首页|Study Data from China University of Petroleum Provide New Insights into Machine Learning (Application of Heterogeneous Ensemble Learning for Co2-brine Interfaci al Tension Prediction: Implications for Co2 Storage)
Study Data from China University of Petroleum Provide New Insights into Machine Learning (Application of Heterogeneous Ensemble Learning for Co2-brine Interfaci al Tension Prediction: Implications for Co2 Storage)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting out of Beijing, People's Re public of China, by NewsRx editors, research stated, "Carbon capture, utilizatio n, and storage (CCUS) is a green engineering technology to reduce CO2 emissions and mitigate climate warming. It is crucial to accurately predict the CO2-brine interfacial tension (IFT) in order to evaluate the carbon storage capacity of sa line aquifers." Our news journalists obtained a quote from the research from the China Universit y of Petroleum, "Traditional experimental methods are time-consuming and costly. The existing empirical correlation methods of IFT have been found to be inaccur ate. Instead, machine learning (ML) methods have a superior ability to predict I FT. However, the existing machine learning methods lack an in-depth examination of the main factors influencing IFT, as well as the simultaneous improvement str ategy of accuracy and time cost and further reliability verification. In this pa per, we first propose a heterogeneous ensemble learning IFT prediction model bas ed on XGBoost and LightGBM. The new model is simultaneously optimized in terms o f both accuracy and time cost. Our proposed model has been proven to be the most accurate and time-efficient through several comparative studies. The variable t rend analysis, the leverage method, and Shapley values (SV) are also used to inv estigate the effectiveness and interpretability of the model. The density differ ences parameter is used for the first time as an input parameter in the model wh ich is found to be an appropriate parameter. A potential law between temperature and IFT can also be derived from the new model."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningChina University of Petrole um
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