Projects

Personal Projects

  1. Recycling Plant Simulator:

    rcplant is an advanced open-source Python package that simulates a recycling plant environment. Its main goal is to help evaluate and test various classification methods for recycling challenges. This comprehensive framework allows for in-depth analysis and testing of recycling processes. The package has gained popularity in the community, as shown by its Downloads downloads.

  1. Robust Economic Model Predictive Control for Solar Thermal Systems:
    • My PhD research focuses on developing an innovative smart control system that combines Gaussian process machine learning with model predictive control (MPC). This new approach aims to create a more accurate and adaptable control system capable of handling recurring but unpredictable disturbances, such as changing hot water demand. I’m currently exploring how to apply this control system to domestic solar thermal systems, where it could significantly improve energy efficiency and reduce waste.
  1. Adaptive Optics Control Using Transverse Actuators:
    • For my master’s research project, I worked on a complex inverse dynamic problem related to shape control of deformable mirrors, which are crucial components in modern large telescopes. Using the advanced PDE-constrained optimization method, I successfully solved this complex problem, generating new insights that could potentially revolutionize the field of astrophysics. This work showed my commitment to precision and ability to apply advanced mathematical concepts to real-world challenges.

Industrial Projects

  1. Train Monitoring System:
    • To improve train safety and reliability, I designed and developed an advanced portable data-logger for monitoring key performance metrics. This device accurately captures and records both vertical and axial acceleration of a moving train, as well as wheelset temperature. The system enables real-time analysis of the train’s performance and can alert the control center to any problems. This innovative data-logging device represents a significant improvement in train monitoring technology, with the potential to greatly enhance the efficiency and safety of railway transportation systems.
  1. Condition Monitoring and Fault Diagnostics for GM Locomotive DC Traction Motors:
    • This project focused on developing an intelligent monitoring system for DC electric motors, specifically for General Motors (GM) locomotives. By using advanced techniques such as vibration analysis and discrete wavelet transform (DWT), I gained valuable insights into motor condition and performance. I used a Learning Vector Quantization (LVQ) artificial neural network to analyze data and provide real-time fault diagnostics. This approach offers a more efficient and accurate method for monitoring and maintaining GM locomotive DC traction motors, potentially improving safety, reducing downtime, and enhancing overall locomotive operational efficiency.
  1. Magnetic Electron Lens for Transmission Electron Microscopy (TEM):
    • Over three months, I designed and implemented a high-precision magnetic electron lens specifically for use in a transmission electron microscope (TEM). This cutting-edge lens was built to the highest standards, using state-of-the-art materials and manufacturing techniques to ensure optimal performance within the TEM environment. The magnetic electron lens plays a crucial role in shaping and focusing electron beams to produce high-resolution images of specimens at the atomic scale.
  1. Spectrum Analysis of Y25 Bogie Using SRSS and CQC Methods:
    • Our project team developed a new method to accurately evaluate forces applied to rail bogies without relying on computationally-intensive dynamic simulations. By combining advanced analytical techniques, such as Finite Element Analysis (FEA), with experimental data, we achieved precise force measurements with minimal computational burden. This breakthrough has significant implications for railway engineering, enabling more efficient and accurate evaluation of bogie behavior and facilitating the development of improved rail system designs.
  1. Dynamic Analysis of MD523 Bogie with ADAMS/Rail:
    • Using detailed manufacturing documents as a reference, I created an accurate model of the MD523 bogie in the software program Adams/Rail. Building on this model, I conducted a comprehensive dynamic analysis of the bogie’s performance across four distinct rail classes. To ensure real-world accuracy, I included a passenger car as a representative wagon in the simulations. This thorough investigation provided deep insights into the MD523 bogie’s behavior under various conditions and laid the groundwork for potential future design improvements and optimizations.
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