A SCACE-UP STUDY OF NOZZLE TOWER SPRAY DRYERS

A quasi-one dimensional model taking into account hindered drying beyond the critical tmisture content and droplet size distribution is used to scale-up nozzle tower type of a spray dryer with uniformly distributed air entry, based on pilot plant data. A pilot plant with 8 m high and 1.7 m diameter spray dryer is used to collect the drying data. Equilibrium and critical mistwe contents of the particles are also experimentally msured. The index of drying rate in the hindered drying period is calculated frun the pilot plant data. This index is used in the M e 1 to determine the drying capacity and thereby the mixinnnn particle size that can be produced ina 3.5 m dimoeter 12 m high nozzle t- type of spray dryer. Experiments are carried out in this spray dryer to verify the calculated values of the spray drying capacity. It is s h m that the model when used along with the pilot plant data is adequate for scale-up calaculations for nozzle taer type of spray dryers.     

Characteristics of nitrogen doped diamond-like carbon thin films grown by microwave surface-wave plasma CVD

Nitrogen doped diamond-like carbon (DLC:N) thin films were deposited on p-type silicon (p-Si) and quartz substrates by microwave (MW) surface-wave plasma (SWP) chemical vapor deposition (CVD) at low temperature (< 100 °C). For films deposition, argon (Ar: 200 sccm), acetylene (C₂H₂:10 sccm) and nitrogen (N: 5 sccm) were used as carrier, source and doping gases respectively. DLC:N thin films were deposited at 1000 W microwave power where as gas composition pressures were ranged from 110 Pa to 50 Pa. Analytical methods such as X-ray photoelectron spectroscopy (XPS), UV-visible spectroscopy, FTIR and Raman spectroscopy were employed to investigate the chemical, optical and structural properties of the DLC:N films respectively. The lowest optical gap of the film was found to be 1.6 eV at 50 Pa gas composition pressure.     

Discrete software reliability growth modeling for errors of different severity incorporating change-point concept

Several software reliability growth models (SRGM) have been developed to monitor the reliability growth during the testing phase of software development.In most of the existing research available in the literatures,it is considered that a similar testing effort is required on each debugging effort.However,in practice,different types of faults may require different amounts of testing efforts for their detection and removal.Consequently,faults are classified into three categories on the basis of severity:simple,hard and complex.This categorization may be extended to (?) type of faults on the basis of severity.Although some existing research in the literatures has incorporated this concept that fault removal rate (FRR) is different for different types of faults,they assume that the FRR remains constant during the overall testing period.On the contrary,it has been observed that as testing progresses,FRR changes due to changing testing strategy,skill,environment and personnel resources.In this paper,a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept.Then,the models are formulated for two particular environments.The models were validated on two real-life data sets.The results show better fit and wider applicability of the proposed models as to different types of failure datasets.     

Industry 4.0 as an enabler of sustainability diffusion in supply chain: an analysis of influential strength of drivers in an emerging economy

Industry 4.0 (I4.0) and sustainability are recent buzzwords in manufacturing environments. However, the connection between these two concepts is less explored in the literature. In the current business context, the future generation of manufacturing systems is greatly influenced by the rapid advancement of information technology. Therefore, this study aims to examine the drivers of I4.0 to diffuse sustainability in Supply Chains (SCs). This research identifies the most relevant drivers through the literature and discusses them with area experts. Afterwards, an empirical analysis is conducted to validate the key drivers. Finally, the Grey based DEMATEL method is employed to examine the influential strength of the identified drivers and to build an interrelationship diagram. ‘Government supportive policies’ and ‘Collaboration and transparency among supply chain members’ were reported as highly significant drivers of I4.0. This study is an initial effort that investigates the key drivers of I4.0 to achieve high triple bottom line (ecological-economic-social) gains in SCs by taking an example from an emerging economy, i.e. India. This study may help managers, practitioners and policy makers interested in I4.0 applications to diffuse sustainability in SCs.     

Silicate Solubilization and Plant Growth Promoting Potential of Rhizobium Sp. Isolated from Rice Rhizosphere

Silicon - Silicon is a beneficial plant nutrient with a vital role in maintaining plant growth and enhancing tolerance to both biotic and abiotic stresses. The polymeric insoluble silica present in...     

Security Enhancement in Mobile Ad-Hoc Network Using Novel Data Integrity Based Hash Protection Process

Wireless Personal Communications - Nowadays, Mobile Ad hoc Networks (MANET) are utilized in numerous applications like disaster management, environmental monitoring, tactical operations, and so on....     

Recent Developments in Spatio-Temporal EEG Source Reconstruction Techniques

EEG is gaining recognition in the field of real-time applications. However, the EEG inverse problem leads to poor spatial resolution in brain source localization. This paper presents an overview of the existing EEG inverse solution methods. Further, a comparative analysis of recent techniques has been presented. This work discusses the challenges associated with the existing source reconstruction algorithms. The main focus is on the recent reports in this field that have combined the EEG denoising in the pre-processing phase along with the inverse localization approaches. Out of various existing techniques, SLORETA offers better localization results but its noise sensitivity is very high. It has been validated in a comparative analysis for simulated dipole sources with no noise. To illustrate the advantage of using pre-processed data with inverse localization, the classification accuracy of conventional methods has been compared. The accuracy has been analyzed for depression signals using the Naïve Bayes, RF, and SVM classifiers. The VMD- SLORETA method shows better accuracy as compared to EMD-SLORETA and SLORETA only. The existing EEG localization methods are efficient but the spatial resolution is still to be improved in the presence of various noise sources in raw EEG. More accurate localization is achieved by implementing denoising in combination with the source localization framework. There is a need to investigate further stages of EEG signal processing along with optimal feature selection. Further, additional studies should be conducted to improve the noise sensitivity of other existing localization systems using pre-processing approaches.

Graphic Abstract

     

Investigation on the effect of Fe impurity pickups during ball milling and Ni dispersion on the microwave absorption performance of ball milled Fe impurities-Ni/SiC composites

Journal of Materials Science: Materials in Electronics - The development of microwave absorbers to mitigate the problem of electromagnetic pollution has become a necessity in today’s...