PSG College of Technology

About: PSG College of Technology is a based out in . It is known for research contribution in the topics: Machining & Thin film. The organization has 3174 authors who have published 3575 publications receiving 40690 citations.

Pretreatment of lignocellulosic sugarcane leaves and tops for bioethanol production

Abstract: Energy has been an inseparable part in all the sectors of the economy mainly for industries and transportation. On the other hand, there is a drastic reduction in the nonrenewable fossil fuels, which are the major contributors for energy generation. Undoubtedly, the world should seek an alternative renewable resource to replace the depleting fossil fuels. Bioethanol is one of the most effective replacements for the conventional petrol. Meanwhile, the production of bioethanol should not affect the food supply. One of the best solutions to overcome all these problems is to produce bioethanol from lignocellulosic biomass. Among the available resources, sugarcane tops (SCT) are becoming a promising feedstock for bioethanol production. However, the presence of lignin in the feedstock will affect the ethanol production, and an efficient pretreatment is mandatory for maximum lignin removal. This chapter summarizes different physical, chemical, biological, and hybrid pretreatment methods and hydrolysis techniques employed for SCT conversion.

Different Topologies of Inverter: A Literature Survey

Abstract: DC to AC control change is a key job in the cutting edge set up of age, transmission, appropriation, and use. DC to AC control converters assume key job in variable recurrence drives, uninterruptible power supplies, cooling, and high-voltage DC control transmission, electric vehicle drives, and static VAR compensators. This paper exhibits a survey on most significant topologies and strategies of control of inverters.

Task Scheduling in Distributed Systems Using Heap Intelligent Discrete Particle Swarm Optimization

Abstract: The optimal mapping of tasks to the processors is one of the challenging issues in heterogeneous computing systems. This article presents a task scheduling problem in distributed systems using discrete particle swarm optimization (DPSO) algorithm with various neighborhood topologies. The DPSO is a recent metaheuristic population-based algorithm. In DPSO, the set of particles in a swarm flies through the N-dimensional search space by learning from both the personal best position and a neighborhood best position. Each particle inside the swarm belongs to a specific topology for communicating with neighboring particles in the swarm. The neighborhood topology affects the performance of DPSO significantly, because it determines the rate at which information transmits through the swarm. The proposed DPSO algorithm works on dynamic topology that is binary heap tree for communication between the particles in the swarm. The performance of the proposed topology is compared with other topologies such as star, ring, fully connected, binary tree, and Von Neumann. The three well-known performance measures such as Makespan, mean flow time, and reliability cost are used for the comparison of the proposed topology with other neighborhood topologies. Computational simulation results indicate that the performance of DPSO algorithm has shown significant improvement with binary heap tree topology used for communication among the particles in the swarm.

Nested particle swarm optimisation for multi-depot vehicle routing problem

Abstract: Vehicle routing problem (VRP) is a well-known non-deterministic polynomial hard problem in operations research. VRP is more suited for applications having one warehouse. A variant of VRP called as multi-depot vehicle routing problem (MDVRP) has more than one warehouse. Cluster first and route second is the methodology used for solving MDVRP. An improved k-means algorithm is proposed for clustering that reduces the MDVRP to multiple VRP. In this work, MDVRP is considered with more than one objective and nested particle swarm optimisation with genetic operators is proposed for solving each VRP. Master particle swarm optimisation forms the group within each cluster. Slave particle swarm optimisation generates the route for each group. The objective of MDVRP is to minimise the total travel length along with route and load balance among the depots and vehicles. The results obtained are better in balancing load, route length and the number of vehicles, rather than minimisation of total cost.