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Toward robust models for predicting carbon dioxide absorption by nanofluids

GREENHOUSE GASES-SCIENCE AND TECHNOLOGY 2022年第4期12卷 537-551页

作者： Amar, Menad Nait Djema, Hakim Belhaouari, Samir Brahim Zeraibi, Noureddine Sonatrach Dept Etud Thermodynam Div Labs Ave 1er Novembre Boumerdes 35000 Algeria Hamad Bin Khalifa Univ Div Informat & Comp Technol Coll Sci & Engn Qatar Fdn POB 34110 Doha Qatar Univ MHamed Bougara Boumerdes Lab Hydrocarbons Phys Engn Fac Hydrocarbons & Chem Ave Independance Boumerdes 35000 Algeria

The application of nanofluids has received increased attention across a number of disciplines in recent years. Carbon dioxide (CO2) absorption by using nanofluids as the solvents for the capture of CO2 is among the attractive applications, which have recently gained high popularity in various industrial aspects. In this work, two robust explicit-based machine learning (ML) methods, namely group method of data handling (GMDH) and genetic programming (GP) were implemented for establishing accurate correlations that can estimate the absorption of CO2 by nanofluids. The correlations were developed using a comprehensive database that involved 230 experimental measurements. The obtained results revealed that the proposed ML-based correlations can predict the absorption of CO2 by nanofluids with high accuracy. Besides, it was found that the GP-based correlation yielded more precise predictions compared to the GMDH-based correlation. The GP-based correlation has an overall coefficient of determination of 0.9914 and an overall average absolute relative deviation of 3.732%. Lastly, the carried-out trend analysis confirmed the compatibility of the proposed GP-based correlation with the real physical tendency of CO2 absorption by nanofluids. (c) 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

关键词： nanofluids carbon dioxide machine learning genetic programming group method of data handling

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Forecasting of Wind Speed by Using Three Different Techniques of Prediction Models

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Annals of data Science 2023年第3期10卷 679-711页

作者： Verma, Manoj Ghritlahre, Harish Kumar Chhattisgarh Bhilai491107 India

Wind energy plays a major role in meeting the world’s growing power demand, due to which wind speed forecasting is essential for power system management, energy trading and maintaining the balance between consumption and generation for a stable electricity market. In this article, three different types of predicting techniques have been implemented for estimating wind speed by means of different meteorological parameters. group method of data handling (GMDH), multi linear regression (MLR) and artificial neural network (ANN) models have been developed. For these models, data sets of 05 years (12 datasets from each year) were collected from the National Renewable Energy Laboratory (NREL). Five different types of input variables, which are ambient temperature (Ta), atmospheric pressure (Pr), wind direction (WD), relative humidity (RH) and precipitation (Pc) were selected as independent variables in all models. The collected wind speed (Wv) is selected as output or dependent variable. In this study, 48 sets of data were picked for training process and 12 datasets were selected for testing. The performances of models were examined using statistical parameters such as RMSE, MAPE and R2. MLR, GMDH and ANN techniques accurately performed with values of correlation coefficient (R) being obtained as 0.90552, 0.95542 and 0.97617 respectively. Comparative study of all models reveals that out of these three techniques, ANN performs the best. In the ANN model, the values of RMSE, MAE and R2 obtained were 0.17476, 0.12984 and 0.95210 respectively, which are optimal results when compared to those of other models. After ANN, GMDH performed better than MLR. Above analysis reveals that the wind speed was predicted with the highest accuracy by the neural technique. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： group method of data handling Multi linear regression Artificial neural network Wind speed Wind energy Prediction model Forecasting Artificial neural network Wind group method of data handling Wind speed Wind power Statistical parameter Wind direction

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Estimation of Compressive Strength of Rubberised Slag Based Geopolymer Concrete Using Various Machine Learning Techniques Based Models

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2024年第2期49卷 1157-1172页

作者： Yeluri, Sesha Choudary Singh, Karan Kumar, Akshay Aggarwal, Yogesh Sihag, Parveen Longspan Structures Pvt LTD Mukai Chowk Kiwale Pune 412101 India School of Civil Engineering Lovely Professional University Punjab Phagwara 144411 India School of Architecture Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Civil Engineering N.I.T Kurukshetra Haryana Kurukshetra 136119 India Department of Civil Engineering Chandigarh University Punjab Mohali 140413 India

In the quest for sustainable construction practices, researchers have been exploring alternative materials that can reduce the reliance on traditional cement in concrete production. Geopolymer concrete (GPC) has surfaced as a promising alternative due to its potential ecological benefits. The formulation of GPC mixtures is a challenging task as there is no specific code provision to determine the mix design. The complexity of determining the optimal mix proportions is compounded by the influence of various factors, including the Na₂SiO₃/NaOH ratio, the quantities of sodium silicate (Na₂SiO₃) and sodium hydroxide (NaOH), and differing curing periods, all of which significantly impact the concrete’s mechanical properties. A variety of predictive modeling techniques, including multivariate adaptive regression splines (MARS), group method of data handling (GMDH), M5P, and linear regression (LR), are used in the estimation of the compressive strength of rubberized slag-based GPC. This study utilizes a dataset comprising 186 observations, which are divided into a training dataset of 130 observations and a testing dataset of 56 observations. The investigation considers various input parameters such as the molarity of NaOH (S), Na₂SiO₃ quantity (SS), sand quantity (S), coarse aggregate quantity (CA), NaOH quantity (M), the quantity of copper slag (C), rubber aggregate quantity (RA), curing period (D), and fly ash (FA), with the compressive strength serving as the output constraint. The efficacy of these approaches is assessed using performance indices such as the coefficient of correlation (CC), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), and scattering index (SI). The findings indicate that the MARS model outperforms the other soft computing techniques, with a testing CC of 0.9634, MAE of 1.4509, RMSE of 1.8465, SI of 0.0480, and NSE of 0.9265. Conversely, the L

关键词： Compressive strength Geopolymer concrete group method of data handling M5P Multivariate adaptive regression splines

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Resilient Model Predictive Adaptive Control of Networked Z-Source Inverters Using GMDH

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IEEE TRANSACTIONS ON SMART GRID 2022年第5期13卷 3723-3734页

作者： Ahmadi, Amirhossein Asadi, Yasin Amani, Ali Moradi Jalili, Mahdi Yu, Xinghuo Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada Shahid Bahonar Univ Kerman Dept Elect Engn Kerman *** Iran RMIT Univ Sch Engn Melbourne Vic 3001 Australia

Power grids are increasingly evolving into more efficient smart grids thanks to advancements in information technology. However, these intelligent communication-dependant power systems are becoming more susceptible to cyberattacks than their traditional peers. Power inverters are one of the main network connected devices supporting grid stability, which can potentially be targets for cyber-threats. This article presents an attack-resilient model predictive adaptive controller for a class of inverters, called Z-source inverters, to protect them against deception attacks. The proposed control structure includes a model predictive controller equipped with an unknown input Kalman filter and an estimator for system perturbations. The group method of data handling technique is used to estimate system uncertainties and make the system robust against perturbations. The unknown input Kalman filter is also adopted to estimate the states and unknown inputs in the presence of noisy measurements and cyberattacks on the control signal. We mathematically prove that in the presence of noise and perturbations, the proposed controller guarantees the stability of the system under a deception attack, which causes a delay, less than a sampling time, in control signals. Simulation results reveal the effectiveness of the presented controller in protecting the system against pulse, scaling and random attacks in the presence of system uncertainties, source and load fluctuations and output noises.

关键词： Z-source inverter attack resilient model predictive adaptive control group method of data handling unknown input Kalman filter

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Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第15期29卷 21935-21954页

作者： Khodakhah, Hedieh Aghelpour, Pouya Hamedi, Zahra Gorgan Univ Agr Sci & Nat Resources Dept Water Engn Gorgan Golestan Iran Bu Ali Sina Univ Fac Agr Dept Water Engn Hamadan Hamadan Iran Univ Birmingham Comp Sci Dept Birmingham W Midlands England

River flow variations directly affect the hydro-climatological, environmental, and ecological characteristics of a region. Therefore, an accurate prediction of river flow can critically be important for water managers and planners. The present study aims to compare different data-driven models in predicting monthly flow. Two river catchments located in the Guilan province in Iran, where rivers play an essential role in agricultural productions (mainly rice), are studied. The monthly river flow dataset was provided by Guilan Regional Water Authority during 1986-2015. The models are derived from two different numerical types of stochastic and machine learning (ML) models. The stochastic model is seasonal autoregressive integrated moving average (SARIMA), and the MLs are least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH). The inputs were selected by autocorrelation and partial autocorrelation functions (ACF and PACF) from the flow rates of the previous months. The data was divided into 75% of training and 25% of testing phases, and then the mentioned models were implemented. Predictions were evaluated by the criteria of root mean square error (RMSE), normalized RMSE (NRMSE), and Nash Sutcliff (NS) coefficient. According to the calculated values of different criteria during the test phase, RMSE = 1.138 cms, NRMSE = 0.109, and NS = 0.826, it can be concluded that the SARIMA model was superior to its ML competitors. Among the ML models, GMDH had the best performance (by RMSE = 1.290 cms, NRMSE = 0.124, and NS = 0.777) because it has more optimization parameters and sample space for network make-up. The models were also evaluated in hydrological drought conditions of both rivers. It was resulted that the rivers' flow can be well predicted in drought conditions by using these models, especially the SARIMA stochastic model. According to the NRMSE values (ranged between 0.1 and 0.2), the accurac

关键词： River flow variations Stochastic models Hydrological simulation group method of data handling Support vector machine

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Developing a robust correlation for prediction of sweet and sour gas hydrate formation temperature

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Petroleum 2022年第2期8卷 204-209页

作者： Mohammad Mesbah Samaneh Habibnia Shahin Ahmadi Amir Hossein Saeedi Dehaghani Sareh Bayat Young Researchers and Elites Club Science and Research BranchIslamic Azad UniversityTehranIran Department of Chemical Engineering Sahand University of TechnologyTabrizIran Department of Environmental Health Engineering Faculty of Environment Health EngineeringZabol University of Medical SciencesZabolIran Department of Petroleum Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityTehranIran Department of Environmental Health Engineering School of Public HealthHamedan University of Medical SciencesHamedanIran

There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas *** study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling(GMDH)for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour *** establish the hybrid GMDH,the total experimental data of 343 were obtained from open *** selection of input variables was based on the hydrate structure formed by each gas *** modeling resulted in a strong algorithm since the squared correlation coefficient(R2)and root mean square error(RMSE)were 0.9721 and 1.2152,*** comparison to some conventional correlation,this model represented not only the outstanding statistical parameters but also its absolute superiority over *** particular,the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and *** statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the *** to this algorithm,approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.

关键词： Hydrate formation temperature HFT Wide range of natural gas mixtures Unified correlation group method of data handling GMDH Outlier detection

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Soil infiltration, its prediction, and GIS-mapping in calcareous soils in northwest Iran

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ARCHIVES OF AGRONOMY AND SOIL SCIENCE 2022年第2期68卷 242-256页

作者： Kang, Xin Ma, Xing Fan, Haibo Liu, Haitao Shojaaddini, Abouzar Shaanxi Prov Land Engn Construct Grp Xian Shaanxi Peoples R China Tarbiat Modares Univ Coll Agr Soil Sci Dept Tehran Iran

Deriving accurate pedo-transfer functions (PTFs) for predicting difficult-to-measure soil properties such as soil cumulative infiltration (CI) is an essential issue for saving time and cost. This study aimed to develop MLR (multiple-linear-regression), ANN (artificial-neural-network) and GMDH (group-method-of-data-handling) PTFs for predicting CI at different time intervals (i.e. 1, 2, 5, 15, 30, 60 and 120 min) and mapping in GIS in calcareous soils in northwest Iran. Soil infiltration measurements with a double-ring infiltrometer were carried out at 124 points with three replications. At each point, various easily measurable soil properties were measured. The results indicated that ANN-based PTFs provided the highest E (from 0.84 to 0.97) and the lowest RMSE (root mean square error) (from 0.19 to 8.91) compared to GMDH-based PTFs (E = 0.43-0.81 and RMSE = 0.34-19.59) and MLR-based PTFs (E = 0.41-0.83 and RMSE = 0.34-16.50). In addition, the map of soil CI at different time intervals was generated based on the best-derived PTFs (i.e. ANN-based PTFs) in GIS. We concluded that ANN-based PTFs provided an accurate prediction and a high-quality map to such costly studies by using easily measurable basic soil properties as input data in calcareous soils.

关键词： Artificial neural network cumulative infiltration group method of data handling multiple linear regression

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Application of GMDH model for predicting the fundamental period of regular RC infilled frames

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STEEL AND COMPOSITE STRUCTURES 2022年第1期42卷 123-137页

作者： Viet-Linh Tran Kim, Seung-Eock Sejong 1 Dept Civil & Environm Engn 98 Gunja Dong Seoul 05006 South Korea Vinh Univ Dept Civil Engn Vinh 461010 Vietnam

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.

关键词： fundamental period group method of data handling reinforced concrete infilled frame structure soft computing

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A novel TS Fuzzy-GMDH model optimized by PSO to determine the deformation values of rock material

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NEURAL COMPUTING & APPLICATIONS 2022年第18期34卷 15755-15779页

作者： Harandizadeh, Hooman Armaghani, Danial Jahed Hasanipanah, Mahdi Jahandari, Soheil Shahid Bahonar Univ Kerman Fac Engn Dept Civil Engn Pajoohesh SqPOB 76169133 Kerman Iran South Ural State Univ Inst Architecture & Construct Dept Urban Planning Engn Networks & Syst 76 Lenin Prospect Chelyabinsk 454080 Russia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Western Sydney Univ Ctr Infrastruct Engn Penrith NSW 2751 Australia

Since determining the rock deformation directly in the laboratory is costly and time consuming, it is important to reliably determine/estimate this parameter through the use of several simple rock index tests. This study develops a new hybrid intelligent technique according to Takagi-Sugeno Fuzzy Inference System-group method of data handling optimized by the particle swarm optimization, called TS Fuzzy-GMDH-PSO for prediction of the rock deformation. The PSO role in this advanced system is to optimize the membership functions of TS Fuzzy-GMDH model for enhancing the level of prediction capacity. In this research, four rock index tests including Schmidt hammer, p-wave velocity, porosity and point load were selected and conducted in laboratory in order to establish a suitable database for prediction purposes. To demonstrate the feasibility and applicability of the advanced hybrid model, two base models of TS Fuzzy and GMDH were also modeled to forecast rock deformation. After conducting several sensitivity analyses on the mentioned models to get the highest performance capacity, their prediction levels were evaluated using some statistical indices, such as root mean square error and correlation coefficient (R). The comparative results confirmed the superiority of the TS Fuzzy-GMDH-PSO over other two models, namely TS Fuzzy and GMDH in terms of both train and test phases. It can be concluded that the TS Fuzzy-GMDH-PSO can be recommended as a powerful, capable and new model to solve the problems related to rock strength and deformation.

关键词： Rock deformation Particle swarm optimization TS Fuzzy group method of data handling

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Identification of Shepherd Guidance Model Based on Real Shepherding data 62

Identification of Shepherd Guidance Model Based on Real Shep...

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62nd Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Tsunoda, Yusuke Sueoka, Yuichiro Wada, Teruyo Osuka, Koichi Osaka Univ Dept Mech Engn Osaka Japan

ISBN: (纸本)9784907764807

Shepherding is a typical collective behavior arising from heterogeneous interactions between swarms and individuals of other species. Understanding the herding model by which a few shepherds command a sheep flock can lead to guide robot groups and enable control of artificial objects that indirectly guide swarms. Most previous studies have proposed empirical guidance models based on human observation and analyzed the navigation performance for group models in simulations. Thus few research attempts have been made to reproduce the models for real shepherds. In this study, we aim to estimate a minimal navigation model of the shepherd resembling the behaviors of shepherds by using system identification based on positioning data acquired during actual herding. For this purpose, we adopted the nonlinear system identification method GMDH (group method of data handling). Using thsi method, we constructed estimation functions by natural selection from as many input variables as possible to determine the factors influencing the estimation target.

关键词： shepherding problem group method of data handling group navigation swarm system identification

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