Group Method Of Data Handling Research Articles

The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal—A Case Study

The quality of coking coal is vital in steelmaking, impacting final product quality and process efficiency. Conventional forecasting methods often rely on empirical models and expert judgment, which may lack accuracy and scalability. Previous research has explored various methods for forecasting coking coal quality parameters, yet these conventional methods frequently fall short in terms of accuracy and adaptability to different mining conditions. Existing forecasting techniques for coking coal quality are limited in their precision and scalability, necessitating the development of more accurate and efficient methods. This study aims to enhance the accuracy and efficiency of forecasting coking coal quality parameters by employing neural networks and artificial intelligence algorithms, specifically in the context of Knurow and Szczyglowice mines. The research involves gathering historical data on various coking coal quality parameters, including a proximate and ultimate analysis, to train and test neural network models using the Group Method of Data Handling (GMDH). Real-world data from Knurow and Szczyglowice mines’ coal production facilities form the basis of this case study. The integration of neural networks and artificial intelligence techniques significantly improves the accuracy of predicting key quality parameters such as ash content, sulfur content, volatile matter, and calorific value. This study also examines the impact of these quality indicators on operational costs and highlights the importance of final indicators like the Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR) in expanding industrial reserve concepts. Model performance is evaluated using metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate the effectiveness of these advanced techniques in enhancing predictive modeling in the mining industry, optimizing production processes, and improving overall operational efficiency. Additionally, this research offers insights into the practical implementation of advanced analytics tools for predictive maintenance and decision-making support within the mining sector.

Influence of delamination on uncertain dynamic characteristics of variable angle tow laminates using polynomial neural network

Influence of delamination on uncertain dynamic characteristics of variable angle tow laminates using polynomial neural network

Development of Prediction Model for Oil Formation Volume Factor for Sudanese Crude Oil

Understanding Oil Formation Volume Factor βo is crucial for effective oil field development, impacting well performance analysis, reservoir simulation, and production engineering calculations. Traditionally, βo is determined through costly and time-consuming laboratory tests, prompting the need for accurate mathematical correlations. Existing correlations such as Vasquez-Beggs, Standing-Glaso, and others have been widely used but show varying degrees of accuracy across different operating conditions. In this study, these correlations were evaluated against 95 datasets of experimental βo data for Sudanese crude oils. Statistical analysis revealed that Vasquez-Beggs and Standing- Glaso models performed best, with average absolute errors of 3.4219 and 3.4477, and correlation coefficients of 0.7563 and 0.7213 respectively. Motivated by the limitations of existing correlations, a new ap- proach using Polynomial Neural Networks (PNN) was developed. This model utilized reservoir temperature, gas gravity, gas oil ratio, and API as input parameters, trained on 70% of the dataset and tested on the remaining 30%. The PNN model exhibited superior predictive performance with a relative average absolute error of 2.8607 and a correlation coefficient of 0.9080. This study contributes a robust predictive tool for estimating βo in Sudanese oil fields, offering enhanced accuracy over traditional correlations and facilitating more reliable reservoir management decisions.

Autonomous and reliable fingerprint map maintenance for indoor positioning system

In WiFi-based crowdsourced fingerprinting indoor positioning systems (IPS), the signal space of WiFi APs changes over time. To autonomously maintain a highly reliable localization accuracy, the fingerprint database needs regular maintenance and updates. However, this process is labor-intensive and time-consuming due to the appearance of faulty access points (APs) in crowdsourced samples received from target clients, which are mainly used for autonomous maintenance and updates. Additionally, if some APs are relocated, removed, or replaced in the indoor environment after fingerprint map construction, it can severely impact IPS accuracy. In this paper, we present Dynamic Online Fingerprint Learning with Altered APs status-aware and updating (DOFLAAU) for IPS. Our systematic methodology involves several key steps. First, we employ the Modified Group Method of Data Handling (GMDH) neural network regression for autonomous AP status monitoring and alteration detection. Next, we perform autonomous APs subset sample selection using a modified Thomson model, followed by outlier feature detection and removal based on the weighted sample density rate. Thirdly, we propose a dynamic online fingerprint map updating model using the cosine similarity distance metric. Lastly, we adopted an effective signal distance-based weighted k-nearest neighbor algorithm to improve IPS accuracy. The experiment results validate the efficiency and effectiveness of our proposed algorithms in terms of better localization accuracy compared to peer algorithms.

A Neural Network Potential Energy Surface and Quantum Dynamics Study of Ca(1S) + H2(v 0 = 0, j 0 = 0) → CaH + H Reaction.

The reactive collision between Ca and H2 molecules has attracted great interest experimentally due to the key role of the product CaH molecule in the field of astrophysics and cold molecules. However, quantum dynamics calculations for this system have not been reported due to the lack of a global potential energy surface (PES). Herein, a globally accurate PES of the ground-state CaH2 is developed by combining 11365 high-level ab initio points and permutation invariant polynomial neural network method. Based on the newly constructed PES, the state-to-state quantum dynamics calculations for the Ca(1S) + H2 (v 0 = 0, j 0 = 0) → CaH + H reaction are carried out using the time-dependent wave packet method. The dynamic results reveal that the reaction follows the complex-forming mechanism near the reactive threshold, whereas both the indirect insertion mechanism and direct abstraction mechanism have effects at higher collision energies. The newly constructed PES can be used to further study the influence of isotope substitution, rovibrational excitation, and spatial orientation of reactant molecules on reaction dynamics.

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

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.

Simplified methods for the design of landfill double composite liners using neural network

Double composite liners (DCLs) have been widely used in landfills to protect the surrounding environment. This study aims to develop simplified empirical equations for calculating breakthrough times of DCLs based on analytical equations or experimental data. The artificial intelligence neural network called Group Method of Data Handling (GMDH) type neural network was used to perform equation simplification. New empirical equations in polynomial formats are obtained by a layer-summation method and a series of numerical experiments based on analytical solutions for contaminant transport in double composite liners. The accuracy of empirical equations is demonstrated by comparing them with the existing solutions and numerical results. The performance of four types of DCLs were then investigated. The mean absolute percentage errors (MAPEs) for each type of DCLs with different leachate heads and soil liner thicknesses are all lower than 10%. Additionally, a trend for the improvement of the GMDH equation accuracy with the increase of Δh1 is observed. The presented equations can perform well in high leachate head conditions (e.g. > 5 m) where DCLs are required.

Damage detection of truss structures using meta-heuristic algorithms and optimized group method of data handling surrogate model

Optimization-based methods are increasingly being implemented for structural damage detection (SDD) problems through the minimization of the objective functions based on vibration data. Meanwhile, the challenge of high computational cost hinders the applied use of SDD methods, especially upon addressing large-scale structures. In this study, a two-stage damage detection approach is proposed for damage localization as well as extent quantification which reduces the computational time of SDD problems. In the first stage of the proposed framework, suspected locations of damage are identified by employing a residual force vector. Then in the second stage, damage extent of already identified elements will be assessed by model updating method through three different optimization algorithms, namely particle swarm optimization (PSO), differential evolution (DE), and enhanced colliding bodies optimization (ECBO). To spare time-consuming modal analysis in each iteration of optimization algorithms, a PSO-based group method of data handling (GMDH) polynomial neural network (PNN) surrogate model is proposed. Performance of GMDH PNN strongly depends on the input variables, the number of input variables, and the order of the polynomial. The PSO-based GMDH PNN, developed in this study, employs PSO for the optimum selection of these parameters. Furthermore, the efficiency of Elemental Energy Quotient Indicator (EEQI) as a new damage index proposed in this paper is compared with the approach based on the natural frequency-based index. This paper also deals with the incomplete modal data measured from limited number of sensors. This problem is addressed by employing the model reduction technique and solving the SDD problem with modal information obtained from the reduced model. Numerical examples show that the suggested PSO-based GMDH surrogate model is an accurate tool for various types of SDD problems, and it can provide promising results with a significant reduction in computational time.

Hybrid particle swarm optimization and group method of data handling for the prediction of ultimate strength of concrete-filled steel tube columns

This study presents a hybrid model coupling particle swarm optimization (PSO) with group method of data handling (GMDH) for predicting the ultimate strength of rectangular concrete-filled steel tube (RCFST) columns. A large database of 490 data samples collected from the existing literature was used to construct the model. Compared with the optimal model among the nine existing models, the coefficient of variation (COV), mean absolute percentage error (MAPE) and root relative squared error (RRSE) values of all datasets of the PSO-GMDH model were decreased by 58.38 %, 69.22 % and 64.27 %, respectively; while the coefficient of determination (R2) and a20-index values were increased by 34.32 % and 8.65 %, respectively. The results show that the predicted results of PSO-GMDH model are in good agreement with the experimental results and can accurately predict the ultimate strength of rectangular RCFST columns. In addition, a graphical user interface (GUI) has been developed to facilitate the application of the PSO-GMDH model.

Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study

Groundwater salinity is a critical factor affecting water quality and ecosystem health, with implications for various sectors including agriculture, industry, and public health. Hence, the reliability and accuracy of groundwater salinity predictive models are paramount for effective decision-making in managing groundwater resources. This pioneering study presents the validation of a predictive model aimed at forecasting groundwater salinity levels using three different validation methods and various data partitioning strategies. This study tests three different data validation methodologies with different data-partitioning strategies while developing a group method of data handling (GMDH)-based model for predicting groundwater salinity concentrations in a coastal aquifer system. The three different methods are the hold-out strategy (last and random selection), k-fold cross-validation, and the leave-one-out method. In addition, various combinations of data-partitioning strategies are also used while using these three validation methodologies. The prediction model’s validation results are assessed using various statistical indices such as root mean square error (RMSE), means squared error (MSE), and coefficient of determination (R2). The results indicate that for monitoring wells 1, 2, and 3, the hold-out (random) with 40% data partitioning strategy gave the most accurate predictive model in terms of RMSE statistical indices. Also, the results suggested that the GMDH-based models behave differently with different validation methodologies and data-partitioning strategies giving better salinity predictive capabilities. In general, the results justify that various model validation methodologies and data-partitioning strategies yield different results due to their inherent differences in how they partition the data, assess model performance, and handle sources of bias and variance. Therefore, it is important to use them in conjunction to obtain a comprehensive understanding of the groundwater salinity prediction model's behavior and performance.

Deferred correction neural network techniques for solving ordinary differential equations

Neural network technology is widely used to solve problems across science and engineering fields. In this paper, a neural network technique is applied to solve ordinary differential equations (ODEs), instead of using conventional time marching techniques with discretization for ODEs. A numerical solution to ODEs is defined with polynomial basis, and each coefficient of the expansion is calculated through an unsupervised neural network structure in the entire time domain. To make the calculated solution more accurate, we consider the deferred correction technique for calculating a numerical residual with a physics-informed neural network. The empirical results show that the proposed scheme with polynomial basis can influence calculation accuracy depending on the order of basis polynomials, providing the possibility of improving accuracy and efficiency by increasing the order of basis. Several numerical tests confirm that the proposed deferred correction network (DCNet) model has an accuracy approximately 100 and 10 times higher than that of the learning polynomial neural network (LPNet) and the standard existing method, physics-informed neural network (PINN), respectively. Additionally, the proposed schemes generate stable results even for stiff problems and preserve the conservation property for Hamiltonian systems.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

Application of deep learning through group method of data handling for interfacial tension prediction in brine/CO2 systems: MgCl2 and CaCl2 aqueous solutions

Capillary/residual CO2 trapping is one of the main mechanisms of CO2 storage in underground formations. Therefore, it is required to estimate the brine/CO2 interfacial tension under different conditions. Although many methods have been proposed so far, the error of estimation is still high. This paper proposes a novel deep learning method to estimate the brine/CO2 interfacial tension at various temperatures, pressures, and salinities. The proposed method is a neural network with the Group Method of Data Handling (GMDH) learning method. The GMDH has the advantage of handling the structural and parametric optimization of the network automatically. The proposed method is tested on an experimental dataset of brine/CO2 interfacial tension with CalCl2 and MgCl2 salts. The results of the proposed method were compared with four of the best performing methods in the literature. The Average Absolute Percentage Error (AAPE) of the method on the training, testing and all data was 1.3 %, 2.95 %, 1.73 %, respectively, while the best method from the literature could reach an AAPE of 8.16 % on all data. Therefore, the proposed method performs far better than the existing methods. Also, a sensitivity analysis was done to determine the most influential inputs to estimate the output. The contribution of this work is to show the applicability of the GMDH method to construct more optimal data-driven models to estimate the brine/CO2 interfacial tension. Also, the utilized dataset is collected under a wide range of pressure, temperature and salinity conditions that increases the generality of the model.

Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms

The research used an artificial neural network (ANN) model to examine the rheological properties of hybrid non-Newtonian ferrofluids (HNFFs) composed of Fe-CuO, water, and ethylene glycol. The performance of neural network was optimized using seven regression methods (RMs), namely Group Method of Data Handling (GMDH), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Multiple Linear Regression (MLR). The findings highlighted GMDH method's superior performance when compared to neural networks. R and RMSE values attained by GMDH for the objective function (OF) μnf were 0.99436 and 2.0135, respectively. For the torque function OF, the values were 0.97652 and 4.8952. Margin of difference (MOD) calculations across various algorithms, such as MLP, SVM, RBF, D-Tree, ELM, MLR, and GMDH-Algos revealed significant disparities, indicating GMDH's efficacy. Comparison of R, RMSD, and standard deviation values between GMDH and MLR algorithms further underscored performance discrepancies. Specific parameters for which NSGA II Algo was rated highest among evaluation indices were as follows: a crossover rate of 0.7, a mutation rate of 0.02, a population size of 50, and 500 generations. Post-optimization, optimal values for μnf and torque (To) were determined as 6.595 and 3.543, respectively, with corresponding values for φ, T, and γ obtained as 0.185, 49.372, and 3.163, respectively. This comprehensive analysis sheds light on the effectiveness of various regression methods in modeling the rheological behavior of hybrid non-Newtonian ferrofluids, contributing to advancements in fluid dynamics research.

Accurate neural-network-based fitting of full-dimensional N2 –Ar and N2–CH4 two-body potential energy surfaces aimed at spectral simulations

We describe the development of machine-learned (ML) potentials for flexible, weakly interacting monomers. A recently suggested permutationally invariant polynomial neural network (PIP-NN) approach is utilised to represent the full-dimensional two-body component of the molecular pair energy. To ensure the asymptotic zero-interaction limit, a tailored subset of the full invariant polynomial basis set is utilised, and their variables are modified to achieve a better fit of the correct asymptotic behaviour at a long range. This new technique is used to build full-dimensional potentials for the two-body N 2 –Ar and N 2 –CH 4 interactions by fitting databases of ab initio energies calculated at the coupled-cluster level of theory. The second virial coefficient, fully accounting for molecular flexibility, is then calculated within the classical framework using the obtained PIP-NN potential surfaces. A trajectory-based simulation of the N 2 –Ar collision-induced absorption is conducted, covering both the far- and mid-infrared ranges.

Machine learning prediction and characterization of sigma-free high-entropy alloys

Precipitation hardening provides a great potential to achieve superior strength-ductility trade-off in high-entropy alloys (HEAs). However, the formation of some precipitates such as brittle sigma phases may significantly deteriorate the ductility. Empirical criteria such as PSFE, VEC, and Md were proposed to predict the formation of undesirable phases during aging, nevertheless, they are insufficient to describe their stability and are not dependable for the noted purpose. Accordingly, for the first time, machine-learning models were conducted for this prediction. A feature selection based on Pearson correlation and mutual information has been used to improve the performance of models. Linear regression, second-order polynomial regression, decision tree, and neural network are conducted by machine learning methods in this paper. Among the implemented methods, the neural network had the best performance which improved the accuracy of the prediction by ∼20% compared to the conventional methods. Additionally, a new parameter based on linear regression, which is more reliable and user-friendly than thermodynamic parameters was developed in this investigation. The results showed that some elements such as Cr, Mo, and V may promote sigma precipitation. A HEA with a sigma-free microstructure was developed for validation of the model. We strongly believe that this HEA has a great potential for overcoming the strength-ductility trade-off because of not only the lack of sigma precipitate but also the presence of NiAl precipitate. The results are a step forward in designing alloys with the potential of microstructure engineering to achieve superior properties.

Optimal operation of the dam reservoir in real time based on generalized structure of group method of data handling and optimization technique

The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method’s main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam’s inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam’s outflow in the shortest amount of time. This is achieved by accounting for the environment’s changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir’s optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model’s performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time.

Artificial intelligence -based prediction of heat transfer enhancement in ferrofluid flow under a rotating magnetic field: Experimental study

The utilization of various techniques, both passive and active, to enhance heat transfer in fluids by scientists is continually advancing. One of the active methods being explored is the impact of a magnetic field (B) on flow to improve heat transfer. This study focuses on experimentally evaluating the effectiveness of B and the rotation of B on heat transfer of ferrofluid. The findings indicate that by increasing the volume fraction of nanoparticles, heat transfer can be enhanced by approximately 2.73–2.82 %. Furthermore, the use of B and intensifying its intensity leads to a 3.75–3.8 % enhancement in heat transfer. Rotating the B and increasing the rotational speed also contribute to improved heat transfer, with a 0.2 rad/s increase in rotation speed resulting in a 5%–12.43 % improvement in heat transfer. By utilizing available data and employing artificial intelligence methods such as Group Method of Data Handling (GMDH) and Long Short-Term Memory (LSTM), an estimation of the Nusselt number (Nu) has been achieved. The outcomes demonstrate that both models have displayed high accuracy in predicting Nu, with GMDH showing superior accuracy compared to LSTM in estimating Nu.

Evaluation and intelligent forecasting of energy and exergy efficiencies of a nanofluid-based filled-type U-pipe solar ETC using three machine learning approaches

The thermodynamic modeling of a filled layer U-type was conducted considering the six water-based and Engine Oil (EO)-based nanofluids (NFs). The results demonstrated that Fe2O3/EO NF is efficient from the exergy efficiency point of view. While MWCNT/water NF outperforms from the energy efficiency aspect. Moreover, the outcome indicated that when (Tin−Tamb)/It increased above 0.04 (K m2/W), Ltube has no considerable effect on the exergy efficiency. Still, the length impact is noticeable regarding outlet temperature and energy efficiency. Furthermore, the advantage of the filled-type over the conventional ETSC is more prominent at low m˙ (e.g. 4.0 kg/h). So that ηex in the filled type with Fe2O3/EO is improved by 2.29 % and 3.70 %, respectively, for (Tin−Tamb)/It of 0 and 0.04. Moreover, the increase in m˙ of the water/based and oil-based NFs, respectively, between 6 and 10 kg/h and 8–20 kg/h is suggested from the energy efficiency aspect. The results of machine learning modeling revealed that three models namely group method for data handling (GMDH), response surface method (RSM), and modified response surface model (MRSM) are capable of estimating ηen and ηex. While MRSM outperforms the two other models in terms of (R2 | ηen = 0.9986 and RMSE| ηen = 0.7849; R2 |ηex = 0.9988 and RMSE| ηex= 0.1513).