Nonlinear Chemical Process Monitoring And Fault Detection Based On Modified Lstm Model

• Main Author: Zambri, Muhammad Ridzuan
• Format: Monograph
• Language: English
• Published: Universiti Sains Malaysia 2022
• Subjects: T Technology; TP155-156 Chemical engineering
• Online Access: http://eprints.usm.my/55494/1/Nonlinear%20Chemical%20Process%20Monitoring%20And%20Fault%20Detection%20Based%20On%20Modified%20Lstm%20Model.pdf; http://eprints.usm.my/55494/
• Institution: Universiti Sains Malaysia

With the development of the chemical industry, fault detection of chemical process has become hard challenge due to the high-dimensional data and complex chemical process and increasing number of equipment. The standard feedforward neural network is not particularly effective at solving these issues...