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Texas A&M Researchers Aim To Accurately Monitor Subsurface Carbon Dioxide Storage

Their novel monitoring system can rapidly monitor carbon dioxide sequestered underground.  Capturing and storing carbon dioxide (CO2) deep underground can help combat climate change, but long-term monitoring of the stored CO2 within a geological storage site is difficult using current physics-based methods. Texas A&M University researchers proved that unsupervised machine-learning methods could analyze the sensor-gathered […]

SecureAmerica Institute, ARM Institute awarded $5M to investigate impact of automated manufacturing processes on supply chain resiliency

The SecureAmerica Institute (SAI) and the Advanced Robotics for Manufacturing (ARM) Institute are partnering to investigate how robotics and automation in manufacturing can enhance the resiliency, flexibility and competitiveness of U.S. industrial base supply chains, thanks to a $5 million grant award from the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST). The project plans to […]

Pistikopoulos Named Association of Former Students Distinguished Achievement Award Recipient 

Texas A&M University and The Association of Former Students have selected 24 outstanding faculty and staff to be honored with the 2022 Distinguished Achievement Awards. Since 1955 the Distinguished Achievement Awards have been awarded to those who exhibit the highest standards of excellence at Texas A&M. Stratos Pistikopoulos, the director of The Texas A&M Energy Institute, has been named a 2022 recipient of one of the Research awards.

Targeted Demand Response Reduces Price Volatility of Electric Grid

To reduce the energy load across the entirety of the state’s grid, traditional demand response studies focus on reducing the energy load in high population centers such as Houston and Dallas. However, Dr. Le Xie, professor in the Department of Electrical and Computer Engineering at Texas A&M University, and his team found that focusing on a few strategic locations across the state outside of those high-population areas is much more cost-effective and can have a greater impact on the price volatility of the grid. A machine learning algorithm is utilized to strategically select these demand response locations based on a synthetic Texas grid model.

Kezunovic inducted into National Academy of Engineering

Dr. Mladen Kezunovic, a Texas A&M Energy Institute Faculty Affiliate from the Texas A&M University College of Engineering is among 111 new members and 22 international members recently elected to the National Academy of Engineering (NAE). This honor is among the highest professional distinctions accorded an engineer. Kezunovic, Regents Professor and Eugene E. Webb Professor […]