Faculty Publications
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Item Green synthesis of iron nanoparticles using different leaf extracts for treatment of domestic waste water(Elsevier Ltd, 2016) Devatha, C.P.; Thalla, A.K.; Katte, S.Y.Green synthesis of iron nanoparticles being cost effective and ecofriendly treatment technique, is gaining importance nowadays. The aim of the present study is to prepare leaf extracts, precursor, and synthesis of iron nanoparticles and to evaluate its efficacy in treating domestic waste water. Synthesis of iron nanoparticles is done using various leaf extracts viz. Mangifera indica, Murraya Koenigii, Azadiracta indica, Magnolia champaca, and to check its potential for treating domestic waste water. Characterization of the synthesized iron nanoparticles is done by UV–Visible spectrophotometer, Scanning Electron Microscopy equipped with X-ray energy dispersive spectroscopy and Fourier Transform Infrared spectroscopy. The characterization results confirm the formation and presence of iron nanoparticles and biomolecules which could help in capping the nanoparticles. The effect of iron nanoparticles thus obtained is evaluated for simultaneous removal of total phosphates, ammonia nitrogen, and chemical oxygen demand. Among the different plant mediated synthesized iron nanoparticles, Azadiracta indica showed 98.08% of phosphate, 84.32% of ammonia nitrogen and 82.35% of chemical oxygen demand removal. Overall performance of Azadiracta indica synthesized iron nanoparticles showed satisfactory results compared to other leaf extracts for treating domestic waste water. © 2016 Elsevier LtdItem Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater(Springer Verlag, 2017) Manu, D.S.; Thalla, A.K.The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, real-time and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014–September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the inter-relationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP. © 2017, The Author(s).Item Effects of chemical pretreatments on material solubilization of Areca catechu L. husk: Digestion, biodegradability, and kinetic studies for biogas yield(Academic Press, 2022) Vannarath, A.; Thalla, A.K.This study aimed to understand the pretreatment-aided anaerobic digestion of lignocellulosic residues and to assess the substrate solubilization capacity of pretreatment processes. We evaluated the feasibility of biogas production using chemically pretreated Areca catechu L. (Arecanut husk, AH). AH was pretreated for 24h at two different temperatures—25 °C and 90 °C with four different chemicals viz. H2SO4 (acidic), NaOH (alkaline), H2O2 (oxidative), and ethanol in 1% H2SO4 (organosolv) under each temperature. AH solubilization assessment included analyses of parameters such as volatile solids to total solids (VS:TS) ratio, soluble chemical oxygen demand, total phenolic content, and biomass composition. Alkaline pretreatment of AH at 90 °C resulted in the maximum biogas yield of 683.89mL/gVS, which was 2.3 times more than that obtained using raw AH without pretreatment. Methane content of biogas produced using AH pretreated with 2–10% of NaOH was found to be between 71.53% and 75.06%; methane content of biogas using raw AH was 62.31%. In order to describe the AH degradation patterns, biogas production potential from pretreated AH was evaluated using bacterial kinetic growth models (First-order exponential, logistic, transference, and modified Gompertz models). The modified Gompertz and logistic models (correlation coefficient >0.99) were found to have the best fit of all kinetic models for the cumulative experimental biogas curve. We formulated a multiple linear regression equation depicting the biodegradability index (BI) as a technical tool to determine biomethane production; BI is represented as a function of biomass composition (cellulose, hemicellulose, and lignin), with a high correlation (>0.95). Based on our analyses of AH pretreatment and substrate utilization for biogas production, we propose that the biochemical composition of lignocellulosic residues should be carefully considered to ensure their biodegradability when subjected to anaerobic digestion. © 2022 Elsevier Ltd