Condition Monitoring of Pharmaceutical Autoclave Germs Removal Using Artificial Neural Network

This paper presents a computer aided mythology for monitoring the performance of Autoclave Chamber used in pharmaceutical industry for removing germs of the medical equipments through sterilization. In order to accomplish that, an Artificial Neural Network (ANN) back propagation algorithm has been used. The artificial neural network (ANN) is trained with all the maximum possible samples of different pressure values, different temperature values of sensors, and different point's values of time. In order to demonstrate the success of proposed method, a group of 14 sensors (13 temperatures and one pressure) were fitted in the autoclave chamber and real time data of temperature and pressure were noted down. These data were used for the training the neural network. The developed ANN module was tested by the same kind of data i.e. numerical values of the temperature, and pressure. This ANN module gives the response in terms of pressure. This value is compared with pressure sensor actual value, in order to validate the methodology.