Bakhtyari Ali, Rasoolzadeh Ali, Vaferi Behzad, Khandakar Amith
Department of Chemical Engineering, Shiraz University, Shiraz, Iran.
Faculty of Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.
Sci Rep. 2023 Jul 27;13(1):12161. doi: 10.1038/s41598-023-39441-7.
The current trend of chemical industries demands green processing, in particular with employing natural substances such as sugar-derived compounds. This matter has encouraged academic and industrial sections to seek new alternatives for extracting these materials. Ionic liquids (ILs) are currently paving the way for efficient extraction processes. To this end, accurate estimation of solubility data is of great importance. This study relies on machine learning methods for modeling the solubility data of sugar alcohols (SAs) in ILs. An initial relevancy analysis approved that the SA-IL equilibrium governs by the temperature, density and molecular weight of ILs, as well as the molecular weight, fusion temperature, and fusion enthalpy of SAs. Also, temperature and fusion temperature have the strongest influence on the SAs solubility in ILs. The performance of artificial neural networks (ANNs), least-squares support vector regression (LSSVR), and adaptive neuro-fuzzy inference systems (ANFIS) to predict SA solubility in ILs were compared utilizing a large databank (647 data points of 19 SAs and 21 ILs). Among the investigated models, ANFIS offered the best accuracy with an average absolute relative deviation (AARD%) of 7.43% and a coefficient of determination (R) of 0.98359. The best performance of the ANFIS model was obtained with a cluster center radius of 0.435 when trained with 85% of the databank. Further analyses of the ANFIS model based on the leverage method revealed that this model is reliable enough due to its high level of coverage and wide range of applicability. Accordingly, this model can be effectively utilized in modeling the solubilities of SAs in ILs.
当前化学工业的发展趋势要求进行绿色加工，特别是使用天然物质，如糖衍生化合物。这一情况促使学术界和工业界寻求提取这些物质的新方法。离子液体（ILs）目前正在为高效提取工艺铺平道路。为此，准确估计溶解度数据至关重要。本研究依靠机器学习方法对糖醇（SAs）在离子液体中的溶解度数据进行建模。初步相关性分析证实，SA-IL平衡受离子液体的温度、密度和分子量以及糖醇的分子量、熔化温度和熔化焓的影响。此外，温度和熔化温度对糖醇在离子液体中的溶解度影响最大。利用一个大型数据库（19种糖醇和21种离子液体的647个数据点）比较了人工神经网络（ANNs）、最小二乘支持向量回归（LSSVR）和自适应神经模糊推理系统（ANFIS）预测糖醇在离子液体中溶解度的性能。在所研究的模型中，ANFIS的准确性最高，平均绝对相对偏差（AARD%）为7.43%，决定系数（R）为0.98359。当使用85%的数据库进行训练时，ANFIS模型在聚类中心半径为0.435时获得了最佳性能。基于杠杆法对ANFIS模型的进一步分析表明，该模型由于其高覆盖率和广泛的适用性而足够可靠。因此，该模型可有效地用于模拟糖醇在离子液体中的溶解度。
