Predicting strontium carbonate precipitation through regression

Abstract

Resource recovery from waste streams is indispensable to shifting from a linear economy to a circular economy. Industrial concentrate is a waste stream that is actively researched to explore and expand the prospects of resource recovery. Strontium (Sr), an alkaline earth metal, while found regularly in concentrate streams has not been a focus for recovery. Through this study, the recovery of Sr as strontium carbonate (SrCO₃) via precipitation from concentrate streams has been investigated. The objective of this study is to characterize SrCO₃ precipitation in terms of factors that influence it. Specifically, the effect of multiple factors has been investigated on the amount of Sr precipitated (as % relative to the initial amount of Sr) as well as the purity of the corresponding SrCO₃ precipitate (%). Factors that could affect either the amount of Sr precipitated or the purity of the precipitated SrCO₃ were identified based on theoretical knowledge. Equilibrium-based simulations were performed using OLI studio to confirm their impact. The conditions associated with the identified influencing factors were investigated in concentrate from a reverse osmosis unit during drinking water production. A Box-Behnken Design was implemented to quantify the relationship between the identified factors and two responses (i) Amount of Sr precipitated and, (ii) Purity of SrCO₃ precipitated. The range of the factors in the design was established based on their ranges as observed in the concentrate. The value of pH and concentrations of strontium, inorganic carbon (C), calcium (Ca), barium (Ba) and inorganic sulfur (S) were hypothesized to have quantitative and qualitative effects on the SrCO₃ precipitated. Magnesium (Mg) was hypothesized to have a qualitative effect on the SrCO₃ precipitated. Direct SrCO₃ precipitation from concentrate was not practical due to considerably higher concentration of other ions. To facilitate SrCO₃ precipitation from concentrate, the concentration of the other ions was reduced by recovering them as precipitates. The recovery of these precipitates was investigated through variation of process conditions via OLI simulations. Theoretically, it is possible to recover 0.0014g of SrCO₃ (100% purity) per liter of concentrate along with 0.684g CaCO₃ (99.95% purity), 0.161g Mg(OH)₂ (96% purity), 0.0083g Ca(OH)₂ (99.99% purity) and  NaCl solution (4.65 M). Using Box-Behnken Design, the mathematical relationship between the amount of Sr precipitated (% relative to initial concentration) and purity of SrCO₃ precipitated (%) subject to the identified factors was established. The equations had a R² of 98.75% and 97.38% respectively. S was found to not have any significant impact on SrCO₃ precipitation while a positive interaction effect between Sr and Ca was also identified. %Sr precipitated=0.82098+0.01319(pH)-0.04004(Sr)+0.11581(C)-0.07343(Ca)-0.01314(pH*pH)-0.04299(Sr*Sr)-0.04073(C*C)-0.02041(Mg*Mg)+0.01818(pH*Ca)+0.05369(Sr*C)+0.07436(Sr*Ca)-0.05763(C*Ca)  SrCO₃ purity(%)=0.5155-0.20923(pH)+0.12593(Sr)-0.03178(C)-0.06115(Ca)-0.07187(Mg)+0.0617(pH*pH)-0.0388(Sr*Sr)-0.0295(C*C)+0.0456(Mg*Mg)-0.0485(pH*Sr)+0.0595(pH*C)+0.0405(pH*Ca)-0.0903(pH*Mg)+0.0481(Sr*C)+0.0614(Sr*Ca)-0.0453(C*Ca) The predictive R² of these equations are 93.45% and 86.32% respectively. This is representative of their prediction accuracy and hence their applicability to replace experiments and simulations. These equations, however, are built on outputs from OLI simulations. Therefore, their accuracy is subject to the predictive capability of OLI software. Experimental results based on the chosen Box-Behnken Design are needed to either replace the OLI simulations results entirely or account for the inaccuracy of OLI simulations.