Intelligent modeling of dye removal by aluminized activated carbon.

Methylene blue (MB) is an important compound in textile and wood processing industries as well as in medical research for combating malaria parasites. Despite these versatilities, direct contact with human beings results in adverse health challenges, and contamination of water bodies affects aquatic biotas. Hence, it is important to treat MB-contaminated wastewaters before disposal into water bodies. Adsorption, which depends on some parameters, proves to be an easy, cheap, and efficient technique to remove pollutants in wastewater. However, investigating these parameters experimentally is a laborious, expensive, and time-consuming process whose efficiency is limited by the conditions imposed on the experiments. Herein, we developed polynomial multiple linear regression (MLR) and the three other machine learning models to study the interplay of five adsorption parameters (descriptors) and their effects on the removal of methylene blue from water using aluminized activated carbon (Al-AC). The optimized machine learning models, that is random forest (R = 0.9905), support vector regression (R = 0.9946), and multilayer perceptron (R = 0.9993), outperformed the best MLR model (R = 0.9845) by small margins. High statistical R and low error values are not enough to satisfactorily classify a model. Hence, the generalizability of the models was further determined under different experimental conditions, and the order of predictive accuracy of the models was established as ANN > SVR > RF > 2-degree MLR. Aluminum loading, adsorbent dosage, and initial adsorbate concentration are the most important factors affecting MB removal. The removal efficiency, which could reach 99.9% at optimum conditions, does not depend on the temperature thus eliminating the need to install temperature control apparatus for practical setup.

Environmental risks and toxicity of surfactants: overview of analysis, assessment, and remediation techniques.

This work comprehensively reviewed the toxicity and risks of various surfactants and their degraded products in the environmental matrices, various analytical procedures, and remediation methods for these surfactants. The findings revealed that the elevated concentration of surfactants and their degraded products disrupt microbial dynamics and their important biogeochemical processes, hinder plant-surviving processes and their ecological niche, and retard the human organic and systemic functionalities. The enormous adverse effects of surfactants on health and the environment necessitate the need to develop, select, and advance the various analytical and assessment techniques to achieve effective identification and quantification of several surfactants in different environmental matrices. Considering the presence of surfactants in trace concentration and environmental matrices, excellent analysis can only be achieved with appropriate extraction, purification, and preconcentration. Despite these pre-treatment procedures, the chromatographic technique is the preferred analytical technique considering its advancement and shortcomings of other techniques. In the literature, the choice or selection of remediation techniques for surfactants depends largely on eco-friendliness, cost-implications, energy requirements, regeneration potential, and generated sludge composition and volume. Hence, the applications of foam fractionation, electrochemical advanced oxidation processes, thermophilic aerobic membranes reactors, and advanced adsorbents are impressive in the clean-up of the surfactants in the environment. This article presents a compendium of knowledge on environmental toxicity and risks, analytical techniques, and remediation methods of surfactants as a guide for policymakers and researchers.

Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector regression model.

BACKGROUND AND OBJECTIVES: The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiology of different diseases. Measurement of the real part of the refractive index remains a challenge due to strong absorption of the hemoglobin especially at relevant high physiological concentrations. So far, only a few studies on direct measurement of refractive index have been reported and there are no firm agreements on the reported values of refractive index of hemoglobin due to measurement artifacts. In addition, it is time consuming, laborious and expensive to perform several experiments to obtain the refractive index of hemoglobin. In this work, we proposed a very rapid and accurate computational intelligent approach using Genetic Algorithm/Support Vector Regression models to estimate the real part of the refractive index for oxygenated and deoxygenated hemoglobin samples. METHODS: These models utilized experimental data of wavelengths and hemoglobin concentrations in building highly accurate Genetic Algorithm/Support Vector Regression model (GA-SVR). RESULTS: The developed methodology showed high accuracy as indicated by the low root mean square error values of 4.65 × 10-4 and 4.62 × 10-4 for oxygenated and deoxygenated hemoglobin, respectively. In addition, the models exhibited 99.85 and 99.84% correlation coefficients (r) for the oxygenated and deoxygenated hemoglobin, thus, validating the strong agreement between the predicted and the experimental results CONCLUSIONS: Due to the accuracy and relative simplicity of the proposed models, we envisage that these models would serve as important references for future studies on optical properties of blood.

Treating MTBE-contaminated water using sewage sludge-derived activated carbon.

In this study, sewage sludge-derived activated carbon (SDAC) was synthesized, characterized, and tested for its potential as an adsorbent of methyl tert-butyl ether (MTBE). The SDAC was produced by activating the sludge with zinc chloride and subsequently subjected to various ranges of pyrolytic temperatures. It was then characterized using SEM/EDX, BET, and TGA. The SEM-EDX analyses showed that impurities like Fe, Al, Mg, Mn, Ca, and Na of the raw sludge were removed by the higher pyrolytic temperature and acid-washing procedures. TGA showed the thermal stability of the produced material. Results of the BET revealed a significant increase in surface area of the sludge from 1.5 m2/g to 385 m2/g after acid washing. The MTBE removal efficiency of 70% was achieved after 60 min with 2 g/L of SDAC at pH 6, and initial MTBE concentration of 1 ppm. The adsorption kinetics of SDAC fitted into pseudo-second-order reactions. This work demonstrated a beneficial use of a bio-waste material (sewage sludge) in water treatment technologies.

Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach.

The optical properties of blood play crucial roles in medical diagnostics and treatment, and in the design of new medical devices. Haemoglobin is a vital constituent of the blood whose optical properties affect all of the optical properties of human blood. The refractive index of haemoglobin has been reported to strongly depend on its concentration which is a function of the physiology of biological cells. This makes the refractive index of haemoglobin an essential non-invasive bio-marker of diseases. Unfortunately, the complexity of blood tissue makes it challenging to experimentally measure the refractive index of haemoglobin. While a few studies have reported on the refractive index of haemoglobin, there is no solid consensus with the data obtained due to different measuring instruments and the conditions of the experiments. Moreover, obtaining the refractive index via an experimental approach is quite laborious. In this work, an accurate, fast and relatively convenient strategy to estimate the refractive index of haemoglobin is reported. Thus, the GA-SVR model is presented for the prediction of the refractive index of haemoglobin using wavelength, temperature, and the concentration of haemoglobin as descriptors. The model developed is characterised by an excellent accuracy and very low error estimates. The correlation coefficients obtained in these studies are 99.94% and 99.91% for the training and testing results, respectively. In addition, the result shows an almost perfect match with the experimental data and also demonstrates significant improvement over a recent mathematical model available in the literature. The GA-SVR model predictions also give insights into the influence of concentration, wavelength, and temperature on the RI measurement values. The model outcome can be used not only to accurately estimate the refractive index of haemoglobin but also could provide a reliable common ground to benchmark the experimental refractive index results.

Cadmium Removal from Contaminated Water Using Polyelectrolyte-Coated Industrial Waste Fly Ash.

Fly ash (FA) is a major industrial waste generated from power stations that add extra cost for proper disposal. Recent research efforts have consequently focused on developing ways to make use of FA in environmentally sound applications. This study, therefore, investigates the potential ability of raw fly ash (RFA) and polyelectrolyte-coated fly ash (PEFA) to remove cadmium (Cd) from polluted water. Using layer-by-layer approach, functionalized fly ash was coated with 20 layers from 0.03% (v/v) of cationic poly(diallyldimethylammonium chloride) (PDADMAC) and anionic polystyrene sulfonate (PSS) solutions. Both surface morphology and chemical composition of the adsorbent (PEFA) were characterized using Field-Emission Scanning Electron Microscope (FE-SEM), X-Ray Diffraction (XRD), Fourier-Transform Infrared (FTIR), and X-Ray Fluorescence (XRF) techniques. The effects of pH, adsorbent dosage, contact time, initial contaminant concentration, and mixing rate of the adsorption of Cd were also studied in batch mode experiments. Results of the study revealed that a 4.0 g/L dosage of PEFA removed around 99% of 2.0 mg/L of Cd in 15 min at 150 rpm compared to only 27% Cd removal achieved by RFA under the same conditions. Results also showed that adsorption by PEFA followed both Langmuir and Freundlich models with correlation coefficients of 98% and 99%, respectively.

Isolation and characterization of PAH-degrading bacteria from the Eastern Province, Saudi Arabia.

Contaminated sediment samples were collected from the Eastern Province, Saudi Arabia for isolation of pyrene- and phenanthrene-degrading bacteria by enrichment method. Four isolates were morphologically characterized as Gram-negative rod strains and 16S rRNA sequence analysis revealed the isolates as closely related to Pseudomonas aeruginosa, P. citronellolis, Ochrobactrum intermedium and Cupriavidus taiwanensis. Degradation of the polycyclic aromatic hydrocarbons (PAHs) by the latter three strains was investigated in liquid cultures. Results of concentration reduction analyzed with gas chromatography show that P. citronellolis_LB was efficient in removing phenanthrene, degrading 94% of 100ppm in 15days while O. intermedium_BC1 was more efficient in pyrene-removal, degrading 62% in 2weeks. Furthermore, bacterial growth assessment using optical density and population counts revealed the latter as more suitable for microbial growth analysis in PAH-containing cultures. In conclusion, the isolated bacterial strains could be further developed for efficient use in biodegradation of PAH.