Supported by NSF, NIH, JDRF, and industry

Multivariable process monitoring, performance assessment, and fault diagnosis

Development of data analytics and machine learning methods to refine process information for assessment of process and equipment status, diagnosis of incipient sensor faults, equipment faults and process performance degradation, and determination of control system effectiveness from continuous and batch process data.

Knowledge-based systems for supervisory process control and intelligent process operations

Development of supervisory knowledge-based systems for automation of process monitoring, fault detection and diagnosis, process performance assessment and fault-tolerant control. Integration of machine learning, and algorithmic techniques, and data analytics to develop an intelligent and fault-tolerant control systems.

Process monitoring, supervision and control with agent-based systems