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Summary

I am a Lecturer in Process Systems Engineering in the Department of Chemical Engineering at ÃÛÌÒ´«Ã½.

I obtained an MEng and MSc in Chemical Engineering from  in Valparaíso, Chile. I completed my PhD in Earth Science and Engineering in the  at Imperial College London in 2022. I worked as a Postdoctoral Researcher in supply chain optimisation for the  in the  at Imperial College London in 2023.

My expertise lies in dynamic modelling for model-based control strategies and optimisation. Currently, I am focused on applying computer-aided tools to real-world problems, specifically on model-based control of mineral processing and multi-scale modelling and optimization of energy systems and decarbonization technologies. One of my key interests is integrating machine learning tools for combining data-driven and physics-based models. This approach combines the strengths of both models, allowing for more accurate and efficient predictions of process performance. By leveraging machine learning algorithms, we can extract meaningful insights from large amounts of data, identifying patterns that may not be obvious with traditional modelling techniques. Then, digital twins (i.e. virtual replicas of real-world systems) can be developed and used to simulate the behaviour of the physical system in real-time, allowing for improved control and optimisation of the process. Combining data-driven and physics-based models can create more accurate representations of complex systems, enabling us to make better-informed decisions and optimise systems more effectively.

Qualifications

• PhD in Earth Science and Engineering, Imperial College London, United Kingdom.
• MSc in Chemical Engineering, Universidad Universidad Técnica Federico Santa María, Chile.#
• MEng in Chemical Engineering, Universidad Universidad Técnica Federico Santa María, Chile.

Responsibility

• Module leader of the Process Systems Engineering (CL5655) module.