Group Method Of Data Handling Research Articles

Modeling of ionic liquids viscosity via advanced white-box machine learning.

Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9MPa) and temperatures (253.15-573K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDHmodel with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.

Predicting Model Biases of the APOLLO3 Neutronics Code Using Machine Learning: Toward a Multiscale and Multifidelity Approach

As part of the verification, validation, and uncertainty quantification process applied to neutronics deterministic codes, there is a requirement to expand the validation domain, especially to accommodate new third-generation reactors. The objective of the present work is to estimate the numerical biases arising from the several approximations used in deterministic codes and across different points in the phase space. Typically, this is accomplished by comparing the deterministic code to be validated with a Monte Carlo or stochastic reference code (without significant approximations). Since these reference calculations are computationally expensive, this paper proposes an alternative approach for predicting model biases of the APOLLO3® deterministic code for third-generation pressurized water reactors using machine learning algorithms. Three types of metamodels are employed (polynomial regression, kriging, and neural networks). Two scales are investigated, from a single assembly to a cluster of 3 × 3 assemblies [small two-dimensional (2-D) core], with model biases evaluated for APOLLO3 schemes with various levels of accuracy (lattice and core solvers, with high- to low-fidelity approaches). For the small 2-D core, numerical biases are observed for reactivity and power peak, representing both global and local quantities of interest. Throughout the study, the best results are achieved using kriging or neural networks, even if polynomial regression provides satisfactory predictions in some cases. The possibility of predicting biases for different quantities is also introduced. In conclusion, this paper discusses the prospects of extending the applicability of these metamodels to small and large third-generation pressurized water reactor cores, the idea being to potentially use these metamodels to support safety demonstrations for the new reactors in the long term.

SHM Implementation on a RPV Airplane Model Based on Machine Learning for Impact Detection

In this study, an on-working structural health monitoring system for impact detection on remote piloted vehicle (RPV) airplane is proposed. The approach is based on the propagation of Lamb waves in metallic structures on which Pb[ZrxTi1−x]O3 (PZT) sensors are bonded for receiving vibrational signals due to impact events. The proposed method can be used to detect impacts in aerospace structures, i.e. skin fuselage and/or wing panels. After the detection, machine learning (ML) algorithms (polynomial regression and neural networks) are applied for processing the acquired ultrasounds waves in order to characterise the impacts, in terms of time of flight (ToF) and relative location. Several test cases are studied: the ML models are tested both without external noise (in laboratory) and introducing external RC engine vibration (on-working conditions). Furthermore, this work presents the implementation of a mini-equipment for acquisition and data processing based on Raspberry Pi. A good agreement between laboratory and in-flight results is achieved, in terms of distance between the actual and calculated impact location.

Optimizing Activated Carbon Production from Waste Cashew Nut Shell with Zinc Chloride: A Box-Behnken Design and Group Method of Data Handling (GMDH) Application

In this study, Response surface methodology (RSM) and innovative Group Method of Data Handling (GMDH) approaches are applied to investigate the optimal process conditions of zinc chloride activated cashew nut production process. The effects of activation conditions (i.e. activation temperature, activation time, and impregnation ratio) on the achievable BET surface areas were studied with the aid of Box Behnken Design (BBD) and GMDH. Comparative analyses of RSM and GMDH-type neural models were further researched. During the process, the polynomial model equations developed were modified and fine-tuned to predict the highest BET surface area(s) using regression analysis and GMDH multi-layered iterative algorithm (MIA). Analysis of Variance (ANOVA) revealed that the significant factor(s) were impregnation ratio, impregnation ratio product, and the 2-way interactions (activation temperature and impregnation ratio) for ZnCl2 activated cashew nut shell. The best activation conditions for producing highest BET surface area of 504 m2.g-1 was activation temperature (873K), activation time (60 min), and impregnation ratio (1.50).The proposed GMDH-type BET model was ascertained to be the best model with average correlation coefficient (R) and root mean square error (RMSE) of 0.925 and 32.0 respectively. Sensitivity analysis conducted for GMDH-type neural network also revealed that the activation temperature and activation time with sensitivity values of 90.6% and 74.1% respectively were the most influential parameters in the basic (ZnCl2) activation process. The results of this study show that RSM and GMDH-type neural network could be applied as effective analytical tools for optimizing the ZCNS (zinc chloride-activated cashew nut shells) manufacturing process.

Estimation of soil organic carbon by combining hyperspectral and radar remote sensing to reduce coupling effects of soil surface moisture and roughness

Soil organic carbon (SOC) is important in the global carbon cycle. Accurate estimation of SOC content in cultivated land is a prerequisite for evaluating the carbon sequestration potential and quality of soils. However, existing SOC prediction studies based on hyperspectral remote sensing neglect the spectral response of the physical properties of surface soil, leading to inadequate model generalization. With the exponential growth of remote sensing data, the development of pixel-level soil spectral correction methods based on multi-source remote sensing data has become an interesting and challenging topic. This method aims to minimize the effect of soil physical properties on spectra, thus addressing the poor spatiotemporal transferability of SOC prediction models due to uncertain variations in surface soil physical properties. In this study, a soil spectral correction strategy is constructed using satellite hyperspectral image (HSI) and synthetic aperture radar (SAR) images through multi-order polynomial regression and convolutional neural networks. This strategy considers soil physical variables such as soil moisture (SM) content and root mean square height (RMSH) of soil surface roughness. The soil spectral correction model and SOC content prediction model were established using 80 soil samples collected from Site 1. Afterward, the performance and transferability of both models were verified using the remaining 25 samples from Site 1 and 50 samples from Site 2. The results showed that: 1) The effect of SM and RMSH on the soil pixel spectrum can be significantly reduced after correcting HSI using soil spectral correction strategy. The correlation coefficients between the corrected pixel spectrum and the ground-based spectrum increase by over 60 % compared with those between the original spectrum and the ground-based spectrum. 2) Soil spectral correction improves the prediction accuracy and mapping capability of HSI for SOC content, with the highest RP2 of 0.743 and RMSEP of 3.455 g/kg at Site 1. 3) Compared with the original HSI-based SOC prediction model, the soil spectral correction strategy based on multi-order polynomial and convolutional neural network reduced the RMSEP of SOC prediction results at Site 2 by 5.082 g/kg and 5.454 g/kg, and the RP2 increased by 0.390 and 0.409, respectively. 4) When predicting SOC content from raw HIS, SM and RMSH contribute to more than 60 % of the bias, with SM having a larger bias than RMSH. The findings of this study emphasize the influence of soil physical properties on SOC prediction and contribute to the existing research on SOC mapping using HSI and SAR data.

Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India

Nitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization and traffic reflux. The current study aims to predicted the NO2 episodes in the Agra city using soft computing models namely, M5P, Random Forest (RF), Group method of data handling (GMDH), Multivariate adaptive regression (MARS), Reduced error pruning tree (REP Tree) and Random tree (RT). The models were generated using 1116 observations, from 2015 to 2020 with input parameters such as Particulate matter (PM2.5), Nitrogen monoxide (NO), Oxides of nitrogen (NOX), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O), Benzene (Be), Toluene, Relative humidity (RH), Wind speed (WS), Wind direction (WD), Solar radiation (SR), Barometric pressure (BP) and Xylene. The performance of each model was evaluated based on the six statistical indices, namely correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), normalized root of mean squared relative error (NRMSE), Willmott's Index (WI), and Legates and McCabe's Index (LMI). The performance evaluation models results of study area, Box plot and Taylor's diagram indicated that M5P is the outperforming model among others with testing CC = 0.9543, RMSE = 5.8006, MAE = 3,9204, NRMSE = 0.1512, WI = 0.9744, and LMI = 0.7549. Based on the sensitivity analysis indicated that NOx is the most influential parameter followed by WD and CO. These results of study area can be helpful to understanding the air pollution causes, health issues, and future NO2 levels around the study area with useful results for air pollution monitoring policy and development.

Hybrid system of computational intelligence based on bagging and group method of data handling

The paper considers the problem of short- and middle-term forecasting in the financial sphere. To solve this problem, a hybrid system of computational intelligence based on the group method of data handling (GMDH) and bagging, as well as an algorithm for its training, is proposed. The odd stacks of the hybrid system are formed by ensembles of parallel connected subsystems. ARIMA and the GMDH-neo-fuzzy hybrid network were chosen as such subsystems. The proposed system does not require a large training data set, automatically determines the number of stacks during training, and provides online operation. The experimental investigations were conducted using the proposed hybrid system, as well as separately using ARIMA and GMDH-neo-fuzzy. The accuracy of the predictions obtained is compared, based on which the feasibility of using the proposed hybrid system is substantiated.

Cardiomyopathy prediction in patients with permanent ventricular pacing using machine learning methods

Pacing-induced cardiomyopathy is a notable issue in patients needing permanent ventricular pacing. Identifying risk groups early and swiftly preventing the ailment can reduce patient harm. However, current prognostic methods require clarity. We employed machine learning to develop predictive models using medical data. Three algorithms — decision tree, group method of data handling, and logistic regression — formed models that forecast pacing-induced cardiomyopathy. These models displayed high accuracy in predicting development, signifying soundness. Factors like age, paced QRS width, pacing mode, and ventricular index during implantation significantly influenced predictions. Machine learning can enhance pacing-induced cardiomyopathy prediction in ventricular pacing patients, aiding medical practice and preventive strategies.

Application of group method of data handling and gene expression programming to modeling molecular diffusivity of CO2 in heavy crudes

Successful enhancement of heavy oil and bitumen production using CO2-based enhanced oil recovery (EOR) techniques requires extensive knowledge about the diffusivity of CO2 in heavy crudes. In this work, two advanced correlative approaches, namely group method of data handling (GMDH) and gene expression programming (GEP), were utilized to establish simple-to-use mathematical correlations by applying a comprehensive dataset including 260 laboratory data of CO2 diffusion coefficient (DC) in heavy crudes and taking into account all the parameters affecting the accuracy of the models. Moreover, the response surface and contour plots were analyzed to obtain the desired response values and operating conditions. The GMDH correlation represented more reliable results with better statistical criteria including average absolute percentage error (AAPRE) values of 7.47%, 7.83%, and 7.54% for training, testing, and the total sets, respectively. Also, the results showed that the increase in pressure and/or temperature caused an increment in the amount of diffusivity of CO2 in heavy crudes; and the maximum amount of CO2 DC was obtained at the upper limit of these operational parameters. At a given temperature and pressure, it appears that the highest CO2 DC in heavy crudes was obtained when the CO2 mass fraction was less than 0.02. Furthermore, the Pearson and Spearman correlation coefficients were used to investigate linear and non-linear relationships between input variables and the responses of GMDH and GEP correlations. The mass fraction of CO2, temperature, pressure, and temperature_PVT had a direct relationship, and on the other hand, the viscosity and density of crudes had a reverse relationship with CO2 DC. Finally, the reliability of the data bank of CO2 DC in heavy crudes along with the high validity of GMDH and GEP correlations was verified by the leverage technique. Notably, no out-of-leverage data were identified, with only seven and five data points considered as suspected for GMDH and GEP models, respectively.

TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization

The paper presents Taylor Map Polynomial Neural Network (TMPNN), a novel form of very high-order polynomial regression, in which the same coefficients for a lower-to-moderate-order polynomial regression are iteratively reapplied so as to achieve a higher-order model without the number of coefficients to be fit exploding in the usual curse-of-dimensionality way. This method naturally implements multi-target regression and can capture internal relationships between targets. We also introduce an approach for model interpretation in the form of systems of differential equations. By benchmarking on Feynman regression, UCI, Friedman-1, and real-life industrial datasets, we demonstrate that the proposed method performs comparably to the state-of-the-art regression methods and outperforms them on specific tasks.

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

This paper presents data-driven modeling and a results analysis. Group method data handling (GMDH), multivariable regression (MVR), artificial neuron network (ANN), and new proposed GMDH-featured ANN machine learning algorithms were implemented to model a field telemetry equivalent mud circulating density (ECD) dataset based on surface and subsurface drilling parameters. Unlike the standard GMDH-ANN model, the proposed GMDH-featured ANN utilizes a fully connected network. Based on the considered eighteen experimental modeling designs, all the GMDH regression results showed higher R-squared and minimum mean-square error values than the multivariable regression results. In addition, out of the considered eight experimental designs, the GMDH-ANN model predicts about 37.5% of the experiments correctly, while both algorithms have shown similar results for the remaining experiments. However, further testing with diverse datasets is necessary for better evaluation.

Plant species recognition with optimized 3D polynomial neural networks and variably overlapping time–coherent sliding window

Plant species recognition with optimized 3D polynomial neural networks and variably overlapping time–coherent sliding window

An improved asynchronous batch gradient method for ridge polynomial neural network

The ridge polynomial neural network composed of pi-sigma modules is a typical higher-order feedforward network, which has good non-linear mapping capabilities. Due to the strong coupling of the network structure, the synchronous gradient method can easily result in significant fluctuations in updated weights. This will reduce the generalization ability of the ridge polynomial neural network for solving classification problems. In this paper, the proposed method is a novel improved asynchronous batch gradient method, and combined with the adaptive parameters of the activation function are trained synchronously. We strictly prove the convergence theorem of the improved method. The numerical experimental results also indicate that our method for training ridge polynomial neural networks can help the weight changes smoothly and the network has good generalization ability. The feasibility and effectiveness of our method are verified from both theoretical and experimental perspectives.

Hydrological dynamics of the Kalisindh and Parbati Rivers: An integrated analysis in the context of the Eastern Rajasthan Canal Project (ERCP)

This study conducts a comprehensive analysis of hydrological patterns in the Kalisindh and Parbati Rivers, highlighting the Eastern Rajasthan Canal Project (ERCP) as pivotal for bolstering regional water security. Employing an array of data sources, this research utilizes Polynomial Regression and neural network forecasting to dissect flow patterns, identifying significant virgin flow peaks in the mid-1980s and early 2000s for the Kalisindh River, and a notable peak in 2006–2007 for the Parbati River. Analysis reveals that the specific discharge rate of the Parbati River is diminishing at twice the rate of the Kalisindh River, with annual decreases of approximately 0.0038 cumecs/km2 for Parbati, compared to 0.0019 cumecs/km2 for Kalisindh. Furthermore, runoff volumes indicate that the Parbati River, specifically at the Khatoli Gauge & Discharge (G&D) site, experiences significantly higher runoff—28,137.912 million cubic meters (MCM)—in contrast to 15,795.094 MCM for the Kalisindh River at the Barod G&D site. The findings accentuate the necessity for science-based water management strategies to effectively combat water scarcity and climate change impacts. The ERCP emerges as a crucial initiative for sustainable management of the Kalisindh and Parbati Rivers in Rajasthan, underscoring its potential to serve as a model for sustainable water and river basin management.

New Full-Dimensional Reactive Potential Energy Surface for the H4 System.

As the most abundant molecule in the universe, collisions involving H2 have important implications in astrochemistry. Collisions between hydrogen molecules also represent a prototype for assessing various dynamic methods for understanding fundamental few-body processes. In this work, we develop a new and highly accurate full-dimensional potential energy surface (PES) covering all reactive channels of the H2 + H2 system, which extends our previously reported H2 + H2 nonreactive PES [J. Chem. Theory Comput., 2021, 17, 6747] by adding 39,538 additional ab initio points calculated at the MRCI/AV5Z level in the reactive channels. The global PES is represented with high fidelity (RMSE = 0.6 meV for a total of 79,000 points) by a permutation invariant polynomial neural network (PIP-NN) and is suitable for studying collision-induced dissociation, single-exchange, as well as four-center exchange reactions. Preliminary quasi-classical trajectory studies on the new PIP-NN PES reveal strong vibrational enhancement of all reaction channels.

Generalized structure of the group method of data handling for modeling iceberg drafts

The iceberg draft prediction is vital to mitigate the collision risk of deep keel icebergs with the seafloor-founded infrastructures, including the subsea pipelines, wellheads, hydrocarbon loading equipment, and communication cables crossing the Arctic and subarctic areas since the drifting icebergs may gouge the ocean floor and the physical and operational integrity of the submarine structures would be threatened. In this study, the iceberg drafts were simulated using the generalized structure of the group method of data handling (GS-GMDH) algorithm for the first time. The parameters affecting the iceberg drafts were determined, and five GS-GMDH models comprising GS-GMDH 1 to GS-GMDH 5 were developed utilizing those parameters governing. A dataset comprising 161 distinct case studies measured in the most significant field investigations of iceberg characteristics was generated, and the GS-GMDH models were trained through 60 % of the data, the rest of the data (i.e., 40 %) were considered for the GS-GMDH models’ validation. By defining different scenarios, the most accurate GS-GMDH model and the most important input parameters were identified. The sensitivity analysis demonstrated that the iceberg width ratio (W/H) and the iceberg shape factor (Sf) were identified as the most influencing input parameters. The comparison between the performance of the premium GS-GMDH model and the group method of data handling (GMDH), artificial neural network (ANN) algorithms, and the empirical models proved that the GS-GMDH model simulated the iceberg drafts with the highest level of precision and correlation along with the lowest degree of complexity. Based on the partial derivative sensitivity analysis (PDSA), the magnitude of iceberg drafts grew by increasing the value of the iceberg width and iceberg length. Ultimately, a GS-GMDH-based equation was presented to estimate the iceberg drafts for practical applications, particularly in the early stages of iceberg management projects and engineering designs.

Sensitivity analysis and performance prediction of a micro plate heat exchanger by use of intelligent approaches

Performance of the heat exchanger with micro dimensions is influenced by variety of factors that have been evaluated in different experimental studies. Similar to other thermal devices, it is possible to propose model for performance characteristics of micro heat exchangers by means of intelligent methods. The aim of the present study is development of models based on intelligent techniques for estimation of Nusselt number in a micro heat exchanger using hybrid nanofluid. Furthermore, sensitivity analysis is implemented to assess the importance of considered inputs in the models. In this study, experimental data from another research work on the nanofluidic micro heat exchanger is used to propose model based on multilayer perceptron (MLP) and Group Method of Data Handling (GMDH) neural networks. Comparison between the outputs of the model and data provided in the experimental study reveals great exactness of the proposed models. It is found that the exactness of the model and errors in the prediction are dependent on the applied configuration and algorithm. R2 of the models by use of GMDH and MLP are 0.9948 and 0.9972, respectively. Average Absolute Relative Deviation (AARD) of the models based on the noted approaches are 2.43 % and 1.56 %, respectively. In addition to modeling, sensitivity analysis is carried out and it is observed that concentration of nanomaterial, TiO2-ZnO, in the working fluid has higher importance than Reynolds number and height of plate in the value of Nusselt number.

Toward Predicting Interfacial Tension of Impure and Pure CO2-Brine Systems Using Robust Correlative Approaches.

In the context of global climate change, significant attention is being directed toward renewable energy and the pivotal role of carbon capture and storage (CCS) technologies. These innovations involve secure CO2 storage in deep saline aquifers through structural and capillary processes, with the interfacial tension (IFT) of the CO2-brine system influencing the storage capacity of formations. In this study, an extensive data set of 2811 experimental data points was compiled to model the IFT of impure and pure CO2-brine systems. Three white-box machine learning (ML) methods, namely, genetic programming (GP), gene expression programming (GEP), and group method of data handling (GMDH) were employed to establish accurate mathematical correlations. Notably, the study utilized two distinct modeling approaches: one focused on impurity compositions and the other incorporating a pseudocritical temperature variable (Tcm) offering a versatile predictive tool suitable for various gas mixtures. Among the correlation methods explored, GMDH, employing five inputs, exhibited exceptional accuracy and reliability across all metrics. Its mean absolute percentage error (MAPE) values for testing, training, and complete data sets stood at 7.63, 7.31, and 7.38%, respectively. In the case of six-input models, the GEP correlation displayed the highest precision, with MAPE values of 9.30, 8.06, and 8.31% for the testing, training, and total data sets, respectively. The sensitivity and trend analyses revealed that pressure exerted the most significant impact on the IFT of CO2-brine, showcasing an adverse effect. Moreover, an impurity possessing a critical temperature below that of CO2 resulted in an elevated IFT. Consequently, this relationship leads to higher impurity concentrations aligning with lower Tcm values and subsequently elevated IFT. Also, monovalent and divalent cation molalities exhibited a growing influence on the IFT, with divalent cations exerting approximately double the influence of monovalent cations. Finally, the Leverage approach confirmed both the reliability of the experimental data and the robust statistical validity of the best correlations established in this study.

A self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecasting

In this study, we propose a new self-organizing deep network architecture of fuzzy polynomial neural networks (FPNN) based on Fuzzy rule-based Polynomial Neurons (FPNs) and a long short-term memory (LSTM) network to solve the task of time-series forecasting. In the existing regression model based on polynomial neural networks (PNN), it is difficult to achieve high quality performance when predicting time series data, because this model lacks the ability to extract temporal and spatial information. Therefore, we propose a new architecture consisting of one LSTM (temporal) layer and several fuzzy polynomial (spatial) layers to overcome the above-mentioned shortcomings of PNN and enhance its predictive ability to approximate the data. The temporal layer consists of LSTM neurons that have inherently strong modeling capabilities to learn sequential information. The spatial layers are composed of Rule-based Polynomial Neurons (FPNs) that can effectively reflect the complex nonlinear structure found in the input space and granulate it using of the Fuzzy C-Means (FCM) clustering method. An elitism-driven roulette-wheel selection (E_RWS) is used to select appropriate neurons. E_RWS not only ensures that the neuron with the strongest fitting ability is selected but also increases the diversity of candidate neurons. According to the experimental results, the proposed model has a high prediction performance and outperforms many state-of-the-art prediction methods when applied to the real-world time-series.

Modeling CO2 loading capacity of triethanolamine aqueous solutions using advanced white-box approaches: GMDH, GEP, and GP

The equilibrium solubility of carbon dioxide (CO2) in the solvents is a key essential characteristic that has to be evaluated for successful absorption-based CO2 capture procedures. In this study, the CO2 loading capacity of triethanolamine (TEA) aqueous solutions was estimated using three famous white-box algorithms namely gene expression programming (GEP), genetic programming (GP), and group method of data handling (GMDH). For achieving the aim of this study, 258 data in a wide range of pressure, temperature, and amine concentration were collected from literature. Temperature, partial pressure of CO2, and amine concentration were used as input parameters. The results demonstrated that GMDH correlation is more accurate than GEP and GP with a determination coefficient (R2) of 0.9813 and root mean square error of 0.0222. The R2 values of 0.9713 and 0.9664 for the GEP and GP, respectively, demonstrated that the GEP and GP also showed accurate predictions. In addition, GMDH approach accurately predicted the anticipated trends of the CO2 loading in response to changes in the partial pressure of CO2 and temperature. The Pearson and Spearman correlation analyses were also incorporated in this research which showed that temperature and CO2 partial pressure have almost the same relative effect on CO2 loading, while amine concentration has the lowest effect on it.