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), R², 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.

中文翻译：

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碳酸锂反应结晶中亚稳区宽度的人工神经网络预测

亚稳态区带宽度（MSZW）是溶液结晶过程优化中的关键参数之一，其准确性将决定结晶产品的质量和过程的坚固性。在Li ₂ CO ₃的反应结晶过程中，采用浊度技术测量了碳酸锂反应结晶的MSZWs 。使用三个半经验模型进行MSZW的预测，此外，首次引入了人工神经网络（ANN）来预测MSZW，并与半经验模型进行了比较。然后，通过指标均方根误差（RMSE），R ²，平均绝对百分比误差（MAPE）和c _p。结果表明，人工神经网络模型具有最佳的预测精度。开发并评估了正交数据集训练的ANN模型，该模型显示出最高的效率和第二好的准确性。另外，研究和分析了工艺参数对MSZW的影响，包括Li ₂ SO _4的浓度，工作体积，搅拌速度，杂质，温度以及Na ₂ CO _3的进料速度和浓度。结果表明，温度和浓度与MSZWs具有很强的正相关性，而温度是推荐用于基于MSZWs的结晶过程优化的最敏感参数。