9732831 English This grant provides funding for the development of an effective and efficient framework for the quality monitoring and control of processes generating time dependent data common to the process industries (i.e., chemical processes). First, the empirical covariation chart will be analytically developed, and its performance potential will be evaluated. Second, the interface between statistical process control (SPC) and automatic process control will be further exploited beyond what is found in the literature. Third, other new and existing SPC approaches will be evaluated in the context of a new process disturbance paradigm unique to time dependent data. Fourth, the recursive discrete time Kalman filter (RDTKF) will be modified and evaluated for application in this scenario. If successful, this project will provide a cohesive design of tools for quality monitoring and control for the process industries. The resulting framework should be suitable for application in many manufacturing processes and will be well tested based upon analytical and simulation results. Scenario specific strengths and weaknesses will be documented within the tasks of the research agenda. The impact of the research will be realized through the dissemination of findings in scholarly journals and applications within various industries.
