21 research outputs found

    Novel Linear and Nonlinear Equations for the Higher Heating Values of Municipal Solid Wastes and the Implications of Carbon to Energy Ratios

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    Energy recovery from municipal solid wastes (MSW) offers economic benefits together with improved management of wastes. In the literature, attempts have been made to understand and quantify the potential energy benefits of MSW but the implications of the proportion of the elemental constituents on the heating value of the wastes are rarely discussed. In this investigation, novel linear and nonlinear equations were developed from artificial neural network (ANN) to predict the higher heating values (HHV) of MSW. The new equations perform equally well in comparison with the existing models in the literature for different HHV data from various MSW sources. They also showed consistency in satisfactory performances for predicting HHV values from new data as well as altered elemental compositions. Furthermore, it was found that the change in the proportion of elemental compositions have interesting relation to the magnitude of the HHV for different wastes. Results show that a change in percent hydrogen (%H) changes the HHV in some wastes that possess the thresholds of both HHV magnitude and the carbon to energy ratio (C/HHV). For the waste with low HHV but relatively high C/HHV value, increasing the %H does not significantly alter their HHV value. For those with high HHV value and moderate C/HHV value, HHV increases as the %H increases. Wastes with high HHV value but low C/HHV undergo reverse in the trend of HHV as the %H increases. Typical example of this is found in plastic wastes with high percentage carbon (%C) but low C/HHV. In this waste, as the %H increases the corresponding HHV decreases. Keywords: Municipal solid wastes, linear, nonlinear, artificial neural network, carbon to energy ratio, higher heating values.

    Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

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    © 2016 Taylor & FrancisWell-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

    Nonlinear finite element analysis of axially crushed cotton fibre composite corrugated tubes

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    It is proven experimentally that introducing corrugation along a shell generator together with a proper advanced composite material will enhance the crashworthiness performance of energy device units. This is because corrugation along the shell generator will force the initial crushing to occur at a predetermined region along the tube generator. On the other hand, a proper composite material offers vast potential for optimally tailoring a design to meet crashworthiness performance requirements. In this paper, the energy absorption characteristics of cotton fibre/propylene corrugated tubes are numerically studied. Finite element simulation using ABAQUS/Explicit was carried out to examine the effects of parametric modifications on the tube’s energy absorption capability. Results showed that the tube’s energy absorption capability was affected significantly by varying the number of corrugation and aspect ratios. It is found that as the number of corrugations increases, the amount of absorbed energy significantly increases

    Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

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    Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

    An investigation on the evolution of granule formation by in-process sampling of a high shear granulator

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    Understanding the growth mechanisms in granulation process is an important topic, providing valuable insights and supports control strategies. Typically, observations in high shear granulators are made after stopping the process. In this work, an in-process sampling technique is described and applied to a high shear wet granulation process. Different samples can be collected over the cause of the high shear granulation process. This allowed observation of the evolution of granules during addition of water at a constant flowrate. For a typical pharmaceutical formulation, we observed that granules nucleate in the first 2 minutes after water addition starts and then grows in size and strength to an average size of 200–1200 μm at 12.5 minutes, corresponding to a sharp increase in torque. Longer water addition times lead to oversized granules and eventually a paste and highly fluctuating torque. Sampling was continued after stopping water addition which showed with time larger formed granules smoothen, whilst the smaller weaker ones disintegrate. The work shows the in-process sampling can facilitate the identification of the required binder quantity in high shear granulation

    Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples

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    We describe the MalariaGEN Pf7 data resource, the seventh release of Plasmodium falciparum genome variation data from the MalariaGEN network. It comprises over 20,000 samples from 82 partner studies in 33 countries, including several malaria endemic regions that were previously underrepresented. For the first time we include dried blood spot samples that were sequenced after selective whole genome amplification, necessitating new methods to genotype copy number variations. We identify a large number of newly emerging crt mutations in parts of Southeast Asia, and show examples of heterogeneities in patterns of drug resistance within Africa and within the Indian subcontinent. We describe the profile of variations in the C-terminal of the csp gene and relate this to the sequence used in the RTS,S and R21 malaria vaccines. Pf7 provides high-quality data on genotype calls for 6 million SNPs and short indels, analysis of large deletions that cause failure of rapid diagnostic tests, and systematic characterisation of six major drug resistance loci, all of which can be freely downloaded from the MalariaGEN website

    Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples

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    We describe the MalariaGEN Pf7 data resource, the seventh release of Plasmodium falciparum genome variation data from the MalariaGEN network.  It comprises over 20,000 samples from 82 partner studies in 33 countries, including several malaria endemic regions that were previously underrepresented.  For the first time we include dried blood spot samples that were sequenced after selective whole genome amplification, necessitating new methods to genotype copy number variations.  We identify a large number of newly emerging crt mutations in parts of Southeast Asia, and show examples of heterogeneities in patterns of drug resistance within Africa and within the Indian subcontinent.  We describe the profile of variations in the C-terminal of the csp gene and relate this to the sequence used in the RTS,S and R21 malaria vaccines.  Pf7 provides high-quality data on genotype calls for 6 million SNPs and short indels, analysis of large deletions that cause failure of rapid diagnostic tests, and systematic characterisation of six major drug resistance loci, all of which can be freely downloaded from the MalariaGEN website

    Novel Indices For Broken Rotor Bars Fault Diagnosis In Induction Motors Using Wavelet Transform

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    This paper introduces novel indices for broken rotor bars diagnosis in three-phase induction motors based on wavelet coefficients of stator current in a specific frequency band. These indices enable to diagnose occurrence and determine number of broken bars in different loads precisely. Besides thanks to the suitability of wavelet transform in transient conditions, it is possible to detect the fault during the start-up of the motor. This is important in the case of start-up of large induction motors with long starting time and also motors with frequent start-up. Furthermore, broken rotor bars in induction motor are detected using spectra analysis of the stator current. It is also shown that rise of number of broken bars and load levels increases amplitude of the particular side-band components of the stator currents in the faulty case. An induction motor with 1, 2, 3 and 4 broken bars at the rated load and the motor with 4 broken bars at no-load, 33%, 66%, 100% and 133% rated load are investigated. Time stepping finite element method is used for modeling broken rotor bars faults in induction motors. In this modeling, effects of the stator winding distribution, stator and rotor slots, geometrical and physical characteristics of different parts of the motor and non-linearity of the core materials are taken into account. The simulation results are are verified by the experimental results. © 2012 Elsevier Ltd

    Управление сферой общего образования на муниципальном уровне (на примере города Тюмени)

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    Исследование управления сферой общего образования на муниципальном уровне (на примере города Тюмени)
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