The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value <0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, temperature 46.4 °C, pressure 21 Mpa, and flowrate 27,000 m3/day which is nearly closed to suggested oily content 8.5 ppm. An artificial neural network (ANN) technique was employed in this study to estimate the oil content in the treatment process. An artificial neural network model was remarkably accurate at simulating the process under investigation. A low mean squared error (MSE) and relative error (RE) equal to 1.55 × 10−7 and 2.5, respectively, were obtained during the training phase, whilst the testing results demonstrated a high coefficient of determination (R2) equal to 0.99.
Wireless networks and communications have witnessed tremendous development and growth in recent periods and up until now, as there is a group of diverse networks such as the well-known wireless communication networks and others that are not linked to an infrastructure such as telephone networks, sensors and wireless networks, especially in important applications that work to send and receive important data and information in relatively unsafe environments, cybersecurity technologies pose an important challenge in protecting unsafe networks in terms of their impact on reducing crime. Detecting hacking in electronic networks and penetration testing. Therefore, these environments must be monitored and protected from hacking and malicio
... Show MoreMachine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
... Show MoreIn this study, we design narrow band pass filter for window (3_5) ?m dependent on the needle optimization method , and a comparison with global designs published -Also, the effect of change parameter design on the optical performance of filter was studded and being able to overcome the difficulties of the design.In this study, the adoption of homogeneous optical properties materials as thin film depositing on a substrate of germanium at wavelength design (? = 4 ?m). For design this kind of filters we used advanced computer program (Matlab )to build a model design dependent both matrix characteristic and Needle technique. In this paper we refer to the type of Mert function , which is used for correct optical performance acces
... Show MoreBackground: Bacteriocin is a peptidic toxin has many advantages to bacteria in their ecological niche and has strong antibacterial activity. Objective: The aim of this study was to evaluation of bacteriocin using Streptococcus sanguinis isolated from human dental caries.
Subjects and Methods: Thirty five streptococcus isolates were diagnosed and tested for their production of bacteriocin, and then the optimal conditions for production of bacteriocin were determined. After that, the purification of bacteriocin was made partially by ammonium sulfate at 95% saturation levels, followed by and gel filtration chromatography
... Show MoreA field experiment was conducted in Al-Yusufiya district - Al-Mahmoudiya district, Baghdad province during the winter season 2021, to study improving the efficiency and management of water use and the productivity of lettuce under different irrigation systems. The Nested-Factorial Experiments design was used, where the main plots include the first factor, irrigation levels (I1) 50%, (I2) 75%, (I3) 100, (I4) 125%, (I5) 150% ETpan. After depleting 35% of the available water and in terms of climatic data from the American Evaporative Basin, Class A. Then the main factor is divided into three replicates, and the coefficients of the second factor are distributed randomly within each replicate, which includes the irrigation system: surface drip i
... Show MoreThis research aims to knowledge of the scope of applying the international specification of which is regarded to occupational health and safety management system (OHSAS 18001) second edition for year 2007 by The AL-Mammon Factory / The General Company For Vegetable Oils Industry in order to diagnosis the gap between specification requirements and reality by using the checklist made based upon the items of the specification, after translating the English copy into Arabic. The results of the research and analysis that occupational health and safety management system in comparison to the specification requirements in all of its main items, which was (%22.26) applied and documented partly, this refer to the existence of great gap (%7
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