Group Method Of Data Handling Research Articles

Investigation on the mechanical uncertainty of resistance spot weldings and its effect on structure crashworthiness of automotive B-pillars under side impacts

Resistance spot welding (RSW) is the main connection method for the white body structure of automobiles. Understanding the mechanical uncertainty of RSWs and its effect on structure crashworthiness design is of great significance for vehicle safety and lightweight design. In this work, the mechanical uncertainty of RSW was experimentally investigated and its effect on automotive B-pillar crashworthiness under side impacts was numerically analyzed based on a hybrid response surface model (HRSM). Firstly, the mechanical uncertainty of RSWs and associated failure mechanism were investigated and the fluctuation range of RSW strength under different loading conditions were quantified according to experimental results. Then, a reasonable RSW modeling method and subsystem collision simulation approach were demonstrated and the univariate sensitivity analysis was carried out to reveal the effect of RSWs’ mechanical property on B-pillar crashworthiness. Finally, a HRSM integrating Kriging polynomial and deep neural network (DNN) were proposed as surrogate model, based on which the multi-factor analysis was conducted to explore effects of RSW strength uncertainty on structure crashworthiness design.

Machine learning prediction of the unconfined compressive strength of controlled low strength material using fly ash and pond ash.

The sustainable use of industrial byproducts in civil engineering is a global priority, especially in reducing the environmental impact of waste materials. Among these, coal ash from thermal power plants poses a significant challenge due to its high production volume and potential for environmental pollution. This study explores the use of controlled low-strength material (CLSM), a flowable fill made from coal ash, cement, aggregates, water, and admixtures, as a solution for large-scale coal ash utilization. CLSM is suitable for both structural and geotechnical applications, balancing waste management with resource conservation. This research focuses on two key CLSM properties: flowability and unconfined compressive strength (UCS) at 28days. Traditional testing methods are resource-intensive, and empirical models often fail to accurately predict UCS due to complex nonlinear relationships among variables. To address these limitations, four machine learning models-minimax probability machine regression (MPMR), multivariate adaptive regression splines (MARS), the group method of data handling (GMDH), and functional networks (FN) were employed to predict UCS. The MARS model performed best, achieving R2 values of 0.9642 in training and 0.9439 in testing, with the lowest comprehensive measure (COM) value of 1.296. Sensitivity analysis revealed that cement content was the most significant factor with obtaining R = 0.88, followed by water (R = 0.82), flowability (R = 0.79), pond ash (R = 0.78), curing period (R = 0.73), and fine content (R = 0.68), with fly ash (R = 0.55) having the least impact. These machine learning models provide superior accuracy compared to traditional methods, particularly in handling complex interactions between mix components. The proposed models offer a practical approach for predicting CLSM performance, supporting sustainable construction practices and the efficient use of industrial byproducts. The novelty of this study lies in the development of precise design equations for evaluating UCS, promoting both practical applicability and environmental sustainability.

Predictive modeling of CO2 capture efficiency using piperazine solutions: a comparative study of white-box algorithms

The urgency to mitigate carbon dioxide (CO2) emissions and combat climate change has spurred the development of effective CO2 capture technologies. One of the industry’s most well-known CO2 collection processes is CO2 absorption utilizing amine solvents. However, designing a successful amine scrubbing system in power plants requires precise prediction of CO2 absorption in aqueous amine solutions under various operating circumstances. Using aqueous piperazine (PZ) solutions for chemical absorption is promising due to the favorable reactivity of PZ with CO2. In this study, a comprehensive evaluation of PZ solution performance in CO2 capturing, employing the white-box algorithms, namely, Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH), was performed. Through extensive experimentation and data analysis, several correlations were developed with high and acceptable R2 values, such as 0.933 for GP, 0.949 for GEP, and 0.889 for GMDH, which shows high accuracy and reliability in predicting the CO2 capture efficiency of PZ solutions under varying operating conditions. The results of sensitivity analysis revealed that CO2 partial pressure increased CO2 absorption, while PZ concentration and temperature had negative and decreasing effects. These insights provide essential guidance for optimizing process conditions to enhance the CO2 capture efficiency. Finally, the leverage method was used to assess the reliability of both experimental and predicted data from white-box algorithms. This analysis identified potential outliers and validated the accuracy of the model’s predictions, enhancing the credibility of developed correlations and demonstrating the robustness of the approach.

Machine learning-aided enhancement of white tea extraction efficiency using hybridized GMDH models in microwave-assisted extraction

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes.

Effect of water to cement ratio on mechanical properties of FRC subjected to elevated temperatures: Experimental and soft computing approaches

The deterioration of concrete is greatly dependent on the cracks and microcracks development owing to loading or environmental influences. An effective step to avoid the spread of cracks and microcracks is employing of different fibers in concrete and the manufacture of fiber reinforced concrete. On the other hand, fire is one of the cases that always threatens engineering structures. Elevated temperatures cause obvious chemical and physical changes that lead to concrete deterioration. In this study, the effect of adding various fiber with different volume contents in concrete mixture with various cement content was evaluated experimentally. Results indicate that spalling was more dominant in mixture containing steel fiber and with higher amount of cement, while there is not any spalling in mixture containing polypropylene (PP) fibers. Moreover, the reduction in tensile strength of fiber-free concrete specimens is less pronounced in mixtures with higher cement content. In addition, the positive performance of PP fibers compared to steel fibers was proved at higher temperatures and cement contents. Furthermore, by conducting a comprehensive and accurate survey, this research pinpoints gaps in the current literature. Moreover, the gathered dataset serves as input for training a machine learning approach known as Group Method of Data Handling (GMDH). The GMDH network demonstrates a satisfactory accuracy in predicting experimental results, with a MSE of 0.0044.

Quality evaluation of the forecasting system for the Polish Timescale by means of the Group Method of Data Handling type neural network

The article presents the results aimed at assessing the effectiveness of the applied forecasting system based on the forecasting procedure using a GMDH (Group Method of Data Handling) neural network for the Polish Timescale (UTC(PL)) implemented on the basis of hydrogen maser (HM). The article also aims to show that the application of the forecasting system to implement the UTC(PL) national timescale makes it possible to achieve a quality of this timescale similar to the best of the best timescales. The results of forecasting using the GMDH neural network for UTC(PL) are presented for two prepared time series (TS1 and TS2), which are additionally compared with the UTC - UTC(k) values for this scale. Very good forecasting quality has been achieved for the analysed cases 5 and 6, both for data prepared according to the TS1 and TS2 time series. This is confirmed by the calculated values of forecast quality measures. The obtained research results show that the developed system allows ensuring the accuracy of the UTC(PL) at the level of best timescales. The presented research results will help to convince some of NMIs (National Metrology Institutes), which are not equipped with caesium fountains, to adopt the UTC(k) steering system.

Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection

Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning.

Bayesian polynomial neural networks and polynomial neural ordinary differential equations.

Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of many science and engineering problems. However, these methods provide point estimates for the model parameters and are currently unable to accommodate noisy data. We address this challenge by developing and validating the following Bayesian inference methods: the Laplace approximation, Markov Chain Monte Carlo (MCMC) sampling methods, and variational inference. We have found the Laplace approximation to be the best method for this class of problems. Our work can be easily extended to the broader class of symbolic neural networks to which the polynomial neural network belongs.

Визначення предикторів діагнозу пацієнтів з ПТСР на основі параметрів одновимірних моделей першого порядку BOLD-сигналів структур мозку та МГУА

Introduction. The use of functional magnetic resonance imaging (fMRI) allows for the assessment of processes occurring in the brain. By analyzing the examination results, it is possible to establish the parameters of connections between brain structures, and changes in the values of these parameters can be used as diagnostic conclusion predictors for PTSD-patients. Purpose. To identify predictors for the classification of the PTSD diagnosis using the connectivity parameters of BOLD signals from brain structures. Methods. The technology for identifying predictors of PTSD diagnosis is based on a) the formation of connectivity parameters of BOLD signals from brain structures obtained during resting-state scanning, b) the use of classifier-oriented selection based on inter-class variance and mRMR criteria to select informative features, and c) the classification of PTSD diagnosis using a logistic regression algorithm optimized by the Group Method of Data Handling. Results. The technology proposed in this work enabled the selection of informative features and the identification of their predictive forms, resulting in the formation of classifiers for the diagnosis of PTSD with high accuracy, sensitivity, and specificity. Conclusion. A technology for the formation, selection, and use of connectivity parameters of BOLD signals from brain structures has been proposed for differentiating healthy individuals from those who suffer with PTSD. A list of the most informative features of PTSD and their predictive forms in the form of generalized variables has been obtained, which can be used for diagnostic conclusions. The results obtained indicate the presence of a specific type of connection between the brain areas identified in the study based on levels of excitation (parameters а0 of the models) and the alteration of these levels in the context of PTSD.

White-Box Machine Learning Models for Accurate Interfacial Tension Prediction in Hydrogen-Brine Mixtures

Abstract The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy sources. One intrinsic challenge of renewable energy sources is their intermittent nature, which can be addressed by transforming excess energy into hydrogen and storing it safely for future use. To securely store hydrogen underground, a comprehensive knowledge of the interactions between hydrogen and residing fluids is required. Interfacial tension is an important variable influenced by cushion gases such as CO2 and CH4. This research developed explicit correlations for approximating the interfacial tension of hydrogen-brine mixture using two advanced machine learning techniques: gene expression programming and the group method of data handling. The Interfacial tension of hydrogen-brine mixture was considered as heavily influenced by temperature, pressure, water salinity, and average critical temperature of the gas mixture. The 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 assessed the influence of individual input variables on the correlations’ outputs. Analysis showed that temperature and average critical temperature of the gas mixture had considerable inverse impacts on the estimated interfacial tension values. Finally, the reliability of the gathered databank and the scope of application for the proposed correlations were verified by the leverage approach by illustrating 97.6% of the gathered data within the valid range of the Williams plot.

Estimating Carbon Dioxide Solubility in Brine Using Mixed Effects Random Forest Based on Genetic Algorithm: Implications for Carbon Dioxide Sequestration in Saline Aquifers

Summary Accurate prediction of carbon dioxide (CO2) solubility in brine is crucial for the success of carbon capture and storage (CCS) by means of geological formations like aquifers. This study investigates the effectiveness of a novel genetic algorithm-mixed effects random forest (GA-MERF) model for estimating CO2 solubility in brine. The model’s performance is compared with established methods like the group method of data handling (GMDH), backpropagation neural networks (BPNN), and traditional thermodynamic models. The GA-MERF model utilizes experimental data collected from literature, encompassing key factors influencing CO2 solubility: temperature (T), pressure (P), and salinity. These data are used to train and validate the model’s ability to predict CO2 solubility values. The results demonstrate the superiority of GA-MERF compared to the other models. Notably, GA-MERF achieves a high coefficient of determination (R) of 0.9994 in unseen data, indicating a strong correlation between estimated and actual CO2 solubility values. Furthermore, the model exhibits exceptionally low error metrics, with a root mean squared error (RMSE) of 2×10-8 and a mean absolute error (MAE) of 1.8×10-11, signifying outstanding accuracy in estimating CO2 solubility in brine. Beyond its high accuracy, GA-MERF offers an additional benefit—reduced computational time compared to the other models investigated, with 65 seconds. This efficiency makes GA-MERF a particularly attractive tool for real-world applications where rapid and reliable CO2 solubility predictions are critical. In conclusion, this study presents GA-MERF as a powerful and efficient model for predicting CO2 solubility in brine. Its superior performance compared to existing methods and previous literature highlights its potential as a valuable tool for researchers and engineers working on CCS projects utilizing aquifer storage. The high accuracy, low error rates, and reduced computational time make GA-MERF a promising candidate for advancing the development of effective and efficient CCS technologies.

Hybrid machine learning approach integrating GMDH and SVR for heavy metal concentration prediction in dust samples.

In agricultural regions prone to dust storms, heavy metal contamination of soil and crops from airborne particulates poses significant risks to food safety and public health. This study has assessed the potential of machine learning models for predicting concentrations of toxic heavy metals like arsenic, chromium, and lead in dust from the agricultural Sistan region of southeastern Iran. This region experiences frequent dust storms mobilizing particulates from local dried lakes onto agricultural lands. The metals including nickel, copper, magnesium, cobalt, zinc, chromium, arsenic, and lead were measured in summer dust samples during 2012-2018 across 15 stations. Two hybrid models were developed combining group method of data handling (GMDH) and support vector regression (SVR) machine learning with harmony search optimization (H) so as to predict toxic metals arsenic, chromium, and lead using nickel, copper, magnesium, cobalt, and zinc inputs. Standard error maps were uncertainty higher in southern and western areas, and they are most impacted by dust storms. Results demonstrated that the hybrid GMDH + H and SVR + H models improved the accuracy of individual GMDH and SVR models in predicting heavy metals. The GMDH + H model performed the best for the lead with an agreement index (d-index) of 0.98, root mean square error (RMSE) of 2.87ppm, normalized RMSE (NRMSE) of 0.12, and coefficient of determination (RR) of 0.96. The SVR + H model showed the highest accuracy for arsenic and chromium, obtaining d-index 0.96, RMSE 0.47ppm, NRMSE 0.09, and RR 0.92 for arsenic, and d-index 0.96, RMSE 11.24ppm, NRMSE 0.16, and RR 0.93 for chromium. Taylor's diagram and heatmap analysis confirmed the superiority of the hybrid techniques. This work demonstrates the utility of state-of-the-art computing for addressing complex environmental health challenges.

An improved permeability estimation model using integrated approach of hybrid machine learning technique and shapley additive explanation

Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production. Conventional methods relying on empirical correlations and assumptions often result in high costs, time consumption, inaccuracies, and uncertainties. This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. The group method of data handling with differential evolution (GMDH-DE) algorithm was used to predict permeability due to its capability to manage complex, nonlinear relationships between variables, reduced computation time, and parameter optimization through evolutionary algorithms. Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing, the GMDH-DE outperformed the group method of data handling (GMDH) and random forest (RF) in predicting permeability with higher accuracy and lower computation time. The GMDH-DE achieved an R2 of 0.9985, RMSE of 3.157, MAE of 2.366, and ME of 0.001 during training, and for testing, the ME, MAE, RMSE, and R2 were 1.3508, 12.503, 21.3898, and 0.9534, respectively. Additionally, the GMDH-DE demonstrated a 41% reduction in processing time compared to GMDH and RF. The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin, Tanzania, which lacks core data. Shapley additive explanations (SHAP) analysis identified thermal neutron porosity (TNPH), effective porosity (PHIE), and spectral gamma-ray (SGR) as the most critical parameters in permeability prediction. Therefore, the GMDH-DE model offers a novel, efficient, and accurate approach for fast permeability prediction, enhancing hydrocarbon exploration and production.

Self-Organizing Hybrid Fuzzy Polynomial Neural Network Classifier Driven Through Dynamically Adaptive Structure and Compound Regularization Technique

Self-Organizing Hybrid Fuzzy Polynomial Neural Network Classifier Driven Through Dynamically Adaptive Structure and Compound Regularization Technique

Modeling interfacial tension of methane-brine systems at high pressure high temperature conditions

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.

Synthesis of expert matrices in inductive system-analytical research based on fuzzy logic algorithm

The object of research is the process of inductive modeling of complex systems. The studies that were conducted related to the application of algorithms of the fuzzy logic theory were to coordinate the conclusions of top-level experts in system information-analytical research (SIAR) in the tasks of innovative design. The possibilities of constructing elements of expert matrices of results, as well as evaluating the effectiveness of such applications, are defined. Thanks to this, obtaining formal expert evaluations in numerical form became possible. Experimental studies have confirmed that the proposed approach to the application of fuzzy logic algorithms to the construction of matrices of expert evaluations of SIAR results is quite effective and simple to implement. In addition, this approach fits well into the general paradigm of the Group Method of Data Handling (GMDH). In particular, it was established that the possibility of «retraining» such a block without significant efforts of professional experts can have a positive result, as well as have a good effect on the economic and time parameters of the research project. The main calculation formulas for the algorithm for building a fuzzy system using a neural network in a system with two rules are given. The construction of a fuzzy information output system trained on expert evaluations in the Matlab system is shown. As a result, a technologically acceptable standard deviation of 0.28268 mg/l was obtained. It has been established that by accumulating a database (knowledge) and/or using an information monitoring system, it is possible to «additionally train» a fuzzy system periodically or according to the established quality criterion in the program mode, without involving experts in this process. Thus, there are reasons to assert the importance of using a fuzzy system as one of the tools in inductive SIAR procedures

Prediction of Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network

A new approach to predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called the group method of data handling (GMDH) and is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in the literature. The model was then evaluated using statistical indices, as well as being compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate the high accuracy of the proposed model, with values of 0.9719 and 0.9513 for training and testing for the dimensionless distance from the nozzle to the return point xr/D and 0.9454 and 0.9565 for training and testing for the dimensionless terminal rise height yt/D. Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of the estimate (SEE) was applied to demonstrate which model performed the best in terms of approaching the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and it was the best model, with an approximately 10% error, which was the lowest error value among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH3OH Reaction with Analytical Forces.

The hydrogen abstraction reaction of OH + CH3OH plays a great role in combustion and atmospheric and interstellar chemistry and has been extensively studied theoretically and experimentally. Theoretically, the numerical gradients with respect to the Cartesian coordinates of atoms in molecular simulations on our recent potential energy surface (PES) for the title reaction trained using the permutationally invariant polynomial neural network (PIP-NN) approach hinder the extensive calculation because of the unaffordable computation cost. To address this issue, we in this work report a new full-dimensional accurate analytical PES for the title reaction using the fundamental invariant neural network (FI-NN) approach based on 140,192 points of the quality UCCSD(T)-F12a/AVTZ. Besides, the spin-orbit (SO) corrections of OH in the entrance channel were determined at the level of complete active space self-consistent field with the AVTZ basis set. As a compromise between computational cost and efficiency, the Δ-machine learning approach was employed to construct the SO-corrected PES. Based on this new FI-NN PES with analytical forces, thermal rate coefficients and various dynamic properties, including the integral cross sections, the differential cross sections, and the product energy partitioning, were determined by running a total of 5.5 million trajectories. The use of analytical gradients of the FI-NN PES accelerated simulations and about 99% of computation cost was saved, compared to that for the PIP-NN PES with numerical gradients. Such a significant acceleration is achieved mainly by replacing PIPs with FIs.

On the evaluation of surface tension of biodiesel

Over time, with the increase in population and the subsequent increase in energy consumption and also due to the non-renewability of fossil fuels, the study of alternative fuels has increased. One of these fuels is biodiesel, which is a suitable alternative to fossil fuels such as diesel and received much attention from researchers today. For this reason, measuring the physical properties of biodiesel is of great importance. Due to the high cost and time-consuming nature of laboratory methods, numerical methods are used to estimate material properties. The novelty of this research was the use of two white box models, including Group method of data handling (GMDH) and Gene expression programming (GEP), which work on the basis of artificial intelligence. By using these models, two simple mathematical equations with high accuracy were presented to predict the surface tension of biodiesel. These models can be used at different temperatures and molecular weights. To do modeling, 78 laboratory data available in the literature were gathered and the data were randomly divided into two groups, train and test, in a ratio of 80 and 20. The input parameters include mass fraction of fatty acid ethyl esters and temperature (T), and esters are divided into three groups according to their molecular weight: less than 200 (Mw1), between 200 and 300 (Mw2), and greater than 300 (Mw3). The statistical error parameters were calculated for the two models developed in this research and after comparing the results, it was found that the GMDH model estimates the surface tension of biodiesel with a higher accuracy. The average absolute relative error for GMDH and GEP models was reported as 0.97 and 1.89, respectively. Also, other statistical error parameters of GMDH such as RMSE, SD, and R2 for the GMDH model were obtained as 0.444, 0.000233, and 0.9233, respectively. Moreover, sensitivity analysis showed that temperature has the highest impact on the surface tension of biodiesel, which is also an inverse effect. Finally, suspicious laboratory and outlier data points were identified using the Leverage technique. According to this analysis, only five data points were identified as outliers and suspicious laboratory data.

Effect of applying serpentine channels and hybrid nanofluid for thermal management of photovoltaic cell: Numerical simulation, ANN and sensitivity analysis

Regarding the importance of thermal management of solar PV cells to achieve higher efficiency and electricity output, a novel configuration by use of serpentine channels for cooling is proposed and numerically investigated in the present article. Moreover, a hybrid nanofluid, (MWCNT)-Fe3O4/water, is employed as the coolant to investigate its potential in further improvement of thermal management. Computational Fluid Dynamics (CFD) is applied to numerically simulate the systems and investigate the impacts of different factors. Performance of the proposed configuration is compared with cooling channels with simple structure without nanofluids. Simulations results revealed that the employment of serpentine channels leads to modification in the thermal management of the cell compared with the simple channel. The maximum improvement rate in this condition compared with simple channels is around 9.63 % that is obtained in case of maximum mass flow rate of coolant and solar radiation intensity. Despite increment in pressure loss by using the serpentine channel compared with the simple channel, 2.6 ×10−3 W and 9.3 ×10−4 W, respectively, in mass flow rate of 0.003 kg/s for water, increase in the generated electricity is much more in this case. Furthermore, numerical results show that applying the hybrid nanofluid with 0.1 % concentration can cause slight reduction in the temperature compared with using water. Moreover, Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) neural network as intelligent methods used for estimation of the cell temperature and the maximum error for the developed model is approximately 1.1 % and 0.098 % for the GMDH- and MLP-based models, respectively. Finally, sensitivity analysis is implemented and it is shown that solar radiation intensity is the most influential factor on the cell temperature in condition of using serpentine channels while mass flow rate of the coolant has the minimum relevancy factor and consequently effect on the temperature of the solar cell.