Applied Research

Artificial Intelligence, Diagnostic and Recommender System for Energy Efficiency and Performance Optimization of Industrial Process Equipment

Leapfrog Energy

Challenge/Opportunity:

Effective equipment optimization protects industry’s investments through minimizing downtime, extending equipment life, reducing energy and optimizing performance. The key goal of this project is to design the next generation of predictive monitoring system, which can improve the operation of industrial process equipment.

Solution/Collaboration:

This project had developed an automated and integrated solution of gathering and analyzing instrumentation data, data cleansing and organization, pattern recognition, prediction and recommendations. LeapFrog would like to continue to providing practical strategies and solutions to lower the risk and to improve both energy and environmental performance through their wealth of theoretical and practical experience. The new and noble solutions for industries were developed throughout the duration of the project.


Researcher:

Khaled Nigim

Area: Renewable Energy Conversion & Storage

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