The β-galactosidase was produced, concentrated, and immobilized on chitosan–genipin supports. Initially, GOS production was conducted in conical flasks, investigating three different variables: enzyme concentration (50–150 U/mL), Porungo cheese whey concentration (200–400 g/L), and temperature (37–43 °C). The highest GOS yields (15.24%) occurred under intermediate process conditions (100 U/mL, 300 g/L, 40 °C), reaching a GOS concentration of 27.04 g/L. These conditions were then used in a packed-bed column bioreactor operated in batch mode, achieving yields of 19.72%. Repeated batches were carried out, and the system was stable until the fifth cycle, with enzyme activity remaining at 83.56% of the initial level. Continuous bioreactors were conducted, varying feed flow rates (1–3 mL/h), with the highest yields and lactose conversion occurring for the longest residence time (24.63% and 68.38%), respectively, with high GOS concentration (44.14 g/L). Microorganisms isolated from Porungo cheese showed the ability to metabolize the GOS produced, demonstrating its prebiotic potential. This work can contribute to optimizing the production of GOS, an important product for pharmaceuticals and food industries. Full article

l-menthol is one of the most popular flavors in the world. The separation of menthol enantiomers is crucial because of the unpleasant taste of d-menthol. This work presents the chiral separation of racemic menthol by simulated moving bed chromatography for the first time. Six preparative columns packed with amylose 3,5-dimethylphenylcarbamate coated on silica gel were used for separation, and a mixture of n-hexane/isopropanol was selected as the mobile phase. The hydrodynamic properties of the SMB columns were studied to minimize the packing asymmetry in the SMB experiment. The binary adsorption isotherm of menthol enantiomers was measured by the adsorption–desorption method. Fixed-bed batch chromatography was carried out to evaluate the adsorption kinetic behavior. Mathematical models, considering the mass transfer resistance and axial dispersion, were applied to describe the dynamics of the chromatographic separation process. The SMB process for chiral separation of racemic menthol was designed by evaluating the separation region using simulations. Reasonable agreements were achieved between the predicted results and the experimental results. Purities for both the extract and raffinate were above 99.0%, and a productivity of 0.267 g_racemate/(L_CSP∙min) and a solvent consumption of 0.431 L/g_racemate were achieved. Full article

This work presents a comprehensive analysis of reactor modeling studies for the methanation of CO_x, with the aim of identifying trends, evaluating modeling strategies, and suggesting a generalized modeling framework. The analysis spans a wide range of configurations, including packed/fixed-bed reactors (immobilized catalyst pellets/particles), fluidized-bed reactors, and structured catalyst reactors, as well as membrane and slurry/bubble-column configurations when applicable. This highlights the diversity of modeling approaches used, ranging from simple 1D pseudo-homogeneous models to complex 2D heterogeneous simulations. Emphasis is placed on the governing assumptions, dimensional formulations, transport phenomena, and kinetic models employed across studies. By systematically comparing these models, this work identifies the most critical modeling assumptions and parameters that govern the prediction reliability of reactor performance (e.g., conversion and temperature profiles) and inform reactor design. The proposed reactor model integrates insights from the literature, balancing model fidelity and computational feasibility, and serves as a foundational tool for future modeling efforts and industrial applications. This work contributes to the field by offering a unified perspective that links model complexity to physical realism, providing valuable guidance in the development of predictive tools for CO_x methanation systems. Full article

This technical note presents a quantitative image-based framework for evaluating the packing and compressibility of granular soils, specifically applied to building foundation design in civil infrastructure projects. The Sedimaging system replicates hydraulic sedimentation in a controlled column, equipped with a high-resolution camera, to visualize particle orientation after deposition. Grayscale images of the settled bed are analyzed using Haar Wavelet Transform (HWT) decomposition to quantify directional intensity gradients. A new descriptor, termed the sediment index (B), is defined as the ratio of vertical to horizontal wavelet energy at the dominant scale, representing the preferential alignment and anisotropy of particles during sedimentation. Experimental investigations were conducted on fifteen granular materials that include natural sands, tailings, glass beads and rice grains with different shapes. The results demonstrate strong correlations between B and both microscopic shape ratios (d₁/d₂ and d₁/d₃) and macroscopic properties. Linear relationships predict the limiting void ratios (e_max, e_min) with mean absolute differences of 0.04 and 0.03, respectively. A power-law function relates B to the compression index (C_c) with an average deviation of 0.02. These findings confirm that the sediment index effectively captures the morphological influence of particle shape on soil packing and compressibility. Compared with conventional physical testing, the Sedimaging-based approach offers a rapid, non-destructive, and high-throughput solution for estimating soil packing and compressibility of cohesionless, sand-sized granular soils directly from post-settlement imagery, making it particularly valuable for preliminary site assessments, geotechnical screening, and intelligent monitoring of granular materials in building foundation design and other infrastructure applications, such as earth–rock dams. Full article

This research seeks to model a packed bed with cocoa waste on an industrial scale using computer software and parametric evaluation to remove Pb(II) in solution. To achieve this, they developed multiple simulations of a packed bed using Aspen Adsorption software using different configurations of inlet flow rate and bed height through a parametric sensitivity study to evaluate the system performance using Langmuir as an isothermal model and the Linear Driving Force (LDF) model as a kinetic model. It was found that the efficiency of the adsorption process reached 99.7% for Pb(II) removal. On the other hand, the best simulation conditions considering efficiency, breakthrough time and saturation were a flow rate of 200 m³/day, a head of 5 m and an initial concentration of 1000 mg/L. This research exhibits a novel engineering perspective to anticipate the potential performance of packed bed with agro-industrial biomasses in the multiscale removal of Pb(II). Full article

The presence of paracetamol and ciprofloxacin in aquatic ecosystems is a cause for great concern due to their harmful effects on human health. The objectives of this investigation are to simulate an industrial-scale adsorption bed for the competitive removal of these pharmaceutical metabolites from effluents using banana-based activated carbon as the adsorbent. Aspen Adsorption simulation software (v.1) was used to model an industrial-scale packed-bed column under different conditions. Freundlich and Langmuir isothermal models were used in combination with the linear driving force (LDF) kinetic formulation. Adsorption efficiencies of 89.57% for paracetamol and 89.57% for ciprofloxacin were achieved using the Freundlich-LDF model, while the Langmuir-LDF model presented efficiencies of 89.60% for paracetamol and 89.59% for ciprofloxacin. This study used machine learning algorithms, combined with analyses of multiple statistical indicators (R², RMSE, and MAE), to evaluate model performance. Coefficient of determination (R²) values of up to 0.99 were observed in validation and testing. The application of these mathematical models yielded high removal efficiencies, demonstrating the potential of this approach for drug-contaminated effluent remediation and for forecasting the performance of packed columns at scaled-up levels. Full article

Rotating packed beds (RPBs) have recently attracted significant attention as a promising approach to intensify the performance of traditional packed columns. Although many lab-scale experimental and numerical studies on RPBs are available in the literature, there is a scarcity of operational data for large-scale RPBs. In this research, high gas flow rates in large-scale RPBs are investigated using computational fluid dynamics (CFD) simulation to predict the dry pressure drop in a rotating bed. A 2D geometry with periodic boundary conditions was applied to simulate the turbulent gas flow in a rotating packed bed. The simulation results provide valuable insights into the gas flow dynamics within rotating beds, highlighting the pressure and velocity variations that occur at high rotational speeds. A semi-empirical correlation successfully replicated the results obtained in this study and can be utilized to predict the pressure drop in large-scale RPBs under operating conditions similar to those studied in this research. Full article

In 2024, the global average temperature reached 1.55 °C above the pre-industrial level for the first time. However, we could still keep the long-term global average temperature below 2 °C if all possible measures are taken to mitigate greenhouse gases. It is widely accepted that methane (CH₄) mitigation can slow global warming in the near term. Among all approaches toward this goal, the utilization of aerobic methanotrophs, which are natural catalysts for the conversion of CH₄, emerges as a promising solution. Previously, we identified a candidate for CH₄ mitigation, Methylotuvimicrobium buryatense 5GB1C, which exhibits a greater growth rate and CH₄ consumption rate than other known methanotrophs at 500 ppm CH₄. In this study, we address aspects of the practical applications of this methanotroph for CH₄ mitigation. We first examined temperature and medium conditions to optimize M. buryatense 5GB1C growth at 500 ppm CH₄. The results show that M. buryatense 5GB1C has a broad optimal temperature range for growth at 500 ppm, from 15 °C to 30 °C, and that its growth rate is consistently improved by 20–30% in 10-fold-diluted medium. Next, to demonstrate the feasibility of CH₄ removal at low concentrations by this methanotroph, we applied it in a laboratory-scale packed-bed column reactor for the treatment of 500 ppm CH₄ and tested different packing materials. The column reactor experiments revealed a maximum elimination capacity of 2.1 g CH₄ m⁻³ h⁻¹ with 2 mm cellulose beads as the packing material. These results demonstrate that with further technological innovation, this methanotroph has the potential for real-world methane mitigation. Full article

Water hyacinth is an invasive weed that can valorize through the production of hydrolytic enzymes via solid-state culture. This study explores the application of Trichoderma harzianum in producing xylanases and endoglucanases on water hyacinth beds. Laboratory-scale packed-bed column bioreactors (PBCBs) with a capacity of 8 grams of dry mass (g_dm) were used to evaluate the effects of temperature (28–36 °C) and initial moisture content (65–80%) on microbial growth and enzyme production. High yields of biomass and enzymes were produced at 30 °C. Moreover, xylanase activity was enhanced in cultures with a moisture content of 65% (~71.24 U/g_dm), and endoglucanase activity at 75–80% moisture (~20.13 U/g_dm). The operational conditions identified for xylanase production were applied to 6 L bench-scale cross-flow internally stirred bioreactors, packed to 40% capacity with 450 g_dm. Two stirring regimes were tested: intermittent and continuous. The results showed that continuous stirring promotes both microbial growth and xylanase activity. In fact, xylanase activity in continuous stirring conditions was comparable to that achieved in PBCBs. Consequently, continuous stirring enables a 56-fold increase in bioreactor capacity without compromising xylanase production. The approaches developed in this study can support the design of large-scale bioprocesses for the valorization of water hyacinth. Full article

Methylene blue and safranin are dyes that may have harmful effects on both aquatic ecosystems and human health. This research aims to simulate an industrial-scale operational adsorption column for competitively removing these dyes from wastewater, employing Cedrela odorata L. as a bioadsorbent material. Aspen Adsorption (v.1) software simulated an industrial-scale packed-bed adsorption column under various configurations. Moreover, machine learning algorithms were applied to predict the results generated by Aspen, representing an advancement in the development of new strategies in this field. The kinetic model employed was the Linear Driving Force (LDF) model. Adsorption efficiencies of 96.1% were achieved for both methylene blue and safranin using the Langmuir–LDF model. The Freundlich–LDF model showed efficiencies of 94.8% for methylene blue and 96% for safranin. Meanwhile, the Langmuir–Freundlich–LDF model achieved up to 96.1% for methylene blue and 94.8% for safranin. This study demonstrated the feasibility of simulating the competitive adsorption of dyes in solution at an industrial scale using Cedrela odorata L. as a bioadsorbent. The application of LDF kinetic models and adsorption isotherms (Langmuir, Freundlich, and Langmuir–Freundlich) resulted in high adsorption efficiencies, highlighting the potential of this approach for the remediation of dye-contaminated effluents as a viable method for predicting the performance of full-scale packed columns. Machine learning algorithms were implemented in this research, obtaining R² higher than 0.996 for validation and testing stages for the responses of the model. Full article

Numerous studies have been conducted employing various techniques to remove pollutants from water bodies. Among these techniques, adsorption a surface phenomenon that utilises adsorbents derived from agricultural residues has shown considerable potential for the removal of contaminants such as heavy metals. However, most of these investigations have been carried out at the laboratory scale, with limited efforts directed towards predicting the performance of these systems at an industrial level. Accordingly, the present study aims to model a packed bed column at industrial scale for the removal of Pb(II) and Ni(II) ions from aqueous solutions, employing biomass derived from oil palm residues as the adsorbent material. To achieve this, Aspen Adsorption was used as a modelling and simulation tool to evaluate the impact of bed height, inlet flow rate, and initial concentration through a parametric assessment. This evaluation incorporated the Freundlich, Langmuir, and Langmuir–Freundlich isotherm models in conjunction with the Linear Driving Force (LDF) kinetic model. The results indicated that the optimal operating parameters included a column height of 5 m, a flow rate of 250 m³/day, and an initial metal concentration of 5000 mg/L. Moreover, all models demonstrated removal efficiencies of up to 94.6% for both Pb(II) and Ni(II). An increase in bed height resulted in longer breakthrough and saturation times but led to a reduction in adsorption efficiency. Conversely, higher flow rates shortened these times yet enhanced efficiency. These findings underscore the potential of computational modelling tools as predictive instruments for evaluating the performance of adsorption systems at an industrial scale. Full article

The objective of this study was to model an adsorption column bed with biomass residues using computational software to remove Pb (II) at the industrial level and analyse the effects of parametric variation. For this purpose, several simulations of the adsorption column were performed using Aspen Adsorption software, evaluating the effects of varied height, inlet flow rate, and initial concentration on the adsorption process performance. The Langmuir II and Freundlich models are established as isotherm models, and the linear driving force (LDF) model is established as the kinetic model. The findings showed that Freundlich–LDF obtained efficiencies of up to 99.9% and Langmuir II–LDF efficiencies of up to 99.7%. The optimal simulation conditions were a column height of 4 m, an initial Pb (II) concentration of 3000 mg/L, and an inlet flow rate of 250 m³/d. This study presents a novel engineering approach to predict the potential performance of columns packed with organic waste-derived biomasses in multi-scale Pb (II) removal using computer-aided engineering tools. Full article

In this study, a novel adsorbent was developed by synthesizing Zn-Al layered double hydroxide (LDH) incorporated with silver nanoparticles (Ag-NPs), and its effectiveness in bromide removal from aqueous solutions was systematically evaluated. The X-ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FTIR) analyses confirmed the integration of Ag-NPs within the LDH, ensuring uniform chemical composition and structural integrity. A series of controlled batch trials, each varying a single parameter (adsorbent dose, contact time, or temperature) confirmed that over 95% of bromide (initially 5320 μg/L) was removed under optimized conditions. LDH/Ag-NPs exhibited superior performance, with kinetics well described by a second-order reaction model. Thermodynamic analysis confirmed the spontaneous and exothermic nature of bromide adsorption, with ΔG° values ranging from −2.03 to −0.73 kJ/mol as the temperature increased from 22 °C to 52 °C. In continuous-flow experiments, packed-bed column tests illustrated that LDH/Ag-NPs maintained more effective bromide removal than LDH alone over extended periods. Conductivity measurements further supported this enhancement, with LDH/Ag-NPs reducing final conductivity to 139 µS/cm, compared to 212 µS/cm for LDH. Furthermore, this study revealed the notable antimicrobial activity of LDH/Ag-NPs, as evidenced by a significant reduction in bacterial growth compared to LDH alone, highlighting its dual functionality for both bromide adsorption and water disinfection. Overall, the incorporation of Ag-NPs into LDH offers a promising strategy for developing multifunctional and sustainable water treatment systems. Full article

Cuboid packed-bed devices developed for chromatographic separation typically have shorter bed heights and larger cross-sectional areas than their equivalent cylindrical columns. These devices can be operated at low back pressures and give comparable or even better resolution than their equivalent columns. However, the bed height of a cuboid packed-bed device could potentially affect its separation performance. To examine this, three devices having 5, 10 and 19.5 mm bed heights were fabricated and packed with the same resin media. A mathematical model was first developed to predict the effect of bed height on the performance of these cuboid devices. This prediction was performed based on the residence time heterogeneity (RTH) in these devices, which increased slightly as the bed height was decreased. However, this was not likely to affect the separation efficiency very significantly. The relative performances of these three cuboid devices were then compared based on the resolution obtained during ion-exchange chromatography of multi-protein mixtures. As predicted by the mathematical model, the loss in resolution due to the decrease in bed height was relatively small (0.83 to 0.73 in binary protein separation). Also, this loss could easily be compensated for by slightly lowering the flow rate or by extending the elution gradient. The results discussed in this paper demonstrate that with cuboid packed-bed devices, the dimensions could be altered in a reasonably flexible manner without adversely affecting separation performance. Such flexibility is advantageous from the point of view of process design and optimization, which is critically important for developing large-scale processes for the purification of biologics. Full article