A neuro-fuzzy approach to automatic diagnosis and location of stator inter-turn faults in CSI-fed PM brushless DC motors

The paper presents a neuro-fuzzy-based perspective to the automation of diagnosis and location of stator-winding interturn short circuits in CSI-fed brushless dc motors. Performance of the drive under normal and short-circuit conditions are obtained through classical lumped-parameter network models. Waveforms of the electromagnetic torque and summation of phase voltages are monitored to develop two independent diagnostic algorithms. Diagnostic indices derived from the characteristic waveforms using discrete Fourier transform (DFT) lead to identifying the number of shorted turns. Fault location is achieved through a different set of indices extracted by the short-time Fourier transform (STFT). Adaptive neuro-fuzzy inference systems (ANFIS) are trained based on simulation results to automate the diagnostic process. ANFIS testing along with the good agreement between simulated and measured waveforms show the effectiveness of the proposed techniques.     

Detection of stator short circuits in VSI-fed brushless DC motors using wavelet transform

The paper presents methodologies to detect and locate short-circuit faults on the stator winding of VSI-fed PM brushless dc motors. Normal performance characteristics of the motor are obtained through a discrete-time lumped-parameter network model. The model is modified to accommodate short-circuit faults in order to simulate faulty operation. Fault signatures are extracted from the waveforms of electromagnetic torque and phase-voltage summation using wavelet transform. Three independent detection techniques are introduced. Experimental measurements agree acceptably with simulation results, and validate the proposed methods. This work sets forth the fundamentals of an automatic fault detector and locator, which can be used in a fault-tolerant drive.     

Study of defect levels in CdTe using thermoelectric effect spectroscopy

We have studied the defect levels in as grown and post growth processed cadmium telluride (CdTe) using thermoelectric effect spectroscopy (TEES) and thermally stimulated current (TSC) techniques. We have extracted the thermal energy (E_th) and trapping cross section (σ_th) for the defect levels using the initial rise and variable heating rate methods. We have identified 10 different defect levels in the crystals. Thermal ionization energy values obtained experimentally were compared to theoretical values of the transition-energy levels of intrinsic and extrinsic defects and defect complexes in CdTe determined by first-principles band-structure calculations. On the basis of this comparison, we have associated the observed ionization levels with various native defects and impurity complexes.     

Non-oxidative Decomposition of CH4 Over CeO2 and CeO2–SiO2 Supported Bimetallic Ni–Mo Catalysts

Catalysis Letters - The development of highly active and durable catalysts for H2 production through CH4 decomposition process is still a great challenge. In this study, CeO2 and CeO2–SiO2...     

Synthesis of multi-walled carbon nanotubes via pyrolysis of plastic waste using a two-stage process

The pyrolysis of different plastic waste types such as low density polyethylene (LDPE), high density polyethylene (HDPE), polypropylene (PP), polyethylene terephthalate (PET) and polystyrene (PS) for producing multi-walled carbon nanotubes (MWCNTs) using a two-stage process has been investigated. Firstly, the cracking of plastic wastes was carried out at a temperature of 700°C to produce hydrocarbon gases. In the second stage, the produced hydrocarbon gases were decomposed at 650°C on the surface of the Ni-Mo/Al₂O₃ catalyst to form CNTs. Various analytical tools such as XRD, TPR, TGA, Raman spectroscopy and TEM were used to describe both the fresh catalyst and the obtained CNTs. The results showed that the amount of the hydrocarbon gases was related to the type of plastic waste and hence the CNT yield. Accordingly, LDPE or PP was decomposed to produce the largest gases yield of 72.5 or 70.7 wt%, respectively. As a result, a large CNTs yield of 5.8 and 5 g/g_cat can be achieved by pyrolysis of PP and LDPE waste, respectively. However, a small yield of CNTs with little quality and low purity was obtained by using PS or PET waste as the carbon feedstock.     

Synthesis of carbon nanotubes by CCVD of natural gas using hydrotreating catalysts

Carbon nanotubes have been successfully synthesized using the catalytic chemical vapor deposition (CCVD) technique over typical refining hydrotreating catalysts (hydrodesulfurization and hydrodenitrogenation) containing Ni–Mo and Co–Mo supported on Al₂O₃ catalysts at 700°C in a fixed bed horizontal reactor using natural gas as a carbon source. The catalysts and the as-grown CNTs were characterized by transmission electron microscopy, HRTEM, X-ray diffraction patterns, EDX and TGA–DTG. The obtained data clarified that the Ni–Mo catalyst gives higher yield, higher purity and selectivity for CNTs compared to Co–Mo catalyst. XRD, TEM and TGA reveal also that the Ni–Mo catalyst produces mostly CNTs with different diameters whereas the Co–Mo catalyst produces largely amorphous carbon.     

Direct Conversion of Natural Gas to Petrochemicals Using Monofunctional Mo/SiO2 and H-ZSM-5 Zeolite Catalysts and Bifunctional Mo/H-ZSM-5 Zeolite Catalyst

Abstract

The components of the standard catalyst globally used for natural gas direct conversion (6% Mo/H-ZSM-5) have been separately prepared and examined to represent: (a) monofunctional metallic component (6% Mo/SiO₂) and (b) acidic component (H-ZSM-5 zeolite) to numerically investigate the extent of activity of each catalytic component in comparison to the activity of the standard catalyst in a fixed-bed flow-type reactor. The temperature and gas hourly space velocity are 700°C and 1500 mLg^?1 h ^?1, respectively, which are very close to those used in the industrial gas conversion reactions. Time on stream up to 240 min was examined. The gaseous products were ethylene, propylene, and hydrogen, whereas the liquid products were benzene, toluene, and naphthalene. Carbon was also produced as deposited particles on the catalyst.     

Direct conversion of natural gas into COx-free hydrogen and MWCNTs over commercial Ni–Mo/Al2O3 catalyst: Effect of reaction parameters

A commercial hydrotreating nickel molybdate/alumina catalyst was used for the direct conversion of natural gas (NG) into CO_x-free hydrogen and a co-valuable product of multi-walled carbon nanotubes (MWCNTs). The catalytic runs were carried out atmospherically in a fixed-bed flow reactor. The effect of reaction temperature between 600 and 800 °C, and dilution of the NG feed with nitrogen as well as pretreatment of the catalyst with hydrogen were investigated. At a reaction temperature of 700 °C and dilution ratio of NG/N₂ = 20/30, the optimum yield of H₂ (～80%) was obtained with higher longevity. However, using the feed ratio of NG/N₂ = 30/20, the optimum yield of MWCNTs was obtained (669%). X-ray diffraction pattern for the catalyst after the reaction showed that the MWCNTs were grown on the catalyst at all reaction temperatures under study. TEM pictures revealed that the as-grown MWCNTs at 600, 650 and 800 °C are short and long with a low graphitization degree. At 700 °C a forest of condensed CNTs is formed, whereas both carbon nanofibers and CNTs were formed at 750 °C.     

Simple method for synthesis of carbon nanotubes over Ni-Mo/Al2O3 catalyst via pyrolysis of polyethylene waste using a two-stage process

Synthesis of valuable multi-walled carbon nanotubes (MWCNTs) by thermal pyrolysis of low-density polyethylene (LDPE) waste was investigated via a two-stage process. The first stage was the thermal pyrolysis of LDPE to gaseous hydrocarbons, and the second stage was the catalytic decomposition of the pyrolysis gases over Ni-Mo/Al₂O₃ catalysts. Two catalysts with the compositions of 5.2%Ni-10.96%Mo/Al₂O₃ and 10%Ni-9.5%Mo/Al₂O₃ were tested for carbon nanotubes (CNTs) formation. The catalyst containing 10%Ni showed better activity in terms of CNTs production. Accordingly, the impact of either pyrolysis or decomposition temperatures was investigated using the 10%Ni-9.5%Mo/Al₂O₃ catalyst. TEM, XRD, Raman spectroscopy, TGA, TPR, and BET analysis tools were used to characterize the fresh catalysts as well as the obtained carbon nanomaterials. TEM images proved that MWCNTs with various morphological structures were obtained at all pyrolysis and decomposition temperatures. Moreover, cup-stacked carbon nanotubes (CS-CNTs) were observed at the decomposition temperature of 600°C. MWCNTs with the best quality were produced at decomposition temperature of 750°C. The optimum pyrolysis and decomposition temperatures in terms of CNTs production were at 700 and 650°C, respectively.     

Estimation of Regional Sub-Daily Rainfall Ratios Using SKATER Algorithm and Logistic Regression

Developing Intensity-Duration-Frequency (IDF) curves is a paramount input in stormwater systems design. To construct these IDF curves, rainfall records at sub-daily durations, provided by continuous rainfall recorders, are required; however, these recorders are seldom available in many locations of interest. To fill this gap, available meteorological and topographical information for a study area in Saudi Arabia are investigated to get an estimate of the ratios of sub-daily rainfall depths to the 24-h depths (sub-daily ratios or SDRs), via applying the following methodology. A spatially constrained regionalization approach is implemented, using the SKATER algorithm, based on 60 gauging stations, to form regions of contiguous stations, based on the similarities of their SDRs. Four different regions are formed, where each region shows consistent SDRs; yet distinctly different from other regions. Subsequently, a multinomial logistic regression model is built and trained, with commonly available meteorological and topographical information as explanatory variables, to determine to which region a specific location belongs. The model is validated based on a hold-out validation method and assessed through confusion matrix statistics to evaluate the model performance. The model shows high performance in predicting the correct regional SDR and it is extended to produce a gridded map covering ungauged areas. Based on this procedure, one can develop the IDF curve for any location within the study area, even if there is no rainfall recorder in that location.