Abstract
Abstract—The primary objective of this study is to enhance
fault identification capabilities in power to methanol systems
by combining simulation based fault injection with machine
learning techniques. In this study, a mathematical model of
a methanol synthesis reactor was simulated in MATLAB and
time series data was generated using input data from an
Aspen plus simulation of the reactor. The developed simulation
environment was exploited to simulate several fault scenarios and
a comprehensive dataset was generated containing both fault
free and faulty data. A one dimensional convolutional neural
network(1D CNN) was employed to classify the injected faults
in the dataset. The implemented 1D CNN structure, with simple
and compact configurations, achieved high accuracy by learning
optimal features directly from the provided data.
fault identification capabilities in power to methanol systems
by combining simulation based fault injection with machine
learning techniques. In this study, a mathematical model of
a methanol synthesis reactor was simulated in MATLAB and
time series data was generated using input data from an
Aspen plus simulation of the reactor. The developed simulation
environment was exploited to simulate several fault scenarios and
a comprehensive dataset was generated containing both fault
free and faulty data. A one dimensional convolutional neural
network(1D CNN) was employed to classify the injected faults
in the dataset. The implemented 1D CNN structure, with simple
and compact configurations, achieved high accuracy by learning
optimal features directly from the provided data.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 8th International Conference on System Reliability and Safety |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-6654-5397-4 |
| Publication status | Accepted/In press - 22 Oct 2024 |