Fixed-Bed Adsorption: Comparisons of Virgin and Zirconium Oxide-Coated Scoria for the Removal of Fluoride from Water

Fixed-bed column adsorption modeling using Zr biocomposites for fluoride removal

This research involved conducting continuous adsorption experiments to assess fluoride elimination from drinking water achieved by utilizing biocomposites created from the peels of oranges and apples, which were impregnated with zirconium (Zr), to form BOP-Zr and BAP-Zr, respectively. The findings from the experimental data indicate that BOP-Zr and BAP-Zr are effective biosorbents with a solid ability to remove fluoride selectively. Additionally, these biosorbents were found to be stable, as they do not release Zr into the treated water. Notably, these environmentally friendly biosorbents are derived from renewable sources and enhance the value of waste materials. The study employed various empirical models, including Bohart-Adamas, Thomas, Yoon-Nelson, BDST, Clark, Yan, and Woolborska, to elucidate the mechanisms and crucial parameters involved in fluoride adsorption within packed bed columns. The Yan model demonstrated the highest correlation among these models, indicating a chemical adsorption process with kinetics following a pseudo-second-order pattern. BOP-Zr and BAP-Zr exhibited a maximum adsorption capacity of 59.3 and 47.5 mg/g, respectively, under a flow rate of 4 mL/min and an inlet fluoride concentration of 25 mg/L. The analysis of mass transfer coefficients revealed that the primary step governing the adsorption procedure was diffusion through pores. Consequently, the study conclusively establishes that BOP-Zr and BAP-Zr biocomposites, originating from lignocellulosic biomass remains, present a practical and competitive choice for eliminating fluoride from water. These materials surpass waste materials in performance and rival more expensive options in efficiency and performance.

Understanding fluoride adsorption from groundwater by alumina modified with alum using PHREEQC surface complexation model

Fluoride is recognized as a vital ion for human and animal growth because of the critical role it plays in preventing skeletal and dental problems. However, when it is ingested at a higher concentration it can cause demineralization of teeth and bones resulting in fluorosis, therefore, the production of high-adsorptive capacity material which is also cost-effective is necessary for the treatment of fluorides. In this study, aluminium foil is valorised into alumina nanoparticles. The as-prepared alumina was modified with alum in two different ratios of 1:0.5 and 1:1 (alumina to alum w/w%) and later used as adsorbents for the removal of fluoride from groundwater. The adsorbents were characterized by Fourier transform infrared spectroscopy, point of zero charge and X-ray diffraction. Different factors that influence the removal efficiency of fluorides such as pH, initial concentrations, contact time and adsorbent dosage were studied and optimized using a simulated fluoride solution. The optimum conditions obtained were used to test real groundwater. The static experiment conditions were used to calibrate a PHREEQC geochemical model which was later used to simulate the fluoride sorption onto the modified alumina at different conditions. PHREEQC was also coupled with parameter estimation software to determine equilibrium constants for the surface reactions between the fluoride species and the adsorbent in a way that the simulations accurately reflect the outcomes of laboratory experiments. Isotherm studies were carried out on the adsorbents. Both Langmuir and Freundlich's non-linear models fitted well for the equilibrium data. However, with a higher coefficient of regression and low chi-square test values, the adsorption process was more of chemisorption on a monolayer surface. Kinetic studies were also carried out by using the non-linear equations from the pseudo-first-order and pseudo-second-order models. The pseudo-second-order model fitted well for the equilibrium data. The mechanism for the fluoride ion adsorption was also studied by the intraparticle (IP) diffusion model and was found that IP was not the rate-determining factor, and therefore the most plausible mechanism for the sorption process was ion exchange or attraction of fluoride ions to the sorbent surface. The findings obtained from this research show that readily available aluminium waste could be valorised into a useful product that could be employed in the removal of fluoride from water samples, including groundwater, that may contain too much fluoride and pose a risk to the general public's health.

Application of artificial neural network for prediction of fluoride removal efficiency using neutralized activated red mud from aqueous medium in a continuous fixed bed column

The present research work approaches the removal of fluoride from aqueous medium using neutralized activated red mud (NARM) in a continuous fixed bed column. Artificial neural network (ANN) technique was applied effectively for optimization of the model for the practicability of the removal process. The consequences of various experimental variables, like bed length, adsorbate concentration, experimental time, and adsorbate solution flow rate are studied to know the breakthrough point and saturation times. The highest removal potentiality of NARM was considered to be 3.815 mg g^-1 of F^- in the bed height of 15 cm, starting concentration 1 ppm, susceptible time 120 min, adsorbate solution flow rate 0.5 mL min^-1, and constant room temperature, respectively. Bohart-Adams and Thomas models were considered to describe the fixed bed column effect to the bed height and adsorbate concentrations. The experimental data were applied to a back propagation (BP) learning algorithm programme with a four-seven-one architecture model. The artificial neural network model was considered to be functioning correctly as absolute relative percentage error throughout the learning period. Differentiation between the predicted outcomes from ANN model and actual results from experimental analysis affords a high degree of correlation (R² = 0.998) stipulating that the model was able to predict the adsorption efficiency. Experimented adsorbent materials were characterized using different instrumental analysis that is scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD).