The present study utilised date palm fibre (DPF) waste residues to adsorb Congo red (CR) dye from aqueous solutions. The features of the adsorbent, such as its surface shape, pore size, and chemical properties, were assessed with X-ray diffraction (XRD), BET, Fourier-transform infrared (FTIR), X-ray fluorescence (XRF), and field emission scanning electron microscope (FESEM). The current study employed the batch system to investigate the ideal pH to adsorb the CR dye and found that acidic pH decolourised the dye best. Extending the dye-DPF waste mixing period at 25°C reportedly removed more dye. Consequently, the influence of the starting dye and DPF waste quantity on dye removal was explored in this study. At 5 g/L dye concentration, 48% dye removal was achieved, whereas at low dye concentrations, only 40% of the dye was removed. The current study also evaluated the DPF particle size created for dye adsorption, yielding a 66% optimal powder size removal. The heat impact assessment performed in this study indicated that increased temperature affected the amount of dye eliminated from aqueous solutions, where a 72% removal was recorded at 45°C. The pseudo-first- and pseudo-second-order models were utilised to predict the maximum CR dye adsorption with DPF waste. Resultantly, the Langmuir-Freundlich experimental DPF waste CR adsorption documented pseudo-second-order kinetics. In a fixed bed reactor, the DPF waste has been reported to remove CR dye constantly. Consequently, several factors affecting the removal process, including the effects of primary dye, the flow rate of the liquid inside the column, the depth of the filling inside the column, and flow rate were assessed. The results were simulated in the COMSOL® program and compared to practical experiments, which yielded a 99% match. Conclusively, DPF waste could remove several colours from wastewater via active removal.
General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k
... Show MoreUse of computer simulation to quantify the effectiveness of blowing agents can be an effective tool for optimizing formulations and for the adopting of new blowing agents. This paper focuses on a mass balance on blowing agent during foaming including the quantification of the amount that stays in the resin, the amount that ends up in the foam cells, and the pressure of the blowing agent in the foam cells. Experimental data is presented both in the sense of developing the simulation capabilities and the validating of simulation results.
This study aimed to show the relationship between mental health and shyness for university students in Baghdad and Al – Mustansiria university which its subject was (200) students , ( 100) males and ( 100) females , Mental Health scale which is constructed by (Al – Janabi 1991) and developed by (Hassan 2006) was used for this aim ,The scale of shyness was built according to a questioner to the students and according to previous publications and studies .
Multiple regulation analysis step - wise was used for data analysis in order to identify the possibility to find single or couple indications for the independent variable (mental
... Show MoreMoment invariants have wide applications in image recognition since they were proposed.
The present work is to investigate the feasibility of removal vanadium (V) and nickel (Ni) from Iraqi heavy gas oil using activated bentonite. Different operating parameters such as the degree of bentonite activation, activated bentonite loading, and operating time was investigated on the effect of heavy metal removal efficiency. Experimental results of adsorption test show that Langmuir isotherm predicts well the experimental data and the maximum bentonite uptake of vanadium was 30 mg/g. The bentonite activated with 50 wt% H2SO4 shows a (75%) removal for both Ni and V. Results indicated that within approximately 5 hrs, the vanadium removal efficiencies were 33, 45, and 60% at vanadium loadings of 1
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