Validated stoichiometric model for mineral phase quantification in commercial gypsums

Gypsum production is an energy-intensive process that contributes significantly to CO2 emissions, particularly in regions that rely on biomass or fossil fuels for calcination. Improving mineralogical control is essential for reducing energy waste and enhancing the environmental performance of the gypsum industry. This study proposes and validates a stoichiometric model implemented in Python to estimate the proportions of dihydrate, hemihydrate, and anhydrite in commercial gypsums based on chemical composition and hydration water. The model was applied to natural gypsum ore and commercial casting and plastering gypsums from the Araripe Gypsum Cluster, Brazil’s main production region. Differential thermogravimetric analysis (DTG) was used for validation, and strong agreement was observed between experimental and simulated results, with maximum deviations of approximately 2.11%. The findings demonstrate that accurate mineral phase estimation can support more precise control of calcination temperature and residence time. Consequently, the proposed model has the potential to contribute to reductions in specific fuel consumption, CO2 emissions, and mineral waste generation when incorporated into industrial process-control strategies. The proposed approach provides a low-cost and scalable tool for environmental monitoring and decision support in gypsum production. The novelty of this work, in contrast to conventional analytical approaches, lies in the development and experimental validation of a stoichiometric computational model capable of estimating gypsum mineral phases from chemical composition and hydration water content. The Python implementation constitutes an accessible computational platform that enables rapid application of the proposed model in research and industrial environments.