Accurate determinations of water (H₂O) content in natural gases especially in the methane (CH₄) phase are highly important for chemical engineers dealing with natural gas processes. To this end, development of a high performance model is necessary. Due to importance of the solubility of methane in the aqueous solutions for natural gas industries, two novel models based on the Decision Tree (DT) and Adaptive Neuro-Fuzzy Interference System (ANFIS) have been employed. To this end, a total number of 204 real methane solubility points in aqueous solution containing NaCl under different pressure and temperature conditions have been gathered. The comparisons between predicted solubility values and experimental data points have been conducted in visual and mathematical approaches. The R² values of 1 for training and testing phases express the great ability of proposed models in calculation of methane solubility in pure water systems.

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开发用于测定水系统中甲烷溶解度的人工智能方法

准确测定天然气中尤其是甲烷 (CH _{4 ) 相中的水 (H 2} O) 含量对于处理天然气工艺的化学工程师来说非常重要。为此，有必要开发高性能模型。由于甲烷在天然气工业水溶液中的溶解度很重要，因此采用了两种基于决策树 (DT) 和自适应神经模糊干扰系统 (ANFIS) 的新型模型。为此，在不同的压力和温度条件下，共收集了 204 个实际甲烷在含 NaCl 水溶液中的溶解点。预测溶解度值和实验数据点之间的比较已经在视觉和数学方法中进行。这训练和测试阶段的R ²值 1 表示所提出的模型在计算纯水系统中的甲烷溶解度方面的强大能力。