Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks

Abstract

Batch process usually differs from the continuous process because of its time-varying variables and the process parameters. An early detection and isolation of faults in the process will help to reduce the process upsets and keep it safe and reliable. This paper discusses on the application of multi-layer perceptron neural network in detecting various faults in batch chemical reactor based on an esterification process that involves the reaction of ethanol and acetic acid catalyzed by sulfuric acid. A multi-layer feed forward neural network with double hidden layers has been used in the neural network architecture. The detection was based on the different patterns generated between normal and faulty conditions. An optimum network configuration was found when the network produced the minimal error with respect to the training, testing and data validation.