This award is to provide research support to Dr. Zafiriou under the National Science Foundation's Presidential Young Investigator Awards (PYIA) Program. The objectives of the PYIA Program are to provide support to the Nation's most outstanding and promising young science and engineering faculty. The awards are intended to improve the capability of U.S. academic institutions to respond to the demand for highly qualified science and engineering personnel for academic and industrial research and teaching. Dr. Zafiriou's field of esearch is process control theory and applications. In particular, he will to work in three areas: (1) Model Predictive Control (MPC): MPC algorithms use a model to predict future values of the process outputs. Modeling error creates the need for designing controllers which are robust with respect to model/plant mismatch. The problem is more complex when hard constraints are present either as physical limitations or as performance and safety specifications, because in that case the overall closed-loop system is nonlinear, even if the plant dynamics are assumed linear. His goal is development of computer-aided design and tuning methodologies that directly account for modeling error. (2) Batch Control: Optimal control theory is often used to determine the input profiles for fed-batch (semi-batch) processes. These profiles, however, may not perform well when applied to the actual process, since they are only optimal for the model that was used. The PI is looking at the development of methods that appropriately modify the input profile during the course of successive batches, by utilizing both the model and plant information from the previous batches. This work is applicable to both polymerization and biochemical systems. (3) Neural Computing in Process Control: The PI's focus will be on the use of neural networks for sensor/actuator failure detection. Significant interactions exist between the operation of a control system and that of the diagnostic module. The main source of these interactions is the presence of model-plant mismatch. He will be examining the use of neural networks for distinguishing between patterns characteristic of control system failure and those corresponding to performance deterioration caused by modeling error.