In this communication, modeling of carbon dioxide absorption by various amino acid solutions is presented as a function of operational parameters using the Least‐Squares Support Vector Machine (LSSVM) algorithm integrated with three different evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hybrid GA and PSO (HGAPSO). A databank containing 255 data of carbon dioxide absorption by amino acids of potassium taurate, potassium glycinate, potassium prolinate, and potassium lysinate at different temperatures, partial pressures, and concentrations was prepared from different sources to train and test the proposed algorithms. The models were applied to estimate carbon dioxide absorption by amino acid solutions in terms of temperature, molar concentration, equilibrium partial pressure, molecular weight, number of hydrogen bond donor, number of hydrogen bond acceptor, and number of rotatable bond. The R² values of LSSVM optimized by HGAPSO, PSO, and GA are 0.9944, 0.9915, and 0.9891, respectively, and the various errors were determined close to zero. On the other hand, the visual comparison of models outputs and actual carbon dioxide adsorption data was used to clarify performances of the models. By comparison analysis, it was found that the LSSVM‐HGAPSO is the most accurate model for estimation of carbon dioxide loading. Also, comparison of our proposed models results with previously reported artificial neural network results indicates the impressive estimation capability of LSSVM algorithm. According to sensitivity analysis, it becomes obvious that pressure is the most effective parameter on carbon dioxide absorption.

中文翻译：

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使用计算方案对氨基酸溶液的CO2吸收能力进行建模

在本交流中，使用最小二乘支持向量机（LSSVM）算法和三种不同的进化算法，即遗传算法（GA），粒子群算法，将各种氨基酸溶液吸收的二氧化碳建模为操作参数的函数优化（PSO），以及混合GA和PSO（HGAPSO）。从不同来源准备了一个数据库，其中包含255个牛磺酸钾，甘氨酸钾，脯氨酸钾和赖氨酸钾在不同温度，分压和浓度下的氨基酸吸收二氧化碳数据，以训练和测试提出的算法。该模型用于估算氨基酸溶液在温度，摩尔浓度，平衡分压，分子量，氢键供体的数目，氢键受体的数目和可旋转键的数目。的通过HGAPSO，PSO和GA优化的LSSVM的R ²值分别为0.9944、0.9915和0.9891，并且确定的各种误差接近于零。另一方面，使用模型输出和实际二氧化碳吸附数据的视觉比较来阐明模型的性能。通过比较分析，发现LSSVM-HGAPSO是估算二氧化碳负荷最准确的模型。此外，我们提出的模型结果与先前报道的人工神经网络结果的比较表明，LSSVM算法具有令人印象深刻的估计能力。根据灵敏度分析，很明显压力是吸收二氧化碳最有效的参数。