On The Estimation Of Phase Behavior Of CO2-Based Binary Systems Using ANFIS Optimized By GA Algorithm

M. Motie, A. Bemani, R. Soltanmohammadi
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引用次数: 5

Abstract

Since the world average temperature is on the rise, severe measurements should be considered due to decrease the concentration of greenhouse gases which are the main reason of global warming. Geological sequestration of the CO2 speculated as one of the most efficient method for mitigate the problem. As the injected CO2 stream is not always a pure one, a more accurate assessment of the impurities effects on various part of the sequestration process would be desired. As equations of state are not able to completely support the thermodynamic attributes of impure CO2 injected stream, developed computational modeling would be more appropriate. In this study, due to obtain a way of predicting vapor liquid equilibrium of CO2 binary mixtures, not fully depending on the experimental data, a novel and accurate computational method is presented. This alternative, uses Adaptive Neuro-Fuzzy Interference System (ANFIS) together with Genetic Algorithm as an optimization tool. As a result, the developed model shows a great i
用遗传算法优化的ANFIS估计co2二元系统的相位行为
由于世界平均温度正在上升,由于温室气体的浓度降低,应该考虑采取严厉的措施,这是全球变暖的主要原因。地质封存二氧化碳被认为是缓解这一问题最有效的方法之一。由于注入的二氧化碳流并不总是纯净的,因此需要更准确地评估杂质对封存过程各个部分的影响。由于状态方程不能完全支持不纯CO2注入流的热力学属性,开发的计算模型将更为合适。在本研究中,为了获得一种不完全依赖实验数据的预测CO2二元混合物汽液平衡的方法,提出了一种新颖而准确的计算方法。该方案采用自适应神经模糊干扰系统(ANFIS)和遗传算法作为优化工具。结果表明,所建立的模型具有良好的经济性
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