Synthesis and Characterization of Natural Extracted Precursor Date Palm Fibre-Based Activated Carbon for Aluminum Removal by RSM Optimization

The Powder-Activated Carbon (PAC) under optimum conditions from a new low-cost precursor Date Palm Fibre (DPF) biomass through a carbonization followed by KOH activation has been synthesized by response surface methodology (RSM) combined with central composite design (CCD). The special effects of activation temperature, time, and impregnation ratio on bio-PAC Aluminum (Al3+) removal and uptake capacity were examined. The optimum conditions for synthesized bio-PAC were found to be 99.4% and 9.94 mg·g−1 for Al3+ removal and uptake capacity, respectively at activation temperature 650 °C, activation time 1h and impregnation ratio 1. The optimum bio-PAC was characterized and analyzed using FESEM, FTIR, XRD, TGA, BET, and Zeta potential. RSM-CCD experimental design was used to optimize removal and uptake capacity of Al3+ on bio-PAC. Optimum conditions were found to be at bio-PAC dose of 5 mg with pH 9.48 and contact time of 117 min. Furthermore, at optimized conditions of Al3+ removal, kinetic, and isotherm models were investigated. The results reveal the feasibility of DPF biomass to be used as a potential and cost-effective precursor for synthesized bio-PAC for Al3+ removal

Data-driven models for atmospheric air temperature forecasting at a continental climate region

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.Validerad;2022;Nivå 2;2022-11-07 (joosat);</p

Modification of Polyethersulfone Ultrafiltration Membrane Using Poly(terephthalic acid-co-glycerol-g-maleic anhydride) as Novel Pore Former

In this research, poly terephthalic acid-co-glycerol-g-maleic anhydride (PTGM) graft co-polymer was used as novel water-soluble pore formers for polyethersulfone (PES) membrane modification. The modified PES membranes were characterized to monitor the effect of PTGM content on their pure water flux, hydrophilicity, porosity, morphological structure, composition, and performance. PTGM and PES/PTGM membranes were characterized by field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy (FTIR), and contact angle (CA). The results revealed that the porosity and hydrophilicity of the fabricated membrane formed using a 5 wt.% PTGM ratio exhibited an enhancement of 20% and 18%, respectively. Similarly, upon raising the PTGM ratio in the casting solution, a more porous with longer finger-like structure was observed. However, at optimum PTGM content (i.e., 5%), apparent enhancements in the water flux, bovine serum albumin (BSA), and sodium alginate (SA) retention were noticed by values of 203 L/m2.h (LMH), 94, and 96%, respectively. These results illustrated that the observed separation and permeation trend of the PES/PTGM membrane may be a suitable option for applications of wastewater treatment. The experimental results suggest the promising potential of PTGM as a pore former on the membrane properties and performance

Modification of Poly(vinylidene fluoride-co-hexafluoropropylene) Membranes with DES-Functionalized Carbon Nanospheres for Removal of Methyl Orange by Membrane Distillation

Chemical pollutants, such as methyl orange (MO), constitute the main ingredients in the textile industry wastewater, and specifically, the dyeing process. The use of such chemicals leads to huge quantities of unfixed dyes to make their way to the water effluent and consequently escalates the water pollution problem. This work investigates the incorporation of hydrophobic carbon nanospheres (CNS) prepared from the pyrolysis of acetylene using the chemical vapor deposition technique with poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) in order to enhance its hydrophobicity. Moreover, a deep eutectic solvent (DES) was used to enhance the membrane’s porosity. The former was based on the quaternary ammonium salt (N,N-diethyl-ethanol-ammonium chloride) as a chemical addition throughout the membrane synthesis. Direct contact membrane distillation (DCMD) was employed to assess the performance of the modified membrane for treatment of MO contaminated water. The phase inversion method was used to embed various contents of CNS (i.e., 1.0, 3.0, and 5.0 wt.%) with 22:78 wt.% of PVDF-co-HFP/N-Methyl-2-pyrrolidone solution to prepare flat-sheet membranes. The membrane embedded with 5 wt.% CNS resulted in an increase in membrane hydrophobicity and presented considerable enhancement in DCMD permeation from 12 to 35 L/h.m2 with salt rejection >99.9%. Moreover, the composite membrane showed excellent anti-biofouling and mechanical characteristics as compared to the pristine counterpart. Using this membrane, a complete rejection of MO was achieved due to the synergistic contribution of the dye negative charge and the size exclusion effect

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them

Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques

Publisher Copyright: © 2023 by the authors.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.Peer reviewe