Article
Mathematics
Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Zana Azeez Kakarash, Ali Awadh Al-Qahtani, Abdullah K. Alanazi, Hala H. Alhashimi, Ehsan Eftekhari-Zadeh, Ehsan Nazemi
Summary: This study presents a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe using artificial intelligence networks. The proposed system is able to accurately determine the volume percentages regardless of the type of flow regime and the amount of scale inside the pipe. By using feature extraction techniques, the system reduces costs and increases accuracy.
Article
Multidisciplinary Sciences
Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Karwan Mohammad Hamakarim, Ali Awadh Al-Qahtani, Abdullah K. Alanazi, John William Grimaldo Guerrero, Hala H. Alhashim, Ehsan Eftekhari-Zadeh
Summary: This research proposes a detection system for monitoring oil pipelines, which utilizes feature extraction and neural network technology to accurately estimate the types and volume fractions of oil products passing through the pipelines. The system shows significant advantages in terms of accuracy, data interpretation, and cost, increasing its application value in the oil industry.
Article
Engineering, Multidisciplinary
Tzu-Chia Chen, Hani Almimi, Mohammad Sh. Daoud, John William Grimaldo Guerrero, Rafal Chorzepa
Summary: This study proposes a method for identifying petroleum products using an X-ray tube-based system, feature extraction, and neural network algorithms. The system demonstrates high accuracy in predicting the volume ratio of ethylene glycol, crude oil, gasoil, and gasoline.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Polymer Science
Siavash Hosseini, Osman Taylan, Mona Abusurrah, Thangarajah Akilan, Ehsan Nazemi, Ehsan Eftekhari-Zadeh, Farheen Bano, Gholam Hossein Roshani
Summary: Measuring fluid characteristics is crucial for various industries, with the efficiency of multiphase flow meters strongly dependent on flow parameters. This study utilized MCNP code for simulation, successfully classifying flow regimes and calculating void fraction percentages using wavelet transform and feature extraction methods.
Article
Engineering, Multidisciplinary
Mohammadmehdi Roshani, Giang Phan, Gholam Hossein Roshani, Robert Hanus, Behrooz Nazemi, Enrico Corniani, Ehsan Nazemi
Summary: A new system based on fan-beam photon attenuation was proposed to recognize the type of flow regime and predict gas-oil-water volume fractions in a three phase flow, achieving correct recognition of flow patterns and volume fraction prediction with RMS error less than 3.1.
Article
Thermodynamics
Jae Gyu Hwang, Myung Kyu Choi, Dong Hyuk Choi, Hang Seok Choi
Summary: The increase in energy consumption from heating, power generation, and transportation is causing global warming and environmental pollution. Global efforts are being made to achieve net zero emissions by 2050, including the use of bioenergy as a carbon-neutral alternative to fossil fuels. This study focuses on reducing tar levels in syngas through catalytic reforming, with the goal of improving efficiency in gas engines and turbines.
Article
Engineering, Electrical & Electronic
Lili Ren, Cheng Kong, Xiaohui Weng, Rongsheng Zhao, Zongwei Yao, Zhiyong Chang
Summary: This article investigates the ability and feasibility of using electronic nose (E-nose) for oil content recognition in oil shale for the first time. The study uses transient and steady-state fusion feature extraction method to obtain more comprehensive information of oil shale, and further discusses the influence of feature extraction methods and temperatures on oil content recognition. The experimental results show that E-nose combined with the proposed feature extraction method can realize rapid in situ recognition of oil content at low temperatures. This work will contribute to the development of more effective methods for evaluating and developing oil shale.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Lei OuYang, Ningde Jin, Weikai Ren
Summary: Uncovering the dynamic behavior of different flow patterns is crucial for multiphase flow research. In this study, a novel deep neural network framework is proposed to extract deep characteristic information of different flow patterns. The model combines BiLSTM and CNN, and also introduces attention mechanism and residual connection to improve performance. Experimental results demonstrate that the proposed model achieves more precise flow pattern identification, providing a new approach for investigating industrial multiphase flow.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
OuYang Lei, Ningde Jin, Weikai Ren
Summary: A novel deep neural network framework is proposed in this study, combining BiLSTM and CNN to extract deep characteristic information of different flow patterns, and introducing attention mechanism and residual connection to improve network performance. The model has been verified to output more precise flow pattern identification, opening up a new way for investigating industrial multi-phase flow.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Energy & Fuels
Yanpeng Xu, Liguo Wang, Xiangjun Chen, Yangyang Fan
Summary: This paper addresses the common problem of borehole deflection in coal mines and proposes anti-inclination drilling technology. By improving drilling equipment and parameters, the boreholes can be drilled straight, significantly reducing deviation and improving gas drainage efficiency, ensuring safe mine production.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2022)
Article
Engineering, Environmental
Yosuke Muranaka, Taisuke Maki, Daiki Nakayoshi, Shusaku Asano, Katsuya Ikebata, Aiichiro Nagaki, Yosuke Ashikari, Kyoko Mandai, Kazuhiro Mae
Summary: The study demonstrated the chiral separation of a water-soluble amino acid derivative using a multiphase segmented flow technique. By utilizing simultaneous extraction and back-extraction with multiple phases, the accumulation of target material was prevented in the extraction phase and efficiently removed in the recovery phase, resulting in extraction performance beyond equilibrium.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Chemistry, Multidisciplinary
Abdullah K. Alanazi, Seyed Mehdi Alizadeh, Karina Shamilyevna Nurgalieva, Slavko Nesic, John William Grimaldo Guerrero, Hala M. Abo-Dief, Ehsan Eftekhari-Zadeh, Ehsan Nazemi, Igor M. Narozhnyy
Summary: This innovative non-invasive system uses a dual-energy gamma source and two detectors, along with artificial intelligence, to determine the flow pattern and volume percentage of two-phase flow by considering the thickness of scales in the pipelines.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Benyamin Yadali Jamaloei, Mingzhe Dong, Nader Mahinpey
Summary: The enhanced cyclic solvent process (ECSP) shows improved recovery efficiency compared to traditional methods, especially with a larger propane-slug size. The cyclic inverse water-alternating-gas (WAG) injection significantly enhances recovery rates and reduces gas consumption. The addition of an oil-soluble foaming surfactant in the cyclic solvent process (CSP) greatly improves recovery factors and rates, making them comparable to the performance of ECSP with methane and a large propane slug.
SPE RESERVOIR EVALUATION & ENGINEERING
(2021)
Article
Construction & Building Technology
Hansong Xiao, Jingfeng Shi, Zixu Yang, Baolong Wang, Wenxing Shi, Meng Li
Summary: This study proposes two methods to improve the accuracy of the CSEC method in VRF system performance measurement, and the effectiveness of these two methods is experimentally verified. The experiment also measures and analyzes the capacity distribution and heat loss of the VRF system.
BUILDING AND ENVIRONMENT
(2022)
Article
Energy & Fuels
Yongshui Kang, Zhi Geng, Bin Liu, Jianben Chen
Summary: Gas hazards pose a serious threat to the safety of coalmines. Gas extraction through in-seam boreholes is an important technical approach for preventing gas hazards and achieving rational exploitation of gas as a clean resource. Improper extraction design may result in inefficient gas extraction and create the risk of gas emission overrun, or even coal and gas burst during mining process.
GEOMECHANICS FOR ENERGY AND THE ENVIRONMENT
(2023)
Article
Instruments & Instrumentation
T-C Chen, O. Taylan, S. M. Alizadeh, M. T. Yilmaz, E. Nazemi, M. Balubaid, G. H. Roshani, D. Karaboga
Summary: This study investigates the application of X-ray tubes combined with feature extraction and artificial intelligence techniques in determining volumetric percentages in two-phase flows. The results show that the use of the GMDH neural network model with extracted time-domain features effectively predicts volume percentages.
MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA
(2023)
Article
Engineering, Chemical
Tzu-Chia Chen, Seyed Mehdi Alizadeh, Marwan Ali Albahar, Mohammed Thanoon, Abdullah Alammari, John William Grimaldo Guerrero, Ehsan Nazemi, Ehsan Eftekhari-Zadeh
Summary: This research presents an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The system includes an X-ray tube, a Pyrex glass pipe, and two sodium iodide detectors. By simulating different flow regimes and volume percentages, the system extracts characteristics from the recorded signals and applies a particle swarm optimization algorithm to determine the best combination of features. The accuracy in determining volume percentages is significantly improved compared to previous works.
Article
Engineering, Chemical
Shivan Mohammed, Lokman Abdulkareem, Gholam Hossein Roshani, Ehsan Eftekhari-Zadeh, Ezadin Haso
Summary: This study utilizes gamma-ray and electrical capacitance sensors to analyze void fraction data in multiphase flows. By combining the data from both techniques using a neural network model, more accurate results can be obtained compared to individual measurements. This hybrid measurement system represents a step towards an adaptive observation system for multiphase flow applications.
Article
Medicine, General & Internal
Yanjie Lu, Nan Zheng, Mingtao Ye, Yihao Zhu, Guodao Zhang, Ehsan Nazemi, Jie He
Summary: Air kerma is a crucial parameter in medical diagnostic radiology, used to evaluate organ doses and patient hazards. An intelligent technique based on radial basis function neural network (RBFNN) is presented to predict air kerma at any point within the X-ray beam's field of view in medical diagnostic imaging systems. The trained RBFNN model can estimate air kerma at random positions within the range of medical diagnostic radiology.
Correction
Biology
Yule Wang, Osman Taylan, Abdulaziz S. Alkabaa, Ijaz Ahmad, Elsayed Tag-Eldin, Ehsan Nazemi, Mohammed Balubaid, Hanan Saud Alqabbaa
Article
Mathematics, Applied
Aryan Veisi, Mohammad Hossein Shahsavari, Gholam Hossein Roshani, Ehsan Eftekhari-Zadeh, Ehsan Nazemi
Summary: In this study, experimental validation was conducted to accurately determine phase fractions in two-phase flows in power plants, petroleum, and petrochemical industries. The simulations for water-air, two-phase flow in an annular pattern were verified through finite element simulations and experimental investigations. The results indicated a relatively low relative error between the simulation and experiment, validating the accuracy of the simulations. An Artificial Neural Network (ANN) model was developed to predict void fractions in different liquid-gas two-phase flows, and the void fraction was accurately measured using the proposed metering system.
Article
Engineering, Electrical & Electronic
Guodao Zhang, Yisu Ge, Haojie Xu, Abdulilah Mohammad Mayet, Yanjie Lu, Mingtao Ye, Ehsan Nazemi
Summary: This paper presents a high-speed, low-cost, and efficient digital circuit for emulating the plausible calcium-dynamic-based model of astrocyte which has spontaneous oscillations. The proposed model is able to accurately simulate biological cell behaviors and consumes only 2% of the resources of a Virtex 4 board. Timing analysis shows that the proposed model works at a high frequency of 371.56 MHz.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Green & Sustainable Science & Technology
Xiaohua Hou, Bo Cheng, Zhiliang Xia, Haijun Zhou, Qi Shen, Yanjie Lu, Ehsan Nazemi, Guodao Zhang
Summary: In order to promote ecological sustainability, researchers are increasingly interested in the issue of sulphur dioxide emissions. However, current research mainly focuses on the relationship between sulphur dioxide (SO2) emissions, foreign direct investment (FDI), and trade, and the effects of trade on SO2 emissions, without giving sufficient consideration to the impact of the institutional environment and economic growth on SO2 emissions. This paper empirically tests the institutional environment and economic growth of sulphur dioxide (SO2) emissions using provincial panel data from 2008 to 2017, employing a fixed effects model. The results indicate an inverted U-shaped relationship between GDP growth and SO2 emissions. The institutional environment and higher government intervention in the region lead to a significant decrease in SO2 emissions, and the institutional environment and level of government intervention have a negative regulatory role on economic growth and SO2 emissions. The paper provides empirical evidence based on environmental governance research, particularly in the context of regional environmental governance and government environmental performance audit policy, thus promoting sustainable ecological and environmental development.
Article
Computer Science, Information Systems
Mohammad Hossein Shahsavari, Aryan Veisi, Gholam Hossein Roshani, Ehsan Eftekhari-Zadeh, Ehsan Nazemi
Summary: This paper investigates the measurement of volume fractions in multi-phase flows in petrochemical industries. Experiments and simulations were conducted to evaluate the performance of vertical concave, horizontal concave, and double-ring sensors in stratified two-phase flow. The simulation results were validated with experimental data. To extract more data, the COMSOL Multiphysics software was used due to the limited availability of experimental data, and the sensitivity of different directions of concave and double-ring sensors was compared. The results show that the overall sensitivity of the concave sensor is higher than the double-ring sensor, and the horizontal concave sensor has higher momentary sensitivity at higher void fractions, while the vertical concave sensor has higher sensitivity at lower void fractions.
Article
Medicine, General & Internal
Licheng Zhang, Fengzhe Xu, Lubing Wang, Yunkui Chen, Ehsan Nazemi, Guohua Zhang, Xicai Zhang
Summary: The amount of energy given off by a radioactive substance, known as the air kerma, is crucial for medical specialists using radiation for cancer diagnosis. This study presents a model-based approach using GMDH neural networks to predict air kerma at different positions within the radiation field of medical imaging instruments. The trained neural network accurately determines air kerma at any location in the X-ray field of view with a Mean Relative Error (MRE) of less than 0.25%.
Article
Mathematics, Interdisciplinary Applications
Abdullah M. M. Iliyasu, Abdallah S. S. Benselama, Dakhkilgova Kamila Bagaudinovna, Gholam Hossein Roshani, Ahmed S. Salama
Summary: The increasing global demand for fossil fuels has led to a rise in the importance of flow measurement in the oil sector, creating a new submarket in the flowmeter business. This study proposes a fraction detection system for gamma-based two-phase flowmeters using time-feature extraction methods, a particle swarm optimization (PSO) based feature selection system, and an artificial neural network. Utilizing these techniques, the study achieves high accuracy while reducing calculation load and improving the precision of the system.
FRACTAL AND FRACTIONAL
(2023)
Article
Mathematics, Interdisciplinary Applications
Abdullah M. M. Iliyasu, Farhad Fouladinia, Ahmed S. S. Salama, Gholam Hossein Roshani, Kaoru Hirota
Summary: Determining the void fraction in multiphase flows is crucial for the oil, chemical and petrochemical industries. Capacitance based two phase flow meters are highly sensitive to fluid properties, and changes in liquid density can cause significant errors. This study proposes an AI-based method that eliminates the need for recalibration and can accurately measure void fraction regardless of liquid phase changes. The method was validated using COMSOL Multiphysics software and a concave sensor, and a Multi-Layer Perceptron (MLP) neural network model trained with simulated data achieved a Mean Absolute Error (MAE) of 1.74 in predicting void fraction.
FRACTAL AND FRACTIONAL
(2023)
Article
Mathematics
Abdullah M. Iliyasu, Dakhkilgova Kamila Bagaudinovna, Ahmed S. Salama, Gholam Hossein Roshani, Kaoru Hirota
Summary: This study proposes a detecting system using a Pyrex-glass pipe, X-ray tube, and NaI detector to determine volume percentages in oil transmission lines. Simulation and feature extraction were conducted to improve the accuracy of this system. The novel aspects of this research include the use of time, frequency, and wavelet characteristics, as well as a PSO-based feature selection method.
Article
Engineering, Mechanical
Budi Rochmanto, Hari Setiapraja, Ihwan Haryono, Siti Yubaidah
Summary: This study calibrates a turbine flowmeter for compressed natural gas (CNG) application by using air as a substitute and simulating the kinematic viscosity property of CNG. The research shows that by using air instead of CNG, the flowmeter can achieve accurate measurements with a measurement uncertainty of less than 1%.
FLOW MEASUREMENT AND INSTRUMENTATION
(2024)
Article
Engineering, Mechanical
Mona Mary Varghese, Chaithanya P. Devan, Samiksha M. Masram, Teja Reddy Vakamalla
Summary: This work investigated the influence of particle shape on fluidization behavior at different inlet superficial gas velocities. The experiments were conducted using a laboratory-scale 3D circular fluidized bed column with Geldart D particles of various shapes. The results showed that non-spherical particles had lower minimum fluidization velocities and higher bed expansion compared to spherical particles. Particle shape significantly affected solids holdup, with spherical particles exhibiting higher solids holdup at the same superficial velocity. Frequency domain analysis of pressure signals using Fast Fourier Transform (FFT) and Power Spectral Density (PSD) revealed flow regime transitions associated with changes in particle shape.
FLOW MEASUREMENT AND INSTRUMENTATION
(2024)
Article
Engineering, Mechanical
Ruiming Yu, Yunyan Ma, Kuaile Liu, Xiangyu Liu
Summary: A single-seat control valve with stable flow regulation is researched and designed to address technical problems such as unstable flow at small openings and uneven force on the valve core. The mechanical and flow characteristics, as well as thermal stress, are analyzed through simulations and tests. The results show that the designed valve meets the requirements.
FLOW MEASUREMENT AND INSTRUMENTATION
(2024)
Article
Engineering, Mechanical
Alcemir Costa de Souza, Ewerton Emmanuel da Silva Calixto, Fernando Luiz Pellegrini Pessoa, Valeria L. da Sila, Luiz Octavio Vieira Pereira
Summary: This study proposes a simple CO2 meter for accurately measuring the CO2 content in Brazilian pre-salt production flows. By analyzing the pressure change during a heating assay of an imprisoned sample, the proposed meter is capable of identifying the mixture properties under different CO2 levels.
FLOW MEASUREMENT AND INSTRUMENTATION
(2024)
Article
Engineering, Mechanical
Mehdi Asadi, S. Abbas Hosseini, Kaveh Ahangari
Summary: Due to technical issues with bottom intake racks, porous intakes made of rock, gravel, or sand can be a viable alternative. This study used an experimental model to assess the performance of porous bottom intakes (PBI) and examined the impacts of various parameters such as channel slope, grain size distribution of the porous media, intake structure geometry, and water depth in the channel on diverted flow rates during sediment-free flow. The study also compared the performance of one-sided and three-sided PBI models under the same conditions. The findings suggest that a slope of 1% yields higher discharge coefficient and diverted flow compared to a slope of 1.68%, and three-sided PBI models outperform one-sided models in terms of flow rate. A formula utilizing nonlinear multivariate regression, experimental data, and dimensional analysis was proposed for calculating the discharge coefficient of PBI, with a high accuracy rate of over 95%.
FLOW MEASUREMENT AND INSTRUMENTATION
(2024)
