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:

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.

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.

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)