Puducherry, India

Pondicherry Engineering College is an engineering institution situated in South India on East Coast Road in Pondicherry, Puducherry on the exact border of Tamil Nadu and Pondicherry.The college is an institution sponsored by the Union Territory of Puducherry. The college was started in 1984 under the VII Five Year Plan. It is an autonomous institution for the purposes of administration, staff recruitment and College development and is managed by a Board of Governors. The college is affiliated to the Pondicherry University for academic purposes. The institute offers eight Undergraduate, eight Postgraduate and PhD programme in the disciplines of engineering and technology.It is the only college which offers off shore structure course in civil engineering.This college is the best college in Pondicherry owing to its placement results.It was ranked the 9th best college in south India. Wikipedia.

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Senthilvelan T.,Pondicherry Engineering College
Engineering Failure Analysis | Year: 2013

Sugar industry plays an important role in economic development of country. Cogeneration is an important source of income for sugar industries. Boiler is one of the essential components used in cogeneration process. Unscheduled boiler outages in sugar mills are major problem resulting loss of production. The boiler may be failed due to number of reasons; some of the reasons such as mechanical failure, electrical failure and temperature sensors failure. This paper describes the failures of the fuel feeding system frequently occurred in the cogeneration boiler and gives the solution to rectify these failures by using three important tools, namely, cause and effect diagram, Failure Mode and Effect Analysis and Taguchi method. © 2012 Elsevier Ltd.

Ananthi Christy A.,SRM University | Vimal Raj P.A.D.,Pondicherry Engineering College
International Journal of Electrical Power and Energy Systems | Year: 2014

This article presents a new approach based on a hybrid algorithm consisting of biogeography based optimization (BBO) with an adaptive mutation scheme and the concept of predator-prey optimization technique for solving the multi-objective optimal power flow problems. The adaptive mutation scheme, based on distance-to-average point diversity measure, avoids the dominance of highly probable solutions through increasing the population diversity. The predators search around the best prey in a concentrated manner, while the preys explore the solution space so as to stay away from the predators. These mechanisms enhance the exploitation and exploration capabilities of the BBO search process, provide a mean of escaping from the suboptimal solutions and force the population to arrive at the global best solution. The proposed method is tested on IEEE 30 bus test system with different objectives that reflect fuel cost minimization, loss reduction, voltage profile improvement and voltage stability enhancement. The comparison of results with those of the existing approaches illustrates the effectiveness and robustness of the suggested method. © 2014 Elsevier Ltd. All rights reserved.

Suresh K.,Pondicherry Engineering College | Suresh K.,Annamalai University | Kumarappan N.,Pondicherry Engineering College | Kumarappan N.,Annamalai University
Swarm and Evolutionary Computation | Year: 2013

This paper presents a model for maintenance scheduling (MS) of generators using hybrid improved binary particle swarm optimization (IBPSO) based coordinated deterministic and stochastic approach. The objective function of this paper is to reduce the loss of load probability (LOLP) and minimizing the annual supply reserve ratio deviation for a power system which are considered as a measure of power system reliability. Genetic algorithm (GA) operators are introduced in the IBPSO to acquire diversified solutions in the search space. Moreover, in this paper, the hybrid IBPSO based economic dispatch (ED) has been decomposed as a sub-problem in the maintenance model that results to a more practical maintenance schedule. A case study for the real power system model in Odisha (India) is considered. Comprehensive studies have also been carried out for the different power system consisting of 5-unit system, 21-unit system and IEEE reliability test system (RTS). It shows that the proposed algorithm can accomplish a significant levelization in the reliability indices over the planning horizon for reliable operation of the power system and demonstrates the usefulness of the proposed approach. The proposed method yields better result by means of improved search performance and better convergence characteristics which are compared to the other optimization methods and conventional method. © 2012 Elsevier B.V. All rights reserved.

Gopalakannan S.,Adhiparasakthi Engineering College | Senthilvelan T.,Pondicherry Engineering College
Measurement: Journal of the International Measurement Confederation | Year: 2013

The newly fabricated metal matrix nano-composite (MMNC) of Al 7075 reinforced with 1.5 wt% SiC nano-particles was prepared by a novel ultrasonic cavitation method. The high resolution scanning electron micrograph (SEM) and field emission scanning electron micrograph (FESEM) shows uniform distribution and good dispersion of the SiC nanoparticles within the aluminum metal matrix. Electrical discharge machining (EDM) was employed to machine MMNC with copper electrode by adopting face centered central composite design of response surface methodology. Analysis of variance was applied to investigate the influence of process parameters and their interactions. Further a mathematical model has been formulated in order to estimate the machining characteristics. It has been observed that pulse current was found to be the most important factor affecting all the three output parameters such as material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR). The optimum parameter of combination setting has been identified for the MMNC are voltage 50.00 V, pulse current 8.00 A, Pulse on time 8.00 μs and pulse off time 9.00 μs. Finally the parameters were optimized for maximizing MRR, minimizing EWR and SR using desirability function approach. © 2013 Elsevier Ltd. All rights reserved.

Radjaram B.,Pondicherry Engineering College | Saravanane R.,Pondicherry Engineering College
Bioresource Technology | Year: 2011

Anaerobic co-digestion of press mud with water or sewage at ratios of 1:7.5, 1:10 and 1:12.5 were performed in continuously fed UASB reactors for hydrogen production. At a constant hydraulic retention time of 30 h, the specific hydrogen production rate was 187 mL/g volatile solids (VS) reduced during maximum biohydrogen production of 7960 mL/day at a 1:10 ratio of press mud to sewage. Chemical oxygen demand (COD) and VS reductions of 61% and 59% were noted on peak biohydrogen yield. A pH range of 5-6 was suitable at ambient temperature for entire process; a lower pH was inhibitory. Co-digestion of acidic press mud with sewage controlled pH for fermentation. Hence press mud can be exploited for biohydrogen production. © 2010 Elsevier Ltd.

Asir Rajan C.C.,Pondicherry Engineering College
International Journal of Electrical Power and Energy Systems | Year: 2011

This paper presents a new approach to solve the hydro-thermal unit commitment problem using Simulated Annealing embedded Evolutionary Programming approach. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A utility power system with 11 generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consist of 25, 44 and 65 units. Numerical results are shown comparing the cost solutions and computation time obtained by conventional methods. © 2011 Elsevier Ltd. All rights reserved.

Saravanan L.,Pondicherry Engineering College | Senthilvelan T.,Pondicherry Engineering College
Materials and Design | Year: 2015

Hot deformation behaviour analysis of an extruded aluminium alloy-Al2O3 nanocomposite is presented in this paper. The cast-extruded nanocomposite specimens were subjected to hot compression test in the temperature range of 573-773K and in the strain rate range of 0.001-0.1s-1 up to a strain of 0.5. The constitutive equations relating the strain rate and flow stress were developed for the fabricated nanocomposite. The average activation energy for the aluminium nanocomposite is found to be 307.23kJ/mol, which is significantly larger than the 196kJ/mol value of the corresponding matrix alloy. The processing map was developed and the kinetic analysis was carried out to arrive at the optimum operating conditions. The optimum hot working zone is found to be the temperature region of 620-740K with strain rate of 0.01-0.1s-1. In the case of Al-Al2O3 nanocomposite, the safe region obtained is significantly larger in size compared to that of matrix alloy. When the strain was increased up to 0.7 in the instability zone, surface cracking was observed. Microstructure analysis had also been done to verify and validate the deformation mechanisms. © 2015 Elsevier Ltd.

Shunmugapriya P.,Pondicherry Engineering College | Kanmani S.,Pondicherry Engineering College
Swarm and Evolutionary Computation | Year: 2013

A Classifier Ensemble combines a finite number of classifiers of same kind or different, trained simultaneously for a common classification task. The Ensemble efficiently improves the generalization ability of the classifier compared to a single classifier. Stacking is one of the most influential ensemble techniques that applies a two level structure of classification namely the base classifiers level and the meta-classifier level. Finding suitable configuration of base level classifiers and the meta-level classifier is always a tedious task and it is domain specific. The Artificial Bee Colony (ABC) Algorithm is a relatively new and popular meta-heuristic search algorithm proved to be successful in solving optimization problems. In this work, we propose the construction of two types of stacking using ABC algorithm: ABC-Stacking1 and ABC-Stacking2. The proposed ABC based stacking is tested using 10 benchmark datasets. The results show that the ABC-Stacking yields promising results and is most useful in selecting the optimal base classifiers configuration and the meta-classifier. © 2013 Elsevier B.V.

Arounassalame M.,Pondicherry Engineering College
International Journal of Automation and Computing | Year: 2012

In electrical circuit analysis, it is often necessary to find the set of all direct current (d. c.) operating points (either voltages or currents) of nonlinear circuits. In general, these nonlinear equations are often represented as polynomial systems. In this paper, we address the problem of finding the solutions of nonlinear electrical circuits, which are modeled as systems of n polynomial equations contained in an n-dimensional box. Branch and Bound algorithms based on interval methods can give guaranteed enclosures for the solution. However, because of repeated evaluations of the function values, these methods tend to become slower. Branch and Bound algorithm based on Bernstein coefficients can be used to solve the systems of polynomial equations. This avoids the repeated evaluation of function values, but maintains more or less the same number of iterations as that of interval branch and bound methods. We propose an algorithm for obtaining the solution of polynomial systems, which includes a pruning step using Bernstein Krawczyk operator and a Bernstein Coefficient Contraction algorithm to obtain Bernstein coefficients of the new domain. We solved three circuit analysis problems using our proposed algorithm. We compared the performance of our proposed algorithm with INTLAB based solver and found that our proposed algorithm is more efficient and fast. © 2012 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

Christober Asir Rajan C.,Pondicherry Engineering College
International Journal of Electrical Power and Energy Systems | Year: 2010

This paper develops a new approach for solving the Economic Load Dispatch (ELD) using an integrated algorithm based on Evolutionary Programming (EP) and Simulated Annealing (SA) on large scale power system. Classical methods employed for solving Economic Load Dispatch are calculus-based. For generator units having quadratic fuel cost functions, the classical techniques ignore or flatten out the portions of the incremental fuel cost curves and so may be have difficulties in the determination of the global optimum solution for non-differentiable fuel cost functions. To overcome these problems, the intelligent techniques, namely, Evolutionary Programming and Simulated Annealing are employed. The above said optimization techniques are capable of determining the global or near global optimum dispatch solutions. The validity and effectiveness of the proposed integrated algorithm has been tested with 66-bus Indian utility system, IEEE 5-bus, 30-bus, 118-bus system. And the test results are compared with the results obtained from other methods. Numerical results show that the proposed integrated algorithm can provide accurate solutions within reasonable time for any type of fuel cost functions. © 2009 Elsevier Ltd. All rights reserved.

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