Experimental study of heat transfer coefficients in air-liquid-solid fluidized beds were carried out by measuring the heat rate and the overall temperature differences across the heater at different operating conditions. The experiments were carried out in Q.V.F. glass column of 0.22 m inside diameter and 2.25 m height with an axially mounted cylindrical heater of 0.0367 m diameter and 0.5 m height. The fluidizing media were water as a continuous phase and air as a dispersed phase. Low density (Ploymethyl-methacrylate, 3.17 mm size) and high density (Glass beads, 2.31 mm size) particles were used as solid phase. The bed temperature profiles were measured axially and radially in the bed for different positions. Thermocouples were connected to an interface system and these measurements were monitored by computer on line. Theoretical analysis has been carried out to solve the differential equation governing heat transfer in the gas-liquid-solid fluidized system with its boundary conditions. Finite difference technique was used as a suitable numerical method to find the solution. By applying the temperature profiles found experimentally in solved equation, effective thermal conductivity values were found.
Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To
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An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to
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New series of metal ions complexes have been prepared from the new ligand 1,5- Dimethyl-4- (5-oxohexan-2- ylideneamino) -2-phenyl- 1H-pyrazol-3 (2H)-one derived from 2,5-hexandione and 4-aminophenazone. Then, its V(IV), Ni(II), Cu(II), Pd(II), Re(V) and Pt(IV) complexes prepared. The compounds have been characterized by FT-IR, UV-Vis, mass and 1H and 13C-NMR spectra, TGA curve, magnetic moment, elemental microanalyses (C.H.N.O.), chloride containing, Atomic absorption and molar conductance. Hyper Chem-8 program has been used to predict structural geometries of compounds in gas phase, the heat of formation, (binding, total and electronic energy) and dipole moment at 298 K.
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This work deals with thermal cracking of three samples of extract lubricating oil produced as a by-product from furfural extraction process of lubricating oil base stock in AL-Dura refinery. The thermal cracking processes were carried out at a temperature range of 325-400 ºC and atmospheric pressure by batch laboratory reactor. The distillation of cracking liquid products was achieved by general ASTM distillation (ASTM D -86) for separation of gasoline fraction up to 220 ºC from light cycle oil fraction above 220 ºC. The comparison between the conversions at different operating conditions of thermal cracking processes indicates that a high conversion was obtained at 375°C, according to gasoline production. According to gasoline produ
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Heat treatment by solid solution method in the ?+? phase region was used at 970°C for Ti-5Al-2.5Fe alloy. The specimens cooled under different cooling media [water quenched (WQ), air cooled (AC) and furnace cooled (FC)], and subsequently aged at 550°C for 4 hours. Five specimens from each treatment were immersed in simulated body fluid SBF for a period of time (3 months). The dependence of corrosion rate on compositional variation in the phases resulted from various type of cooling rates are discussed based on immersion tests. The EDXA results show the precipitation of phosphate and calcium compounds on the alloy after 3 months of immersion in blood plasma solution forming a bone-like apatite, which enhanced the alloy biocompatibility ma
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A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
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The purpose of my thesis is to prepare four new ligands (L1-L4) that have been used to prepare a series of metal complexes by reacting them with metal ions: M=(Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II), Hg(II) Where succinyl chloride was used as a raw material for the preparation of bi-dented ligands (L1-L4) by reacting it with potassium thiocyanate as a first step and then reacting with (2-aminobenzothiazole, Benzylamine, 4-aminoantipyrine, Sulfamethoxazole) respectively as a second step with the use of dry acetone as a solvent, the chemical formula of the four ligands prepared in succession: N1,N4-bis(benzo[d]thiazol-2-ylcarbamothioyl)succinamide (L1) N1,N4-bis(benzylcarbamothioyl)succinami
... Show MoreBackground: Denture fracture is one of the most common problems encountered by the patients and prosthodontists. The objective of present study was to evaluate the transverse strength of nylon denture base resin repaired by using conventional heat polymerized, autopolymerized and visible light cure {VLC} resins, surface treatment that used for repair and adjustment of insufficient nylon denture bases and in case of addition of artificial teeth. As these corrective procedures are common chair side procedures in dental clinic. Materials and methods: One hundred twenty nylon specimens were prepared by using metal patterns with dimension of (65x10x2.5 mm) length, width, and thickness respectively for transverse strength test while for tensile b
... Show MoreBackground: The polymethyl methacrylate is the most reliable material for the construction of complete and partial dentures, despite satisfying esthetic demand itsuffered from having unsatisfactory properties like impact strength and transverse strength. This study was designed to improve the impact strength and transverse strength of heat cure acrylic resin by adding untreated and oxygen plasma treated polypropylene fibers and investigate the effect of this additive on some properties of acrylic resin materials. Materials and methods: Untreated and oxygen plasma treated polypropylene fibers was added to PMMA powder by weight 2.5 %. Specimens were constructed and divided into 5 groups according to the using tests; each group was subdivided
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