ZnO nanorods surface-decorated by WO3 nanoparticles for photocatalytic degradation of endocrine disruptors under a compact fluorescent lamp

Nanoscaled tungsten oxide (WO₃) particles coated on ZnO nanorods (ZNRs) were newly synthesized by combining a hydrothermal technique with a chemical solution process. The structure, morphologies and compositions of the as-prepared WO₃–ZNR nanocomposites were characterized through XRD, FESEM, TEM and Raman measurements. The results revealed that pure monoclinic WO₃ nanoparticles with an average size range of 18–26 nm were distributed on the surfaces of ZNRs and attached strongly. Particularly, the optical properties as well as photocatalytic characteristics of pure ZNRs and WO₃–ZNR nanocomposites with different loadings of WO₃ were also examined. The absorption of WO₃–ZNR nanocomposites was redshifted due to effective immobilization of WO₃ on ZNRs. Under irradiation of a 55 W compact fluorescence lamp, the photocatalytic activities of the WO₃–ZNR nanocomposites were superior to those of pure ZNRs and P25 in the degradation of resorcinol (ReOH). Furthermore, WO₃–ZNR nanocomposites showed very favorable recycle use potential and high sedimentation rate. Other endocrine disrupting chemicals (EDCs) such as phenol, bisphenol A (BPA) and methylparaben were also successfully photodegraded under identical conditions. These characteristics showed the practical applications of the WO₃–ZNR nanocomposites in indoor environmental remediation.     

Multiclassification of tool wear with support vector machine by manufacturing loss consideration

Tool wear is a dynamic process, as a tool progresses from sharp to worn state and possibly to breakage. Thus the multiclassification of tool states is preferred, which can provide more timely and accurate estimation of tool states. Based on acoustic emission (AE) sensing, this paper proposes a new performance evaluation function for tool condition monitoring (TCM) by considering manufacturing loss. Firstly, two types of manufacturing loss due to misclassification (loss caused by under prediction and loss caused by over prediction) are analyzed, and both are utilized to compute corresponding weights of the proposed performance evaluation function. Then the expected loss of future misclassification is introduced to evaluate the recognition performance of TCM. Finally, a revised support vector machine (SVM) approach coupled with one-versus-one method is implemented to carry out the multiclassification of tool states. With this approach, a tool is replaced or continued not only based on the tool condition alone, but also the risk in cost incurred due to underutilized or overused tool. The experimental results show that the proposed method can reliably perform multiclassificaion of tool flank wear, and reduce the potential manufacturing loss.     

MICROFLORA CHANGES IN TUNA MINCE DURING CONVECTION AIR DRV1NG

Microorganisms grow in tuna during drying at low temperatures. The drying temperature of 50°C or below is not lethal to the microflora. The decimal reduction time (D-value) varied from 12.66 to 2.63 hr when drying temperature increased from 60 to 100°C, respectively. These values can be used to estimate the lethal effect of drying on the natural microflora in tuna.     

Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.     

An improved control method of buried-type IPM bearingless motors considering magnetic saturation and magnetic pull variation

An improved control method of the buried-type interior permanent magnet (IPM) bearingless motors has been presented. It is shown that the conventional method is not applicable to this type of IPM bearingless motor for loaded conditions. In IPM bearingless motors, the armature reaction flux is present due to high magnetic permeance with thin permanent magnets and small airgap length. An increase in d-axis flux linkage is caused by armature reaction as a torque-component flux is increased. Thus, it is likely that magnetic saturation occurs in the stator teeth. In addition, a magnetic attractive force caused by the displacement force factor is dependent on the armature reaction flux. A new decoupling controller for the IPM bearingless motor considering magnetic saturation is proposed in this paper. It also considers the influence of magnetic attractive force variations. In addition, a new parameter identification method for the decoupling controller is also proposed. The new controller is found quite suitable to realize successful stable operation of the experimental IPM bearingless motor.     

Groundwater Exploration and Assessment in Rural Communities of Yobe State, Northern Nigeria

The provision of adequate water supply and sanitation to the rapidly growing urban populations is increasingly becoming a problem for governments throughout the world. The continuing expansion of the numbers of people in cities who need water and sanitation services form a continuous pressure to either invest in additional production capacity or to stretch the available supplies to serve more people. Due to rapid increase in population growth in the Yobe State north of Nigeria, there is a shortage in the water supply to Damaturu city the capital of the state and surrounding villages. At the present the total water supply is about 10,000 m³/day abstracted form the shallow alluvial groundwater aquifer using 29 production wells. Due to the expected increase in water demand and the limited potentiality of shallow aquifer system, other deep aquifers were explored and investigated to evaluate their potentiality for future water demand. Vertical Electrical Sounding Method was used for the geophysical survey of the study area. Groundwater flow model was developed and calibrated against the historical information. Three wellfields were designed to provide Damaturu city and surrounding villages with the required water. The calibrated model has been used to evaluate the aquifer potentiality and the effect of future withdrawals on the deep aquifer system. It was found that the aquifer system within the study area consists of two main layers. The upper layer is the Chad formation comprises an alluvial sand and gravel with intercalation of thin sility clay layers. The second layer is Keri-Keri formation consists of sandstone formation which is not explored before. During this study the Kerri-Kerri aquifer system was investigated as an alternative source for groundwater for future demand. The study presents an integrated groundwater resources management strategy for present and future water supply for rural communities.     

Mapping Aquifer Vulnerability Indices Using Artificial Intelligence-running Multiple Frameworks (AIMF) with Supervised and Unsupervised Learning

DRASTIC-based vulnerability indices and their variations for an aquifer are investigated in this paper, each of which is regarded as a framework since their rationale of using seven DRASTIC data layers is consensual and lacks empirical or theoretical formulations. The Basic DRASTIC framework (BDF) is implemented by a set of prescribed rules; whereas its three variations involve unsupervised learning from the data, which comprise: (i) learning the rates by the Wilcoxon test (WDF) but using BDF weights; (ii) using BDF rates but learning the weights by Genetic Algorithm (BDF-GA); and (iii) learning rates as in WDF and the weights as in BDF-GA (WDF-GA). These four frameworks are not supervised, but the novelty of the paper is to introduce supervised learning at the second stage by Artificial Intelligence to run Multiple Frameworks (AIMF), for which the paper uses Support Vector Machine (SVM). AIMF uses the outputs of the four frameworks as its input data and a function of observed nitrate-N values as its target data. The AIMF strategy is evaluated in the aquifer of Ardabil plain, which is exposed to anthropogenic contamination such as nitrate-N. The coefficient of correlation (r-values) between the results and nitrate-N values for the above frameworks are: 0.2, 0.37, 0.38 and 0.45; whereas AIMF enhances it to 0.84; attributable to the supervised learning.     

Heat transfer from a spinning disk during semi-confined axial impingement from a rotating nozzle

Conjugate heat transfer from a uniformly heated spinning solid disk of finite thickness and radius during a semi-confined liquid jet impingement from a rotating nozzle is studied. The model covers the entire fluid region including the impinging jet on a flat circular disk and flow spreading out downstream under the spinning confinement plate and free surface flow after exposure to the ambient gaseous medium. The model examines how the heat transfer is affected by adding a secondary rotational flow under semi-confined jet impingement. The solution is made under steady state and laminar conditions. The study considered various plate materials such as aluminum, copper, silver, constantan and silicon. Ammonia, water, flouroinert FC-77 and MIL-7808 oil were used as working fluids. The range of parameters covered included Reynolds number (220–900), Ekman number (7.08 × 10^?5–∞), nozzle-to-target spacing (β = 0.25–1.0), disk thicknesses to nozzle diameter ratio (b/d_n = 0.25–1.67), Prandtl number (1.29–124.44) and solid to fluid thermal conductivity ratio (36.91–2222). It was found that a higher Reynolds number increased local heat transfer coefficient reducing the interface temperature difference over the entire disk surface. The rotational rate also increased local heat transfer coefficient under most conditions. An engineering correlation relating the Nusselt number with other dimensionless parameters was developed for the prediction of the system performance.     

Effect of particle properties on particle percolation behaviour in a packed bed

Spontaneous inter-particle percolation is a very important phenomenon related to the mixing/segregation of particulate materials. Many studies have been conducted to understand the mechanisms governing the percolation behaviours in the past. However, previous work has a lack of systematic investigation on the effect of particle properties. In this work, a layer of small particles passing through a packed bed under gravity is analysed by means of the discrete element method. Percolation behaviour in terms of percolation velocity and radial dispersion is studied. The effect of percolating particle properties, including sliding friction and damping coefficients between percolating and packing particles, density and diameter ratios of percolating to packing particles and Young’s modulus of percolating particles, on the percolation behaviour is considered. It is observed that the damping coefficient and diameter ratio are the two dominant parameters that significantly affect the percolation behaviour. In particular, increasing the damping coefficient or decreasing the diameter ratio would increase the percolation velocity and decrease the radial dispersion. The sliding friction coefficient, Young’s modulus and density ratio have limited effects on the percolation behaviour. Two formulas have been, respectively, proposed to describe the dependences of percolation velocity and interaction force between percolating and packing particles on damping coefficient and diameter ratio.