About me

I am a Control and Automation Engineer who graduated from UFMG. I have a Data Science specialization, a Master’s degree in optimization at UFMG (PPGEE) and I am currently pursuing my doctoral degree at TU Wien (Institute for Energy Systems and Thermodynamics ) as part of the MSCA Doctoral Network CESAREF. My research focuses in how optimization and machine learning can be combined to make industrial processes energy-efficient and sustainable.

With 10 years of experience in Industrial IT and Data Science, I have practical experience with machine learning, optimization, and software engineering/architecture applied to the steel and mining industries. Solid skills in Python, C#, Web development (HTML/CSS/React/Typescript), SQL, operations research, mixed integer linear programming (MILP), non-linear optimization, and machine learning. Strong presentation, communication and project management skills.

Projects

Checkout some of my projects!

1. Piecewise-linear model fitting heuristic - CESAREF (2024)
2. Real-time optimization (Master’s thesis) - PPGEE (2022)
3. Tenesse Eastman Process Anomaly Detection using LSTM Autoencoders - Udacity (2022)
4. Artificial Neural Networks - PPGEE (2020)
5. Evolutionary Computing - PPGEE (2020)
6. Network Optimization - PPGEE (2020)
7. VSB Power Line Fault Detection - Udacity (2019)

Notebooks

• Real-Time Optimization Tutorial
• Modifier Adaptation as a Contrained Bayesian Optimization problem

Blog Posts

• LSTM Autoencoders — Detecting anomalies in the Tennessee Eastman Process
• Why Does it Matter to Analyze your Indoor Cycling Training Data
• From Data to Decisions: Unlocking Advanced Analytics for Optimal Steel Ladle Management

Publications

Peer-reviewed

1. V. Ruela, P. Van Beurden, S. Sinnema, R. Hofmann, and F. Birkelbach, “A Global Solution Approach to the Energy-Efficient Ladle Dispatching Problem With Refractory Temperature Control,” IEEE Access, vol. 11, pp. 137718–137733, 2023, doi: 10.1109/ACCESS.2023.3339392.

2. V. Ruela, P. Van Beurden, B. Luchini, R. Hofmann, and F. Birkelbach, “Optimizing the Steel Ladle Thermal Management: Toward a Sustainable and Cost‐Effective Ladle Fleet Logistics,” steel research int., vol. 96, no. 2, p. 2400616, Feb. 2025, doi: 10.1002/srin.202400616.

Conferences

2021 Conference on Control and Automation Technology - CCTA (Online)

Investigation of Initial Data and Optimizer in Real-Time Optimization Performance via Modifier Adaptation with Gaussian Processes

• Manuscript: Click here
• Source: [Notebook] [Code]

UNITECR 2023 (Frankfurt, Germany)

Advanced Analytics Applied to Improve the Energy Efficiency of Steel Ladle Logistics

• Poster: Click here

33rd European Conference on Operational Research - EURO 2024 (Copenhagen, Denmark)

A heuristic for fitting piecewise-linear models of bivariate functions with simplices and its application to an industrial use case

• Manuscript and Presentation: Click here
• Source code: Code

Iron and Steel Technology Conference - AISTech 2025 (Nashville, TN - USA)

Advanced analytics for sustainable steel ladle logistics

• Manuscript: Click here

Certificates

• Machine Learning Engineer Nanodegree (2019)
• Agile Development with Design Patterns (2017)
• Principles of Agile Software Development (2017)
• Agile Development with Advanced Java (2017)

Contact information

• LinkedIn: https://www.linkedin.com/in/victorspruela/
• E-mail: victorspruela@gmail.com