Faculty Publications
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Item Prediction of ammonia contaminants in the aquaculture ponds using soft computing coupled with wavelet analysis(Elsevier Ltd, 2023) Thotakura, T.V.; Sunil, B.M.; Chaudhary, B.; Durga Prasad, C.D.; Gobinath, G.Intensive aquaculture practices generate highly polluted organic effluents such as biological oxygen demand (BOD), alkalinity, total ammonia, nitrates, calcium, potassium, sodium, iron, and chlorides. In recent years, Inland aquaculture ponds in the western delta region of Andhra Pradesh have been intensively expanding and are more concerned about negative environmental impact. This paper presents the water quality analysis of aquaculture waters in 64 random locations in the western delta region of Andhra Pradesh. The average water quality index (WQI) was 126, with WQI values ranging from 21 to 456. Approximately 78% of the water samples were very poor and unsafe for drinking and domestic usage. The mean ammonia content in aquaculture water was 0.15 mg/L, and 78% of the samples were above the acceptable limit set by the World Health Organization (WHO) of 0.5 mg/L. The quantity of ammonia in the water ranged from 0.05 to 2.8 mg/L. The results show that ammonia levels exceed the permissible limits and are a significant concern in aquaculture waters due to toxicity. This paper also presents an intelligent soft computing approach to predicting ammonia levels in aquaculture ponds, using two novel approaches, such as the pelican optimization algorithm (POA) and POA coupled with discrete wavelet analysis (DWT-POA). The modified and enhanced POA with DWT can converge to higher performance when compared to standard POA, with an average percentage error of 1.964 and a coefficient of determination (R2) value of 0.822. Moreover, it was found that prediction models were reliable with good accuracy and simple to execute. Furthermore, these prediction models could help stakeholders and policymakers to make a real-time prediction of ammonia levels in intensive farming inland aquaculture ponds. © 2023 Elsevier LtdItem Improving landfill liner performance with bentonite-slag blend permeated with ammonia for a Municipal solid waste landfill(Academic Press, 2024) Aswathy, A.; Sunil, B.M.Leachate emanating from landfills contains ammonia which may cause serious health effects on living things. An effectively designed clay barrier should not allow the contaminant to infiltrate the soil and groundwater systems. The utilization of certain industrial by-products in engineered landfill barriers, not only reduces the need for conventional liner materials but also helps in sustainable waste management. This study investigated the hydraulic conductivity, unconfined compressive strength, compaction, and adsorption characteristics of lithomargic clay blended with an optimum percentage of bentonite (10%) and granulated blast furnace slag (15%) permeated with ammonia. The results revealed that increasing the content of granulated blast furnace slag decreased the maximum dry density while increasing the optimum moisture content. In comparison to lithomargic clay, the hydraulic conductivity of the amended soil liner permeated with ammonia decreased from a value of 3 × 10−8 m/s to 5 × 10−10 m/s. The unconfined compressive strength of the amended soil specimens showed an increasing trend with curing times (i.e., 0, 14, 28, and 56 days). The batch adsorption results revealed that Freundlich and Langmuir's isotherm fits the equilibrium adsorption data and the adsorption of ammonia on clay liner follows non-linear behaviour. Overall, the experimental results implied that lithomargic clay blended with 10% bentonite and 15% granulated blast furnace slag can be used as an impermeable soil reactive barrier in engineered landfills. © 2024 Elsevier Ltd