Directionally sensitive weighted adaptive multivariate CUSUM mean charts

In many service and manufacturing industries, process monitoring involves multivariate data, instead of univariate data. In these situations, multivariate charts are employed for process monitoring. Very often when the mean vector shifts to an out-of-control situation, the exact shift size is unknown; hence, multivariate charts for monitoring a range of the mean shift sizes in the mean vector are adopted. In this paper, directionally sensitive weighted adaptive multivariate CUSUM charts are developed for monitoring a range of the mean shift sizes. Directionally sensitive charts are useful in situations where the aim lies in monitoring either an increasing or a decreasing shift in the mean vector of the quality characteristics of interest. The Monte Carlo simulation is used to compute the run length characteristics in comparing the sensitivities of the proposed and existing multivariate CUSUM charts. In general, the directionally sensitive and weighted adaptive features enhance the sensitivities of the proposed multivariate CUSUM charts in comparison with the existing multivariate CUSUM charts without the adaptive feature or those that are directionally invariant. It is also found that the variable sampling interval feature enhances the sensitivities of the proposed and existing charts as compared to their fixed sampling interval counterparts. The implementation of the proposed charts in detecting upward and downward shifts in the in-control process mean vector is demonstrated using two different datasets.     

Properties enhancement using oil palm shell nanoparticles of fibers reinforced polyester hybrid composites

Oil palm shell (OPS) nanoparticles were utilized as filler in fibers reinforced polyester hybrid composites. The OPS nanoparticles were successfully produced from the raw OPS using high-energy ball milling process. Fundamental properties including morphology, crystalline size, and particle size of the OPS nanoparticles were determined. Tri-layer natural fiber reinforcement (kenaf–coconut–kenaf fiber mat) polyester hybrid composites were prepared by hand lay-up techniques. The influences of the OPS nanoparticles loading in the natural fibers reinforced polyester hybrid composites were determined by analyzing physical, mechanical, morphological, and thermal properties of the composites. Results showed that the incorporation of the OPS nanoparticles into the hybrid composites enhanced the composite properties. Further, the natural fibers reinforced polyester hybrid composite had the highest physical, mechanical, morphological, and thermal characteristics at 3 wt.% OPS nanoparticles loading.     

An application of a merit function for solving convex programming problems

This paper presents a gradient neural network model for solving convex nonlinear programming (CNP) problems. The main idea is to convert the CNP problem into an equivalent unconstrained minimization problem with objective energy function. A gradient model is then defined directly using the derivatives of the energy function. It is also shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. It is also found that a larger scaling factor leads to a better convergence rate of the trajectory. The validity and transient behavior of the neural network are demonstrated by using various examples.     

Artificial neural network approach for solving fuzzy differential equations

The current research attempts to offer a novel method for solving fuzzy differential equations with initial conditions based on the use of feed-forward neural networks. First, the fuzzy differential equation is replaced by a system of ordinary differential equations. A trial solution of this system is written as a sum of two parts. The first part satisfies the initial condition and contains no adjustable parameters. The second part involves a feed-forward neural network containing adjustable parameters (the weights). Hence by construction, the initial condition is satisfied and the network is trained to satisfy the differential equations. This method, in comparison with existing numerical methods, shows that the use of neural networks provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples.     

An Artificial Neural Network Model to Solve the Fuzzy Shortest Path Problem

Neural Processing Letters - In the present research, we are going to obtain the solution of the fuzzy shortest path (FSP) problem. According to our search in the scientific reported papers, this is...     

RETRACTED ARTICLE: Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks

In the present study, compressive strength results of geopolymers produced by ordinary Portland cement (OPC) as aluminosilicate source have been modeled by artificial neural networks. Six main factors including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time and water curing regime each at 4 levels were considered for designing. A total of 32 experiments were conducted according to the L32 array proposed by the method. The neural network models were constructed by 10 input parameters including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time, water curing regime, water glass content, NaOH content, Portland cement content and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.

     

Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling

Water coning in petroleum reservoirs leads to lower well productivity and higher operational costs. Adequate knowledge of coning phenomena and breakthrough time is essential to overcome this issue. A series of experiments using fractured porous media models were conducted to investigate the effects of production process and pore structure characteristics on water coning. In addition, a hybrid artificial neural network (ANN) with particle swarm optimization (PSO) algorithm was applied to predict breakthrough time of water coning as a function of production rate and physical model properties. Data from the literature combined with experimental data generated in this study were used to develop and verify the ANN‐PSO model. A good correlation was found between the predicted and real data sets having an absolute maximum error percentage less than 9%. The developed ANN‐PSO model is able to estimate breakthrough time and critical production rate with higher accuracy compared to the conventional or back propagation (BP) ANN (ANN‐BP) and common correlations. The presence of vertical fractures was found to accelerate considerably the water coning phenomena during oil production. Results of this study using combined data suggest the potential application of ANN‐PSO in predicting the water breakthrough time and critical production rate that are critical in designing and evaluating production strategies for naturally fractured reservoirs. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1905–1919, 2014     

Photocatalysis of xenobiotic organic compounds in greywater using zinc oxide nanoparticles: a critical review

The direct discharge of raw bathroom greywater has increased the concentrations of various pollutants in the water bodies. Typically, greywater contains large quantities of xenobiotic organic compounds (XOCs) owing to an increase in consumption of personal care and bath products. Therefore, it urges for a suitable technology to eliminate these compounds from contaminated waters. Photocatalytic degradation using Zinc Oxide nanoparticles (ZnO NPs) has the potentiality to eliminate various XOCs. However, ZnO NPs have high tendency to aggregate, which may lower the photocatalytic degradation rate. Therefore, there is an urgency to modify ZnO NPs to overcome the limitation. The present review was conducted to determine a suitable method for the modification ZnO NPs. Besides, the potential of the modified ZnO NPs in degrading XOCs in greywater as a photocatalyst was also discussed.     

Hybrid connectionist model determines CO_2–oil swelling factor

In-depth understanding of interactions between crude oil and CO_2 provides insight into the CO_2-based enhanced oil recovery(EOR) process design and simulation. When CO_2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO_2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine(LS-SVM) to calculate the CO_2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters(у and б~2) of the LS-SVM model. This model showed a high coefficient of determination(R~2= 0.9953) and a low value for the mean-squared error(MSE = 0.0003) based on the available experimental data while estimating the CO_2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO_2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO_2–oil swelling factor when adequate experimental data are not available.     

Treatment of landfill waste,leachate and landfill gas: A review

This review aims at the treatment of the entire landfill, including the waste mass and the harmful emissions: leachate and landfill gas. Different landfill treatments (aerobic, anaerobic and semi-aerobic bioreactor landfills, dry-tomb landfills), leachate treatments (anaerobic and aerobic treatments, anammox, adsorption, chemical oxidation, coagulation/flocculation and membrane processes) and landfill gas treatments (flaring, adsorption, absorption, permeation and cryogenic treatments) are reviewed. Available information and the gaps present in current knowledge is summarized. The most significant areas to expand are landfill waste treatments, which in recent years has begun to grow but there is an opportunity for much more. Another area to explore is the treatment of landfill gas, a very large field to which not much effort has been put forth. This review is to compare different treatment methods and give direction to future research.