This study shows that it is possible to fabricate and characterize green bimetallic nanoparticles using eco-friendly reduction and a capping agent, which is then used for removing the orange G dye (OG) from an aqueous solution. Characterization techniques such as scanning electron microscopy (SEM), Energy Dispersive Spectroscopy (EDAX), X-Ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) were applied on the resultant bimetallic nanoparticles to ensure the size, and surface area of particles nanoparticles. The results found that the removal efficiency of OG depends on the G‑Fe/Cu‑NPs concentration (0.5-2.0 g.L-1), initial pH (2‑9), OG concentration (10-50 mg.L-1), and temperature (30-50 °C). The batch experiments showed

Electrochemical oxidation in the presence of sodium chloride used for removal of phenol and any other organic by products formed during the electrolysis by using MnO2/graphite electrode. The performance of the electrode was evaluated in terms fraction of phenol and the formed organic by products removed during the electrolysis process. The results showed that the electrochemical oxidation process was very effective in the removal of phenol and the other organics, where the removal percentage of phenol was 97.33%, and the final value of TOC was 6.985 ppm after 4 hours and by using a speed of rotation of the MnO2 electrode equal to 200 rpm.

One of the most important and common problems in petroleum engineering; reservoir, and production engineering is coning; either water or gas coning. Almost 75% of the drilled wells worldwide contains this problem, and in Iraq water coning problem is much wider than the gas coning problem thus in this paper we try to clarify most of the reasons causing water coning and some of applicable solutions to avoid it using the simulation program (CMG Builder) to build a single well model considering an Iraqi well in north of Iraq black oil field with a bottom water drive, Coning was decreased by 57% by dividing into sub-layers (8) layers rather than (4) layers, also it was decreased (Coning) by 45% when perforation numbers and positions was chang

Research Summary

First: the problem of research and its importance

      The teacher's success in facilitating the students' learning and growth according to the educational and educational goals set out, he must identify the problems of discipline of students in the classroom in terms of sources and reasons and types and methods of prevention and treatment and the teacher to remember that success in his teaching and instruction is not completed more fully once he has the information And knowledge of the subject of the lesson, but must understand the dynamics of the group (class group) and master the skills of classroom management, su

In many applications such as production, planning, the decision maker is important in optimizing an objective function that has fuzzy ratio two functions which can be handed using fuzzy fractional programming problem technique. A special class of optimization technique named fuzzy fractional programming problem is considered in this work when the coefficients of objective function are fuzzy. New ranking function is proposed and used to convert the data of the fuzzy fractional programming problem from fuzzy number to crisp number so that the shortcoming when treating the original fuzzy problem can be avoided. Here a novel ranking function approach of ordinary fuzzy numbers is adopted for ranking of triangular fuzzy numbers with simpler an

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob