NOPR Collection:

Neural network for short-term predictions of ambient particulate matter around thermal power plant

2006-08-01T00:00:00Z

Title: Neural network for short-term predictions of ambient particulate matter around thermal power plant Authors: Sriram, G; Mohan, N Krishna; Gopalasarny, V Abstract: A three-layer neural network model has been developed to predict suspended particulate matter (SPM) in and around a thermal power plant at Neyveli. The feed forward supervised neural network-the back propagation was used to train the network. Utilizing the meteorological data along with SPM data for all the six monitoring stations surrounding the thermal power plant, the neural gave better predictions. The aim of the present research work is to construct a forecasting model that would be suitable for the use of future planning and developments. Page(s): 639-645

Modelling the effects of industrial discharged waters to Degirmendere River (South Eastern Black Sea)

2006-08-01T00:00:00Z

Title: Modelling the effects of industrial discharged waters to Degirmendere River (South Eastern Black Sea) Authors: Sivri, N; Kose, E; Feyzioglu, A M Abstract: In this study, modeling the effect of discharge waters from various industrial establishments located along the Degirmendere basin in Trabzon was investigated. Discharges from industrial establishments directly influence the water quality of the Degirmendere River, which is also influenced by inputs resulting from sewage, industrial processes, and agricultural run-off water discharge. Wastewaters are often discharged continuously into this river. The organic compounds found in the water are utilized by aquatic micro organisms and consequently reduce the dissolved oxygen (DO) concentration in the river. Models of DO profiles are useful in predicting possible consequences of additional river discharges or aeration strategies on the river. Input data for the model was the average of data collected between 1996 and 2004. The resulting four different scenarios were indicative of the effects of industrial discharge water, especially the dynamics of DO, BOD₅, temperature, and fecal coliform in the Degirmendere River. Page(s): 632-638

Design of robust model based neural controller for controlling vibration of active suspension system

2006-08-01T00:00:00Z

Title: Design of robust model based neural controller for controlling vibration of active suspension system Authors: Yildirim, Sahin; Eski, Ikbal Abstract: This paper presents a new robust model based neural controller (NC) for active suspension system (ASS)'s vibrations via feedback control approach. The proposed control system consists of a NC, a robust feedback controller, a third-order linear reference model and dynamics of ASS. The simulation examples with various standard input signals are included to demonstrate the effectiveness of the proposed control method and show significant improvement over the existing PID controller method. The robustness of the proposed NC is also analyzed with white noise disturbances on the suspension system. It is shown that the control system is robustly stable for all road disturbances. Page(s): 646-654

Removal of Reactofix Red 3BFN from industrial effluent using adsorption techniques

2006-08-01T00:00:00Z

Title: Removal of Reactofix Red 3BFN from industrial effluent using adsorption techniques Authors: Jain, Rajeev; Varshney, Shaily; Sikarwar, Shalini Abstract: Wheat husk has been used to adsorb dye using a series of batch experiments. Langmuir model provides best correlation of experimental data. Isotherms have also been used to obtain thermodynamic parameters such as free energy, enthalpy and entropy of adsorption. Various parameters (pH, adsorbent dose, initial dye concentration, temperature, particle size etc.) were optimized. Page(s): 680-683