In this study, ultraviolet (UV), ozone techniques with hydrogen peroxide oxidant were used to treat the wastewater which is produced from South Baghdad Power Station using lab-scale system. From UV-H₂O₂ experiments, it was shown that the optimum exposure time was 80 min. At this time, the highest removal percentages of oil, COD, and TOC were 84.69 %, 56.33 % and 50 % respectively. Effect of pH on the contaminants removing was studied in the range of (2-12). The best oil, COD, and TOC removal percentages (69.38 %, 70 % and 52 %) using H₂O₂/UV were at pH=12. H₂O₂/ozone experiments exhibited better performance compared to

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 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

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

This work deals with thermal cracking of slack wax produced as a byproduct from solvent dewaxing process of medium lubricating oil fraction in AL-Dura refinery. The thermal cracking process was carried out at a temperature ranges 480-540 ºC and atmospheric pressure. The liquid hourly space velocity (LHSV) for thermal cracking was varied between 1.0-2.5 . It was found that the conversion increased (61 - 83) with the increasing of reaction temperature (480 - 540) and decreased (83 - 63) with the increasing of liquid hourly space velocity (1.0 - 2.5).
The maximum gasoline yield obtained by thermal cracking process (48.52 wt. % of feed) was obtained at 500 ºC and liquid hour space velocity 1 . The obtaining liquid product at the best op

Crude soybean peroxidase (SBP), isolated from soybean seed coats (hulls) at unusually low concentrations, catalyses the oxidative polymerisation of hazardous aqueous benzidine and its 3,3′-dichloro, 3,3′-dimethyl and 3,3′-dimethoxy derivatives in the presence of hydrogen peroxide. The optimum operating conditions for oxidation of 0·10 mM benzidine were investigated. At pH 5, the hydrogen peroxide-to-substrate concentration ratio was 1·5 and the minimum SBP concentration required to achieve at least 95% conversion of the benzidine in synthetic wastewater was 0·43 mU/ml. Progress curves were established for the conversion of the four substrates, and apparent first-order rate constants were derived. Enzyme-catalysed polym