Modeling Solubility of Anhydrite and Gypsum in Aqueous Solutions: Implications for Swelling of Clay-Sulfate Rocks

Abstract

The swelling of clay-sulfate rocks is a well-known phenomenon often causing threats to the success of various geotechnical projects, including tunneling, road and bridge construction, and geothermal drilling. The origin of clay-sulfate swelling is usually explained by physical swelling due to clay expansion combined with chemical swelling associated with the transformation of anhydrite (CaSO4) into gypsum (CaSO4∙2H2O). The latter occurs through anhydrite dissolution and subsequent gypsum precipitation. Numerical models that simulate rock swelling must consider hydraulic, mechanical, and chemical processes. The simulation of the chemical processes is performed by solving thermodynamic equations, which usually contribute a significant portion of the overall computation time. This paper employs feed-forward neural network (FFNN) and cascade-forward neural network (CFNN) models trained with a Bayesian regularization (BR) algorithm as an alternative approach to determine the solubility of anhydrite and gypsum in the aqueous phase. The network models are developed using calcium sulfate experimental data collected from the literature. Our results indicate that the FFNN-BR is the most accurate model for the regression task. The comparison analysis with the Pitzer ion interaction model as well as previously published data-driven models shows that the FFNN-BR model is highly accurate in determining the solubility of sulfate minerals in acid and salt-containing solutions. We conclude from our results that the FFNN-BR model can be used to determine the solubility of anhydrite and gypsum needed to address typical subsurface engineering problems such as swelling of clay-sulfate rocks.

Anion effect on the redox properties of copper ions in ionic liquids and deep eutectic solvents

“…For the CuSCN system, the most likely identity of the solid species is insoluble CuSCN, as the solubility of this species in water at room temperature is 4.1 × 10 −6 mol kg −1 . 40 For the CuSO 4 system, it is more likely that CaSO 4 is present (solubility of <0.05 mol kg −1 ), 41 rather than undissolved CuSO 4 (solubility of >1 mol kg −1 ). While the molar extinction coefficient values for the spectra of these two systems are much smaller than for the other investigated systems, the spectra still correspond to the presence of copper chloride complexes.…”

Experimental Data on Solubility of the Two Calcium Sulfates Gypsum and Anhydrite in Aqueous Solutions

“…Therefore, the solubility values are presented in their original units. Taherdangkoo et al (2022) [43] provided a dataset containing 1912 experimental solubility data of the here-presented datasets but reported in a unified unit, namely molality. Their dataset includes calcium sulfate solubilities taken from [6,8,9,[11][12][13]15,18,21,24,26,27,[30][31][32][33][34][36][37][38]40,44,45].…”

Chemical Corrosion-Water-Confining Pressure Coupling Damage Constitutive Model of Rock Based on the SMP Strength Criterion

“…According to the experimental results [ 15 ], the duplication of chemical damage has a tremendous impact on the quality, wave velocity, porosity, and compression failure characteristics of the rock. To assess the solubility of anhydrite and gypsum in the aqueous phase, Taherdangkoo et al [ 25 ] used feed-forward neural network (FFNN) and cascade-forward neural network (CFNN) models trained with a Bayesian regularization (BR) algorithm. Shi et al [ 26 ] provided evidence for the effective depiction of rock deformation characteristics under freeze–thaw loading by the SMP criterion.…”