Rafael Natalio Fontana Crespo – Sustainable Development – Young Scientist Award

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Rafael Natalio Fontana Crespo - Sustainable Development - Young Scientist Award

Young Scientist Award - Italy

AUTHOR PROFILE

ORCID

RAFAEL NATALIO FONTANA CRESPO 🌊

Rafael Natalio Fontana Crespo is a distinguished researcher in the field of power systems and renewable energy forecasting. His expertise lies in applying machine learning techniques to optimize grid power forecasting and improve the reliability of renewable energy systems. He is particularly known for his innovative work in wave energy converters, making him a leader in the intersection of artificial intelligence and sustainable energy technologies.

RESEARCH IN WAVE ENERGY 🔋

Rafael's research primarily focuses on short-term grid power forecasting, where he leverages machine learning techniques to enhance the prediction accuracy of wave energy converters. His recent comparative analysis of these techniques has highlighted new methods for improving uncertainty analysis in energy forecasting, which is crucial for integrating renewable sources into the grid.

EXPERT IN ARTIFICIAL INTELLIGENCE 🤖

With a strong foundation in engineering applications of artificial intelligence, Rafael has made significant contributions to the field. His work on LSTM (Long Short-Term Memory) models for grid power forecasting provides novel insights into how AI can be applied to improve the stability and efficiency of energy systems. His research paves the way for smarter, AI-driven energy management solutions.

INNOVATIONS IN ADDITIVE MANUFACTURING 🛠️

Rafael's contributions extend beyond energy systems to manufacturing technologies. He played a key role in developing a distributed software platform for additive manufacturing, showcased at the IEEE International Conference on Emerging Technologies and Factory Automation. This platform aims to streamline the manufacturing process, pushing forward the boundaries of Industry 4.0.

PUBLICATIONS AND IMPACT 📚

Rafael has authored several influential papers in prestigious journals and conferences. His work on machine learning and energy systems has been widely cited, including his 2024 paper on grid power forecasting published in the Engineering Applications of Artificial Intelligence journal. He continues to influence the scientific community with his cutting-edge research in AI applications.

CONFERENCE CONTRIBUTIONS 🌍

Rafael regularly presents his research at international conferences, such as the IEEE Annual Computers, Software, and Applications Conference (COMPSAC). His presentations on wave energy forecasting and AI-based solutions for energy grid management have garnered attention, solidifying his reputation as a forward-thinking engineer and researcher in global platforms.

FUTURE-ORIENTED RESEARCH 🧠

With a clear vision for the future, Rafael aims to continue advancing AI-driven solutions for renewable energy systems. His ongoing work explores the integration of artificial intelligence in real-time energy management, making energy grids more adaptive, resilient, and capable of supporting large-scale renewable energy adoption.

NOTABLE PUBLICATION

Title: A comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters
Authors: Rafael Natalio Fontana Crespo, Alessandro Aliberti, Lorenzo Bottaccioli, Edoardo Pasta, Sergej Antonello Sirigu, Enrico Macii, Giuliana Mattiazzo, Edoardo Patti
Year: 2024
Journal: Engineering Applications of Artificial Intelligence

Title: A Distributed Software Platform for Additive Manufacturing
Authors: Rafael Natalio Fontana Crespo, Davide Cannizzaro, Lorenzo Bottaccioli, Enrico Macii, Edoardo Patti, Santa Di Cataldo
Year: 2023
Conference: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)

Title: LSTM for Grid Power Forecasting in Short-Term from Wave Energy Converters
Authors: Rafael Natalio Fontana Crespo, Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Giorgio Fighera, Edoardo Patti
Year: 2023
Conference: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

Mohammad Mirzehi – Life Cycle Assessment – Best Researcher Award

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Mohammad Mirzehi - Life Cycle Assessment - Best Researcher Award

Tarbiat Modares - Iran

AUTHOR PROFILE

GOOGLE SCHOLAR

Based on the provided information about Mohammad Mirzehi, he appears to be a strong candidate for the Research for Community Impact Award for several reasons:

RESEARCH FOCUS AND IMPACT

Mohammad's research focuses on optimizing mine planning and operations using advanced techniques such as operations research, data science, and machine learning. His work includes addressing environmental concerns like greenhouse gas emissions and dust through innovative planning strategies.

EXPERIENCE AND EXPERTISE:

With four years of experience in mining engineering research, Mohammad has contributed significantly to projects involving open-pit and underground mining operations. His involvement in projects like sustainable stochastic long-term planning of open-pit mines demonstrates a commitment to integrating environmental considerations into mining practices.

PUBLICATION RECORD:

Mohammad has publications that showcase his innovative approach, such as developing novel models for short-term planning in open-pit mines and forecasting copper prices using advanced algorithms. These publications indicate his ability to produce impactful research outputs that can influence both academic and industry practices.

ACADEMIC ENGAGEMENT:

As a Research Assistant and Teacher Assistant at Tarbiat Modares University, Mohammad has actively collaborated on various mining engineering projects and courses. His teaching role also underscores his commitment to sharing knowledge and fostering the next generation of mining engineers.

TRACK RECORD OF IMPACTFUL RESULTS:

Mohammad's track record includes addressing real-world challenges in mining operations, from optimizing equipment performance to incorporating geo-metallurgical uncertainty in planning. His work in revitalizing ecosystems affected by mining activities further demonstrates a holistic approach to sustainable mining practices.

IN CONCLUSION

Mohammad Mirzehi's blend of technical expertise, commitment to sustainability, and impactful research outcomes makes him a suitable candidate for the Research for Community Impact Award. His contributions not only advance the field of mining engineering but also promote environmentally responsible practices within the industry.

NOTABLE PUBLICATION

A hybrid model for back-break prediction using XGBoost machine learning and metaheuristic algorithms in Chadormalu iron mine 2023 (11)

Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP): a case study from an iron ore mine 2023 (8)

A novel hybrid XGBoost methodology in predicting penetration rate of rotary based on rock-mass and material properties 2024 (6)

Sustainable long-term production planning of open pit mines: An integrated framework for concurrent economical and environmental optimization 2024

Reliable novel hybrid extreme gradient boosting for forecasting copper prices using meta-heuristic algorithms: A thirty-year analysis 2024

Application of XGB-based metaheuristic techniques for prediction time-to-failure of mining machinery 2023 (2)