Continuous pressure on profit margins and ever stringent environmental limits have led to a need to develop advanced control structures that force optimal process behavior and simultaneously satisfy strict process and product quality constraints. Over the past twenty years model predictive control (MPC) has become a powerful tool that is extensively used by the chemical industry. The control action in MPC is calculated by repeatedly solving online a finite-horizon open-loop optimization problem. As the control action is computed during process evolution, MPC has the capability to suppress the external disturbances and tolerate model inaccuracies during the course of forcing the system to follow a certain optimal path that respects the process constraints. Issues that significantly limit the practical implementation of MPCs is their computational requirements, the need for development of specialized search algorithms and performance degradation due to model uncertainty.
Motivated by the above, this work aims to extend the applicability of MPC designs to complex processes and address their computational requirements which have so far prevented their implementation to fast evolving and unstable processes. The intellectual objective is to develop a general and systematic MPC synthesis methodology that is specifically tailored for processes in the chemical and energy fields. The work will resolve fundamental computational issues associated with a) the dynamic nature of optimal control problems, and b) spatial variations due to the interplay of transport phenomena and reaction. Individual project aims include: o Development of a computationally efficient algorithm to derive nonlinear low-order, approximate algebraic models that describe the dynamic process behavior. o Characterization of error and enforcement of user-defined error bounds. o Construction of practically implementable MPC designs via reformulation of the underlying dynamic optimization problem as an algebraic one that is amenable to standard search algorithms. o Computational acceleration of the MPC designs. Characterization of model nonlinearity and uncertainty effects on MPC results. o Integration of the research results into the graduate curriculum and dissemination of software tools; involvement of undergraduate students into selected parts of the research and revision of undergraduate curriculum.
Broader Impact:
A wide range of complex industrial processes could benefit from the results of this research. Examples include both batch reactors and reactive distillation columns for the production of unsaturated polyesters, and microelectronics manufacturing processes (e.g. vapor phase epitaxy, chemical vapor deposition, etching & electrodeposition). The latter processes are extensively used for the production of both organic and inorganic photovoltaic systems. The PI aims to implement the research results in real-life industrial processes focusing on economic operation and tight control of key process and product characteristics. The predictive controllers will be used in collaborative efforts with NTUA and PSU experimentalists to design more efficient experiments, discover crucial process parameters and identify optimal time dependent operating conditions. In return, the developed methodologies will be evaluated in real-life experimental reactors; relevant weaknesses will be identified and addressed.
To transfer the results and insight of the research to the industrial sector, the PI will also actively seek collaborations with industry and will develop and disseminate software tools with a transparent user-machine interaction interface. In addition, The PI plans a number of activities 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. Finally, the PI will employ current available venues to efficiently disseminate the software to other research groups within Penn State, other educational institutions and industries.
