An inexpensive embedded electronic Continuous Stirred Tank Reactor (CSTR) based on neural networks

The high nonlinearity and the high cost of the Continuous Stirred Tank Reactor (CSTR) system need to realize a low cost embedded system based on an inexpensive microcontroller using the full neural networks. The two parts, training and validation are used to adapt the network parameters in real time as proposed in this paper. The realized card simulates the CSTR model. The well-known backpropagation algorithm is implemented to train a neural network model. Both the training and the validation parts are shown through an alphanumeric liquid crystal display. A comparison was made between the realized embedded system and the computer results.