Group Method Of Data Handling Method Research Articles

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.

Attenuation Relationships for the Horizontal Component of Peak Ground Acceleration (PGAH) Using Gene Expression Programing (GEP) and Group Method of Data Handling (GMDH)

Earthquake is one of the natural disasters that has always influenced human life. It is not currently possible to predict exactly when and where an earthquake will occur, nor how large it will be. It is also impossible to prevent an earthquake. However, by designing seismic-resistant structures, the amount of financial losses and casualties can be reduced. This resistant design requires the use of earthquake risk analysis. By using the earthquake risk analysis, it will be possible to estimate the parameters of the strong ground motion, including acceleration, velocity, and displacement in each area. Estimating the parameters of strong ground motion will be possible just by obtaining the appropriate attenuation relationship. The aim of this paper is to present an appropriate attenuation relationship to estimate the horizontal component of the possible occurrence of peak ground acceleration in each region. Two methods were used to calculate attenuation relationship: gene expression programing (GEP) and group method of data handling (GMDH). In the first step, an up-to-date and comprehensive catalog consisting of 1,185 earthquake records that occurred around the world has been prepared. In the next step, the parameters of magnitude, hypocentral distance, and shear wave velocity of these records have been used as variables of the attenuation relationship. Then, the fitness function (f) was determined, and attenuation relationships were calculated using GEP and GMDH. The amount of fitness function (f) was obtained 766.12 from 1,000 in the GEP method and 767.77 from 1,000 in the GMDH method. The values of the fitness function, residuals and comparison plots showed a high-agreement between “the values predicted by the attenuation relationships” and “the actual values observed in the earthquakes.” Finally, according to the results of this research, it can be said that the use of GEP and GMDH methods has provided better results than the other similar researches. Also, the use of up-to-date records makes the results of this research more reliable than the previous researches.

Double diffusive Buoyancy‐driven flow in a fluid‐saturated elliptical annulus with a neural network‐based prediction of heat and mass transfer

AbstractThis paper presents a numerical study of buoyancy‐driven double‐diffusive convection within an elliptical annulus enclosure filled with a saturated porous medium. An in‐house built FORTRAN code has been developed, and computations are carried out in a range of values of Darcy–Rayleigh number Ram (10 ≤ Ram ≤ 500), Lewis number Le (0.1 ≤ Le ≤ 10), and the ratio of buoyancy forces N (−5 ≤ N ≤ 5). In addition, three methods are used, namely the multi‐variable polynomial regression, the group method of data handling (GMDH), and the artificial neural network (ANN) for the predictions of heat and mass transfer rates. First, results are successfully validated with existing numerical and experimental data. Then, the results indicated that temperature and concentration distributions are sensitive to the Lewis number and thermal and mass plumes are developing in proportion to the Lewis number. Two particular values of Lewis number Le = 2.735 and Le = 2.75 captured the flow's transition toward an asymmetric structure with a bifurcation of convective cells. The average Nusselt number tends to have an almost asymptotic value for Le » 5. For the case of aiding buoyancies N > 1, the average Nusselt Number decreased by 33% when the Lewis number increased to its maximum value. Then, it increased by 10% when the Lewis number increased to Le = 1 for the case of opposing buoyancies N < 1 and then decreased by 33% when the Lewis number increased to its maximum value., contrary to the behavior of the average Sherwood number that increased by 700% for both cases N > 1 and N < 1. New correlations of , and as a function of Ram, Le, and N are derived and compared with GMDH and ANN methods, and the ANN method showed higher performance for the prediction of and with R2 exceeding 0.99.

Application of GMDH model to predict pore pressure

Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas industry, especially in reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industry. This research aims to develop a algorithm to better predict PP in subsurface -formations. Based on this, information from three wells (F1, F2, and F3) representing one of the Middle East oil fields was used in this research. The input variables used in this research include; laterolog (LLS), photoelectric index (PEF), compressional wave velocity (Vp), porosity (NPHI), gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), shear wave velocity (Vs), caliper (CALI), resistivity (ILD), and sonic transit time (DT). Based on the results presented in the heat map (Spearman’s correlation), it can be concluded that the pairs of parameters RHOB-PEF, CGR-SGR, RHOB-CALL, DT-PEF, PP-RHOB, Vs-RHOB, ILD-LLS, DT-CGR, and DT-NPHI are connected. In this research the GS-GMDH methods is used for modeling which is based on the Group method of data handling (GMDH). The results of this research show that this algorithm has an average error of RMSE = 1.88 Psi and R2 = 0.9997, indicating its high-performance accuracy. The difference between this method and the conventional GMDH method is that it can use three or more variables instead of two, which can improve prediction accuracy. Furthermore, by using the input of each neuron layer, the proposed model can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.

Comprehensive power swing detection by current signal modeling and prediction using the GMDH method

Power swing is an undesirable variation in power flow. This can be caused by large disturbances in demand load, switching, disconnection or reclosing lines. This phenomenon may enter the zones of distance relays and cause relay malfunction leading to the disconnection of healthy lines and undermining network reliability. Accordingly, this paper presents a new power swing detection method based on the prediction of current signal with a GMDH (Group Method of Data Handling) artificial neural network. The main advantage of the proposed method over its counterparts is the immunity to noise effect in signals. In addition, the proposed method can detect stable, unstable, and multi-mode power swings and is capable of distinguishing them from the variety of permanent faults occurring simultaneously. The method is tested for different types of power swings and simultaneous faults using DIgSILENT and MATLAB, and compared with some latest power swing detection methods. The results demonstrate the superiority of the proposed method in terms of response time, the ability to detect power swings of different varieties, and the ability to detect different faults that may occur simultaneously with power swings.

Epistemic Uncertainty Treatment Using Group Method of Data Handling Algorithm in Seismic Collapse Fragility

Abstract Developing fragility functions is the essential step in incorporating important uncertainties in next-generation performance-based earthquake engineering (PBEE) methodology. The present paper is aimed to involve record-to-record variability as well as modelling uncertainty sources in developing the fragility curves at the collapse limit state. In this article, in order to reduce the dispersion of uncertainties, Group Method of Data Handling (GMDH) in combination with Monte Carlo Simulation (MCS) is used to develop structural collapse fragility curve, considering effects of epistemic and aleatory uncertainties. A Steel Moment Resisting Frame (SMRF) is chosen as the tested structure. The fragility curves obtained by the proposed method which belongs to GMDH approaches are compared with those resulted from simple and well-known available methods such as First-Order Second-Moment (FOSM), Approximate Second-Order Second-Moment (ASOSM) and Monte Carlo (MC)/Response Surface Method (RSM), somehow, as an accurate method. The results of the application of the proposed approach indicate increasing accuracy and precision of the outputs as well as power with the same computational time compared to aforementioned methods. The GMDH method introduced here can be applied to the other performance levels.

Prediction of hydraulics performance in drain envelopes using Kmeans based multivariate adaptive regression spline

This study suggests a new modeling strategy, hybridized multivariate adaptive regression spline (MARS) and Kmeans clustering, for estimating coefficients of hydraulic conductivity using various input combinations of the useful variables, hydraulic head H (cm), geotextile filters size O90 (nm), time T (min) and discharge of drain Q (cm3/s). The results of the newly developed method (MARS-Kmeans) were compared with the single MARS, M5 model tree (M5 Tree) and group method of data handling (GMDH) with respect to four assessing statistics of root mean square errors (RMSE), mean absolute errors (MSE), Nash Sutcliffe efficiency (NSE), and coefficient and determination (R2) together with Wilcoxon rank-sum test and visual evaluation via scatterplots, boxplots, and Taylor diagram. The results indicated the superiority of the MARS-Kmeans method over the M5 Tree, MARS, and GMDH in estimating envelope hydraulic conductivity and soil-envelope hydraulic conductivity. The accuracy of the M5 Tree, MARS and, GMDH methods were improved using MARS-Kmeans in estimating Kse by 45%, 57%, and 77% and estimating Ke by 31%, 38%, and 45% for RMSE.

Combination of Group Method of Data Handling (GMDH) and Computational Fluid Dynamics (CFD) for Prediction of Velocity in Channel Intake

This paper utilizes computational fluid dynamics as well as a group method of data handling (GMDH) method to predict the mean velocity of intake. Firstly, the three dimensional flow pattern in a 90-degree intake is simulated with ANSYS-CFX at a transverse ratio equal to one (W*b/W*m = 1) that W*m is the width of the main channel and W*b is the width of the branch channel. The comparison of mean velocity in the simulated intake and experimental channel represents the high accuracy of ANSYS-CFX modeling (mean absolute percentage error (MAPE) = 5% and root mean square error (RMSE) = 0.017). A group method of data handling (GMDH) is one type of artificial intelligence approach that presents elementary equations for calculating the problem’s target parameter and performing well in complex nonlinear systems. In this research, to train and test the GMDH method, input data is needed in all parts of the channel. Since there is not enough laboratory data in all parts of the channel, to increase the benchmarks, the laboratory model is simulated by the Computational Fluid Dynamics (CFD) numerical model. After ensuring the proper accuracy of the numerical results, the built-in CFD numerical model has been used as a tool to create primary benchmarks in the channel points, especially in areas where there is no laboratory data. This generated data has been used in training and testing the GMDH method. The diversion angle with the longitudinal direction of the main channel (θ), the longitudinal coordinates in the intake (y*), and the ratio of the branch channel width to the main channel (Wr) have been applied as the input training data in the GMDH method to estimate mean velocity. The results of the statistical indexes used to quantitatively examine this model, (R2 = 0.86, MAPE = 10.44, RMSE = 0.03, SI = 0.12), indicated the accuracy of this model in predicting the mean velocity of the flow within open channel intakes.

Prediction and pareto-based multi-objective optimization of moisture and fatigue damages of asphalt mixtures modified with nano hydrated lime

Moisture sensitivity can lead to damages due to adhesion and cohesion in asphalt mixtures that cause serious damages to the pavement. Adhesion between bitumen and aggregate is a basic prerequisite for the proper performance of asphalt mixtures. The role of nano hydrated lime (NHL) as an anti-stripping agent has been extensively studied in asphalt pavements. In this study, in addition to investigating the effect of NHL as bitumen modifier on moisture and fatigue damages, and bitumen-aggregate adhesion and bitumen membrane cohesion using surface free energy (SFE) concept, the prediction model to achieve optimal additive percentage with two objectives of moisture sensitivity and fatigue failure by multivariate regression (MVR), group method of data handling (GMDH) and genetic programming (GP) were provided. By the use of the modified non-dominated sorting genetic algorithm II (NSGA-II) approach, Pareto fronts were then plotted. In addition, boiling water testing was applied to examine the sensitivity of mixtures to moisture. To simulate environmental conditions, asphalt mixtures were placed in freeze–thaw (F-T) cycles of 1, 3 and 5. 85–100 penetration grade base bitumen was modified with 0.5%, 1% and 1.5% NHL (by the weight of bitumen). The results indicated that by decreasing the number of yellow pixels in the image processing of modified mixtures with NHL, modification of bitumens decreased the moisture sensitivity of specimens. Using NHL increased the resistance of the specimens to moisture and fatigue damages. The amount of indirect tensile strength (ITS) and fatigue life of asphalt mixtures reduced by rising the number of F-T cycles. The highest increase in ITS and fatigue life was obvious in modified mixtures with 1.5% and 1% NHL, respectively. The results of calculating the surface free energy (SFE) components of the base and modified bitumens showed that using NHL enhanced the total SFE amount of bitumens. Also, using the NHL increased cohesion free energy (CFE), adhesion free energy (AFE) and debonding energy (DE) of modified bitumens and decreased the permeability of asphalt mixtures (PAMs) amount. Among the various methods, the GMDH method had the highest R2. So that the R2 value of GMDH for tensile strength ratio (TSR) and the ratio of fatigue life (NFR) was 93.9% and 86.9%, respectively. The two-objective optimization results indicated that 1.29% NHL was the best optimal amount to maximize the TSR and NFR values simultaneously.

Using the GMDH and ANFIS methods for predicting the crack resistance of fibre reinforced high RAP asphalt mixtures

In this paper, the effectiveness of the group method of data handling (GMDH) and the adaptive neuro-fuzzy inference system (ANFIS) methods in modelling the fracture parameters of asphalt mixtures were studied. For this aim, the models were investigated on the fracture energy and J-integral results of hot mix asphalt in terms of temperature, RAP content and fibre content. It was found that the fibres have an outstanding effect on the fracture behaviour of asphalt mixtures especially at intermediate and high temperatures and can be considered as an alternative to enhance the fracture resistance of recycled asphalt mixtures. The fracture data of asphalt mixtures can be successfully modelled by the ANFIS method with a high level of correlation. The GMDH was unable to model the J-integral results, however, it had a fair correlation with the results of fracture energy.

Experimental Investigation and Model Development of Geometric Characteristics of Negatively Buoyant Jets Inclined at 15° and 52° using GMDH Method

Alfaifi, H.; Mohammadian, A., and Bonakdari, H., 2020. Experimental investigation and model development of geometric characteristics of negatively buoyant jets inclined at 15° and 52° using GMDH method. Journal of Coastal Research, 36(3), 636–653. Coconut Creek (Florida), ISSN 0749-0208.The goal of this study was to provide more experimental data on the main geometric characteristics of an inclined negatively buoyant jet discharged into deep stagnant water, as well as to use the data for developing and evaluating the performance of the proposed model. Series of laboratory experiments were performed using a high-resolution digital camera to determine the main geometric characteristics of two jet discharge angles, 15° and 52°. Various densimetric Froude numbers (Frd) were used in this study, ranging from 11 to 75. The results showed that the behavior of the jet is significantly affected by the Frd and the initial discharge angle (θ). A comparison of the present results with previously reported data showed good agreement for all geometric parameters, especially for the angle of 15°, while the 52° values differed slightly. A nonlinear method (artificial neural network, ANN) called the group method of data handling (GMDH) was employed in this study to develop explicit equations for the geometric characteristics of the two studied angles. This method has not previously been used in the prediction of mixing of inclined dense jets. Moreover, different statistical indices were utilized to evaluate the accuracy of the proposed equations. The values of R-squared (R2) and the mean absolute percentage error (MAPE) for the geometric characteristic ranged between 0.964 to 0.995 and from 2.6% to 8.4% respectively, which indicate that the GMDH model performed very well. The GMDH results provided good predictions for both angles for the data used within this study.

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions

Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water pipelines. Even though, there is a wide range of numerical models to estimate coefficient of longitudinal dispersion, these mathematical techniques may often come in quite few inaccuracies due to complex mechanism of convection-diffusion processes in pollutant transition in water pipelines. In this research work, to obtain more accurate prediction of LDC, general structure of group method of data handling (GMDH) is modified by means of extreme learning machine (ELM) conceptions. In fact, with getting inspiration from ELM, a novel GMDH method, called GMDH network based on using extreme learning machine (GMDH-ELM), is proposed in which weighting coefficients of quadratic polynomials applied in conventional GMDH are no longer required to be updated either using back propagation technique or other evolutionary algorithms through training stage. In fact, an intermediate parameter is employed to establish a relationship between the input and output in each neuron of the GMDH model. In this way, a well-known and reliable dataset (233 experimental data) related to LDC in water network pipelines, as output vector, is applied to conduct training and testing phases. Through datasets, the Re number, the average longitudinal flow velocity, the friction factor of pipeline and the diameter of pipe are considered as inputs of the proposed approach. The results of GMDH-ELM model indicate a highly satisfying level of precision in both training and testing phases. Furthermore, feed forward structure of GMDH model was improved by particle swarm optimization (PSO) and gravitational search algorithm (GSA) to predict LDC. Through a sound judgment, a comparison is drawn between the performance of GMDH-ELM and other developed GMDH models. Moreover, several empirical equations existing in literature have been applied for comparisons. Overall, results of GMDH-ELM have permissible superiority over the other soft computing tools and conventional predictive models.

Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm

Pressure drop (Δp) and collection efficiency (η) are used to evaluate the separation performance of the cyclone separator. In this study, we conducted comparative study of cyclone models using response surface methodology (RSM), back propagation neural network (BPNN), and group method of data handling (GMDH) networks to develop optimal predictive cyclone models. Also, we conducted multi-objective optimization for maximizing model and minimizing model using genetic algorithm (GA). CFD was performed instead of experimental method to get the estimated values for modeling of Δp and η. The validation results of CFD showed 0.5% and 2% errors for Δp and η, respectively, compared with the experimental data. Second, design of experiment (DOE) analysis for 10 cyclone geometrical parameters was executed to obtain the significant geometrical parameters. Vortex finder diameter D x, inlet width a, inlet height b and cone height H co have a significant effect on η and Δp. However, interaction effects between the geometrical parameters have small effects. The cyclone models by RSM, BPNN and GMDH based on 25 CFD training set were developed. The predictive performance results by the cyclone models were compared by 25 CFD test set. The GMDH method achieved the best prediction for Δp (, RMSE = 0.102) , RMSE = 0.0119) than the RSM, BPNN cyclone models. Additionally, uncertainty analysis was performed to estimate the quantitative performance of cyclone models. The results show that the uncertainty width of GMDH models achieved the best prediction (: ±0.0065, Δp: ±0.0188). Finally, GA was applied to optimize the GMDH models simultaneously. GA generated 70 non-dominant solutions. Reproducibility of five optimal points was validated by using CFD. The trade-off optimal point showed improvement by 24.31%, 18% and 8.79% for Δp and , respectively.

Numerical analysis of natural frequency and stress intensity factor in Euler–Bernoulli cracked beam

In this paper, the evaluation procedures of the natural frequency and the stress intensity factor in the opening mode are established for the Euler–Bernoulli cracked beam by using (a) a technique in the framework of the finite element method, (b) a group method of data handling (GMDH), and (c) the software ABAQUS software. In the first one, the stiffness and mass matrices of the beam are enriched according to the depth and location of the crack for the determination of the natural frequency. A discrete spring model is used to simulate the crack in the structure based on the energy release rate. The continuity conditions in a cracked element are applied to connect two sub-elements of both sides of the crack. In the second method, the natural frequency and the stress intensity factor are determined using the GMDH algorithm. Design of experiments technique is employed to create an optimum arrangement for the application in the GMDH neural network. A few case studies are examined to investigate the results of the analysis, in addition, to identify the priority and the comparison of the three methods. The procedure of the analysis explains the advantages and limitations of the finite element-based technique, the GMDH method, and ABAQUS.

A comparative study in the prediction of thermal conductivity enhancement of nanofluids using ANN-MLP, ANN-RBF, ANFIS, and GMDH methods

In this work, four types of data mining methods, namely adaptive neuro-fuzzy inference system, artificial neural network—multilayer perceptron algorithm (ANN-MLP), artificial neural network—radial basis function algorithm (ANN-RBF), and group method of data handling (GMDH) have been used to predict the enhancement of the relative thermal conductivity of a wide range of nanofluids with different base fluids and nanoparticles. The total number of experimental data used in this work is 483 from 18 different nanofluids. The input parameters are thermal conductivity of base fluid and nanoparticles, volume fraction percent, the average size of nanoparticles, and temperature. Although the results showed that all four models are in relatively good agreement with experimental data, the ANFIS method is the best. The average absolute relative deviations (AARD%) between the experimental data and those of obtained using ANFIS, ANN-MLP, ANN-RBF, and GMDH methods were calculated as 2.7, 2.8, 4.2, and 4.3, respectively, for the test sets and as 1.1, 2.4, 3.9, and 4.5, respectively, for the training sets. Comparison between the predictions of the proposed ANN-MLP, ANN-RBF, ANFIS, and GMDH models and those predicted by traditional models, namely Maxwell and Bruggeman models showed that much better agreements can be obtained using the four models especially ANFIS model. Accordingly, the ANFIS method can able us to predict the relative thermal conductivity of new nanofluids in different conditions with good accuracy.

Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned

Water alternating gas (WAG) injection process is a proven enhanced oil recovery (EOR) technology with many successful field applications around the world. WAG pilot projects demonstrate that WAG incremental recovery factor typically ranges from 5 to 10% of original oil in place, though up to 20% has been observed in some fields. Despite its proven success, WAG application growth has been very slow. One of the reasons is the unavailability of robust analytical predictive tools that could estimate WAG incremental recovery factor, which is required for preliminary economic analysis before committing to expensive and time-consuming detailed technical studies and field pilot test that often requires a lot of input data. A semi-numerical model for WAG incremental recovery factor prediction was developed based on data mining of published WAG pilots to fill this gap. An extensive review of published WAG pilot projects was carried out, and consequently, 33 projects from 28 fields around the world were selected for this research study. Field WAG incremental recovery factor and parameters with total of one hundred and seventy-seven (177) observations were inputted to the predictive model. A predictive model was developed using both regression and group method of data handling (GMDH) techniques; 70% of the 33 WAG pilot projects data were used as validation, whereas remaining 30% of data set were used for validation. The predictive model results achieved with coefficient of determination (R2) from the regression method were ranging from 0.892 to 0.946 and 0.854 to 0.917 for training and validation sets, respectively. However, the prediction model coefficient of determination (R2) using GMDH method was ranging from 0.964 to 0.981 and 0.934 to 0.974 for training and validation, respectively. The developed predictive model can predict WAG incremental recovery factor versus multiple input parameters that include rock type, WAG process type, hydrocarbon pore volume of injected gas, reservoir permeability, oil gravity, oil viscosity, reservoir pressure, and reservoir temperature. The results of the study demonstrated that few input parameters have a significant impact on WAG incremental recovery factor as reservoir permeability and hydrocarbon pore volume of injected gas. This research study uses a novel approach in pre-defining the expected incremental WAG recovery factor before committing resources for building complex numerical reservoir simulation models and running WAG pilot tests, which are very time-consuming and costly and require extensive data input.

Novel approach for predicting water alternating gas injection recovery factor

Water alternating gas (WAG) injection process is a proven EOR technology that has been successfully deployed in many fields around the globe. The performance of WAG process is measured by its incremental recovery factor over secondary recovery. The application of this technology remains limited due to the complexity of the WAG injection process which requires time-consuming in-depth technical studies. This research was performed for a purpose of developing a predictive model for WAG incremental recovery factor based on integrated approach that involves reservoir simulation and data mining. A thousand reservoir simulation models were developed to evaluate WAG injection performance over waterflooding. Reservoir model parameters assessed in this research study were horizontal and vertical permeabilities, fluids properties, WAG injection scheme, fluids mobility, trapped gas saturation, reservoir pressure, residual oil saturation to gas, and injected gas volume. The outcome of the WAG simulation models was fed to the two selected data mining techniques, regression and group method of data handling (GMDH), to build WAG incremental recovery factor predictive model. Input data to the machine learning technique were split into two sets: 70% for training the model and 30% for model validation. Predictive models that calculate WAG incremental recovery factor as a function of the input parameters were developed. The predictive models correlation coefficient of 0.766 and 0.853 and root mean square error of 3.571 and 2.893 were achieved from regression and GMDH methods, respectively. GMDH technique demonstrated its strength and ability in selecting effective predictors, optimizing network structure, and achieving more accurate predictive model. The achieved WAG incremental recovery factor predictive models are expected to help reservoir engineers perform quick evaluation of WAG performance and assess a WAG project risk prior launching detailed time-consuming and costly technical studies.

An Application for the Acquisition and Understanding of Expert Knowledge in a Maintenance Department

Abstract The knowledge of experienced and competent workers, or expert knowledge, so called, is an important element in the decision-making process in a maintenance department. Motivated by the need for collecting and storing a unique expert knowledge of maintenance, the Maintenance Business Processes Assignment Model, namely, the Eknow-Maintenance Model, was developed, by using the Group Method of Data Handling (GMDH). The Eknow-Maintenance Model includes: Expert Maintenance Identification (E1), Acceptance of Expert Maintenance using FAHP (E2), Expert Knowledge Acquisition (E3), Expert Knowledge Representation (E4) and Expert Knowledge Understanding using the GMDH method (E5). This model is implemented in the web-application.

Brain Tumors Diagnosis and Prediction Based on Applying the Learning Metaheuristic Optimization Techniques of Particle Swarm, Ant Colony and Bee Colony

Brain tumors are intensively studied and many techniques and algorithms have been proposed to extract the features of the brain MRI images and diagnose the tumors. The different techniques are distinguished and favored based on their accuracy and speed. This led to apply optimization methods to improve them. In this paper, we apply three methaheuristic optimization methods which recently gained much interest. These are: the Binary Particle Swarm Optimization (B-PSO), the Ant colony Travel salesman problem (ACO-TSP) and the Artificial Bee colony optimization (ABCO-BFS). These methods are carried out in two main steps. The first step is the segmentation process of the brain MRI images using K-means cluster and the second steps is the features extraction from them. We compare the results of these methods to obtain the best solution for feature extraction. The group method of data handling (GMDH) is applied to classify the extracted features for the tumors diagnosis. Prediction is also carried on by applying the GMDH method for any future complications. The accuracy of classification and prediction is obtained to be range from 88.9% to 98.8%.