No solutions exist for predicting the likelihood of future undesirable consequences across various industrial settings. The lack of these solutions subjects personnel, the public, and the environment to potentially catastrophic consequences.

The Need

Essential services such as power plants, pump stations, water treatment sites, hydroelectric dams, manufacturing, textiles plants, and other facilities must safely operate without significant disruption. These facilities generate real-time data for operators to observe and determine the operation and safety of the facility. Operators require extensive historical and up-to-date data and analysis available to make informed decisions during emergencies. Unfortunately, limited tools are available for operators to predict dangerous events and rapidly mitigate crises. New automated technologies are needed to improve plant uptime and reduce the risk of catastrophic events.

The Technology

This technology describes an artificial intelligence/machine learning (AI/ML) platform for the predictive analysis of future events to provide emergency guidelines within industrial facilities. It uses Deep Learning (DL) to evaluate simulations of accidents and monitored facility variables to predict the likely outcomes of emergencies based on prior data. Using the software's prediction capabilities, plant operators can be aware of possible consequences in the early stages of an emergency. Operators can alert state and local government officials such that the proper emergency response plan can be coordinated to ensure public safety.

Commercial Applications

This software can be used at a multitude of industrial facilities, such as nuclear power plants, hydroelectric dams, water treatment plants, pump stations, manufacturing plants and oil & gas facilities, and for predicting and evaluating emergency responses.

Benefits/Advantages

The technology could prevent and/or minimize undesired and dangerous events, thereby improving many industrial plants' overall safety and operation. In addition, it could guide public health officials in the event of dangerous situations.