Comparing the efficiency of unmodified dried sludge adsorbents and those modified via chemical and microwave methods in removing 2,4-dinitrophenol from aqueous solutions

2,4-dinitrophenol (DNP) is found in small amounts in the effluent of many wastewater treatment plants. The contamination of drinking water with this pollutant, even in trace amounts, causes toxicity, health problems, and unfavorable taste and odor. This study aims to compare the efficiency of non-modified and modified dried sludge adsorbents in removing 2,4 DNP from aqueous solutions. The results of 2,4DNP removal by high-performance liquid chromatography method at the wavelength of 360 nm in a batch mode were obtained by changing the influential factors including contact time, pH, initial concentration of the contaminant, and adsorbent dosage. Eventually, the results were analyzed by kinetic and isotherm models. In this research, the optimal time was obtained as 60 min and pH as seven for all three adsorbents. The results showed that the removal percentage increases by rising adsorbent dosage and reducing contaminant concentration. The correlation coefficient value of linear and non-linear led that in kinetic studies, it follows the pseudo-second order model. In contrast, in isotherm studies, examining linear and non-linear models of isotherms showed that the data for every three types of adsorbents follow the Freundlich model well. The adsorption process is highly dependent on pH and affects the adsorbent surface properties, ionization degree, and removal percentage. At high pH, hydroxide ions (OH) compete with 2,4 DNP molecules for the adsorption sites. The adsorption occurs quickly and gradually reaches a constant value because, over time, the adsorption sites are occupied until reaching a saturated limit. By increasing the adsorbent dosage, the adsorption percentage increased significantly, which is due to the fact that higher amounts of adsorbent cause more adsorption sites. © 2020, Springer Nature Switzerland AG

Municipal solid waste management during COVID-19 pandemic: effects and repercussions

The COVID-19 pandemic has an adverse effect on the environment. This epidemic�s effect on the waste composition and management and the impacts of municipal solid waste management (MSWM) on disease transmission or controlling are considered a compelling experience of living in the COVID-19 pandemic that can effectively control the process. This systematic review research was conducted to determine the effects of COVID-19 on the quantity of waste and MSWM. Searches were conducted in three databases (using keywords covid 19, coronaviruses, and waste), and among the published articles from 2019 to 2021, 56 ones were selected containing information on the quantity and waste management during the COVID-19 pandemic. The results showed that COVID-19 caused the quantity variation and composition change of MSW. COVID-19 also has significant effects on waste recycling, medical waste management, quantity, and littered waste composition. On the other hand, the COVID-19 pandemic has changed waste compounds� management activities and waste generation sources. Recognizing these issues can help plan MSWM more efficiently and reduce virus transmission risk through waste. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature

Investigation of Nano Alumina Efficiency for Removal of Acid Red 18 Dye from Aqueous Solutions

Background: Textile and dying industries are considered most important industries of each country. In these industries due to the use of different colors in different processes, their wastewater is highly colored and must be treated before discharge to the environment. The objective of this study was Investigation of nano alumina efficiency for removal of Acid Red 18 dye from aqueous solutions. Methods: This study was carried out in the laboratory scales. Synthetic solution was made from Acid Red 18 dye stock and effect of different parameters such as dye concentration, pH solution, nano alumina concentration and contact time on dye removal efficiency were evaluated. Also kinetic and isotherm models of adsorption process were evaluated. Results: Results from experiments showed that dye removal was increased with increasing contact time and nano alumina powder concentration, while decreased with increasing of pH and dye concentration. Experimental data were best fitted to Longmuir isotherm model (r2=0.994). The maximum adsorption capacity for Acid Red 18 was found 83.33 mg g-1. The results from kinetic studies showed that removal of Acid Red 18 was best described by pseudo-second order kinetic model (r2=0.999). Conclusion: The present study shows nano alumina powder is promising adsorbent for removal of Acid Red 18 from aqueous solution

Comparison of LSSVM and RSM in simulating the removal of ciprofloxacin from aqueous solutions using magnetization of functionalized multi-walled carbon nanotubes: Process optimization using GA and RSM techniques

This inquiry focuses on acquiring empirical models to predict ciprofloxacin removal using magnetization of functionalized multi-walled carbon nanotubes (FMWCNTs-Fe3O4) from an aqueous solution. The response surface methodology (RSM) and support vector regression (SVR) as data mining techniques were adopted to develop models. Critical parameter effects comprising pH (3-10), adsorbent dose (0.2-1 g/L), contact time (5-60 min) and ciprofloxacin concentrations (30-100 mg/L) were analysed. The Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isothermal models were utilized to fit the empirical data. FMWCNTs-Fe3O4 prepared by chemical co-precipitation method was loaded by Fe3O4 nanoparticles (using sonication) to synthesize functionalized multi-walled carbon nanotubes to remove ciprofloxacin (CIP). FMWCNTs-Fe3O4 were characterized by fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), vibrating sample magnetometer (VSM) methods. The Langmuir model was utilized to precisely describe the maximum adsorption capacity(qmax) of 107.66 mg/g with R2 = 0.998. In this study, the pseudo-second-order model exactly described the adsorption process(R2 = 0.99). The results illustrated that the LSSVM (least squares support vector machine) model efficiently predicted the CIP removal percentage with very high accuracy in the training phase (R2 = 0.975) and the test phase (R2 =0.970). Moreover, the highest removal percentages in optimized step were achieved for RSM (pH 5.4, dose 0.78 g/L, time of 24.5 min, and CIP concentrations of 59 mg/L) and GA (genetic algorithm) (pH 4.4, dose 0.74 g/L, time of 42 min, and CIP concentrations of 38 mg/L) techniques by 88 and 99.1, respectively. The FMCNTs-Fe3O4 efficiency has decreased by 12 even after five used cycles relative to the optimal conditions (regeneration). Therefore, FMWCNTs-Fe3O4 adsorption was considered to be an effective technique for CIP removal in the aqueous environment. © 2021 Elsevier Ltd

Gait Recognition Based on EMG Information with Multiple Features

Part 10: Image UnderstandingInternational audienceIn order to evaluate the effects of time domain (TD) and frequency domain (FD) features as well as muscle number on gait classification recognition, eight channels of electromyography (EMG) signals were collected from four thigh and four lower leg muscles, and two TD features and two FD features were extracted in this study. The method of support vector machine (SVM) was presented to investigate the classification property. For the classification stability and accuracy, 3-fold cross validation was verified and selected to classify the lower limb gait. The results show that the FD features can obtain higher accuracy than TD features. In addition, accuracy of gait recognition increased with the augment of muscle number

A novel human–machine interface based on recognition of multi-channel facial bioelectric signals

This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi-channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Frontalis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed. © 2011 Australasian College of Physical Scientists and Engineers in Medicine