A Review of Fluoride Removal from Phosphorous Gypsum: A Quantitative Analysis via a Machine Learning Approach.

This review comprehensively explores fluoride removal from phosphogypsum, focusing on its composition, fluorine-containing compounds, characterization methods, and defluorination techniques. It initially outlines the elemental composition of phosphogypsum prevalent in major production regions and infers the presence of fluorine compounds based on these constituents. The study highlights X-ray photoelectron spectroscopy (XPS) as a pivotal method for characterizing fluorine compounds, emphasizing its capability to determine precise binding energies essential for identifying various fluorine species. Additionally, the first-principle density functional theory (DFT) is employed to estimate binding energies of different fluorine-containing compounds. Significant correlations are observed between the total atomic energy of binary fluorides (e.g., of alkali metals, earth metals, and boron group metals) and XPS binding energies. However, for complex compounds like calcium fluorophosphate, correlations with the calculated average atomic total energy are less direct. The review categorizes defluorination methods applied to phosphogypsum as physical, chemical, thermal, and thermal-combined processes, respectively. It introduces neural network machine learning (ML) technology to quantitatively analyze and optimize reported defluorination strategies. Simulation results indicate potential optimizations based on quantitative analyses of process conditions reported in the literature. This review provides a systematic approach to understanding the phosphogypsum composition, fluorine speciation, analytical methodologies, and effective defluorination strategies. The attempts of adopting DFT simulation and quantitative analysis using ML in optimization underscore its potential and feasibility in advancing the industrial phosphogypsum defluorination process.

Clean Process to Utilize the Potassium-Containing Phosphorous Rock with Simultaneous HCl and KCl Production via the Steam-Mediated Reactions.

In this paper, a clean process based on the steam-mediated reactions for simultaneous HCl and KCl production using the potassium (K)-containing phosphorous rock as a precursor is proposed. Through hydrochloric acid (HCl) leaching, not only the generation of H3PO4 and CaCl2 (via further precipitation) were realized but also the acid-insoluble residue [phosphorous-rock slag (PS)] rich in elements, that is, K, Al, Si, and so on, in the form of microcline (KAlSi3O8) and quartz (SiO2) was obtained and became readily available for further HCl and KCl generation. Over 95% of the elements, that is, K, Al, and Si, come into the final products, and the overall acid consumption (based on HCl) is significantly reduced (90%) due to recovery of acids. The impacts of the key operational parameters such as temperature, duration, and reagent impregnate ratio were rigorously analyzed via a supervised machine learning approach, and the optimal conditions were determined [reaction temperature, X1, 850 °C; reaction duration, X2, 40 min; and impregnate ratio (PS over CaCl2), X3, 2.5] with approximately ±10% uncertainties. Thermodynamic analysis indicates that the introduction of steam to PS + CaCl2 not only enhances the chemical potential for the formation of HCl and KCl but also provides the transport advantage in continuously removing the generated products, that is, HCl and KCl, out of the system. Molecular simulation indicates that the presence of both steam and SiO2 in the PS matrix plays critical roles in decomposing PS + CaCl2 at high temperature. The shrinking core model shows that both the intrinsic kinetics and transport are influential with the activation energy being around 14.63 kJ/mol. The potential reaction pathway is postulated.