M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/42
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item A Data-Driven Approach for Design and Development of Batch Process Automation System of a Plant(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muhammad Subiyyal Fayyaz; SP20-REE-001; Dr. Aamer Bilal Asghar; LHR TP 8070Industrial automation processes make use of control systems to operate and observe machines as well as processes. Automation in industrial workplace gives advantages of enhancing quality and productivity whereas minimizing errors, improving safety, and adding flexibility to manufacturing process. The aim of this work is to implement the simulation-based data driven control automation in which I have to design, develop and automate the complete plant through Programmable Logic controllers (PLC). Keeping this in view, I have designed the redundant controller architecture by using Device Level Ring (DLR) topology; a cost-effective control solution in which the single point of failure on networking and SCADA/HMI layer will be minimized. V-Cycle process model will be followed during detail design and development phase of the project till complete simulation. Control Logix soft computing technique used for logic development. I have developed complete simulated data driven control project in which I have utilized different soft computing techniques and software’s for industrial automation including RS-Logix5000/Studio 5000 for logic development, RSLinx for communication between PLC controllers and SCADA/HMI layer are used. Factory talk View Studio will be used for graphics development. Also, I have used the soft computing techniques for reporting purpose. SQL Database management console utility have utilized for reporting DB. For simulation activity, I have used RS-Simulator. Simulated data driven automated SCADA/HMI system are ensure to meet the performance parameters i.e. increase productivity, increase efficiency, reduce variability and ensure safety. Major parameters are showed in form of Bar-Trends. Health Statuses of control hardware and communication diagnostics are very helpful for system health checks and monitoring in diagnostic HMI.Item Module level power electronics in distrubted power system for solar PV application(COMSATS University Islamabad Lahore Campus, 0022) Muhammad Talha Naveed; , SP20-REE-015; Dr. Muhammad Yaqoob Javed; LHR TP 7895hotovoltaic (PV) solar energy is as promising as other renewable energies. Different researchers and engineers are attempting to increase the efficiency of solar PV system. As a result, for PV modules, this enhancement may be accomplished at almost the same level. As is well known, solar PV systems are less efficiency as a result of changing climatic conditions. Module-level power electronics (MLPE) do this by providing the performance improvements of a distributed transmission system in both partial and full shading conditions. As a result, MLPE successfully harvests the distributed maximum power point (DMPP) from solar to accept DC from PV or the grid. Each PV module is connected to the power system via a separate dc/dc converter with Maximum Power Point Tracking (MPPT) capabilities in the DMPP scheme. Each PV panel has a built in power optimization or micro-inverter that helps it work better in partial shade. In order to design the MLPE the efficiency of contemporary string inverters are comparing. The efficiency may be measured in a variety of situations, including uniform irradiation and partial shade. As a result, a DC-DC converter that is attached to each PV module is required to offset shading losses. The optimizer identifies the Local peak using a DC-DC converter from the unit, shuts down the modules during fire situations, troubleshoots, and monitoring a module in a highly efficient manner in this work. On the Matlab software tool, the effectiveness of the proposed power optimizer is displayed. To calculate efficiency, several firms such as SMA, Solar Edge, Huawei, Tigo, and Enphase can use helioscope to compare production and loses data. The results suggest that MLPE produces superior outcomes.Item Enhancing Security and privacy of machine learning based outdoor Air pollution prediction system using IOTA B(COMSATS University Islamabad Lahore Campus, 2020) Muneeba Malik,; SP20-REE-004; Dr. Abbas Javed Assistant Professor; LHR TP 7893Internet of Things (IoT) is a combination of devices, which are network-enabled and work for one shared cause. IoT has transformed several fields and continue to do so, for instance, agriculture, smart housing, development and planning, security systems, and communication networks. The security of IoT networks and devices remains compromised and has not been particularly worked upon. It poses many threats and challenges like jamming networks, insecure physical interface, sleep-deprivation attack and high-level attacks like Sybil attacks, authentication and communication, buffer reservation attacks. To deal with the issue of security, Blockchain Technology have been using advanced security algorithms for hashing and different resources like miners to compute the proof of work. It implements centralized consensus to verify and add more block to the chain acting as an authentication scheme. Over the course of last decade, the decentralized applications and their performance have been under observation while work on them is continuously in progress. Distributed Ledger Technology (DLT) has emerged as an advanced system to record and confirm transactions and authenticate the network to add another node to it. DLT have been used to verify, secure, improve the data that is produced and is sent over any network in its domain. As IoT continues to make progress, it continues to encounter verification challenges. To counter one these issues, a DLT called IOTA Tangle has exclusively been designed for IoT – machine to machine communication. This project aims to work on enhancing security and privacy concerns of machine learning based air pollution prediction system using IOTA Tangle as its resource. IOTA light node and full node will be developed to upload the sensor data securely on Tangle. In this work, performance of the network will be evaluated in terms of end-to-end node delay, power consumption and accuracy of air quality index (AQI).