The aim of this study is to utilize the electromembrane extraction (EME) system as a manner for effective removal of zinc from aqueous solutions. A novel and distinctive electrochemical cell design was adopted consisting of two glass chambers, a supported liquid membrane (SLM) housing a polypropylene flat membrane infused with 1-octanol and a carrier. Two electrodes were used, a graphite as anode and a stainless steel as cathode. A comprehensive examination of several influential factors including the choice of carrier, the applied voltage magnitude, the initial pH of the donor solution, and the initial concentration of zinc was performed, all in a concerted effort to ascertain their respective impacts on the efficiency of zinc elim

Deep drawing process to produce square cup is very complex process due to a lot of process parameters which control on this process, therefore associated with it many of defects such as earing, wrinkling and fracture. Study of the effect of some process parameters to determine the values of these parameters which give the best result, the distributions for the thickness and depths of the cup were used to estimate the effect of the parameters on the cup numerically, in addition to experimental verification just to the conditions which give the best numerical predictions in order to reduce the time, efforts and costs for producing square cup with less defects experimentally is the aim of this study. The numerical analysis is used to study

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

In most manufacturing processes, and in spite of statistical control, several process capability indices refer to non conformance of the true mean (µc ) from the target mean ( µT ), and the variation is also high. In this paper, data have been analyzed and studied for a blow molded plastic product (Zahi Bottle) (ZB). WinQSB software was used to facilitate the statistical process control, and process capability analysis and some of capability indices. The relationship between different process capability indices and the true mean of the process were represented, and then with the standard deviation (σ ), of achievement of process capability value that can reduce the standard deviation value and improve production out of theoretical con

Four samples were collected from the wastewater of State Battery Manufacturing Company (SBMC); Babylon 2 factory in AL-Waziriya district, as triplicates. Physical and chemical measurements were carried out such as temperature, pH, Lead concentrations and their ranges were: (19.5-34.5) °C, (6.1-6.4) and (4.5-6.5) mg/L, respectively. Six dominant Bacillus spp. isolates were isolated from these samples; namely, Bacillus subtilis N1, Bacillus subtilis N2, Bacillus subtilis N3, Bacillus cereus N4, Bacillus cereus N5 , Bacillus cereus N6. These isolates were capable of removing Lead from aqueous solutions in a capacity reached 27.6 ± 1.4, 10.1 ± 1.7, 74.5 ± 0.7, 8.93 ± 2.8, 8.1 ± 3.5, 1.6± 0.7 mg/L, respectively. Whereas cell walls,

This work investigates the effect of the gas nitriding process on the surface layer microstructure and mechanical properties for steel 37, tool steel X155CrVMo12-1 and stainless steel 316L. Nitriding was conducted at a temperature of 550 °C for 2 hours during the first stage and at 750 °C for 4 hours during the second stage. SEM and X-ray diffraction tests were performed to evaluate the microstructural features and the major phases formed after surface treatment. SEM and X-ray diffraction tests were performed to assess the microstructural features and the primary phases formed after surface treatment. The new secondary precipitates were identified as γ′-Fe4N, ε (Fe2–3N), and α-Fe, exhibiting an uneven chain-like pattern wit