Modeling of Rotating Disc Contactor (RDC) Column

Liquid-liquid extraction is one of the most important separation processes. Different kinds of liquid-liquid extractor such as Rotating Disc Contactor (RDC) Column being used in industries. The study of liquid-liquid extraction in an RDC column has become a very important subject to be discussed not just among chemical engineers but mathematician as well. In this research, the modeling of small diameter RDC column using the chemical system involving cumene/isobutryric asid/water are analyzed by the method of Artificial Neural Network (ANN). In the previous research, we begin the process of analyzed the data using methods of design of the experiments (DOE) to identify which factor and their interaction factor are significant and to determine the percentage of contribution of the variance for each factor. From the result obtained, we continue the research by discussed the development and validation of an artificial neural network model in estimating the concentration of continuous and concentration of dispersed outlet for an RDC column. It is expected that an efficient and reliable model will be formed to predict RDC column performance as an alternative to speed up the simulation process.