A Novel Accelerator Combination for the Low Temperature Curing of Silica-Filled NBR Compounds

Zinc salts of ethyl, isopropyl, and butyl xanthates were prepared in the laboratory. The effect of these xanthates in combination with zinc diethyldithiocarbamate (ZDC) on the vulcanization of silica-filled NBR compounds has been studied at different temperatures. The cure times of these compounds were compared with that of NBR compounds containing tetramethylthiuram disulphide/dibenzthiazyl disulphide. The rubber compounds with the xanthates and ZDC were cured at various temperatures from 60 to 150°C. The sheets were molded and properties such as tensile strength, tear strength, crosslink density, elongation at break, compression set, abrasion resistance, flex resistance, heat buildup, etc. were evaluated. The properties showed that zinc salt of xanthate/ZDC combination has a positive synergistic effect on the cure rate and mechanical properties of NBR compounds.

Studies on Xanthate/Dithiocarbamate Accelerator Combination in NR/BR Blends

Zinc butyl xanthate [Zn(bxt)2] was prepared in the laboratory . The effect of this xanthate with zinc diethyl dithiocarbamate (ZDC) on the vulcanization of natural rubber ( NR), polybutadiene rubber (BR), and NR/BR blend has been studied at different temperatures. The amounts of Zn (bxt)2 and ZDC in the compounds were optimized by varying the amount of ZDC from 0 . 75 to 1.5 phr and Zn (bxt)2 from 0 . 75 to 1 .5 phr. The cure characteristics were also studied . HAF filled NR, BR, and NR / BR blend compounds were cured at different temperatures from 60 to 150 C. The sheets were molded and properties such as tensile strength, tear strength, crosslink density and elongation at break, compression set, abrasion resistance, etc. were evaluated. The results show that the mechanical properties of 80NR/20BR blends are closer to that of NR vulcanizates, properties of 60NR/40BR blends are closer to BR vulcanizates, while the 70NR/30BR blends show an intermediate property.

Investigations on the Dynamics of Combination Maps and the Analysis of Bifurcation in a Discontinuous Logistic Map

This thesis is a study of discrete nonlinear systems represented by one dimensional mappings.As one dimensional interative maps represent Poincarre sections of higher dimensional flows,they offer a convenient means to understand the dynamical evolution of many physical systems.It highlighting the basic ideas of deterministic chaos.Qualitative and quantitative measures for the detection and characterization of chaos in nonlinear systems are discussed.Some simple mathematical models exhibiting chaos are presented.The bifurcation scenario and the possible routes to chaos are explained.It present the results of the numerical computational of the Lyapunov exponents (λ) of one dimensional maps.This thesis focuses on the results obtained by our investigations on combinations maps,scaling behaviour of the Lyapunov characteristic exponents of one dimensional maps and the nature of bifurcations in a discontinous logistic map.It gives a review of the major routes to chaos in dissipative systems,namely, Period-doubling ,Intermittency and Crises.This study gives a theoretical understanding of the route to chaos in discontinous systems.A detailed analysis of the dynamics of a discontinous logistic map is carried out, both analytically and numerically ,to understand the route it follows to chaos.The present analysis deals only with the case of the discontinuity parameter applied to the right half of the interval of mapping.A detailed analysis for the n –furcations of various periodicities can be made and a more general theory for the map with discontinuities applied at different positions can be on a similar footing

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year