Login / Signup

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

Yunshan WangLufang ShiHouli LiYixiao WangZhiying WangXuebin AnMingzhu TangGang YangJun HeJing HuYong Sun
Published in: ACS omega (2022)
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 H 3 PO 4 and CaCl 2 (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 (KAlSi 3 O 8 ) and quartz (SiO 2 ) 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, X 1 , 850 °C; reaction duration, X 2 , 40 min; and impregnate ratio (PS over CaCl 2 ), X 3 , 2.5] with approximately ±10% uncertainties. Thermodynamic analysis indicates that the introduction of steam to PS + CaCl 2 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 SiO 2 in the PS matrix plays critical roles in decomposing PS + CaCl 2 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.
Keyphrases
  • machine learning
  • high temperature
  • heavy metals
  • room temperature
  • risk assessment
  • human health
  • magnetic nanoparticles
  • deep learning
  • virtual reality