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

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.

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.

Compaction Curves and Strength of Clayey Soil Modified with Micro and Nano Silica.

Some Clayey soils are generally categorized as weak soils, and structures lying on such soils have been exposed to severe damage. Therefore, the central thesis of this paper is the impact of a waste material known as a silica fume as nano and micro material on soil's behaviour. To evaluate the effects of those additives on Atterberg limits, compaction characteristics and unconfined compressive strength, clayey soil samples have been transformed using micro and nano silica fume (by-product materials). In the current investigation, silica fume is used at four different percentages: 0, 2, 4, and 7%. The results show that the plasticity index of soil decreases with the addition of micro silica and increases with the addition of nano-silica. Increasing nano silica percentage improves the dry density of the compacted soil and reduces the optimum moisture content. An opposite behavior is observed with adding micro silica to compacted soil. Finally, 4% of silica fume is found to be the optimum dosage to improve the unconfined compressive strength of the treated soil with both additives. As a result, treating the weak clay soil with micro and/or nano-silica fume has the potential to be impactful.

A review of medicinal plant of Middle East and North Africa (MENA) region as source in tuberculosis drug discovery.

Tuberculosis (TB) is a disease that affects one-third of the world's population. Although currently available TB drugs have many side effects, such as nausea, headache and gastrointestinal discomfort, no new anti-TB drugs have been produced in the past 30 years. Therefore, the discovery of a new anti-TB agent with minimal or no side effects is urgently needed. Many previous works have reported the effects of medicinal plants against Mycobacterium tuberculosis (MTB). However, none have focused on medicinal plants from the Middle Eastern and North African (MENA) region. This review highlights the effects of medicinal plants from the MENA region on TB. Medicinal plants from the MENA region have been successfully used as traditional medicine and first aid against TB related problems. A total of 184 plants species representing 73 families were studied. Amongst these species, 93 species contained more active compounds with strong anti-MTB activity (crude extracts and/or bioactive compounds with activities of 0-100 µg/ml). The extract of Inula helenium, Khaya senegalensis, Premna odorata and Rosmarinus officinalis presented the strongest anti-MTB activity. In addition, Boswellia papyrifera (Del) Hochst olibanum, Eucalyptus camaldulensis Dehnh leaves (river red gum), Nigella sativa (black cumin) seeds and genus Cymbopogon exhibited anti-TB activity. The most potent bioactive compounds included alantolactone, octyl acetate, 1,8-cineole, thymoquinone, piperitone, α- verbenol, citral b and α-pinene. These compounds affect the permeability of microbial plasma membranes, thus kill the mycobacterium spp. As a conclusion, plant species collected from the MENA region are potential sources of novel drugs against TB.

Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent.

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

Optimization of the Synthesis of Superhydrophobic Carbon Nanomaterials by Chemical Vapor Deposition.

Demand is increasing for superhydrophobic materials in many applications, such as membrane distillation, separation and special coating technologies. In this study, we report a chemical vapor deposition (CVD) process to fabricate superhydrophobic carbon nanomaterials (CNM) on nickel (Ni)-doped powder activated carbon (PAC). The reaction temperature, reaction time and H2/C2H2 gas ratio were optimized to achieve the optimum contact angle (CA) and carbon yield (CY). For the highest CY (380%) and CA (177°), the optimal reaction temperatures were 702 °C and 687 °C, respectively. However, both the reaction time (40 min) and gas ratio (1.0) were found to have similar effects on CY and CA. Based on the Field emission scanning electron microscopy and transmission electron microscopy images, the CNM could be categorized into two main groups: a) carbon spheres (CS) free carbon nanofibers (CNFs) and b) CS mixed with CNFs, which were formed at 650 and 750 °C, respectively. Raman spectroscopy and thermogravimetric analysis also support this finding. The hydrophobicity of the CNM, expressed by the CA, follows the trend of CS-mixed CNFs (CA: 177°) > CS-free CNFs (CA: 167°) > PAC/Ni (CA: 65°). This paves the way for future applications of synthesized CNM to fabricate water-repellent industrial-grade technologies.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Computational investigation of the microstructural characteristics and physical properties of glycerol-based deep eutectic solvents.

Recently, there has been significant interest in the possibility of using deep eutectic solvents (DESs) as novel green media and alternatives to conventional solvents and ionic liquids (ILs) in many applications. Due to their attractive properties, such as their biodegradability, low cost, easy preparation, and nontoxicity, DESs appear to be very promising solvents for use in the field of green chemistry. This computational study investigated six glycerol-based DESs: DES1 (glycerol:methyl triphenyl phosphonium bromide), DES2 (glycerol:benzyl triphenyl phosphonium chloride), DES3 (glycerol:allyl triphenyl phosphonium bromide), DES4 (glycerol:choline chloride), DES5 (glycerol:N,N-diethylethanolammonium chloride), and DES6 (glycerol:tetra-n-butylammonium bromide). The chemical structures and combination mechanisms as well as the sigma profiles and sigma potentials of the studied DESs were explored in detail. Moreover, density, viscosity, vapor pressure, and IR analytical data were predicted and compared with the corresponding experimental values reported in the literature for these DESs. To achieve these goals, the conductor-like screening model for realistic solvents (COSMO-RS) and the Amsterdam Density Functional (ADF) software package were used. The predicted results were found to be in good agreement with the corresponding experimental values reported in the literature. Further theoretical investigations are needed to confirm the experimental results-regarding both properties and applications-reported for these DESs.

Novel deep eutectic solvent-functionalized carbon nanotubes adsorbent for mercury removal from water.

Due to the interestingly tolerated physicochemical properties of deep eutectic solvents (DESs), they are currently in the process of becoming widely used in many fields of science. Herein, we present a novel Hg2+ adsorbent that is based on carbon nanotubes (CNTs) functionalized by DESs. A DES formed from tetra-n-butyl ammonium bromide (TBAB) and glycerol (Gly) was used as a functionalization agent for CNTs. This novel adsorbent was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, XRD, FESEM, EDX, BET surface area, and Zeta potential. Later, Hg2+ adsorption conditions were optimized using response surface methodology (RSM). A pseudo-second order model accurately described the adsorption of Hg2+. The Langmuir and Freundlich isotherm models described the absorption of Hg2+ on the novel adsorbent with acceptable accuracy. The maximum adsorption capacity was found to be 177.76mg/g.

Allyl triphenyl phosphonium bromide based DES-functionalized carbon nanotubes for the removal of mercury from water.

Recently, deep eutectic solvents (DESs) have shown their new and interesting ability for chemistry through their involvement in variety of applications. This study introduces carbon nanotubes (CNTs) functionalized with DES as a novel adsorbent for Hg2+ from water. Allyl triphenyl phosphonium bromide (ATPB) was combined with glycerol as the hydrogen bond donor (HBD) to form DES, which can act as a novel CNTs functionalization agent. The novel adsorbent was characterized using Raman, FTIR, XRD, FESEM, EDX, BET surface area, TGA, TEM and Zeta potential. Response surface methodology was used to optimize the removal conditions for Hg2+. The optimum removal conditions were found to be pH 5.5, contact time 28 min, and an adsorbent dosage of 5 mg. Freundlich isotherm model described the adsorption isotherm of the novel adsorbent, and the maximum adsorption capacity obtained from the experimental data was 186.97 mg g-1. Pseudo-second order kinetics describes the adsorption rate order.