The objective of this research project is to relax the requirements of data-based nonlinear order reduction methods and extend their applicability towards the control and optimization of dissipative partial differential equation (PDE) systems arising in the context of spatially distributed processes. To achieve this objective, the research will initially focus on creating a computationally efficient data-driven algorithm for: a) the derivation of nonlinear low-order, approximate models for dissipative PDE systems that are specifically tailored for control and optimization purposes, and b) characterization of the error between the low-order model and PDE system solutions. Subsequently, the research will focus on the synthesis of practically implementable feedback control structures that can deal with the issues of nonlinearity, model uncertainty, constrains and limited measurement availability. Concurrently, computational issues of optimization/optimal operation policies for spatially distributed processes will be resolved. This will be achieved via the derivation of a systematic scheme for the formulation of computationally efficient dynamic optimization problems that are amenable to standard search algorithms.
The research results will be transferred into the industrial sector through the development and dissemination of software with a transparent user-machine interaction interface. Analyzing, optimizing and tightly controlling transport-reaction processes will benefit key processes of a wide range of industries such as lithographic reactors for microelectronics and photovoltaics fabrication and advanced catalytic reactors and industrial glass furnaces. Moreover, numerous activities will be pursued to integrate the research with education including incorporation of research results in optimization and control courses, undergraduate student participation in research through the honors program, and the development of educational tools such as matlab add-ons, java applets and wikis.
|Effective start/end date||9/1/13 → 8/31/17|
- National Science Foundation: $339,500.00