Neural networks for process monitoring, control and fault detection: Application to Tennessee Eastman Plant

Abstract

This paper discusses the application of artificial neural networks in the area of process monitoring, process control and fault detection. Since chemical process plants are getting more complex and complicated, the need of schemes that can improve process operations is highly demanded. Artificial neural network can provide a generic, non-linear solution, and dynamic relationship between cause and effect variables for complex and non-linear processes. This paper will describe the application of neural network for monitoring reactor temperature, estimation and inferential control of a fatty acid composition in a palm oil fractionation process and detection of reactor sensor failures in the Tennessee Eastman Plant (TEP). The potential for the application of neural network technology in the process industries is great. Its ability to capture and model process dynamics and severe process non-linearities makes it powerful tools for process monitoring, control and fault detection.