Textile industry production processes generate one of the most highly polluted wastewaters in the world. Unfortunately, the field is also challenged by the availability of relatively cheap and highly effective technologies for wastewater purification. The application of natural zeolite as a depth filter offers an alternative and potential approach for textile wastewater treatment. The performance of a depth filter treatment system can be deeply affected by the column depth and the characteristics of the wastewater to be treated. Regrettably, the information on the potential of these filter materials for the purification of textile wastewater is still scarce. Therefore, this study investigated the potential applicability of natural zeolite in terms of column depth for the treatment of textile wastewater. From the analysis results, it was observed that the filtration efficiencies were relatively low (6.1 to 13.7%) for some parameters such as total dissolved solids, electrical conductivity, chemical oxygen demand, and sodium chloride when the wastewater samples were subjected to the 0.5 m column depth. Relatively high efficiency of 82 and 93.8% was observed from color and total suspended solids, respectively, when the wastewater samples were subjected to the 0.5 m column depth. Generally, the 0.75 m column depth achieved removal efficiencies ranging from 52.3% to 97.5%, whereas the 1 m column depth achieved removal efficiencies ranging from 86.9% to 99.4%. The highest removal efficiency was achieved with a combination of total suspended solids and 1 m column depth (99.4%). In summary, the treatment approach was observed to be highly effective for the removal of total suspended solids, with a 93.8% removal efficiency when the wastewater was subjected to the 0.5 m column depth, 97.5% for 0.75 m column depth, and 99.4% for 1 m column depth. Moreover, up to 218.233 mg of color per g of the filter material was captured. The results derived in this study provide useful information towards the potential applicability of natural zeolite in the textile wastewater treatment field.
Groundwater is one of the main sources of water for irrigation used worldwide. However, the application of the resource is threatened by the possibility of high saline levels, especially in low-lying coastal regions. Furthermore, the lack of readily accessible materials for successful treatment procedures makes the purification of such water a constant challenge. Based on the fact that natural zeolite is one of the easily accessible and relatively cheap filter materials, this study examined the potential use of high-salinity groundwater filtered by natural zeolite for irrigation. Zeolite-filled filters at two different depths (0.5 m and 1 m) were studied. The samples were collected from the low-lying areas of Dar es Salaam City, Tanzania. The study observed that when the raw groundwater samples were exposed to the 0.5 m column depth, sodium (Na+) had the lowest removal efficiency at 40.2% and calcium (Ca2+) had the highest removal efficiency at 98.9%. On the other hand, magnesium (Mg2+) had the lowest removal efficiency, at about 61.2%, whereas potassium (K+) had up to about 99.7% removal efficiency from the 1 m column depth treatment system. Additionally, from the salinity hazard potential analysis, most of the samples fell within C4 (based on the electrical conductivity), which is a “very high salinity” class, and based on the quality it means the water cannot be directly applied for irrigation purposes. From the 0.5 m column depth, most of the samples fell within C3 (the “high salinity” class), and from the 1 m column depth most of the samples fell within C1 (“low salinity” class). The findings of this study offer some valuable insight into the prospective use of natural zeolite for the filtration of saline groundwater before its application for irrigation.
Zeolite materials are among the relatively cheap and readily available materials for wastewater treatment. However, the performance of zeolite-based systems can be highly affected by the material properties. In this study, the treatment system based on natural zeolite materials from Chankanai mines in Kazakhstan was compared with a synthetic zeolite treatment system for the purification of groundwater. Water quality indices were also developed from a set of selected water quality parameters to further assess the state of water quality of raw groundwater and the effluents treated with natural and synthetic zeolite. The lowest removal efficiency of natural zeolite (30%) was observed with zinc, while the lowest removal efficiency (36%) of synthetic zeolite was observed with arsenic. With turbidity and beryllium, we observed the maximum removal efficiency (100%) of natural zeolite, whereas with turbidity, we observed the highest removal efficiency (100%) of synthetic zeolite. When the groundwater samples were put through the natural zeolite treatment system, removal efficiency of 50% and above was obtained with 27 (79.4%) out of the 34 water quality parameters examined. On the other hand, when the groundwater samples were put through the synthetic zeolite treatment system, more than 50% removal efficiency was attained with 30 (88.2%) out of the 34 water quality parameters studied. The aggregated water quality index of raw groundwater was 3278.24, falling in the “water unsuitable for drinking” category. The effluent treated with natural zeolite generated 144.82 as a water quality index, falling in the “poor water” quality category. Synthetic zeolite generated 94.79 as a water quality index, falling in the “good water” quality category. Across the board, it was shown that the synthetic zeolite treatment system outperformed the natural zeolite treatment system according to a number of water quality parameters. The findings of this study offer substantial knowledge that can be used to develop more efficient groundwater treatment technologies.
Using the mixture of carbonized rice husk and shungite from the Kazakhstan Koksu deposit and the experimentally determined oil sorption capacity from contaminated soil with oil originating in the Karazhanbas oil field, a set of Artificial Neural Network (ANN) models were built for sorption predictions. The ANN architecture design, training, validation and testing methodology were performed, and the sorption capacity prediction was evaluated. The ANN models were successfully trained for capturing the sorption capacity dependence on time and on a carbonized rice husk and shungite mixture ratio for the 10% and 15% oil-contaminated soil. The best trained ANNs revealed a very good prediction capability for the testing data subset, demonstrated by the high coefficient of the determination values of R2 = 0.998 and R2 = 0.981 and the mean absolute percentage errors ranging from 1.60% to 3.16%. Furthermore, the ANN sorption models proved their interpolation ability and utility for predicting the sorption capacity for any time moments in the investigated time interval of 60 days and for new values of the shungite and rice husk mixture ratios. The ANN developed models open opportunities for planning new experiments, maximizing the sorption performance and for the design of dedicated equipment.
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