This work deals with separation of the aromatic hydrocarbons benzene, toluene, and xylene (BTX) from reformate. The separation was examined using adsorption by molecular sieve zeolite 13X in a fixed bed process. The concentration of aromatic hydrocarbons in the influent and effluent streams was measured using gas chromatography. The effect of flow rate and bed length of adsorbent on the adsorption of multicomponent hydrocarbons and adsorption capacity of molecular sieve was studied. The tendency of aromatic hydrocarbons adsorption from reformate is in the order: benzene >toluene>xylenes.

This work was conducted to study the recovery of catalyst and desirable components from tar formed in phenol production unit and more particularly relates to such a method whereby better recovery of copper salts, phenol, benzoic acid and benzoate salts from tar by aqueous acid solution was accomplished.
The effect of solvent type, solvent concentration (5, 10, 15, 20, 25 and 30 wt%), agitation speed (100, 200, 300 and 400 rpm), agitation time (5, 10, 15, 20 and 25 min), temperature (90, 100, 110, 120, 130 and 140 oC) , phase ratio (1/1, 2/1, 3/1, 4/1 and 5/1) and number of extraction (1, 2, 3, 4, and 5) were examined in order to increase the catalyst and desirable components extraction.
Four types of solvent were used; hydrochloric

The aim of this work was to prepare zeolite type 13X from locally available kaolin and to study the effects of using some binding materials through the process of agglomeration of this zeolite. This study was focused on using kaolin binder in different weight percents (10,15,25,35 and 45%).Physical and mechanical properties of the agglomerates such as porosity , apparent density , pore volume, crushing strength , loss on attrition , surface area and finally the adsorption capacity had been measured and evaluated .The preparation step was achieved by mixing the reactants consisting of metakaolin , source of silica as ( sodium trisilicate ) and sodium hydroxide . The conditions was temperature of 70° C and time of mixing as 8, 10,24,34,50

The neutron flux in this paper, which is generated as a result of γ incineration of the radioactive fisssion products isotopes has been evaluated .It is obvious from this paper that the neutron flux value depends on the number of incineration nuclei and the nuclear cross-section of the incinerated isotopes, and the neutron flux is directly dependent on γ-ray flux. The neutron flux increases from 1010to 1017n/s.gm as the irradiation flux increases from 1016to 1020 γ/cm2.s. It is concluded that the γ-incineration technique can be used to produce a switchable neutron source of high flux.

A large amount of thermal energy is generated from burning hazardous chemical wastes, and the temperature of the flue gases in hazardous waste incinerators reaches up to (1200 °C). The flue gases are cooled to (40°C) and are treated before emission. This thermal energy can be utilized to produce electrical power by designing a system suitable for dangerous flue gases in the future depending on the results of much research about using a proto-type small steam power plant that uses safe fuel to study and develop the electricity generation process with water tube boiler which is manufactured experimentally with theoretical development for some of its parts which are inefficient in experimental work. The studied system gen

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

 

This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20

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