Process Optimization Without an Algebraic Model

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

1033661 Sahinidis The area of optimization is a strong focus of the process systems engineering community. As a result, a plethora of models, algorithms, and software have been developed for algebraic nonlinear programs (NLPs) and mixed-integer nonlinear programs (MINLPs). These techniques have had and will continue to have an impact in process synthesis, design, and operations. Yet, the algebraic NLP/MINLP paradigm: o often requires modelers to make restrictive assumptions in order to make possible the solution of their models with current optimization software; o is inefficient when expensive simulations must be carried out for modeling complex systems via proprietary software; and o is not in line with engineering practice, where technological developments are almost always based on experimental measurements rather than algebraic models. Experiments, in particular, provide measurements of the objective function to be optimized but no direct information on derivatives or any other information required by algebraic NLP/MINLP optimization. This project aims to develop optimization algorithms and software capable of optimizing without an explicit algebraic model. Towards this goal, the PS plans to: o complete a critical comparison of existing methods for this problem, especially in regard to their ability to find global solutions and improve starting points; o develop novel local and global optimization algorithms for optimizing systems described by any combination of algebraic, simulation, and experimental components; o use previously developed algorithms to optimize systems involving multiple scales, hidden constraints, and noisy objective functions; o develop and make available innovative software that relies on modern cyberinfrastruc- ture and implements the algorithms developed in this research. Intellectual Merit The project will lay the foundations of a new generation of optimization algorithms and software capable of solving complex problems for which algebraic NLPs and MINLPs are not available. Such problems abound in all scientific fields that rely on simulation or experiments for design and optimization. The task of optimizing algebraic NLPs and MINLPs is, in general, a very challenging one. Optimizing without explicit algebraic models can be even more challenging. This research addresses that challenge by capitalizing on recent progress in global optimization of algebraic NLPs and MINLPs to develop new, more efficient algorithms for algebraic-model-free optimization. Broader Impacts The project involves graduate student mentoring, integration of research results in course work, targeted minority student recruitment, and broad dissemination of the results through innovative cyber-enabled software implementing the results of the research. In addition, the research will have an immediate and wide impact on industrial practice as it specifically provides algorithms for experiment-based optimization and design.