Chemical Engineer | Python Developer | Machine Learning Enthusiast
As a Chemical Engineer with a Master of Science degree, I possess a strong foundation in the principles of chemical processes and thermodynamics. My expertise extends into the domain of software development, particularly with the Python programming language.
My academic journey and professional endeavors highlight a commitment to leveraging computational tools to advance scientific research. I focus on bridging the gap between scientific research and practical application, translating theoretical concepts into tangible outcomes.
"The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka!' but 'That's funny...'"
- Isaac Asimov
Innovative application of Graph Neural Networks (GNNs) to estimate the pure-component parameters of the PC-SAFT equation of state. This project addresses a fundamental challenge in thermodynamic modeling by enabling the use of this robust equation without the traditional reliance on prior experimental data.
A web application that allows GNNPCSAFT-aware chat interactions with LLMs (Gemini and Ollama), making it possible to ask for thermodynamic properties predictions in chat. Built with Django and deployed as a Docker container and as an Electron app for easy access.
A web application that provides an intuitive interface for using the GNNPCSAFT model to predict thermodynamic properties. Built with Django and deployed as a Docker container and as an Electron app for easy access.
A kivy application that provides another intuitive interface for using the GNNPCSAFT model to predict thermodynamic properties. Built with Kivy and deployed as a desktop application for easy access.
Implementation of the Model Context Protocol (MCP) specifically for GNNPCSAFT tools, such as predicting thermodynamic properties. This server manages communication and context between models and clients using the standardized MCP protocol, facilitating integration with LLM systems.
A command-line interface for the GNNPCSAFT model, developed so the scientific community can access the model's results easily. Easily installable using pipx.
Developing innovative membranes for carbon capture as part of doctoral research within the Chemical Engineering Department.
Focusing on applying machine learning techniques to thermodynamic modeling and chemical process optimization.
Foundation in chemical processes, thermodynamics, and process modeling. Research on bioprocess engineering and enzyme technology.
This study evaluated the impact of enzymatic hydrolysis on the properties of red rice starch, providing insights into the structural changes and digestibility.
This research optimized the recovery of an important enzyme using chromatographic techniques, demonstrating efficient purification methods.
This book chapter presents an innovative approach using Deep Eutectic Solvents (DES)-based aqueous two-phase systems for enzyme recovery and purification, offering a greener and more efficient alternative to conventional bioproduct separation methods.
This work explored the use of an immobilized enzyme for lactose hydrolysis, a process with significant industrial relevance, particularly in dairy processing.
This investigation demonstrated the potential of using a dairy byproduct for the co-production of valuable biochemicals, contributing to sustainable bioprocessing.