Ionic Liquids (ILs) are increasingly emerging as new innovating green solvents with great importance from academic, industrial, and environmental perspectives. This surge of interest in considering ILs in various applications is owed to their attractive properties. Involvements in the gas sweetening and the reduction of the amounts of sour and acid gasses are among the most promising applications of ILs. In this study, new advanced committee machine intelligent systems (CMIS) were introduced for predicting the solubility of hydrogen sulfide (H₂S) in various ILs. The implemented CMIS models were gained by linking robust data-driven techniques, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath rigorous schemes using group method of data handling (GMDH) and genetic programming (GP). The proposed paradigms were developed using an extensive database encompassing 1243 measurements of H₂S solubility in 33 ILs. The performed comprehensive error investigation revealed that the newly implemented paradigms yielded very satisfactory prediction performance. Besides, it was found that CMIS-GP provided more accurate estimations of H₂S solubility in ILs compared with both the other intelligent models and the best-prior paradigms. In this regard, the developed CMIS-GP exhibited overall average absolute relative deviation (AARD) and coefficient of determination (R²) values of 2.3767% and 0.9990, respectively. Lastly, the trend analyses demonstrated that the tendencies of CMIS-GP predictions were in excellent accordance with the real variations of H₂S solubility in ILs with respect to pressure and temperature.

从学术，工业和环境的角度来看，离子液体（ILs）作为新兴的创新绿色溶剂越来越重要。在各种应用中考虑IL的兴趣激增归因于其吸引人的特性。IL的最有前景的应用包括参与气体脱硫和减少酸性和酸性气体的量。在这项研究中，引入了新的高级委员会机器智能系统（CMIS）来预测硫化氢（H ₂S）在各种IL中。通过将严格的方案下的多层数据感知器（MLP）和级联前向神经网络（CFNN）结合使用数据处理的分组方法（GMDH）和遗传编程（GP），将强大的数据驱动技术链接在一起，从而获得了已实施的CMIS模型。使用广泛的数据库开发了所提出的范例，该数据库包含对_12种在33 ILs中的H ₂ S溶解度的测量。进行的综合误差调查显示，新实施的范例产生了非常令人满意的预测性能。另外，发现CMIS-GP提供了更准确的H ₂估计。与其他智能模型和最佳范例相比，IL中的S溶解度。在这方面，开发的CMIS-GP的总体平均绝对相对偏差（AARD）和测定系数（R ²）值分别为2.3767％和0.9990。最后，趋势分析表明CMIS-GP的预测趋势与IL中H ₂ S溶解度相对于压力和温度的实际变化非常吻合。