Prediction of critical total drawdown in sand production from gas wells: Machine learning approach

Sand production is a critical issue in petroleum wells. The critical total drawdown (CTD) is an essential indicator of the onset of sand production. Although some models are available for CTD prediction, most of them are proven to lack accuracy or use commercial software. Furthermore, the previous correlations have not studied the trend analysis to verify the correct relationships between the parameters. Therefore, this study aims to build accurate and robust models for predicting CTD using response surface methodology (RSM) and support vector machine (SVM). The RSM is utilized to obtain the equation without using any software. The SVM model is an alternative method to predict the CTD with higher accuracy. This study used 23 datasets to develop the proposed models. The CTD is a strong function of the total vertical depth, cohesive strength, effective overburden vertical stress, and transit time with correlation coefficients (R) of 0.968, 0.963, 0.918, and −0.813. Different statistical methods, that is, analysis of variance (ANOVA), F-statistics test, fit statistics, and diagnostics plots, have shown that the RSM correlation has high accuracy and is more robust than correlations reported in the literature. Moreover, trend analysis has proven that the proposed models ideally follow the correct trend. The RSM correlation decreased the average absolute percent relative error (AAPRE) by 12.7% compared to all published correlations' AAPRE of 22.6%–30.4%. The SVM model has shown the lowest AAPRE of 6.1%, with the highest R of 0.995. The effects of all independent variables on the CTD are displayed in three-dimensional plots and showed significant interactions.     

N‐(hydroxymethyl) acrylamide as a multifunctional finish to cotton and a tether for grafting methacrylamide for biocidal coatings

N‐(hydroxymethyl) acrylamide (NMA) was immobilized on cotton surfaces through etherification, and then methacrylamide (MA) was grafted onto the treated surface. The coatings were characterized by ATR‐IR spectroscopy and were rendered biocidal upon exposure to dilute household bleach. The treated fabrics were challenged with Gram‐negative and Gram‐positive bacteria; both NMA and NMA/MA‐treated fabrics inactivated about 8 logs of Escherichia coli O157:H7 and Staphylococcus aureus within only 5 min of contact time. The coatings were also quite stable toward ultraviolet (UVA) light exposure and repeated laundering. Moreover, a substantial improvement in wrinkle recovery angle was obtained for the NMA/MA‐treated fabrics. The new acyclic acrylamide N‐halamine coating should be less expensive to produce and use than previous cyclic N‐halamine coatings developed in these laboratories. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Modelling of a new solar air heater through least-squares support vector machines

This paper reports on a modelling study of new solar air heater (SAH) system efficiency by using least-squares support vector machine (LS-SVM) method. In this study, a device for inserting an absorbing plate made of aluminium cans into the double-pass channel in a flat-plate SAH. A SAH system is a multi-variable system that is hard to model by conventional methods. As regards the LS-SVM, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data. In this study, a LS-SVM based method was intended to adopt SAH system for efficient modelling. For modelling, different mass flow rates in flow duct and collector types are used and then for obtaining the optimum LS-SVM parameters, such as regularization parameter, and optimum kernel function and parameters, several tests have been carried out. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that root mean squared error (RMSE) value is 0.0024, the coefficient of multiple determinations (R²) value is 0.9997 and coefficient of variation (cov) value is 2.1194 for the proposed radial basis function (RBF)-kernel LS-SVM method at 0.03 kg/s air mass flow rate. It is found that RMSE value is 0.0135, R² value is 0.9991 and cov value is 2.9868 for the proposed RBF-kernel LS-SVM method at 0.05 kg/s air mass flow rate. Comparison between predicted and experimental results indicates that the proposed LS-SVM model can be used for estimating the efficiency of SAHs with reasonable accuracy.     

Group sequential testing of homogeneity in genetic linkage analysis

Human genetic linkage studies have the objective of testing whether disease genes are linked to genetic markers based on family genetic data. Sometimes, these studies require many years of recruiting informative families and large amount of funds. One way to reduce the required sample size for such studies is to use sequential testing procedures. In this paper, we investigate two group sequential tests for homogeneity in binomial mixture models that are commonly used in genetic linkage analysis. We conduct Monte Carlo simulations to examine the performance of the group sequential procedures. The results show that the proposed group sequential procedures can save, on average, substantial sample size and detect linkage with almost the same power as their nonsequential counterparts.     

Diagnosis of valvular heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis.     

Prediction of protein cellular localization sites using a hybrid method based on artificial immune system and fuzzy k-NN algorithm

The use of artificial intelligence methods in biological data analysis has been increased recent since performance of the classification and detection systems have improved considerably to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm with and without cross validation in a class of imbalanced problems in bioinformatics. Furthermore, we devise an unsupervised AIS algorithm in a supervised manner which contains a training stage for data reduction and a classification stage using fuzzy k-NN algorithm. The experiments show the efficacy of the proposed method with promising results. Using the Escherichia coli and yeast database, we compare the classification accuracy of the proposed method with those of other methods which have been proposed in the literature. The proposed hybrid system produced much more accurate results than the Horton and Nakai's method [P. Horton, K. Nakai, Better prediction of protein cellular localization sites with the k-nearest neighbors classifier, in: Proceedings of Intelligent Systems in Molecular Biology, Halkidiki, Greece, 1997, pp. 368–383]. Besides the improvement on the classification accuracy, one of the important aspects of the proposed method is the complexity. As the proposed AIS method incorporates data reduction in the training stage, the training complexity is considerably low comparing with the k-NN classifier.     

Thermodynamic re-assessment of the Al–Ir system

The thermodynamic assessment of the Al–Ir binary system was performed using the CALPHAD technique. The B2-AlIr phase was described, using the two sublattice model with the formula (Al,Ir,V a)_1/2(Al,Ir,V a)_1/2, while Al₉Ir₂, Al₃Ir, Al₁₃Ir₄, Al₄₅Ir₁₃, Al₂₈Ir₉, and Al_2.7Ir compounds were treated as stoichiometric compounds. The fcc-based phases (L1₀-AlIr, L1₂-Al₃Ir, L1₂-AlIr₃ and A1) were described using the four sublattice model with the formula, (Al,Ir)_1/4(Al,Ir)_1/4(Al,Ir)_1/4(Al,Ir)_1/4. From ab initio calculations (VASP) the formation enthalpies of the stable/metastable intermetallic phases involved in the Al–Ir system were estimated. The thermodynamic quantities, such as the phase equilibria, invariant reactions, and formation enthalpies of the intermetallic phases, were calculated using the obtained parameter set, and agree well with experimental data.     

One-step preparation of poly(NIPAM-pyrrole) electroconductive composite hydrogel and its dielectric properties

Conductive polymers and hydrogels are two of the hot prospect polymer types that are used for new stimuli responsive materials. In this study, one-step preparation of electroconductive composite hydrogels containing polypyrrole (PPy) and N-isopropylacrylamide (NIPAM) using free radical polymerization technique was achieved with N,N-methylenebisacrylamide as a crosslinker and ammonium peroxy disulphate (APS) as initiator, in mixture of water/isopropyl alcohol. The equilibrium swelling degree of the poly(NIPAM)-pyrrole) electroconductive composite hydrogel was 9.88 g of H₂O/g dry polymer. According to TGA results, the thermal stability of the prepared composite poly(NIPAM-PPy) conductive hydrogel (700°C) hydrogel is higher than that of pure poly(NIPAM) hydrogel (600°C). Furthermore, prepared samples were characterized by FTIR, and SEM analyzes. Later, the samples were pressured into pellets so that electrical impedance spectroscopy (EIS) measurements were taken between 10 and 10 MHz at room temperature. The dielectric constant value of composite poly(NIPAM-PPy) hydrogel at 10 Hz is almost 10 times higher than that of poly(NIPAM) hydrogel. Both samples' real and imaginary parts of dielectric constant decreased with increased frequency. Samples exhibited non-Debye relaxation since experimental data fit into dielectric model of Havriliak-Negami. Moreover, low frequency data yielded d.c. conductivity of the pure and composite samples as 3.74 × 10⁻¹¹ and 1.02 × 10⁻⁸ S/cm, respectively. Real part of impedance at low frequencies also points out ~10³ times lower resistance values at 10 Hz for composite poly(NIPAM-PPy) hydrogel. Therefore, EIS results support that electroconductive composite hydrogel fabrication was achieved using free radical polymerization technique.     

Effects of Mold Pressure on Mechanical, Microstructures, Oxidation Behavior, and Thermodynamic Properties of an Al-Based Alloy

In this study, the effects of mold pressure variation on the microstructure, mechanical, oxidation behavior, and thermodynamics properties of an AlSi12CuNi alloy have been investigated by means of optical microcopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, differential thermal analysis, and thermo-gravimetric analysis techniques. It is observed that hardness and tensile strength of the alloys increase as die-casting mold pressure increases due to an improvement in the distribution pattern of secondary phases and reduction in porosity. Oxidation behavior parameters and thermodynamic parameters such as formation enthalpy, heat capacity, and entropy are determined from TGA and DTA. It is found that mold pressure also has a significant effect on the oxidation behavior and thermodynamic parameters.