Simulation of waste heat recovery system with fuzzy based evaporator model

The organic Rankine cycle (ORC) is one of the promising waste heat recovery (WHR) technologies used to improve the thermal efficiency, reduce the emissions and save the fuel costs of internal combustion engines. In the ORCWHR system, the evaporator is considered to be the most critical component as the heat transfer of this device influences the efficiency of the system. Although the conventional Finite Volume (FV) model can successfully capture the complex heat transfer process in the evaporator, the computation time for this model is high as it consists of many iterative loops. To reduce the computation time, a new evaporator model using the fuzzy inference technique is developed in this research. The developed fuzzy based model can predict the evaporator outputs with an accuracy of over 90% while it reduces the simulation time significantly. This model is then integrated with other components of the ORC to simulate a completed ORC-WHR system for internal combustion engines. The influence of operating parameters on the performance of the WHR system is investigated in this paper

Fault Tolerance Enhance DC-DC Converter Lifetime Extension

One of the most crucial renewable energy sources today is solar energy. Power convertors play an important role in adjusting the output voltage or current of photovoltaic (PV) systems. Using efficient and reliable switches for power converters and inverters is crucial for enhancing the safety and reliability of a platform. Generally, power converters suffer from failure mechanisms, such as wire bond fatigue, wire bond lift up, solder fatigue and loose gate control voltage, which mainly occur in power switches. In this paper, the junction temperature of the Insulated Gate Bipolar Transistor (IGBT) acting as a power switch used in the Impedance-Source DC-DC converter is estimated using an electro-thermal model in order to develop an adaptive thermal stress control (ATSC). The proposed stress control adjusts reference input of the PI control to extend the life expectancy of the device under the mission. The accuracy of results present using The Modified Coffin-Manson Law has been used to determine the life of IGBT and the lifetime has been successfully increased base on implementing imperative ATSC and comparing the result with the constant reference input of the PI controller. The result integrates with converter health management to develop advanced intelligent predictive maintenance

Real-time vision-based multiple object tracking of a production process : industrial digital twin case study

The adoption of Industry 4.0 technologies within the manufacturing and process industries is widely accepted to have benefits for production cycles, increase system flexibility and give production managers more options on the production line through reconfigurable systems. A key enabler in Industry 4.0 technology is the rise in Cyber-Physical Systems (CPS) and Digital Twins (DTs). Both technologies connect the physical to the cyber world in order to generate smart manufacturing capabilities. State of the art research accurately describes the frameworks, challenges and advantages surrounding these technologies but fails to deliver on testbeds and case studies that can be used for development and validation. This research demonstrates a novel proof of concept Industry 4.0 production system which lays the foundations for future research in DT technologies, process optimisation and manufacturing data analytics. Using a connected system of commercial off-the-shelf cameras to retrofit a standard programmable logic controlled production process, a digital simulation is updated in real time to create the DT. The system can identify and accurately track the product through the production cycle whilst updating the DT in real-time. The implemented system is a lightweight, low cost, customable and scalable design solution which provides a testbed for practical Industry 4.0 research both for academic and industrial research purposes

Digital twin in manufacturing : conceptual framework and case studies

The digital twin (DT) concept has a key role in the future of the smart manufacturing industry. This review paper aims to investigate the development of the digital twin concept, its maturity and its vital role in the fourth industrial revolution. Having identified its potential functionalities for the digitalisation of the manufacturing industry, the digital twin concept, its origin and perspectives from both the academic and industrial sectors are presented. The identified research gaps, trends and technical limitations hampering the implementation of digital twins are also discussed. In particular, this review attempts to address the research question on how the digital twin concept can support the realisation of an integrated, flexible and collaborative manufacturing environment which is one of the goals projected by the fourth industrial revolution. To address this, a conceptual framework supporting an integrated product-process digital twin for application in digitised manufacturing is proposed. The application and benefits of the proposed framework are presented in three case studies

Design of a converter for photovoltaic panels testing

A solar panel or module, under different irradiation and temperature conditions, is characterized by its I-V characteristic curve. The evaluation of these curves allows to completely characterize the panel from the performance point of view. As well known, the performance of the photovoltaic (PV) system in terms of generated power depends not only on the photovoltaic panel quality but also on the employed maximum power point tracker algorithms (MPPT). In technical literature many MPPT algorithms have been proposed but a clear method to compare their performances is missing. For this reason a good system for the evaluation of PV technology allowing to test different PV panels with different tracking algorithms in the same environmental conditions could be very useful. In this paper the design and implementation of a system oriented to the characterization of PV systems and to the comparison of MPPT algorithms will be proposed. It allows to measure the characteristic curves of a panel and easily to implement MPPT algorithms in different irradiation and temperature conditions. Experimental tests have been carried out to validate the performance of the system

Simulation of waste heat recovery system with fuzzy based evaporator model

The organic Rankine cycle (ORC) is one of the promising waste heat recovery (WHR) technologies used to improve the thermal efficiency, reduce the emissions and save the fuel costs of internal combustion engines. In the ORCWHR system, the evaporator is considered to be the most critical component as the heat transfer of this device influences the efficiency of the system. Although the conventional Finite Volume (FV) model can successfully capture the complex heat transfer process in the evaporator, the computation time for this model is high as it consists of many iterative loops. To reduce the computation time, a new evaporator model using the fuzzy inference technique is developed in this research. The developed fuzzy based model can predict the evaporator outputs with an accuracy of over 90% while it reduces the simulation time significantly. This model is then integrated with other components of the ORC to simulate a completed ORC-WHR system for internal combustion engines. The influence of operating parameters on the performance of the WHR system is investigated in this paper