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首页> 外文期刊>Journal of water reuse and desalination >Evolving artificial intelligence techniques to model the hydrate-based desalination process of produced water
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Evolving artificial intelligence techniques to model the hydrate-based desalination process of produced water

机译：不断发展的人工智能技术，以模拟生产水的水合物脱盐过程

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摘要

In this study, two artificial intelligence models based on an adaptive neuro-fuzzy inference system (ANFIS) and a support vector machine (SVM) technique have been successfully developed to predict the desalination efficiency of produced water through a hydrate-based desalination treatment process. A genetic algorithm as an evolutionary optimization method has been used to determine the optimal values of SVM model coefficients. To this end, compressed natural gas and CO_2 hydrate formation experiments were carried out, and the desalination efficiency of produced water was measured and utilized for model training and validation. After model development, graphical and statistical analysis approaches have been applied to evaluate the performance of suggested models by a comparison of model predictions with measured experimental data. For the ANFIS model, the coefficient of determination (R~2) and average absolute relative error (AARE) are 0.9927 and 0.58%, respectively. The values of AARE and R~2 for the SVM model are obtained 0.35% and 0.9985, respectively. These statistical criteria confirm excellent accuracy and robustness of intelligent models in predicting the desalination efficiency of produced water through the hydrate-based desalination treatment process. Furthermore, the Leverage statistical technique has been carried out to define the outliers. The obtained results demonstrate that all experimental data are reliable and both ANFIS and SVM models are statistically valid.
机译：在该研究中，已经成功开发了基于自适应神经模糊推理系统（ANFIS）和支持向量机（SVM）技术的两个人工智能模型，以通过水合物的脱盐处理方法预测产水的脱盐效率。作为进化优化方法的遗传算法已用于确定SVM模型系数的最佳值。为此，进行压缩的天然气和CO_2水合物形成实验，并测量产水的脱盐效率并用于模型训练和验证。在模型开发之后，应用了图形和统计分析方法来评估模型预测与测量实验数据的模型预测的性能。对于ANFIS模型，测定系数（R〜2）和平均绝对相对误差（AARE）分别为0.9927和0.58％。 SVM模型的AARE和R〜2的值分别获得0.35％和0.9985。这些统计标准确认了通过水合物的脱盐处理过程预测产水的脱盐效率的良好精度和稳健性。此外，已经进行了利用统计技术来定义异常值。所获得的结果表明，所有实验数据都是可靠的，ANFIS和SVM模型都在统计上有效。

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