Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Removal of Cd(II) from Synthetic Medium on to Gulmohar Fruit Shell: Characterization, Equilibrium and Kinetic studies

Abstract: Innovative process for treating waste water containing heavy metals often involve technologies for reduction of toxicity in order to meet the technology based standards. The present research is based on the sorption of Cd(II) from synthetic water using Gulmohar fruit shell (GFS). Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM) were used for identification of morphology, functional groups of GFS. Equilibrium, kinetic studies were performed by considering the effects of pH, contact time, initial Cd(II) concentration and GFS loading. Results showed that the % removal of Cd(II) ions rised with the rise of pH, contact time and biomass loading and declined with rise of initial Cd(II) concentration. Langmuir isotherm was better described than Fruendlich isotherm. The maximum GFS coverage was 24.03mg/g. Kinetic studies were performed by different kinetic models. Pseudo-second order was excellent suited by kinetics. Thus, it can be concluded that GFS is an efficient biosorbent for the exclusion of Cd(II) from synthetic waste water.

Modelling and comparative analysis of ANN and PID controllers with MFB applied to HESS of electric vehicle

Abstract: A control strategy approach is proposed, in order to achieve the smooth and precise switching of energy sources present in the hybrid energy storage system which includes battery and ultracapacitor. Based on the speed of an electric motor four individual math functions are considered, designed a new controller named as math function-based (MFB) controller. Thereafter the designed MFB controller is integrated with conventional/intelligent controller to full fill the proposed control strategy approach. Two different hybrid controllers MFB with proportional integral derivative, MFB with artificial neural network are proposed and implemented to the electric motor in four different modes based on the load applied. Two-hybrid controllers MATLAB/Simulink output responses are compared and tabulated in the conclusion section.

Reservoir Inflow Forecasting Using Extreme Learning Machines

Abstract: Accurate and reliable forecasting of reservoir inflow is necessary for efficient and effective water resources’ planning and management. In the present study, the capacity of recently developed extreme learning machines (ELMs) modeling approach in forecasting reservoir inflows is assessed and compared to that of equivalent traditional artificial neural network-based models. Performance of wavelet analysis technique is also explored by developing wavelet-based ELMs (WELMs) and wavelet-based ANNs (WANNs) models. Seven years of reservoir inflow data along with outflow data from two upstream reservoirs in the Damodar catchment along with rainfall data of 5 upstream rain gauge stations are considered in this study. Out of 7 years’ daily data, 5 years’ data are used for training the model, one-year data are used for cross-validation, and remaining one-year data are used to evaluate the performance of the developed models. Different performance indices indicated better performance of ELM and WELM models in comparison with MLR, ANN, WMLR, and WANN models. This study demonstrated the effectiveness of proper selection of wavelet functions and appropriate methodology for wavelet-based model development. ELM models were also computationally efficient as demonstrated by faster running time, and consequently, this study advocates the superiority of the WELM model and the significant role of wavelet transformation in order to improve the model’s overall performance for reservoir inflow forecasting modeling.