A Process Integration based Approach for the Analysis of an Evaporator System

A scalable new mathematical model based on the principles of Process Integration has been developed for the analysis of multiple effect evaporator (MEE) systems. It uses the concepts of stream analysis, temperature path and internal heat exchange for the formulation of the model equations. In addition to the above concepts, the model also takes into account the variable physico‐thermal properties of steam/vapor, condensate and liquor, while simulating the MEE system. The present model consists of a set of linear equations and does not present any stability or oscillation problems during solution as is generally seen in the case of models that are based on sets of nonlinear equations. The model equations are automatically generated through a computer program and the model was run for three different liquor and flow sequences to prove its utility. The results obtained are compared with published models.     

Preparation of Sn–3.5Ag nano-solder by supernatant process

The nano-scaled Sn–3.5Ag solder was prepared successfully by a supernatant process in the present study. The morphology of the nano-particle was investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), respectively. It was found from the SEM micrographs that the average diameter of the particle was 137 nm with a standard deviation of ±5 nm. From the TEM studies it was revealed that the particles aggregated into larger particles and the shape of the elongated particles were irregular. The composition of the alloy was also measured by an electron probe microanalyzer (EPMA) and energy dispersive spectroscopy (EDS), qualitatively and quantitatively. The eutectic element, Ag with a weight percentage of 3.5%, was found to be homogenous over all of the particles. Furthermore, the microstructure of the Sn–3.5Ag alloy was identified by X-ray diffraction. It was found that the trace element, Ag was dissolved in the matrix, a tetragonal system, without an intermetallic phase.     

Product selection in Internet business: a fuzzy approach

In this paper, we propose a methodology which helps customers buy products through the Internet. This procedure takes into account the customer's level of desire in the product attributes, which are normally fuzzy, or in linguistically defined terms. The concept of fuzzy number will be used to measure the degree of similarities of the available products to that of the customer's requirements. The degrees of similarities so obtained over all the attributes give rise to the fuzzy probabilities and hence the fuzzy expected values of availing a product on the Internet as per the customer's requirement. Attribute‐wise the fuzzy expected values are compared with those of the available products on the Internet and the product that is closest to the customer's preference is selected as the best product. The multi‐attribute weighted average method is used here to evaluate and hence to select the best product.     

Distribution of mineral species in different coal seams of Talcher coalfield and its transformation behavior at varying temperatures

International Journal of Coal Science & Technology - Mineral phase characterization and thorough understanding of its transformation behavior during combustion are imperative to know the...     

Retracted: Mechanical and thermal properties enhancement of polycarbonate nanocomposites prepared by melt compounding

The following article from the Journal of Applied Polymer Science, “Mechanical and thermal properties enhancement of polycarbonate nanocomposites prepared by melt compounding” by Sanjay K. Nayak, Smita Mohanty, and Sushanta K. Samal, published online on 7 April 2010 in Wiley Online Library (J. Appl. Polym. Sci. 2010 , 117, 2101; http://onlinelibrary.wiley.com/doi/10.1002/app.31222/full ), has been retracted by agreement between the authors, the journal's editors, and Wiley Periodicals, Inc. The retraction has been agreed due to significant overlap with respect to another article, “Effect of hydrogen bonding on the rheology of polycarbonate/organoclay nanocomposites,” by Kyung Min Lee, and Chang Dae Han, published online on 19 June 2003 in Polymer ( 2003 , 44, 4573).     

Experimental studies and empirical models for the prediction of bed expansion in gas–solid tapered fluidized beds

Studies in the expansion behaviour of tapered fluidized bed systems are important for specifying the height of the bed. Data have been obtained on the expanded heights of tapered fluidized beds and bed expansion ratios for spherical and non-spherical particles have been calculated. Based on dimensional analysis, models have been developed as a function of geometry of tapered bed, static bed height, particle diameter, density of solid and gas and superficial velocity of the fluidizing medium. The data used to derive the models cover a wide range of operating conditions, with varying fluidization velocities. Effects of static bed height, particle diameter, density, tapered angle and superficial gas velocity over minimum fluidization velocity on bed expansion ratios have been investigated experimentally. A comparison has been made between the calculated values of bed expansion ratios using proposed models and the experimental data. It has been seen that calculated values by models agree well with the experimental values. Models have also been compared with literature data of conventional bed and found its applicability at higher gas velocities with good accuracy.     

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.     

High Thermal Conductivity Polymer Composites Fabrication through Conventional and 3D Printing Processes: State-of-the-Art and Future Trends

The lifespan and the performance of flexible electronic devices and components are affected by the large accumulation of heat, and this problem must be addressed by thermally conductive polymer composite films. Therefore, the need for the development of high thermal conductivity nanocomposites has a strong role in various applications. In this article, the effect of different particle reinforcements such as single and hybrid form, coated and uncoated particles, and chemically treated particles on the thermal conductivity of various polymers are reviewed and the mechanism behind the improvement of the required properties are discussed. Furthermore, the role of manufacturing processes such as injection molding, compression molding, and 3D printing techniques in the production of high thermal conductivity polymer composites is detailed. Finally, the potential for future research is discussed, which can help researchers to work on the thermal properties enhancement for polymeric materials.