Browsing by Author "Alsalhy, Qusay F."
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
- A modified zeolite (Na2SO4 @zeolite NaA) as a novel adsorbent for radium-226,228 from acidic radioactive wastewater : Synthesis, characterization and testing
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-04) Abbas, Taisir K.; Ibrahim, Zaidoon H.; Mustafa, K.; Al-Juboori, Raed A.; Nafae, Takrid M.; Al-Mashhadani, Asia H.; Fal, Mokhatar; Alotaibi, Abdulrahman M.; Alsalhy, Qusay F.The radiological contamination with radium in oil and gas production is challenging environmental and health concerns. This study investigated the synthesis of zeolite NaA modified with Na2SO4 (Na2SO4@zeolite NaA) hydrothermal method and subsequent use as an adsorbent along with manganese dioxide (MnO2) for removing radium isotopes (226Ra and 228Ra) from acidic radioactive wastewater (ARW). ARW was generated through acidic sequential leaching of petroleum-based organic soils collected from the South Rumaila fields in the Basra Governorate, Iraq. The optimum adsorption parameters were MnO2: Na2SO4@zeolite NaA mass ratio of 1:0.5 g.g−1 and a pH of 6.3 resulting in maximum removal of 78.7% and 66.7% for 226Ra and 228Ra, respectively. Higher levels of removal were not attainable due to co-elements effect. The adsorption was endothermic with cation exchange of Na+ with Ra2+ being the main mechanism. The incorporation of Na2SO4 increased the exchange sites available for Ra2+ and the surface area and pore size available for facilitating such reactions. The exhausted column was regenerated and subsequently used for five cycles with a small drop in the removal of 226Ra and 228Ra by 11% and 9.5%, respectively highlighting the propitious application of Na2SO4@zeolite NaA and MnO for treating contaminated wastewater in oil fields. - Novel MXene-Modified Polyphenyl Sulfone Membranes for Functional Nanofiltration of Heavy Metals-Containing Wastewater
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2023-03) Naji, Mohammed Azeez; Salimi-Kenari, Hamed; Alsalhy, Qusay F.; Al-Juboori, Raed A.; Huynh, Ngoc; Rashid, Khalid T.; Salih, Issam K.In this work, MXene as a hydrophilic 2D nanosheet has been suggested to tailor the polyphenylsulfone (PPSU) flat sheet membrane characteristics via bulk modification. The amount of MXene varied in the PPSU casting solution from 0–1.5 wt.%, while a series of characterization tools have been employed to detect the surface characteristics changes. This included atomic force microscopy (AFM), scanning electron microscopy (SEM), contact angle, pore size and porosity, and Fourier-transform infrared spectroscopy (FTIR). Results disclosed that the MXene content could significantly influence some of the membranes’ surface characteristics while no effect was seen on others. The optimal MXene content was found to be 0.6 wt.%, as revealed by the experimental work. The roughness parameters of the 0.6 wt.% nanocomposite membrane were notably enhanced, while greater hydrophilicity has been imparted compared to the nascent PPSU membrane. This witnessed enhancement in the surface characteristics of the nanocomposite was indeed reflected in their performance. A triple enhancement in the pure water flux was witnessed without compromising the retention of the membranes against the Cu2+, Cd2+ and Pd2+ feed. In parallel, high, and comparable separation rates (>92%) were achieved by all membranes regardless of the MXene content. In addition, promising antifouling features were observed with the nanocomposite membranes, disclosing that these nanocomposite membranes could offer a promising potential to treat heavy metals-containing wastewater for various applications. - Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2023-12) Ismael, Bashar H.; Khaleel, Faidhalrahman; Ibrahim, Salah S.; Khaleel, Samraa R.; AlOmar, Mohamed Khalid; Masood, Adil; Aljumaily, Mustafa M.; Alsalhy, Qusay F.; Mohd Razali, Siti Fatin; Al-Juboori, Raed A.; Hameed, Mohammed Majeed; Alsarayreh, Alanood A.Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR–SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR–SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR–SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.