A method to incorporate knowledge about the process structure into a neural network has been developed. This method has been successfully used for identification of nonlinear processes. It has been validated on a pH-level control system. Identification of nonlinear models for processes using sequential state space methods has also been explored. The results are found to be very encouraging and useful for nonlinear identification.
Design methodologies have to a large extent ignored controllability aspects of the final design.
This is likely to yield process designs that are poorly controllable. Integration of Design and Control steps is being explored in the context of heat exchanger networks. Strategies for design of networks that are optimal from energy integration as well as controllability viewpoints, are being developed. Quality variables such as melt index in a polymerization reactor are seldom measured on a frequent basis, due to a lack of online sensers. Strategies, that are based on the used of multivariate statistics, have been developed to predict such quality variables on an online basis.