论文标题
对离子液体氨捕获的机器学习模型的比较分析
Comparative analysis of machine learning models for Ammonia Capture of Ionic Liquids
论文作者
论文摘要
行业在制冷和通风的过程中使用了各种溶剂。其中，离子液体（ILS）是相对较新的溶剂，以其经过验证的环保特征而闻名。在这项研究中，进行了全面的文献综述，以深入了解ILS和用于估计ILS氨溶解度的预测模型。此外，多种先进的机器学习方法，即多层感知器（MLP）以及粒子群优化（PSO）和自适应神经模糊推理系统（ANFIS）模型的组合，用于估计各种离子液在各种离子液体中ammonia的溶解度。影响参数的是分子量，临界温度和IL压力。此外，还可以使用状态的两个方程式来预测可口。在线，在实验和建模结果之间进行了一些比较，这些比较很少进行。研究表明，相比之下，状态方程无法准确估计氨的溶解度，而人工智能方法产生了有希望的结果。
Industry uses various solvents in the processes of refrigeration and ventilation. Among them, the Ionic liquids (ILs) as the relatively new solvents, are known for their proven eco-friendly characteristics. In this research, a comprehensive literature review was carried out to deliver an insight into the ILs and the prediction models used for estimating the ammonia solubility in ILs. Furthermore, a number of advanced machine learning methods, i.e. multilayer perceptron (MLP) and a combination of particle swarm optimization (PSO) and adaptive neuro-fuzzy inference system (ANFIS) models are used to estimate the solubility of ammonia in various ionic liquids. Affecting parameters were molecular weight, critical temperature and pressure of ILs. Furthermore, the salability is also predicted using the two-equation of states. Down the line, some comparisons were drawn between experimental and modeling results which is rarely done. The study shows that the equations of states are not able estimate the solubility of ammonia accurately, by contrast, artificial intelligence methods have produced promising results.
