Sarvajanik College of Engineering and Technology

About: Sarvajanik College of Engineering and Technology is a based out in . It is known for research contribution in the topics: Wireless sensor network & Deep learning. The organization has 336 authors who have published 404 publications receiving 4326 citations.

Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions

Abstract: Current-voltage and power-voltage characteristics of large photovoltaic (PV) arrays under partially shaded conditions are characterized by multiple steps and peaks. This makes the tracking of the actual maximum power point (MPP) [global peak (GP)] a difficult task. In addition, most of the existing schemes are unable to extract maximum power from the PV array under these conditions. This paper proposes a novel algorithm to track the global power peak under partially shaded conditions. The formulation of the algorithm is based on several critical observations made out of an extensive study of the PV characteristics and the behavior of the global and local peaks under partially shaded conditions. The proposed algorithm works in conjunction with a DC-DC converter to track the GP. In order to accelerate the tracking speed, a feedforward control scheme for operating the DC-DC converter is also proposed, which uses the reference voltage information from the tracking algorithm to shift the operation toward the MPP. The tracking time with this controller is about one-tenth as compared to a conventional controller. All the observations and conclusions, including simulation and experimental results, are presented.

A correlation for calculating HHV from proximate analysis of solid fuels

Abstract: Higher heating value (HHV) and composition of biomass, coal and other solid fuels, are important properties which define the energy content and determine the clean and efficient use of these fuels. There exists a variety of correlations for predicting HHV from ultimate analysis of fuels. However, the ultimate analysis requires very expensive equipments and highly trained analysts. The proximate analysis on the other hand only requires standard laboratory equipments and can be run by any competent scientist or engineer. A few number of correlations of HHV with proximate analysis have appeared in the solid fuel literature in the past but were focused on one fuel or dependent on the country of origin. This work introduces a general correlation, based on proximate analysis of solid fuels, to calculate HHV, using 450 data points and validated further for additional 100 data points. The entire spectrum of solid carbonaceous materials like coals, lignite, all types of biomass material, and char to residue-derived fuels have been considered in derivation of present correlation which is given as below: HHV=0.3536FC+0.1559VM−0.0078ASH (MJ/kg) (where FC 1.0–91.5% fixed carbon, VM 0.92–90.6% volatile matter and Ash 0.12–77.7% ash content in wt% on a dry basis). The average absolute error of this correlation is 3.74% and bias error is 0.12% with respect to the measured value of HHV, which is much less than that of previous correlations of the similar kind. The major advantage of this correlation is its capability to compute HHV of any fuel simply from its proximate analysis and thereby provides a useful tool for modeling of combustion, gasification and pyrolysis processes. It can also be used in examining old/new data for probable errors when results lie much outside the predicted results.

Aggregation behavior of quaternary salt based cationic surfactants

Abstract: The aggregation behavior of pure cationic surfactants (quaternary salts) in water has been studied by electrical conductivity (at 293.15–333.15 K), surface tension, dye solubilization and viscosity measurements (at 303.15 K). Critical micelle concentrations (CMCs), degree of counter ion dissociation (β), aggregation number and sphere-to-rod transition for cationic surfactants are reported. Using law of mass action model, the thermodynamic parameters, viz. Gibbs energy (�G ◦), enthalpy (�H ◦) and entropy (�S ◦ m) were evaluated. The plots of differential conductivity, (d k/dc)T,P , versus the total surfactant concentration enables us to determine the CMC values more precisely than the conventional method. Surfactants with longer hydrocarbon chain are adapted to rodlike micelle better than to a spherical micelle. The data are explained in terms of molecular characteristics of surfactants viz. nonpolar chain length, polar head group size and counter ion. © 2004 Published by Elsevier B.V.

Nanofuels: Combustion, engine performance and emissions

Abstract: Experimental investigation was carried out to study the burning characteristics, engine performance and emission parameters of a single-cylinder Compression Ignition (CI) engine using nanofuels which were formulated by sonicating nanoparticles of aluminum (A 1 ), iron (F 1 ) and boron (B 1 ) in base diesel These fuels showed reduced ignition delay, longer flame sustenance and agglomerate ignition Study of engine performance at higher loads revealed drop in peak cylinder pressures and reduction of 7% in specific fuel consumption for A 1 as compared to diesel Improved combustion rates raised exhaust gas temperatures by 8%, 7% and 5% leading to increased brake thermal efficiencies by 9%, 4%, and 2% for A 1 , F 1 , and B 1 respectively, as compared to diesel at maximum loading conditions Volumetric reduction of 25–40% in CO emission, 8% and 4% in hydrocarbon emission was measured when the engine was fueled with A 1 and F 1 respectively as compared to emissions from diesel However, elevated temperatures resulted into marginal rise in NO x emission

A correlation for calculating elemental composition from proximate analysis of biomass materials

Abstract: Elemental composition of biomass is an important property, which defines the energy content and determines the clean and efficient use of the biomass materials. However, the ultimate analysis requires very expensive equipments and highly trained analysts. The proximate analysis on the other hand only requires standard laboratory equipments and can be run by any competent scientist or engineer. This work introduces a general correlation, based on proximate analysis of biomass materials, to calculate elemental composition, derived using 200 data points and validated further for additional 50 data points. The entire spectrum of solid lignocellulosic materials have been considered in the derivation of the present correlation, which is given as: C = 0.637FC + 0.455VM, H = 0.052FC + 0.062VM, O = 0.304FC + 0.476VM, where FC – 4.7–38.4% fixed carbon, VM – 57.2–90.6% volatile matter, C – 36.2–53.1% carbon, H – 4.36–8.3% hydrogen and O – 31.37–49.5% oxygen in wt% on a dry basis. The average absolute error of these correlations are 3.21%, 4.79%, 3.4% and bias error of 0.21%, −0.15% and 0.49% with respect to measured values C, H and O, respectively. The major advantage of these correlations is their capability to compute elemental components of biomass materials from the simple proximate analysis and thereby provides a useful tool for the modeling of combustion, gasification and pyrolysis processes.