Environmental Engineering
N.D. Takarina; N. Matsue; E. Johan; A. Adiwibowo; M.F.N.K. Rahmawati; S.A. Pramudyawardhani; T. Wukirsari
Abstract
BACKGROUND AND OBJECTIVES: Zeolite has been recognized as a potential adsorbent for heavy metals in water. The form of zeolite that is generally available in powder has challenged the use of zeolite in the environment. Embedding powder zeolite in a nonwoven sheet, known as a zeolite-embedded sheet can ...
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BACKGROUND AND OBJECTIVES: Zeolite has been recognized as a potential adsorbent for heavy metals in water. The form of zeolite that is generally available in powder has challenged the use of zeolite in the environment. Embedding powder zeolite in a nonwoven sheet, known as a zeolite-embedded sheet can be an alternative to solve that. Another challenge is that information and models of zeolite-embedded sheet removal efficiency are still limited. The novelty of this study is, first, the development of a zeolite-embedded sheet to remove heavy metals from water, and second, the use of the random forest method to model the heavy metal removal efficiency of a zeolite-embedded sheet in water.METHODS: The heavy metals studied were copper, lead and zinc, considering that those are common heavy metals found in water. For developing the zeolite-embedded sheet, the methods include fabrication of the zeolite-embedded sheet using a heating procedure and heavy metals adsorption treatment using the zeolite-embedded sheet. The machine learning analysis to model the heavy metal removal efficiency using zeolite-embedded sheet was performed using the random forest method. The random forest models were then validated using the root mean square error, mean square of residuals, percentage variable explained and graphs depicting out-of-bag error of a random forest.FINDINGS: The results show the heavy metal removal efficiency was 5.51-95.6 percent, 42.71-98.92 percent and 13.39-95.97 percent for copper, lead and zinc, respectively. Heavy metals were reduced to 50 percent at metal concentrations of 10.355 milligram per liter for copper, 171.615 milligram per liter for lead and 4.755 milligram per liter for zinc. Based on the random forest models, the important variables affecting copper removal efficiency using zeolite-embedded sheet were its contents in water, followed by water temperature and potential of hydrogen. Conversely, lead and zinc removal efficiency was influenced mostly by potential of hydrogen. The random forest model also confirms that the high efficiency of heavy metals removal (>60 percent) will be achieved at water potential of hydrogen ranges of 4.94–5.61 and temperatures equal to 29.1 degrees Celsius.CONCLUSION: In general, a zeolite-embedded sheet can adsorb diluted heavy metals from water because there are percentages of adsorbed heavy metals. The random forest model is very useful to provide information and determine the threshold of heavy metal contents, water potential of hydrogen and temperature to optimize the heavy metal removal efficiency using a zeolite-embedded sheet and reducing pollutants in the environment.
Environmental Engineering
S. Dhanasekar; R. Sathyanathan
Abstract
BACKGROUND AND OBJECTIVES: Recent investigations indicated that continuous use of fertilizers and pesticides in agricultural fields not only deteriorated soil health but also caused a deleterious effect on surface and groundwater bodies. Treating such wastewater using microalgae has shown higher nutrient ...
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BACKGROUND AND OBJECTIVES: Recent investigations indicated that continuous use of fertilizers and pesticides in agricultural fields not only deteriorated soil health but also caused a deleterious effect on surface and groundwater bodies. Treating such wastewater using microalgae has shown higher nutrient removal and biomass efficiency. Moreover, microalgae are proven to be miniature factories that augment the huge potential of biofuel. The aim of this study is to evaluate the different light intensities required for Chlorella vulgaris algae to remove nutrients from synthetic agricultural wastewater in a fabricated bubble column photobioreactor. Additionally, the research findings focus on assessing the degradation of organic pollutants and biomass generation under different light conditions.METHODS: In this study, synthetic agrochemical wastewater was treated in a bubble column photobioreactor with blue, red, sunlight, and white light conditions. The treatment was conducted in a batch process with a hydraulic retention time of 21 days, using light intensity of 1800–2800 luminescence and a temperature maintained at 25–28° degrees Celsius.FINDINGS: Under different lighting conditions, the blue light condition exhibited a higher biomass concentration of 3.99 gram per liter, with an estimated heat energy value of 1.278 kilojoule per liter. Moreover, in the blue light condition, scanning electron microscopy analysis showed no significant changes in the shape of Chlorella vulgaris and energy-dispersive X-ray analysis elemental composition exhibited the lowest oxygen-to-carbon ratio (1.03). Fourier transform infrared spectroscopy was used to illustrate the functional group of microalgae under different lighting conditions. The lipid, protein, carbohydrate, and amino acid contents were 3329–3332, 2116–2139, 1636–1645, and 545–662 per centimeter, respectively. The higher biomass potential from the wastewater treatment shows significant benefit in terms of feedstock and biofuel production.CONCLUSIONS: The present investigation identified the nutrient reduction and biomass productivity to be more in blue light condition for Chlorella vulgaris algae. The investigation also assessed the potential of lipid, carbohydrate, and protein content in Chlorella vulgaris, which indirectly evaluates the biofuel potential of the species.
