低碳化学与化工2023，Vol.48Issue(6)：170-176,7.DOI:10.12434/j.issn.2097-2547.20230008
基于IAO-PNN模型的天然气水合物生成条件预测研究
Study on prediction of nature gas hydrate formation conditions based on IAO-PNN model
摘要
Abstract
In order to mitigate the issues caused by hydrate blockage in flow assurance,experimental data on natural gas hydrate formation was collected to construct a Probabilistic Neural Network(PNN)model.By improving the Aquila Optimizer(AO)algorithm through adaptive weights and hyperbolic tangent function,optimization of smoothing parameters was achieved,resulting in the establishment of an IAO-PNN-based hydrate formation prediction model.A comparison with thermodynamic models and machine learning models validated the superiority of the algorithm.The results show that the improved AO algorithm(IAO)exhibits significantly higher optimization precision and convergence speed compared to intelligent algorithms such as AO,PSO and SSA.The IAO-PNN model exhibits the highest consistency with experimental data,making it suitable for predicting hydrate formation conditions in binary systems,multicomponent systems,acid systems and alcohol-salt systems,and it demonstrates good predictive performance in high-pressure environments.Compared to thermodynamic models and machine learning models,the IAO-PNN model shows excellent generalization performance,with an root mean square error(RMSE)of 0.6176 and an coefficient of determination(R2)of 0.9994 on the training set,and an RMSE of 0.7624 and an R2 of 0.9991 on the test set.Through on-site verification,the IAO-PNN model displays good applicability and can provide reference for formulating on-site remediation measures.关键词
IAO-PNN模型/天然气/水合物/热力学模型/机器学习Key words
IAO-PNN model/natural gas/hydrate/thermodynamic model/machine learning分类
能源科技引用本文复制引用
梁龙贵,张龙,郭仕为,景玉平,梁挺,李姜超..基于IAO-PNN模型的天然气水合物生成条件预测研究[J].低碳化学与化工,2023,48(6):170-176,7.基金项目
中国石油天然气股份有限公司重点项目(S2022013E). （S2022013E）
