Applicability and Limitations of a Capillary-LC Column-Switching System Using Hybrid Graphene-Based Stationary Phases

Graphene oxide sheets fixed over silica particles (SiGO) and their modification functionalized with C18 and endcapped (SiGO-C18ec) have been reported as sorbents for extraction and analytical columns in LC. In this study, a SiGO column was selected as the extraction column and a SiGO-C18ec as the analytical column to study the applicability and limitations of a column-switching system composed exclusively of columns packed with graphene-based sorbents. Pyriproxyfen and abamectin B1a were selected as the analytes, and orange-flavored carbonated soft drinks as the matrix. The proposed system could be successfully applied to the pyriproxyfen analysis in a concentration range between 0.5 to 25 µg/mL presenting a linearity of R² = 0.9931 and an intra-day and inter-day accuracy of 82.2-111.4% (RSD < 13.3%) and 95.5-99.8% (RSD < 12.7%), respectively. Furthermore, the matrix composition affected the area observed for the pyriproxyfen: the higher the concentration of orange juice in the soft drink, the higher the pyriproxyfen the signal observed. Additionally, the SiGO extraction column presented a life use of 120 injections for this matrix. In contrast, the proposed system could not apply to the analysis of abamectin B1a, and the SiGO-C18ec analytical column presented significant tailing compared to a similar approach with a C18 analytical column.

Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns

In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events.

Enhanced Fluorescence in a Scattering Medium

Often only small amounts of sample are available for spectroscopic analytical determinations. This work investigates the enhancement of signal in columns packed with silica particles. We propose that silica particles cause the light to scatter through the column, effectively increasing optical path length. Packed columns are shown to be effective with fluorescence spectroscopy, but results were inconclusive with absorbance spectroscopy.

A novel sorbent Ulva lactuca-derived biochar for remediation of Remazol Brilliant Orange 3R in packed column

An up-flow fixed column study was conducted to remediate Remazol Brilliant Orange 3R (RBO3R) from contaminated solutions using biochar derived from Ulva lactuca biomass. The influences of column parameters on dye sorption were studied in detail, which include initial RBO3R concentration, bed depth, and flow rate. Optimization experiments indicated that maximum RBO3R column uptake of 0.114 mmol/g was observed at 0.25 mmol/L (initial RBO3R concentration), 0.3 L/hr (flow rate), and 25 cm (U. lactuca bed depth). Modeling of column sorption data was performed using the Yoon-Nelson, modified dose-response and Thomas models. The spent biochar was desorbed and rejuvenated using 0.01 M NaOH. The elutant (0.01 M NaOH) exhibited 99.7% efficiency, and the process was completed in 115 min with high overall concentration factor of 8.4. PRACTITIONER POINTS: This study explores the impact of column parameters on the dye removal potential of U. lactuca-derived biochar. At optimized condition, the biochar bed exhibited highest Remazol Brilliant Orange 3R uptake capacity of 0.114 mmol/g. The regeneration and desorption of U. lactuca-derived biochar bed was possible with NaOH (0.01 M) as elutant.

Green alga-mediated treatment process for removal of zinc from synthetic solution and industrial effluent

The present work explored biosorption of Zn(II) ions from aqueous and zinc-bearing factory effluent using marine seaweed Ulva lactuca. The batch pH edge experiments using aqueous zinc solution indicated that Zn(II) uptake by U. lactuca was found to be maximum at pH 4.5 and the batch isotherm trials performed at pH 4.5 resulted in maximum uptake capacity of 128.0 mg Zn(II)/g. With 0.1 M CaCl₂ (pH 3.5, HCl) as elutant, the elution of Zn(II) ions from Zn(II)-laden U. lactuca biosorbent was effective with possible regeneration and reuse for three cycles. The zinc industrial effluent was found to comprise of 87.8 mg/L of zinc ions along with excess co-ions and high total dissolved solids (838.1 mg/L). Owing to this, Zn(II) uptake from electroplating effluent by U. lactuca was suppressed due to competition from other ions. Continuous-flow sorption trials were conducted at flow rate of 5 mL/min in an up-flow fixed column. The existence of surplus competing ions in zinc wastewater influenced the Zn(II) biosorption by U. lactuca. U. lactuca-loaded packed column exhibited uptakes of 78.3 and 70.8 mg Zn(II)/g for aqueous solution and effluent, respectively. The results of three continuous sorption-desorption cycles demonstrated that reuse of U. lactuca biosorbent in remediation of zinc-containing wastewaters was practical and economical.

Mathematical modelling of Pb2+, Cu2+, Ni2+, Zn2+, Cr6+ and Cd2+ ions adsorption from a synthetic acid mine drainage onto chitosan derivative in a packed bed column

Studies on the adsorption of toxic metal ions in batch mode using modified chitosan beads have been reported by several authors in literature; for large-scale operations, packed bed column studies are performed to generate data that are directly applicable in real wastewater treatment. In this work, the application potential of chitosan derivative for the uptake of Pb²⁺, Cu²⁺, Ni²⁺, Zn²⁺, Cr⁶⁺, and Cd²⁺ ions from aqueous solution in a packed bed adsorption column was investigated. On this note, the adsorbent was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Scanning electron microscope (SEM); the effect of breakthrough parameters such as influent concentration, bed height, and feed flow rate was determined. The experimental data were fitted to mathematical column models, namely Bed Depth Service Time (BDST), Yoon-Nelson and Thomas model. The removal efficiency, breakthrough and exhaustion time increased with an increase in bed height but decreased with an increase in the flow rate and influent concentration. The mathematical models applied in this study were successful in describing experimental data. Desorption studies were performed with no loss in the mass of the beads, and the breakthrough and exhaustion time were found to be the same on reusing the beads. Abbreviations: Ch-b: chitosan coated bentonite; GXXB: grafted cross-linked chitosan beads; MMBB: multi metal binding biosorbent; PFOLG: panosized ferric oxide loaded glycidyl; MPSD: Marquardt's percent standard deviation.

Simulation of hydrogen sulphide absorption in alkaline solution using a packed column

In this work, a simulation tool was developed for hydrogen sulphide (H₂S) removal in an alkaline solution in packed columns working at countercurrent. Modelling takes into account the mass-transfer enhancement due to the reversible reactions between H₂S and the alkaline species (CO(²⁻)(3), HCO⁻(3), and HO⁻) in the liquid film. Many parameters can be controlled by the user such as the gas and liquid inlet H₂S concentrations, the gas and liquid flow rates, the scrubbing liquid pH, the desired H₂S removal efficiency, the temperature, the alkalinity, etc. Since the influence of the hydrodynamic and mass-transfer performances in a packed column is well known, the numerical resolutions performed were dedicated to the study of the influence of the chemical conditions (through the pH and the alkalinity), the temperature and the liquid-to-gas mass flow rate ratio (L/G). A packed column of 3 m equipped with a given random packing material working at countercurrent and steady state has been modelled. The results show that the H₂S removal efficiency increases with the L/G, the pH, the alkalinity and more surprisingly with the temperature. Alkalinity has a very significant effect on the removal efficiency through the mass-transfer enhancement and buffering effect, which limits pH decreasing due to H₂S absorption. This numerical resolution provides a tool for designers and researchers involved in H₂S treatment to understand deeper the process and optimize their processes.

Effects of distillation system and yeast strain on the aroma profile of Albariño (Vitis vinifera L.) grape pomace spirits

Orujo is a traditional alcoholic beverage produced in Galicia (northwest Spain) from distillation of grape pomace, a byproduct of the winemaking industry. In this study, the effect of the distillation system (copper charentais alembic versus packed column) and the yeast strain (native yeast L1 versus commercial yeast L2) on the chemical and sensory characteristics of orujo obtained from Albariño (Vitis vinifera L.) grape pomace has been analyzed. Principal component analysis, with two components explaining 74% of the variance, is able to clearly differentiate the distillates according to distillation system and yeast strain. Principal component 1, mainly defined by C6-C12 esters, isoamyl octanoate, and methanol, differentiates L1 from L2 distillates. In turn, principal component 2, mainly defined by linear alcohols, linalool, and 1-hexenol, differentiates alembic from packed column distillates. In addition, an aroma descriptive test reveals that the distillate obtained with a packed column from a pomace fermented with L1 presented the highest positive general impression, which is associated with the highest fruity and smallest solvent aroma scores. Moreover, chemical analysis shows that use of a packed column increases average ethanol recovery by 12%, increases the concentration of C6-C12 esters by 25%, and reduces the concentration of higher alcohols by 21%. In turn, L2 yeast obtained lower scores in the alembic distillates aroma profile. In addition, with L1, 9% higher ethanol yields were achieved, and L2 distillates contained 34%-40% more methanol than L1 distillates.