首页|Findings from Texas A&M University Update Knowledge of Machine Learning [Dynamic Domino Effect Assessment (D2ea) In Tank Farms Using a Machine Learning-based Approach]
Findings from Texas A&M University Update Knowledge of Machine Learning [Dynamic Domino Effect Assessment (D2ea) In Tank Farms Using a Machine Learning-based Approach]
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NETL
NSTL
Elsevier
A new study on Machine Learning is now available. According to news reporting originating from College Station, Texas, by NewsRx correspondents, research stated, “The current work presents a Dynamic Domino Effect Assessment (D2EA) methodology for chemical storage tank farms. While the application of the proposed approach is focused on atmospheric tanks, it applies as well to pressurized tanks.” Financial support for this research came from Mary Kay O’Connor Process Safety Center (MKOPSC) at Texas A & M University, TX, USA. Our news editors obtained a quote from the research from Texas A&M University, “It utilizes the temperature and time-dependent material strength property (yield strength) as a structural health indicator. The D2EA methodology uses random forest (RF) and feed-forward neural network (FFNN) to predict yield strength during fire exposure and applies the predicted yield strength to dynamic failure assessment. A lumped parameter model generates datasets to train the dynamic failure prediction model. Two case studies have been used to demonstrate how the method can be used. The results suggest that RF and FFNN can predict gamma distribution-aided dynamic failure probability assessment. The RF is a better tool than FFNN due to its lower computational cost and good performance.”
College StationTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningTexas A&M University
NETL
NSTL
Elsevier
