Tailoring optimization algorithms to process applications

Abstract

Over the past decade the application of efficient nonlinear programming tools has become a powerful and established strategy for process analysis and design. While simple coupling of standard optimization codes to process applications has demonstrated the effectiveness of this approach, further development is needed for the efficient large-scale use of these tools. This is particularly true for dynamic optimization problems and on-line applications, as well as design problems with complex models. This paper centers on exploiting the structure of process optimization problems. Here we consider the tailoring of Newton-type optimization algorithms for various process applications. In particular, the Successive Quadratic Programming (SQP) algorithm has been successful over the past decade; it has also been extended in a number of ways to optimization problems involving several thousand variables. After describing a large-scale, general purpose NLP solver, we briefly describe specialized SQP algorithms for the following problem classes: optimization of complex, structured process models with few degrees of freedom optimization of dynamic systems, where structured sets of nonlinear equations are created from discretization of differential-algebraic systems (DAEs) treatment of parameter estimation problems with many degrees of freedom Each of these aspects is illustrated with a small process application and efficient techniques for handling these problems are outlined. Also, these methods are briefly compared to general purpose algorithms in order to demonstrate the effectiveness of this approach.