Search

Globally, the petroleum industry plays a very significant role in producing oil to fulfil the demand of the growing population. The improper management of abandoned quantity of petroleum sludge that is one of the byproducts of petroleum industry has posed many environmental as well as socio-economic issues in most of the developing countries. The petroleum sludge contains various toxic substances, like minerals, oil, and other chemicals which are very harmful for biotic as well as abiotic environment. Meanwhile, a huge quantity of livestock manure, especially buffalo dung, is produced in villages and burned as fuel after drying in open atmosphere for domestic application without any treatment which generates indoor air pollution. This study was formulated to analyze the biochemical methane potential of buffalo dung with petroleum sludge at different mixing ratios (i.e., 1:1, 1.5:0.5 and 0.5:1.5) through batch digestion system. The substrates were prepared and characterized before and after batch digestion by using standard methodology. The maximum methane was obtained as 268 Nml/gVS, followed by 326 Nml/gVS and 191 Nml/gVS at mixing ratios of 1:1, 1.5:0.5 and 0.5:1.5, respectively. The results and findings of the study indicated that the co-digestion of buffalo dung with petroleum sludge at mixing ratio of 1.5:0.5 through continuous batch digestion would be the best option to enhance methane production. [ABSTRACT FROM AUTHOR]

The present work is focused on an extensive comparative study of Pb (II) removal by organically tailored silica aerogels with the relevant studies. Briefly, two types of silica aerogels were synthesized using organic solvents, sol–gel method, modified aging techniques, and ambient pressure drying. The aerogels were characterized (SEM, XRD, FTIR) and investigated for Pb (II) removal from synthetic solutions and the results were extensively compared with other studies that synthesized aerogels using inorganic and expensive functionalizing/modifying agents. As per results of the comparative study, aerogel SA-Iso showed excellent removal efficiency and outperforming most of the functionalizing agents used by other studies, whereas SA-Sim showed remarkably decent results. Highest Pb (II) removal by both the aerogels was achieved in adsorbent dose experiments. At adsorbent dose of 0.1 g, pH 6, Pb (II) concentration of 10 ppm, solution volume of 100 mL, and contact time of 8 h, SA-Iso showed removal of 98.45%, which was higher than the removal achieved by functionalizing agents such as chitosan, bispyrazole, AAAPTS, thiamine, glutaraldehyde, TMCS, and hexane. On the other hand, SA-Sim removed 86.9% of Pb (II) ions which was higher than chitosan, bispyrazole, AAAPTS, and thiamine, respectively. The comparative study revealed that, under optimum conditions, such organically modified silica aerogels aged using the given aging techniques could result in an enhanced Pb (II) removal than silica aerogels modified with conventional functionalizing/modifying solvents. [ABSTRACT FROM AUTHOR]

Precise determination of engineering properties of soil is essential for proper design and successful construction of any structure. The conventional methods for determination of engineering properties are invasive, costly, and time-consuming. Geoelectrical survey is a very attractive tool for delineating subsurface properties without soil disturbance. Proper correlations of various soil parameters with electrical resistivity of soil will bridge the gap between geotechnical and geophysical engineering and also enable geotechnical engineers to estimate geotechnical parameters from electrical resistivity data. The regression models of relationship between electrical resistivity and various soil properties used in the current research for the purpose of comparison with artificial neural network (ANN) models were adopted from the work of Siddiqui and Osman (Environ Earth Sci 70:259-26, ). In order to obtain better relationships, ANN modeling was done using same data as regression analysis. The neural network models were trained using single input (electrical resistivity) and single output (i.e., moisture content, plasticity index, and friction angle). Twenty (20) multilayer feedforward (MLFF) networks were developed for each properties, ten (10) each for two different learning algorithms, Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG). The numbers of neurons in hidden layer were experimented from 1 to 10. Best network with particular learning algorithm and optimum number of neuron in hidden layer presenting lowest root mean square error (RMSE) was selected for prediction of various soil properties. ANN models show better prediction results for all soil properties. [ABSTRACT FROM AUTHOR]