The Sequencing Batch Reactor system (SBR) is a major component of the municipal wastewater biological treatment system and water reclamation that provides high-quality water that could be reused in restricted plants that which require large quantities of water despite the lack of water. The research aims to investigate the performance of a pilot plant SBR unit under real operation conditions that was installed and operated in Al-Rustamiya Wastewater Treatment Plant (WWTP), Baghdad, Iraq. Results showed that the BOD5/COD ratio of the raw wastewater was within the average value at 0.66 emphasizing the organic nature of the influent flow and hence the amenability to biological treatment. The results also ensured that the treatment pro

In the present study, 1-ethyl -3-methyllimidazolium acetate ionic liquid is introduced for extractive desulfurization of Iraqi kerosene (1622ppm) and compared with 1-ethyl -3- methyllimidazolium tetrafloroborate. The effect of ionic liquid/ fuel ratio (1/9, 1/4, 1/2), temperature (25, 30,40oC), stirring speed (300,450rpm) and time (10, 30, 90, 180, 360 min) were studied. Sulfur compound analysis was performed using X-Ray fluorescence. The ionic liquid with acetate anion (OAc) showed better performance than tetrafloborate (BF4). The maximum extraction efficiency was 32% achieved at 1/2 IL/Fuel and 40oC after 90min. The oxidation step using hydrogen peroxide (8ml/200ml), catalyzed by acetic acid (2ml) and followed by ionic liquid extraction h

            Liquid – liquid equilibria data were measured at 293.15 K for the pseudo ternary system (sulfolane + alkanol) + octane + toluene. It is observed that the selectivity of pure sulfolane increases with cosolvent methanol but decreases with increasing the chain length of hydrocarbon in 1-alkanol. The nonrandom two liquid (NRTL) model, UNIQUAC model and UNIFAC model were used to correlate the experimental data and to predict the phase composition of the systems studied. The calculation based on NRTL model gave a good representation of the experimental tie-line data for all systems studied. The agreement between the correlated and the experimental results was very good

Poly (3-hydroxybutyrate) (PHB) is a typical microbial bio-polyester reserve material; known as “green plastics”, which produced under controlled conditions as intracellular products of the secondary metabolism of diverse gram-negative/positive bacteria and various extremophiles archaea. Although PHB has properties allowing being very attractive, it is too expensive to compete with conventional and non-biodegradable plastics. Feasibility of this research to evaluate the suitability of using a watermelon-derived media as an alternative substrate for PHB synthesis under stress conditions was examined. Results, include the most nutrients extraction, indicated that the watermelon seeds contain a high content of nutrients makes them a promisi

In this paper, the construction of Hermite wavelets functions and their operational matrix of integration is presented. The Hermite wavelets method is applied to solve nth order Volterra integro diferential equations (VIDE) by expanding the unknown functions, as series in terms of Hermite wavelets with unknown coefficients. Finally, two examples are given

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra