Article
Chemistry, Medicinal
Chao Yang, Yingkai Zhang
Summary: In this study, the robustness and applicability of machine-learning scoring functions were further improved by expanding the training set, developing meaningful features, using a linear empirical scoring function as the baseline, and applying extreme gradient boosting (XGBoost) with Delta-machine learning. The new scoring function demonstrated superior performance in scoring and ranking in various structure types and showed reliability and robustness in virtual screening applications.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Agronomy
Jianzheng Li, Ganqiong Li, Ligang Wang, Denghua Li, Hu Li, Chao Gao, Minghao Zhuang, Jiayu Zhuang, Han Zhou, Shiwei Xu, Zhengjiang Hu, Enli Wang
Summary: This study aims to develop a hybrid approach using machine learning to blend climate data, satellite data, extreme climate events, and process-based modelling results to improve the accuracy of maize yield prediction in Northeast China. The results show that a hybrid model developed with random forest can achieve high performance for predicting yield toward the end of the growing season.
FIELD CROPS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Linda Ait Mohamed, Assia Cherfa, Yazid Cherfa, Noureddine Belkhamsa, Fatiha Alim-Ferhat
Summary: This study presents a new and automatic method for precise segmentation of early gliomas, combining different algorithms and features, achieving competitive results compared to existing methods.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Siwei Xia, Yuehan Yang
Summary: The paper introduces an iterative feature screening procedure called forward recursive selection, which combines random forest and forward selection to improve computational efficiency and address model limitations. Through numerical comparisons and empirical analysis, it is shown that the proposed method performs well.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Food Science & Technology
Huili Zhu, Minyan Wang, Jing Zhang, Fengwang Ma
Summary: This study utilized the Random Forest algorithm to construct a prediction model of aroma components in apple hybrid offspring, with different preprocessing methods tested, showing that SNV is the most effective in noise removal. The characteristic wavelength-aroma chemical group model can accurately predict the aroma components in apples.
Article
Environmental Sciences
Yuying Zhang, Zhizhong Lu, Congying Tian, Yanbo Wei, Fanming Liu
Summary: This study proposes a method to improve wind measurement accuracy affected by ship structure, combining anemometer and X-band radar data to obtain more accurate wind parameters. Using multivariate bias strategy and random forest model to optimize the layout of multiple anemometers and train wind parameter estimation model, improving wind parameter estimation accuracy.
Article
Geosciences, Multidisciplinary
Pin Zhang, Zhen-Yu Yin, Yin-Fu Jin, Tommy H. T. Chan, Fu-Ping Gao
Summary: This study proposes a novel modeling approach using machine learning techniques to predict the compression index C c in geotechnical design, showing that machine learning models outperform traditional empirical prediction formulations. Among the tested machine learning algorithms, random forest and back-propagation neural network models are recommended for predicting C c under different conditions.
GEOSCIENCE FRONTIERS
(2021)
Article
Chemistry, Multidisciplinary
Shengshuai Su, Na Zhang, Peng Wang, Shun Jia, Acacia Zhang, Han Wang, Min Zhang
Summary: This research proposes a new methodology to assist engineers in making fast and scientific decisions on Enhanced Oil Recovery (EOR) selection process by implementing machine learning algorithms. The results show that the random forest classification model has the highest accuracy and can make recommendations of the EOR technique for a new candidate oil reservoir.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Civil
Konstantinos Plataridis, Zisis Mallios
Summary: Floods are a common natural hazard that cause significant economic and human losses. This paper proposes and evaluates four new hybrid models for mapping flood susceptibility using the Spercheios river basin in Greece as a case study. The models combine ensemble algorithms with statistical methods and are optimized using the Artificial Bee Colony (ABC) method. The results show that these models accurately predict flood-prone areas and can assist decision-makers.
JOURNAL OF HYDROLOGY
(2023)
Article
Chemistry, Medicinal
Hui Zhu, Jincai Yang, Niu Huang
Summary: This study investigates the scoring functions used in structure-based virtual screening, finding that machine-learning scoring functions (MLSFs) have unsatisfactory generalization capacity. By combining target-specific patterns with features shared among similar compounds, better performance can be achieved. Therefore, it is recommended to assess the generalization ability of MLSFs using the Pfam-cluster approach and to be cautious with the features learned by MLSFs.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Computer Science, Artificial Intelligence
Yong Dai, Manoj Khandelwal, Yingui Qiu, Jian Zhou, M. Monjezi, Peixi Yang
Summary: This paper introduces a new intelligent method based on random forest and particle swarm optimization for accurately predicting backbreak phenomena and reducing unwanted effects in open-pit blasting. By establishing a dataset with six input parameters and one output parameter, the PSO-RF model is optimized using the PSO algorithm and RF algorithm, and its performance is evaluated using multiple metrics. The results show that the PSO-RF model outperforms other models in predicting backbreak.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Energy & Fuels
Hrishikesh K. Chavan, Rajib K. Sinharay, Vijay Kumar, Darvin Patel
Summary: Screening of an appropriate Enhanced Oil Recovery (EOR) technique is crucial for maximizing oil reservoir recovery and economics. This study utilizes five supervised machine learning techniques to accurately screen EOR methods for complex reservoirs, including two techniques never used before. By analyzing a database of 358 successful EOR projects, the Random Forest classification technique demonstrates the highest accuracy value of 0.91. The study also ranks reservoir and fluid parameters based on their influences on EOR screening, with viscosity being the most impactful factor at 34.6% feature importance.
PETROLEUM SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Aihua Wei, Yuanyao Chen, Duo Li, Xianfu Zhang, Tao Wu, Hui Li
Summary: The main objective of this study is to predict groundwater levels using modified hybrid algorithms. The results showed that the W-SVM-D and W-RF-D hybrid algorithms had the highest predictive accuracies among all the selected wavelets.
EARTH SCIENCE INFORMATICS
(2022)
Article
Remote Sensing
Yihao Zhang, Xing Peng, Qinghua Xie, Yanan Du, Bing Zhang, Xiaomin Luo, Shaobo Zhao, Zhentao Hu, Xinwu Li
Summary: This study combines polarimetric SAR variables and single-polarization TomoSAR features to estimate forest height for the first time. The results confirm the advantages of this method in terms of estimation accuracy and computational efficiency.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Environmental Sciences
Ali Danandeh Mehr, Ali Torabi Haghighi, Masood Jabarnejad, Mir Jafar Sadegh Safari, Vahid Nourani
Summary: This article introduces a new hybrid random forest (RF) model, GARF, which utilizes genetic algorithm (GA) and hybrid RF to improve the prediction accuracy of regression and classification problems in hydrology. Through comparison with other models, the results show that GARF performs the best in predicting drought indices.
Article
Energy & Fuels
Munqith Aldhaheri, Mingzhen Wei, Na Zhang, Baojun Bai
SPE RESERVOIR EVALUATION & ENGINEERING
(2019)
Article
Energy & Fuels
Ze Wang, Baojun Bai, Enze Zhou, Jingyang Pu, Thomas Schuman
Article
Engineering, Chemical
Haifeng Ding, Na Zhang, Yandong Zhang, Mingzhen Wei, Baojun Bai
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2019)
Article
Energy & Fuels
Zhe Li, Wanli Kang, Baojun Bai, Hairong Wu, Congbo Gou, Yongjie Yuan, Derong Xu, Yao Lu, Jirui Hou
Article
Energy & Fuels
Hasan N. Al-Saedi, Yifu Long, Ralph E. Flori, Baojun Bai
Article
Energy & Fuels
Yi-Bo Li, Ya-Qian Zhang, Chen Luo, Hao Gao, Kai Li, Zun-Rong Xiao, Zhi-Qiang Wang, Wan-Fen Pu, Baojun Bai
Article
Energy & Fuels
Ze Wang, Baojun Bai, Xindi Sun, Jiang Wang
Article
Energy & Fuels
Pu Han, Jiaming Geng, Haifeng Ding, Ye Zhang, Baojun Bai
Review
Energy & Fuels
Zhaojie Song, Yilei Song, Yuzhen Li, Baojun Bai, Kaoping Song, Jirui Hou
Article
Energy & Fuels
Munqith Aldhaheri, Mingzhen Wei, Ali Alhuraishawy, Baojun Bai
Summary: Based on 61 field projects, gel treatments typically lead to increased water production for undeveloped conformance problems, and decreased water production for developed conformance issues.
JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
(2021)
Article
Chemistry, Multidisciplinary
Na Zhang, Mingzhen Wei, Baojun Bai, Xiaopeng Wang, Jian Hao, Shun Jia
Summary: In this study, hierarchical clustering algorithm and principal component analysis are used to analyze steam flooding projects worldwide. The goal is to group similar projects together and provide valuable operational design experiences and production performance for decision-making. Additionally, hidden patterns within steam flooding applications can be revealed based on the data characteristics of each cluster.
Review
Energy & Fuels
Junchen Liu, Yandong Zhang, Mingzhen Wei, Xiaoming He, Baojun Bai
Summary: This article summarizes the latest techniques and development history of micro/nanofluidics in oil and gas recovery, including fabrication methods, materials, and applications. Compared with other reviews, this study focuses on solving specific issues in the oil and gas industry, covering most representative studies using micro/nanomodels.
Article
Geochemistry & Geophysics
Na Zhang, Xicheng Wang, Jianmin Zhang, Xiaopeng He, Shaofei Kang, Jingyang Pu, Shuhang Fan, Xu Li
Summary: This study investigates the scaling mechanisms and characteristics in the Huanjiang oilfield. Different types of scale deposition were identified through the analysis of well samples and water samples.
Article
Energy & Fuels
Munqith Aldhaheri, Mingzhen Wei, Na Zhang, Baojun Bai
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2020)
Article
Energy & Fuels
Yingna Du, Chen Huang, Wei Jiang, Qiangwei Yan, Yongfei Li, Gang Chen
Summary: In this study, anionic surfactants modified hydrotalcite was used as a flow improver for crude oil under low-temperature conditions. The modified hydrotalcite showed a significant viscosity reduction effect on crude oil. The mechanism of the modified hydrotalcite on viscosity and pour point of crude oil was explored through characterization and analysis of the modified hydrotalcite and oil samples.
Article
Energy & Fuels
Mohammad Saeid Rostami, Mohammad Mehdi Khodaei
Summary: In this study, a hybrid structure, MIL-53(Al)@MWCNT, was synthesized by combining MIL-53(Al) particles and -COOH functionalized multi-walled carbon nanotube (MWCNT). The hybrid structure was then embedded in a polyethersulfone (PES) polymer matrix to prepare a mixed matrix membrane (MMM) for CO2/CH4 and CO2/N2 separation. The addition of MWCNTs prevented MIL-53(Al) aggregation, improved membrane mechanical properties, and enhanced gas separation efficiency.
Article
Energy & Fuels
Yunlong Li, Desheng Huang, Xiaomeng Dong, Daoyong Yang
Summary: This study develops theoretical and experimental techniques to determine the phase behavior and physical properties of DME/flue gas/water/heavy oil systems. Eight constant composition expansion (CCE) tests are conducted to obtain new experimental data. A thermodynamic model is used to accurately predict saturation pressure and swelling factors, as well as the phase boundaries of N2/heavy oil systems and DME/CO2/heavy oil systems, with high accuracy.
Article
Energy & Fuels
Morteza Afkhamipour, Ebad Seifi, Arash Esmaeili, Mohammad Shamsi, Tohid N. Borhani
Summary: Non-conventional amines are being researched worldwide to overcome the limitations of traditional amines like MEA and MDEA. Adequate process and thermodynamic models are crucial for understanding the applicability and performance of these amines in CO2 absorption, but studies on process modeling for these amines are limited. This study used rate-based modeling and Deshmukh-Mather method to model CO2 absorption by DETA solution in a packed column, validated the model with experimental data, and conducted a sensitivity analysis of mass transfer correlations. The study also compared the CO2 absorption efficiency of DETA solution with an ionic solvent [bmim]-[PF6] and highlighted the importance of finding optimum operational parameters for maximum absorption efficiency.
Article
Energy & Fuels
Arastoo Abdi, Mohamad Awarke, M. Reza Malayeri, Masoud Riazi
Summary: The utilization of smart water in EOR operations has gained attention, but more research is needed to understand the complex mechanisms involved. This study investigated the interfacial tension between smart water and crude oil, considering factors such as salt, pH, asphaltene type, and aged smart water. The results revealed that the hydration of ions in smart water plays a key role in its efficacy, with acidic and basic asphaltene acting as intrinsic surfactants. The pH also influenced the interfacial tension, and the aged smart water's interaction with crude oil depended on asphaltene type, salt, and salinity.
Article
Energy & Fuels
Dongao Zhu, Kun Zhu, Lixian Xu, Haiyan Huang, Jing He, Wenshuai Zhu, Huaming Li, Wei Jiang
Summary: In this study, cobalt-based metal-organic frameworks (Co-based MOFs) were used as supports and co-catalysts to confine the NHPI catalyst, solving the leaching issue. The NHPI@Co-MOF with carboxyl groups exhibited stronger acidity and facilitated the generation of active oxygen radicals O2•, resulting in enhanced catalytic activity. This research provides valuable insights into the selection of suitable organic linkers and broadens the research horizon of MOF hybrids in efficient oxidative desulfurization (ODS) applications.
Article
Energy & Fuels
Edwin G. Hoyos, Gloria Amo-Duodu, U. Gulsum Kiral, Laura Vargas-Estrada, Raquel Lebrero, Rail Munoz
Summary: This study investigated the impact of carbon-coated zero-valent nanoparticle concentration on photosynthetic biogas upgrading. The addition of nanoparticles significantly increased microalgae productivity and enhanced nitrogen and phosphorus assimilation. The presence of nanoparticles also improved the quality of biomethane produced.
Article
Energy & Fuels
Yao Xiao, Asma Leghari, Linfeng Liu, Fangchao Yu, Ming Gao, Lu Ding, Yu Yang, Xueli Chen, Xiaoyu Yan, Fuchen Wang
Summary: Iron is added as a flocculant in wastewater treatment and the hydrothermal carbonization (HTC) of sludge produces wastewater containing Fe. This study investigates the effect of aqueous phase (AP) recycling on hydrochar properties, iron evolution and environmental assessment during HTC of sludge. The results show that AP recycling process improves the dewatering performance of hydrochar and facilitates the recovery of Fe from the liquid phase.
Article
Energy & Fuels
He Liang, Tao Wang, Zhenmin Luo, Jianliang Yu, Weizhai Yi, Fangming Cheng, Jingyu Zhao, Xingqing Yan, Jun Deng, Jihao Shi
Summary: This study investigated the influence of inhibitors (carbon dioxide, nitrogen, and heptafluoropropane) on the lower flammability limit of hydrogen and determined the critical inhibitory concentration needed for complete suppression. The impact of inhibitors on explosive characteristics was evaluated, and the inhibitory mechanism was analyzed with chemical kinetics. The results showed that with the increase of inhibitor quantity, the lower flammability limit of hydrogen also increased. The research findings can contribute to the safe utilization of hydrogen energy.
Article
Energy & Fuels
Zonghui Liu, Zhongze Zhang, Yali Zhou, Ziling Wang, Mingyang Du, Zhe Wen, Bing Yan, Qingxiang Ma, Na Liu, Bing Xue
Summary: In this study, high-performance solid catalysts based on phosphotungstic acid (HPW) supported on Zr-SBA-15 were synthesized and evaluated for the one-pot conversion of furfural (FUR) to γ-valerolactone (GVL). The catalysts were characterized using various techniques, and the ratio of HPW and Zr was found to significantly affect the selectivity of GVL. The HPW/Zr-SBA-15 (2-4-15) catalyst exhibited the highest GVL yield (83%) under optimized reaction conditions, and it was determined that a balance between Bronsted acid sites (BAS) and Lewis acid sites (LAS) was crucial for achieving higher catalytic performance. The reaction parameters and catalyst stability were also investigated.
Article
Energy & Fuels
Michael Stoehr, Stephan Ruoff, Bastian Rauch, Wolfgang Meier, Patrick Le Clercq
Summary: As part of the global energy transition, an experimental study was conducted to understand the effects of different fuel properties on droplet vaporization for various conventional and alternative fuels. The study utilized a flow channel to measure the evolution of droplet diameters over time and distance. The results revealed the temperature-dependent effects of physical properties, such as boiling point, liquid density, and enthalpy of vaporization, and showed the complex interactions of preferential vaporization and temperature-dependent influences of physical properties for multi-component fuels.
Article
Energy & Fuels
Yuan Zhuang, Ruikang Wu, Xinyan Wang, Rui Zhai, Changyong Gao
Summary: Through experimental validation and optimization of the chemical kinetic model, it was found that methanol can accelerate the oxidation reaction of ammonia, and methanol can be rapidly oxidized at high concentration. HO2 was found to generate a significant amount of OH radicals, facilitating the oxidation of methanol and ammonia. Rating: 7.5/10.
Article
Energy & Fuels
Radwan M. EL-Zohairy, Ahmed S. Attia, A. S. Huzayyin, Ahmed I. EL-Seesy
Summary: This paper presents a lab-scale experimental study on the impact of diethyl ether (DEE) as an additive to waste cooking oil biodiesel with Jet A-1 on combustion and emission features of a swirl-stabilized premixed flame. The addition of DEE to biodiesel significantly affects the flame temperature distribution and emissions. The W20D20 blend of DEE, biodiesel, and Jet A-1 shows similar flame temperature distribution to Jet A-1 and significantly reduces UHC, CO, and NOx emissions compared to Jet A-1.
Article
Energy & Fuels
Jiang Bian, Ziyuan Zhao, Yang Liu, Ran Cheng, Xuerui Zang, Xuewen Cao
Summary: This study presents a novel method for ammonia separation using supersonic flow and develops a mathematical model to investigate the condensation phenomenon. The results demonstrate that the L-P nucleation model accurately characterizes the nucleation process of ammonia at low temperatures. Numerical simulations also show that increasing pressure and concentration can enhance ammonia condensation efficiency.
Article
Energy & Fuels
Shiyuan Pan, Xiaodan Shi, Beibei Dong, Jan Skvaril, Haoran Zhang, Yongtu Liang, Hailong Li
Summary: Integrating CO2 capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method for achieving negative emissions. This study develops a reliable data-driven model based on the Transformer architecture to predict the flowrate and CO2 concentration of flue gas in real time. The model validation shows high prediction accuracy, and the potential impact of meteorological parameters on model accuracy is assessed. The results demonstrate that the Transformer model outperforms other models and using near-infrared spectral data as input features improves the prediction accuracy.