Samir K. Pal

Wear behavior of silica filled tire tread compounds by various rock surfaces

Abstract Wear of silica filled tire tread compounds made from natural rubber (NR) and styrene butadiene rubber (SBR) against various rock surfaces at different normal loads and frictional work was investigated. The rocks chosen, i.e. granite, quartzite, limestone and shale, had variation in hardness and chemical composition. Wear, V, increased with normal load, N and frictional work, Fw, and these were related by V=βNα and V=k Fw n, where β,α,k and n were constants and dependent on the nature of the abraded surface and the rubber. The rate of wear of SBR was found to be much lower than that of NR against all rock surfaces. All the results could be explained with the help of dynamic co-efficient of friction, frictional work and structural changes during abrasion. The rate of wear as well as the dynamic coefficient of friction decreased with silica filler loading up to certain loading and then increased. These were 60 phr silica for NR and 70 phr silica for SBR. The surfaces of NR and SBR when abraded again...

Ore grade estimation of a limestone deposit in India using an Artificial Neural Network

This study describes a method used to improve ore grade estimation in a limestone deposit in India. Ore grade estimation for the limestone deposit was complicated by the complex lithological structure of the deposit. The erratic nature of the deposit and the unavailability of adequate samples for each of the lithogical units made standard geostatistical methods of capturing the spatial variation of the deposit inadequate. This paper describes an attempt to improve the ore grade estimation through the use of a feed forward neural network (NN) model. The NN model incorporated the spatial location as well as the lithological information for modeling of the ore body. The network was made up of three layers: an input, an output and a hidden layer. The input layer consisted of three spatial coordinates (x, y and z) and nine lithotypes. The output layer comprised all the grade attributes of limestone ore including silica (SiO2), alumina (Al2O3), calcium oxide (CaO) and ferrous oxide (Fe2O3). To justify the use of the NN in the deposit, a comparative evaluation between the NN method and the ordinary kriging was performed. This evaluation demonstrated that the NN model decisively outperformed the kriging model. After the superiority of the NN model had been established, it was used to predict the grades at an unknown grid location. Prior to constructing the grade maps, lithological maps of the deposit at the unknown grid were prepared. These lithological maps were generated using indicator kriging. The authors conclude by suggesting that the method described in this paper could be used for grade-control planning in ore deposits.

Rock-type classification of an iron ore deposit using digital image analysis technique

In this paper, the rock types of an iron ore deposit were classified using the digital image analysis technique. The image acquisition and analysis of blasted rocks were conducted in a laboratory for six different rock types. A total of 189 features were extracted from the individual rock samples using best-suited segmentation technique selected by validation study. The neural network technique was applied for rock classification model using image features. Five principal components, which accounts for 95% of total data variance, were selected as input parameters for the model. The misclassification error of the model for testing data was 2.4%.

General regression neural network residual estimation for ore grade prediction of limestone deposit

Abstract The aim of the present paper is to provide an improved estimator for the ore grade prediction of a limestone deposit in India. A generalised modelling framework with the help of general regression neural network and the ordinary kriging was formulated to capture the spatial variability of the deposit. In this platform, spatial variability of the deposit is assumed to be characterised by three major components: spatial trend component, regionalised component, and purely random component. The general regression neural network (GRNN) model was used to capture the spatial trend component, and the ordinary kriging technique was implemented to capture the regionalised component. The GRNN model was developed using the spatial coordinates (Northing, Easting and Elevation) as the input parameters and the grade attributes (CaO, Al2O3, Fe2O3 and SiO2) as the output parameters. The performance of the GRNN residual kriging model was tested using a testing data set, and the outputs of this model were compared with the outputs of the GRNN model, and the ordinary kriging. The comparative results show that the GRNN residual kriging model provided significant improvement over the ordinary kriging, however, it only shows a marginal improvement over the GRNN model.

Effect of Filler and Urethane Rubber on NR/BR with Nanosilica: Morphology and Wear

The blends of butadiene rubber (BR), urethane rubber (EU) and natural rubber (NR) was prepared using a blending technique in presence of different types of carbon black. The effect of filler on morphological and wear characteristics was studied. ISAF type of carbon black showed a significant effect on optimum cure time, cure rate index and mechanical properties by reacting at the interface between BR, EU and NR matrix. All the samples show only one melting peak on the DSC curve; this is attributed to the same backbone structure of the matrix and the carbon black reinforcement. Blends containing ISAF type of carbon black showed high abrasion resistant properties against Du-Pont abrader, DIN abrader and different mining rock surfaces and also is found to be the toughest rubber against all types of rock.

Effect of fillers on morphological properties in NR/SBR blends for OTR tyres

Abstract The blends of styrene butadiene rubber (SBR) and natural rubber (NR) are prepared using a two-roll mixing mill in the presence of different types of carbon blacks as reinforcing filler. The effects of fillers on cure characteristics and thermal, dynamic–mechanical, morphological properties of the blends are studied. The ISAF N231 type of carbon black shows a significant effect on tensile, tear and modulus properties by reacting at the interface between SBR/NR matrixes. The dynamic characteristics and storage modulus of SBR/NR with SAF N110 and SRF N774 types of carbon black show distinct characteristics in respect to all other blends in this system. The thermal stability of the rubber vulcanizates containing SAF N110 and SRF N774 types of carbon blacks is higher than other blend types. With the increasing percentage of SBR to NR, the thermal stability of the blend is increased. However, the heat buildup of the blends increases with the increase in SBR percentage.

Elastomeric Nanocomposites for Tyre Applications

In this study the epoxidized natural rubber (ENR) and organoclay (Cloisite 20A) composites were prepared by solution mixing process. The obtained nanocomposites were incorporated in natural rubber (NR) and styrene butadiene rubber (SBR) blends in presence of varying types of carbon black as reinforcing fillers. Morphology, curing characteristics, mechanical and thermal properties were characterized and analyzed. Also, the wear characteristics of the nanocomposites against Du-Pont and DIN abrader were determined and discussed. The morphology of the organoclay incorporated in ENR shows a highly intercalated structure. ISAF type of carbon black shows a significant effect on curing and mechanical properties by reacting at the interface between SBR and NR matrix. Blends containing ISAF N234 type of carbon black shows high abrasion resistant properties against Du-Pont and DIN abrader.

Organic/Silica Nanocomposite Membranes

Nanoscience and nanotechnology has become a versatile and promising subject for producing new materials with enhanced properties and potential applications. In this regard, nanoparticles (NPs) have received growing attention in every sector of science and technology. The size, shape, structure, and chemical properties of engineered NPs open a vast range of technical applications and novel approaches in basic research science. Among the variety of NPs, silica NP is of a particular interest due to its ease of synthesis, functionalization, and precise controlling of size and distribution of particles. Superior features of polymeric membranes especially flexibility and processability have made them one of the best candidates for commercial applications. Meanwhile, despite of the outstanding characteristics of these membranes, their application is still limited due to the trade-off trend between gas permeability and selectivity.

Organic/Montmorillonite Nanocomposite Membranes

In this chapter, organic/montmorillonite nanocomposite membrane and membrane fabrication techniques are discussed. The fabrication technique, properties of the fabricated membranes, and performance are explained in detail and compared. With the addition of clay addition, important parameters which affect the membrane performance, such as crystallinity, porous structure, hydrophobicity/hydrophilicity, membrane charge, and surface roughness were analyzed. Despite the fact that extensive knowledge exist on membrane pore structure after clay incorporation including its surface properties and cross-section morphology by selection of appropriate fabrication methods, there is still a challenge to produce reliable membranes with antifouling properties, thermal resistance, chemical resistance, high-mechanical strength with high flux and selectivity. To ensure progress in polymer–clay membrane performance, further improvements are needed for common membrane fabrication techniques, such as solution casting, phase inversion, and interfacial polymerization. At the same time, the potential of novel fabrication techniques such as electro spinning and track-etching are also assessed. A comprehensive understanding between structure-surface properties and performance is a key for further development and progress in organic/montmorillonite membrane technology.

Development of calcium titanium oxide coated silicon solar cells for enhanced voltage generation capacity

Abstract Depletion of fossil fuel based energy sources drive the present scenario towards development of solar based alternative energy. Polycrystalline silicon solar cells are preferred due to low cost and abundant availability. However, the power conversion efficiency of polycrystalline silicon is lesser compared to monocrystalline one. The present study aims at analyzing the effect of calcium titanium oxide (CaTiO3) antireflection (AR) coating on the power conversion of polycrystalline solar cells. CaTiO3 offers unique characteristics, such as non-radioactive and non-magnetic orthorhombic biaxial structure with bulk density of 3.91 g/cm3. CaTiO3 film deposition on the solar cell substrate has been carried out using Radio Frequency (RF) magnetron sputter coating technique under varying time durations (10 min to 45 min). Morphological studies proved the formation of CaTiO3 layer and respective elemental percentages on the coated substrate. Open circuit voltage studies were conducted on bare and coated silicon solar substrates under open and controlled atmospheric conditions. CaTiO3 coated on a solar cell substrate in a deposition time of 30 min showed 8.76 % improvement in the cell voltage compared to the bare solar cell.