The reactive crystallization of lithium carbonate (Li2CO3) from lithium sulfate (Li2SO4) and sodium carbonate (Na2CO3) solutions is a key process in harvesting solid lithium, whether from ores, brines, or clays. However, the process kinetics and mechanism remain poorly understood and the modelling of the reactive crystallization of Li2CO3 is not available. Hence, this work aims to determine the kinetics and mechanisms of the nucleation and growth of Li2CO3 reactive crystallization by induction time measurements and to model and optimize the crystallization process using response surface methodology. Induction time measurements were carried out as functions of initial supersaturation and temperature using a laser method. It was found that the primary nucleation mechanism of Li2CO3 varies with solution supersaturations, in which, expectedly, the heterogenous nucleation mechanism dominates at low supersaturations while the homogeneous nucleation mode governs at high supersaturations. The transition point between heterogenous and homogenous nucleation was found to vary with temperatures. Growth modes of Li2CO3 crystals were investigated by relating induction time data with various growth mechanisms, revealing a two-dimensional nucleation-mediated growth mechanism. The modelling and optimization of a complex reactive crystallization were performed by response surface methodology (RSM), and the effects of various crystallization parameters on product and process performances were examined. Solution concentration was found to be the critical factor determining the yield of crystallization, while stirring speed was found to play a dominant role in the particle size of Li2CO3 crystals. Our findings may provide a better understanding of the reactive crystallization process of Li2CO3 and are critical in relation to the crystallization design and control of Li2CO3 production from lithium sulfate sources.

Metastable zone widths (MSZWs) are one of the crucial parameters in solution crystallization process optimization whose accuracy would determine the crystalline product quality and process robustness. In this paper, the MSZWs of lithium carbonate-reactive crystallization were measured by turbidity technology during the reactive crystallization process of Li2CO3. Three semiempirical models were used to proceed with the prediction of MSZWs, and further, artificial neural networks (ANN) were introduced for the first time to predict MSZWs and compared with semiempirical models. Then, the prediction models were evaluated by the indicators root mean square error (RMSE), R 2, mean absolute percentage error (MAPE), and c p. The results indicated that the ANN model has the best prediction accuracy. An orthogonal-dataset-trained ANN model was developed and evaluated, and it showed the highest efficiency and the second-best accuracy. In addition, the effects of process parameters on the MSZWs were investigated and analyzed, including Li2SO4 concentrations, working volumes, agitation speeds, impurities, temperatures, and Na2CO3 feed speeds and concentrations. The results showed that temperatures and concentrations had a strong positive correlation with MSZWs, and temperature is the most sensitive parameter that is recommended for MSZWs-based crystallization process optimization.