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GEOENERGY SCIENCE AND ENGINEERING 2024年 242卷

作者： Mehrjoo, Hossein Riazi, Mohsen Rezaei, Farzaneh Ostadhassan, Mehdi Hemmati-Sarapardeh, Abdolhossein Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran Shiraz Univ IOR EOR Res Inst Enhanced Oil Recovery EOR Res Ctr Shiraz Iran Amirkabir Univ Technol Dept Petr Engn Tehran Iran Univ Kiel Inst Geosci Marine & Land Geomech & Geotecton D-24118 Kiel Germany

In the present study, two white-box correlative models, namely gene expression programming (GEP) and group method of data handling (GMDH), were implemented to predict the interfacial tension (IFT) of brine-methane system. To achieve this, 790 data points were collected from literature, with pressure, temperature, and salinity as inputs and IFT as the output. The range of salinity, pressure, and temperature is from 0 to 107000 ppm, 0.01-260 MPa, and 278.1-477.59 K, respectively. The results show that the GMDH model with an R2 of 0.9321 and an average absolute percent relative error (AAPRE) of 3.68% outperforms the GEP, which has an AAPRE of 5.08% and an R2 of 0.8943. Based on our investigation, GMDH and GEP models follow the expected trend of IFT with the variation of pressure. It has been found that the GMDH model exhibits better performance compared to the previously developed correlations in the literature. Furthermore, the sensitivity analysis indicated that temperature has the greatest effect on IFT. By using the leverage approach, we proved that the GMDH model is statistically valid.

关键词： Interfacial tension group method of data handling Gene expression programming Water/brine-methane system

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A Lightweight Chip-Scale Chemical Mechanical Polishing Model Based on Polynomial Network

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2024年第2期37卷 139-145页

作者： Ji, Ruian Chen, Rong Chen, Lan Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Chemical mechanical polishing/planarization (CMP) combines physical grinding and chemical reactions to planarize the wafer surface. The complex mechanism of CMP brings great challenges to the mechanism-based modeling process. The data-driven CMP modeling process is limited by insufficient datasets. At the same time, these two types of models generally have high computational complexity. In this paper, we introduce the group method of data handling (GMDH)-type polynomial network to build the CMP model to address the above challenges. We designed and manufactured the test chip using a 28nm process. The measurement data from the test chip shows that compared with the mechanism-based CMP model, the trained CMP model based on GMDH-type polynomial network has higher accuracy and lower computational complexity, with the average simulation speed being 115x faster. Experiments based on silicon data show that this modeling method has a small demand for data, and 20 randomly selected sets of data can meet the needs for modeling the current CMP process.

关键词： Chemical mechanical polishing polynomial network group method of data handling

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Effect of model selection approach obtained by machine learning tools on predicting the volume reduction of plant-based dehydrated foods

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JOURNAL OF FOOD ENGINEERING 2025年 391卷

作者： Thibault, Bruno Zeynoddin, Mohammad Bonakdari, Hossein Ratti, Cristina Khalloufi, Seddik Laval Univ Soils Sci & Agrifood Engn Dept 2425 Rue Agr Quebec City PQ G1V 0A6 Canada Inst Nutr & Funct Foods 2440 Blvd Hochelaga Suite 1710 Quebec City PQ G1V 0A6 Canada Univ Ottawa Dept Civil Engn 161 Louis Pasteur Private Ottawa ON K1N 6N5 Canada

Previous research has identified eight key factors-product type, initial moisture content, pretreatment method, drying technology, temperature, pressure, microwave power, and sample thickness-that influence the shrinkage coefficient in dehydrated foods. Despite this understanding, there is a lack of comprehensive studies exploring the effect of model selection on predicting shrinkage. This study aims to evaluate the impact of different artificial intelligence (AI) model selection approaches on the prediction accuracy of the shrinkage coefficient for dehydrated foods. Specifically, we compare the performance of three AI techniques: Extreme Learning Machine (ELM), Evolutionary Polynomial Regression (EPR), and group method of data handling (GMDH). Our findings provide valuable insights into the role of model selection in enhancing the predictive accuracy of AI-driven models for food dehydration processes. To achieve this, two model selection approaches were considered: 1) prioritizing higher accuracy and minimizing error, and 2) conducting a comprehensive evaluation of the overall performance of the input variables. The obtained results showed that product type, drying technology, and temperature were consistently involved in the most effective model combinations, underscoring their critical relevance in accurately estimating the shrinkage coefficient. However, the most accurate model involved five inputs (product, technology, temperature, initial moisture content, and pretreatment), was developed with ELM, and produced a correlation coefficient of 0.8713, a root mean square error of 0.0897, and a Nash-Sutcliffe efficiency of 0.7534. EPR and GMDH models had simpler structures than ELM but lower accuracy. An external validation of the models suggested that selecting input combinations according to a comprehensive assessment of the inputs' global performances lead to models being more generalizable. Therefore, the model selection approach was found to be critical for pre

关键词： Extreme learning machine Evolutionary polynomial regression group method of data handling Prediction Volumetric shrinkage coefficient Food drying

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Effects of sonication time on thermophysical properties of ternary hybrid nanofluid and modeling thermophysical properties utilizing two GMDH and SVR models based on machine learning

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2024年 163卷

作者： Shahsavar, Amin Sepehrnia, Mojtaba Moghaddam, Ali Fateh Farahani, Somayeh Davoodabadi Kermanshah Univ Technol Dept Mech Engn Kermanshah Iran Tech & Vocat Univ Dept Mech Engn Qom Iran Shahabdanesh Univ Dept Mech Engn Qom Iran Arak Univ Technol Sch Mech Engn Arak Iran

Background: With the rapid advancement of nanotechnology and the expansion of its applications in various fields, nanofluids have recently been proposed as a novel strategy for heat transfer operations, and extensive research has been conducted in this regard. Ternary hybrid nanofluids (HNFs) can be used in cooling electronic chips, automotive radiator, solar collectors, heat exchangers, heat pipes, and refrigeration systems. methods: The current study investigates the efficacy of sonication time (ST) on the viscosity and thermal conductivity of a ternary HNF consisting of GO-Fe3O4-TiO2 nanoparticles (NPs) dispersed in the hydraulic oil HLP 68. Nanoparticle volume fractions (NPVFs) of 0.05-1%, STs of 30-120 min, and mixing ratios (MRs) of 1:1:1, 1:1:2, 2:1:1, and 1:2:1 are considered. Moreover, the Support Vector Regression (SVR) and group method of data handling (GMDH) machine learning algorithms are used to accurately estimate the thermal conductivity and viscosity based on the available research data. Significant findings: The outcomes indicated that the viscosity and thermal conductivity of the HNF increase with an intensification in the NPVF. The highest value of viscosity and thermal conductivity were detected for the MR of 2:1:1 and 1:1:1, respectively. The ST was found to have a significant impact on the viscosity and thermal conductivity, which is dependent on the MR and NPVF. The minimum viscosity and maximum thermal conductivity were observed at an ST of 90 min. The highest impact of ST on the viscosity and thermal conductivity was observed for an NPVF of 0.05%. By increasing the ST from 30 to 90 min, thermal conductivity increased between 10.07% (at MR = 1:2:1 and ( = 1%) to 14.82% (at MR = 1:1:1 and NPVF = 0.05%) and viscosity decreased between 1.94% (at MR = 2:1:1 and NPVF = 0.5%) to 24.27% (at MR = 1:1:1 and NPVF = 0.25%). The modeling results showed that the R2 value for thermal conductivity and viscosity is 0.9582 and 0.9832 using SVR, and 0.9531 an

关键词： Sonication duration Machine learning Support vector regression group method of data handling Thermophysical properties Ternary hybrid nanofluid

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White-box machine-learning models for accurate interfacial tension prediction in hydrogen-brine mixtures

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Clean Energy 2024年第5期8卷 252-264页

作者： Qichao Lv Jinglei Xue Xiaochen Li Farzaneh Rezaei Aydin Larestani Saeid Norouzi-Apourvari Hadi Abdollahi Abdolhossein Hemmati-Sarapardeh College of Carbon Neutral Energy China University of Petroleum(Beijing)BeijingChina Unconventional Petroleum Research Institute China University of Petroleum(Beijing)BeijingChina CNPC Bohai Drilling Engineering Company Limited TianjinChina Department of Petroleum Engineering Amirkabir University of TechnologyTehranIran Department of Petroleum Engineering Shahid Bahonar University of KermanKermanIran

The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy *** intrinsic challenge with renewable energy sources is their intermittent nature,which can be addressed by transforming excess energy into hydrogen and storing it safely for future *** securely store hydrogen underground,a comprehensive knowledge of the interactions between hydrogen and residing fluids is *** tension is an important variable influenced by cushion gases such as CO_(2) and *** research developed explicit correlations for approximating the interfacial tension of a hydrogen–brine mixture using two advanced machine-learning techniques:gene expression programming and the group method of data *** interfacial tension of a hydrogen–brine mixture was considered to be heavily influenced by temperature,pressure,water salinity,and the average critical temperature of the gas *** results indicated a higher performance of the group method of data handling-based correlation,showing an average absolute relative error of 4.53%.Subsequently,Pearson,Spearman,and Kendall methods were used to assess the influence of individual input variables on the outputs of the *** showed that the temperature and the average critical temperature of the gas mixture had considerable inverse impacts on the estimated interfacial tension ***,the reliability of the gathered databank and the scope of application for the proposed correlations were verified using the leverage approach by illustrating 97.6%of the gathered data within the valid range of the Williams plot.

关键词： underground hydrogen storage interfacial tension cushion gas correlation gene expression programming group method of data handling

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A Novel Hybrid Machine Learning Approach and Basin Modeling for Thermal Maturity Estimation of Source Rocks in Mandawa Basin, East Africa

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NATURAL RESOURCES RESEARCH 2024年第5期33卷 2089-2112页

作者： Mkono, Christopher N. Shen, Chuanbo Mulashani, Alvin K. Ngata, Mbega Ramadhani Hussain, Wakeel China Univ Geosci Key Lab Tecton & Petr Resources Minist Educ Wuhan 430074 Peoples R China Mbeya Univ Sci & Technol Coll Engn & Technol Dept Geosci & Min Technol Box 131 Mbeya Tanzania China Univ Geosci Key Lab Theory & Technol Petr Explorat & Dev Hubei Wuhan 430074 Peoples R China China Univ Geosci Sch Geophys & Geomat Hubei Multiscale Image Key Lab Wuhan 430074 Peoples R China

Basin modeling and thermal maturity estimation are crucial for understanding sedimentary basin evolution and hydrocarbon potential. Assessing thermal maturity in the oil and gas industry is vital during exploration. With artificial intelligence advancements, more accurate evaluation of hydrocarbon source rocks and efficient thermal maturity estimation are possible. This study employed 1D basin modeling using PetroMod and a novel hybrid group method of data handling (GMDH) neural network optimized by a differential evolution (DE) algorithm to estimate thermal maturity (Tmax) and assess kerogen type in Triassic-Jurassic source rocks of the Mandawa Basin, Tanzania. The GMDH-DE addresses the limitations of conventional methods by offering a data-driven approach that reduces computational time, overcomes overfitting, and improves accuracy. The 1D thermal maturity basin modeling suggests that the Mbuo source rocks reached the gas-oil window in late Triassic times and began expulsion in the early Jurassic while located in an immature-to-mature zone. The GMDH-DE model effectively estimated Tmax with high coefficient of determination (R2 = 0.9946), low root mean square error (RMSE = 0.004), and mean absolute error (MAE = 0.006) during training. When tested on unseen data, the GMDH-DE model yielded an R2 of 0.9703, RMSE of 0.017, and MAE of 0.025. Moreover, GMDH-DE reduced the computational time by 94% during training and 87% during testing. The results demonstrated the model's exceptional reliability compared to the benchmark methods such as artificial neural network-particle swarm optimization and principal component analysis coupled with artificial neural network. The GMDH-DE Tmax model offers a unique and independent approach for rapid real-time determination of Tmax values in organic matter, promoting efficient resource assessment in oil and gas exploration.

关键词： Thermal maturity 1D basin modeling group method of data handling Source rock Well logs Differential evolution

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Estimation of the compressive strength of ultrahigh performance concrete using machine learning models

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2025年 25卷

作者： Kumar, Rakesh Kumar, Divesh Ranjan Wipulanusat, Warit Thongchom, Chanachai Samui, Pijush Rai, Baboo Dayananda Sagar Coll Engn Dept Civil Engn Bengaluru 560111 India Thammasat Univ Fac Engn Thammasat Sch Engn Res Unit Data Sci & Digital TransformatDept Civil Pathum Thani Thailand Thammasat Univ Fdn Engn Fac Engn Thammasat Sch EngnDept Civil Engn Pathum Thani Thailand Natl Inst Technol Dept Civil Engn Patna India

The compressive strength of ultrahigh performance concrete (UHPC) is influenced by the composition, quality, and quantity of its constituent elements. Using traditional statistical methods, it is difficult for us to quantify the relationships between the technical properties of UHPC and the composition of the mixture because of their complexity and nonlinearity. This work aims to develop advanced prediction models for estimating UHPC compressive strength over a large spectrum of supplementary cementitious material combinations and aggregate sizes. The models trained on the UHPC mixture dataset with 15 input variables included the group method of data handling, recurrent neural networks, long short-term memory, and bidirectional long short-term memory (Bi-LSTM). These models routinely forecast UHPC compressive strength according to sensitivity analysis, external validation, and statistical performance measures. During testing, the Bi-LSTM model outperformed the other models, with an RMSE of 0.0482 and an R2 value of 0.9464. These results maximize component selection by showing how effectively the Bi-LSTM model might reduce UHPC formulation development and lower the cost and testing time span.

关键词： Ultrahigh performance concrete group method of data handling Long short-term memory Bidirectional long short-term memory Recurrent neural network

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Improvement in measurement of radiation based two-phase flowmeters independent of flow regime and scale thickness using ant colony optimization and GMDH

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NUCLEAR ENGINEERING AND TECHNOLOGY 2024年第11期56卷 4826-4836页

作者： Mayet, Abdulilah Mohammad Gorelkina, Evgeniya Ilyinichna AlShaqsi, Jamil Parayangat, Muneer Guerrero, John William Grimaldo Raja, M. Ramkumar Muqeet, Mohammed Abdul Mohammed, Salman Arafath King Khalid Univ Elect Engn Dept Abha 61411 Saudi Arabia Peoples Friendship Univ Russia Inst Ecol Dept Green Technol 6 Miklukho Maklay St Moscow 117198 Russia Sergo Ordzhonikidze Russian State Univ Geol Prospe Dept Dev & Operat Oil & Gas Fields 23 Miklukho Maklay St Moscow 117485 Russia Sultan Qaboos Univ Dept Informat Syst POB 20PC 123 Muscat Oman Univ La Costa Dept Energy Barranquilla 080001 Colombia

The formation of scales in pipes is one element that has a major impact on the efficiency of machinery used in the oil and gas sector. With the help of artificial intelligence, this new, non-invasive device was able to figure out the volume fraction of a two-phase flow by taking into account the thickness of the scale in the tested pipeline. The proposed design uses an isotope pair of barium-133 and cesium-137 as a dual-energy gamma generator. One detector records photons that are transmitted, and another detector records photons that are scattered. The signals from the detectors were simulated using the Monte Carlo N-Particle (MCNP) code, and then ten frequency and wavelet characteristics were extracted. To choose the best inputs from the collected features for computing the volume fraction, an ant colony optimization (ACO)-based method is applied. Six attributes, representing the optimal combination, were developed using this method. In order to forecast the volume percentage of two-phase flows independently of flow regime and scale thickness, we fed the characteristics introduced by ACO into a group method of data handling (GMDH) neural network. Volume fraction calculations had a maximum RMSE of 0.056, which is quite little compared to previous research. By using the ACO to choose the best characteristics, the current work has significantly increased its accuracy in identifying volume fractions.

关键词： Dual-energy gamma source group method of data handling Scale thickness Two phase-flows Ant colony optimization

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Prediction of Stress-Dependent Soil Water Retention Using Machine Learning

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2024年第5期42卷 3939-3966页

作者： Mojtahedi, Seyed Farid Fazel Akbarpour, Ali Darzi, Ali Golaghaei Sadeghi, Hamed van Genuchten, Martinus Theodorus Univ Melbourne Dept Infrastruct Engn Parkville Australia Univ Oklahoma Sch Civil Engn & Environm Sci Norman OK USA Sharif Univ Technol Dept Civil Engn Tehran Iran Univ Fed Rio de Janeiro Dept Nucl Engn Rio De Janeiro Brazil

The soil water retention curve (SWRC) provides information for a wide range of geoenvironmental problems, such as analyses of transient two-phase flow, the bearing capacity and shear strength of unsaturated soils. Many past studies have shown experimentally the effects of stress on the SWRC. Unfortunately, direct stress-dependent water retention measurements are relatively time-consuming and generally require special equipment and a certain level of expertise. This study primarily aimed to develop a novel predictive framework within the context of soft computing to capture the dependency of the SWRC on several variables, with an emphasis on stress and soil type. To achieve this, the three shape parameters of van Genuchten's water retention model were estimated using a comprehensive database of 102 SWRC tests retrieved from the literature. In this study, 60% of the datasets were employed for model training, with an additional 20% being designated for validation, while the remaining 20% were set aside for testing the model's performance. The data were analyzed using two machine learning techniques: the group method of data handling and multi-layer perceptron approaches. Results showed excellent performance of the two methods. A sensitivity analysis was conducted to explore the relative significance of the different variables. Interestingly, net stress was found to be almost as significant as soil type. The introduced artificial intelligence based predictive framework provided a very effective method of integrating theory and practice.

关键词： Soil water retention Net stress Machine learning group method of data handling Multilayer perceptron

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Proposing new models to predict pile set-up in cohesive soils

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GEOMECHANICS AND ENGINEERING 2023年第3期33卷 231-242页

作者： Moghadam, Sara Banaei Khanmohammadi, Mohammadreza Isfahan Univ Technol Dept Civil Engn Esfahan *** Iran

This paper represents a comparative study in which Gene Expression Programming (GEP), group method of data handling (GMDH), and multiple linear regressions (MLR) were utilized to derive new equations for the prediction of time -dependent bearing capacity of pile foundations driven in cohesive soil, technically called pile set-up. This term means that many piles which are installed in cohesive soil experience a noticeable increase in bearing capacity after a specific time. Results of researches indicate that side resistance encounters more increase than toe resistance. The main reason leading to pile setup in saturated soil has been found to be the dissipation of excess pore water pressure generated in the process of pile installation, while in unsaturated conditions aging is the major justification. In this study, a comprehensive dataset containing information about 169 test piles was obtained from literature reviews used to develop the models. to prepare the data for further developments using intelligent algorithms, data mining techniques were performed as a fundamental stage of the study. To verify the models, the data were randomly divided into training and testing datasets. The most striking difference between this study and the previous researches is that the dataset used in this study includes different piles driven in soil with varied geotechnical characterization;therefore, the proposed equations are more generalizable. According to the evaluation criteria, GEP was found to be the most effective method to predict set-up among the other approaches developed earlier for the pertinent research.

关键词： cohesive soils gene expression programming group method of data handling pile foundations set-up

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