|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Synthesis of Two Novel Additives and Study of Their Tribological Properties in Rapeseed Oil显示文摘二新奇的灰更少添加剂 - benzothiazole 衍生物包含硼和氯, OBC 和 BBC,被综合。在在油菜籽的添加剂上油的不同集体比率(RO ) 的 OBC 和 BBC 的 tribological 表演在一台四球的机器上被检验。油样品润滑的更低的钢球的穿的表面借助于扫描电子显微镜学(SEM ) 被分析。测试结果显示出那 OBC, BBC 在底有好溶解度上油,并且能有效地增加基础油的带负担的能力。包含 1.5 m% BBC 的油样品的最大的非抓住负担是 1117 N,它 2.3 倍于基础油的。OBC 和 BBC 能改进禁止性能和基础油的热稳定性的反穿和腐蚀,谁的起始的分解温度在 350 ° C 上面。然而,在不同集中的 OBC 和 BBC 能增加基础油的磨擦系数。油润滑的钢球的 SEM 形态学取样包含 1.5 m% 添加剂似乎比形成的基础油,和疤的更一致、光滑是很浅的。 | Zhou Maolin Li Fenfang Zeng Xiaojun Fan Chengkai | 2007 | China Petroleum Processing & Petrochemical Technology2007，9，3： | 1 |
| 2 | Initial exploration of tribological performance of novel triazine derivatives in water显示文摘 | Sheng Liping Li Fenfang Fan Chengkai | 2009 | Lubrication Science2009，21，： | 1 |
| 3 | Initial exploration of tribological performance of novel triazine derivatives in water显示文摘 | SHENG LIP1NG LI FENFANG FAN CHENGKAI | 2009 | Lubrication Science2009，21，4： | 1 |
| 4 | Tribological Behaviors of S,B-Containing Morpholine Derivatives as Additives in Rapeseed Oil显示文摘无灰的二篇小说和非磷 S , 包含B morpholine 衍生物, MBOC 和 MBOD ,被准备,他们在油菜籽油( RSO )的 tribological 行为用四球的 tester.Thermal 降级被评估测试被进行用穿的 thermo-gravimetric analyzer.The 识别他们的热稳定性钢球的表面被扫描 .The 结果显示了的电子显微镜学( SEM )调查添加剂拥有了高热的稳定性并且好带负担的 | Fan Chengkai Li Fenfang Sheng Liping | 2008 | China Petroleum Processing & Petrochemical Technology2008，10，4： | 0 |
| 5 | Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines显示文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines. | Chengkai Fan Na Zhang Bei Jiang Wei Victor Liu | 2024 | Journal of Rock Mechanics and Geotechnical Engineering2024，16，3： | 0 |
| 6 | A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill显示文摘The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB. | Chathuranga Balasooriya Arachchilage Chengkai Fan Jian Zhao Guangping Huang Wei Victor Liu | 2023 | Journal of Rock Mechanics and Geotechnical Engineering2023，15，11： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员