ORCID Profile
0000-0001-6917-0751
Current Organisation
University of Adelaide
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 06-2019
Publisher: American Association for the Advancement of Science (AAAS)
Date: 20-10-2023
Publisher: Elsevier BV
Date: 2021
Publisher: Society of Petroleum Engineers (SPE)
Date: 22-03-2023
DOI: 10.2118/207877-PA
Abstract: A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments however, the work can be extended to capture the imbibition cycle as well.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 23-06-2023
Abstract: The trade-off between activity and stability of oxygen evolution reaction (OER) catalysts in proton exchange membrane water electrolyzer (PEMWE) is challenging. Crystalline IrO 2 displays good stability but exhibits poor activity amorphous IrO x exhibits outstanding activity while sacrificing stability. Here, we combine the advantages of these two materials via a lattice water–incorporated iridium oxide (IrO x · n H 2 O) that has short-range ordered structure of hollandite-like framework. We confirm that IrO x · n H 2 O exhibits boosted activity and ultrahigh stability of hours (~8 months) with a record-high stability number of 1.9 × 10 7 n oxygen n Ir −1 . We evidence that lattice water is active oxygen species in sustainable and rapid oxygen exchange. The lattice water–assisted modified OER mechanism contributes to improved activity and concurrent stability with no apparent structural degradation, which is different to the conventional adsorbate evolution mechanism and lattice oxygen mechanism. We demonstrate that a high-performance PEMWE with IrO x · n H 2 O as anode electrocatalyst delivers a cell voltage of 1.77 V at 1 A cm −2 for 600 hours (60°C).
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1MA00681A
Abstract: Carbon intercalated 1T-MoS 2 (MoS 2 -2C) for efficiently electrocatalytic H 2 evolution is synthesized via a glucose-assisted approach, which not only stablizes the active 1T-MoS 2 but also modifies the electronic structure of the in-plane sites.
Publisher: SPE
Date: 07-12-2309
DOI: 10.2118/207877-MS
Abstract: A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, three different machine learning models was developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. The three different models that were tested and compared were extreme gradient boosting (XGB), deep neural network (DNN) and recurrent neural network (RNN) algorithms. Based on existing mathematical models, a leading edge framework was developed where a large database of Kr and Pc curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were utilized to estimate Kr curves based on Darcy's law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the machine learning model. The entire data set was split into 80% for training and 20% for testing. K-fold cross validation technique was applied to increase the model accuracy by splitting the 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, 9 folds were used for training and the remaining one was used for model validation. Once the model is trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The machine learning model learns to capture fluid flow behavior inside the core from the training dataset. The trained/tested model was thereby employed to estimate Kr curves based on available experimental results. The performance of the developed model was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective cross plots along with comparisons of ground-truth versus AI predicted curves indicate that the model is capable of making accurate predictions with error percentage between 0.2 and 0.6% on history matching experimental data for all the three tested ML techniques (XGB, DNN, and RNN). This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The results also include a comparison between classical machine learning approaches, shallow and deep neural networks in terms of accuracy in predicting the final Kr curves. The various models discussed in this research work currently focusses on the prediction of Kr curves for drainage steady-state experiments however, the work can be extended to capture the imbibition cycle as well.
Publisher: MDPI AG
Date: 14-08-2019
DOI: 10.3390/NANO9081161
Abstract: Electrochemical oxygen reduction and oxygen evolution are two key processes that limit the efficiency of important energy conversion devices such as metal–air battery and electrolysis. Perovskite oxides are receiving discernable attention as potential bifunctional oxygen electrocatalysts to replace precious metals because of their low cost, good activity, and versatility. In this review, we provide a brief summary on the fundamentals of perovskite oxygen electrocatalysts and a detailed discussion on emerging high-performance oxygen electrocatalysts based on perovskite, which include perovskite with a controlled composition, perovskite with high surface area, and perovskite composites. Challenges and outlooks in the further development of perovskite oxygen electrocatalysts are also presented.
Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 05-2019
Publisher: Elsevier BV
Date: 03-2020
Publisher: Wiley
Date: 28-02-2017
No related grants have been discovered for Xu Jun.