Nanofiltration (NF) ceramic membrane have found increasing applications particularly in wastewater and water treatment. In order to estimate and optimize the performance of NF membranes, the membrane should be characterized correctly in terms of their basic parameters such as effective pore radius (r_p) and equivalent effective thickness as well as effective surface charge ( ), the effective charge density ( ) and Donnan potential ( ). The impact of electrokinetic (zeta) potential on the membrane surface charge density, effective membrane charge density and Donnan potential at two different concentrations of the reference solutions 0.001, 0.01 M sodium chloride at various pH values from 3 to 9, and effective po

This work was conducted to study the extraction of eucalyptus oil from natural plants (Eucalyptus camaldulensis leaves) using water distillation method by Clevenger apparatus. The effects of main operating parameters were studied: time to reach equilibrium, temperature (70 to100°C), solvent to solid ratio (4:1 to 8:1 (v/w)), agitation speed (0 to 900 rpm), and particle size (0.5 to 2.5 cm) of the fresh leaves, to find the best processing conditions for achieving maximum oil yield. The results showed that the agitation speed of 900 rpm, temperature 100° C, with solvent to solid ratio 5:1 (v/w) of particle size 0.5 cm for 160 minute give the highest percentage of oil (46.25 wt.%). The extracted oil was examined by HPLC.

The CO2-Assisted Gravity Drainage process (GAGD) has been introduced to become one of the mostinfluential process to enhance oil recovery (EOR) methods in both secondary and tertiary recovery through immiscibleand miscible mode. Its advantages came from the ability of this process to provide gravity-stable oil displacement forenhancing oil recovery. Vertical injectors for CO2 gas have been placed at the crest of the pay zone to form a gas capwhich drain the oil towards the horizontal producing oil wells located above the oil-water-contact. The advantage ofhorizontal well is to provide big drainage area and small pressure drawdown due to the long penetration. Manysimulation and physical models of CO2-AGD process have been implemented

Aim To develop a low-density polyethylene–hydroxyapatite (HA-PE) composite with properties tailored to function as a potential root canal filling material. Methodology Hydroxyapatite and polyethylene mixed with strontium oxide as a radiopacifier were extruded from a single screw extruder fitted with an appropriate die to form fibres. The composition of the composite was optimized with clinical handling and placement in the canal being the prime consideration. The fibres were characterized using infrared spectroscopy (FTIR), and their thermal properties determined using differential scanning calorimetry (DSC). The tensile strength and elastic modulus of the composite fibres and gutta-percha were compared, dry and after 1 month storage in

The implementation of nanotechnology in all industries is one of most significant research fields. Nanoparticles have shown a promising application in subsurface fields. On the other hand, various surfactants have been used in the oil industry to reduce oil/water interfacial tension and also widely used to stabilize the nano-suspensions. The primary objective of this study was to investigate the improvements of surfactants ability in term of interfacial tension (γ) reduction utilizing addition of silicon dioxide nanoparticles at different temperatures and salinity. The pendant drop technique has been used to measure γ and electrical conductivity has been used to measure the critical micelle concentration (CMC). The synergistic effects of

The present work aims to study the efficiency of coagulation/ flocculation as 1st stage, natural gravity water filter or microfiltration (MF) as 2nd stage and nanofiltration (NF) technology as final stage for treatment of water of main outfall drain (MOD) for injection in Nasiriyah oil field. Effects of operating parameters such as coagulant dosage, speed and time of slow mixing step and settling time in the 1st stage were studied. Also feed turbidity and total suspended solids (TSS) in the 2

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi