The present work was aimed to study the synthesis of silver nanoparticles (Ag NPs) using Tri-Sodium Citrate (TSC), stability study of synthesized Ag NPs and their characterization. Synthesis of Ag NPs has been carried out by maintaining different conditions such as TSC concentration (0.50, 1.00 and 1.50%), AgNO3 concentration (0.50, 1.00 and 1.50 mM) and stirring time (10, 15 and 20 min). The stability study of synthesized Ag NPs conducted for 30 days without adding any stabilizing agents. The characterization of synthesized Ag NPs was carried out for different parameters like particle size and zeta potential using particle size analyzer, absorbance peak by UV-Visible spectrophotometer, morphology by Scanning Electron Microscope (SEM), crystallinity by X-Ray Diffractometer (XRD) and material structural characteristics by Atomic Force Microscope (AFM). The stable chemically synthesized Ag NPs were obtained at C20 (AgNO3 concentration of 1.5 mM, TSC 1.5% and stirring time 20 min) (desirability 99.97%), with average particle size of 22.14 nm and average absorbance peak of 449.85 nm.
The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) for efficient treatment of dairy industry wastewater using RSM and ANN models. The nano-adsorbents were synthesized using chemical precipitation method and characterized by using SEM-EDS and AFM. Maximum %RBOD (96.71 and 87.56%) and %RCOD (90.48 and 82.10%) for CZnO and CTiO2 nano-adsorbents were obtained at adsorbent dosage of 1.25 mg/L, initial BOD and COD concentration of 100 and 200 mg/L, pH of 7.0 and 2.00, contact time of 100 and 60 min, respectively. The results obtained for both the nano-adsorbents were subject to RSM and ANN models for determination of goodness of fit in terms of SSE, RMSE, R2 and Adj. R2, respectively. The well trained ANN model was found superior over RSM in prediction of the treatment effect. Hence, the developed CZnO and CTiO2 nano-adsorbents could be effectively used for dairy industry wastewater treatment.
Abstract. Paddy is a staple food for more than 93 countries and it will stay of life for future generations. Harvesting is one of the vital operations in crop production and timely harvesting is essential for getting maximum yield. Moisture content and forward speed are the two factors to overcome the post harvest losses and minimise the quantitative losses. In this paper, an artificial neural network is introduced to assess the grain losses in the field condition. The simulation result shows that the ANN method is appropriate and feasible to assess the grain losses. However, model results that, an error of (RMSE) 0.1582 for cutter bar loss, for threshing loss was 0.1299 and for separation loss was 0.1321. Hence, the ANN model help the operator / farmer to decide the time of harvest. It also minimizes the post harvest grain losses by considering crop and machine parameters.
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