In wide range of chemical, petrochemical and energy processes, it is not possible to manage without slurry bubble column reactors. In this investigation, time average local gas holdup was recorded for three different height to diameter (H/D) ratios 3, 4 and 5 in 18" diameter slurry bubble column. Air-water-glass beads system was used with superficial velocity up to 0.24 m/s. the gas holdup was measured using 4-tips optical fiber probe technique. The results show that the axial gas holdup increases almost linearly with the superficial gas velocity in 0.08 m/s and levels off with a further increase of velocity. A comparison of the present data with those reported for other slurry bubble column having diameters larger than

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

   The aim of this study is to investigate the kinetics of copper removal from aqueous solutions using an electromembrane extraction (EME) system. To achieve this, a unique electrochemical cell design was adopted comprising two glass chambers, a supported liquid membrane (SLM), a graphite anode, and a stainless-steel cathode. The SLM consisted of a polypropylene flat membrane infused with 1-octanol as a solvent and bis(2-ethylhexyl) phosphate (DEHP) as a carrier. The impact of various factors on the kinetics constant rate was outlined, including the applied voltage, initial pH of the donor phase solution, and initial copper concentration. The results demonstrated a significant influence of the applied voltage on enhancing the rate of c

Aqueous Two Phase System (ATPS) or liquid-liquid extraction is used in biotechnology to recover valuable compounds from raw sources. In Aqueous Two-Phase Systems, many factors influence the Partition coefficient, K, (which is the ratio of protein concentration in the top phase to that in the bottom phase) and the Recovery percentage (Rec%). In this research, two systems of ATPS were used: first, polyethylene glycol (PEG) 4000/Sodium citrate (SC), and the second, PEG8000/ Sodium phosphate (SPH), for the extraction of Bovine Serum Albumin (BSA). The behavior of Rec% and K of pure (BSA) in ATPS has been investigated throughout the study by the effects of five parameters: temperature, concentration of polyethylene glycol (P

Emulsion Liquid Membrane (ELM) is an emerging technology that removes contaminants from water and industrial wastewater. This study investigated the stability and extraction efficiency of ELM for the removal of Chlorpyrifos Pesticide (CP) from wastewater. The stability was studied in terms of emulsion breakage. The proposed ELM included n-hexane as a diluent, span-80 as a surfactant, and hydrochloric acid (HCl) as a stripping agent. Parameters such as mixing speed, aqueous feed solution pH, internal-to-organic membrane volume ratio, and external-to-emulsion volume ratio were investigated. A minimum emulsion breakage of 0.66% coupled with a maximum chlorpyrifos extraction and stripping efficiency were achieved at 96.1% and 95.7% at b

In current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL^-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi