M. A. Stadtherr

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In recent years, it has been shown that strategies based on an interval-Newton approach can be used to reliably solve a variety of nonlinear equation solving and optimization problems in chemical process engineering, including problems in parameter estimation and in the computation of phase behavior. These strategies provide a mathematical and computational(More)
The reliable prediction of phase stability is a challenging computational problem in chemical process simulation, optimization and design. The phase stability problem can be formulated either as a minimization problem or as an equivalent nonlinear equation solving problem. Conventional solution methods are initialization dependent, and may fail by(More)
For the simulation and optimization of large scale chemical processes, the overall computing time is often dominated by the time needed to solve a large sparse system of linear equations. A parallel frontal solver can be used to signiicantly reduce the wallclock time required to solve these linear equation systems using parallel/vector supercomputers. This(More)
For the simulation and optimization of large-scale chemical processes, the overall computing time is often dominated by the time needed to solve a large sparse system of linear equations. We describe here a parallel frontal solver which can significantly reduce the wallclock time required to solve these linear equation systems using parallel/vector(More)
For the simulation of complex equilibrium-stage operations, the overall computing time is often dominated by the solution of large, sparse systems of linear equations. If the modeling equations for such separation systems are grouped by equilibrium stage, the linear systems take on an almost banded form with relatively few oo-band elements. We present here(More)
The solution of chemical process simulation and optimization problems on today's high performance supercomputers requires algorithms that can take advantage of vector and parallel processing when solving the large, sparse matrices that arise. The frontal method can be highly eecient in this context due to its ability to make use of vectorizable dense matrix(More)
For the simulation and optimization of large-scale chemical processes, the overall computing time is often dominated by the time needed to solve a large sparse system of linear equations. We present here a new parallel frontal solver which can signiicantly reduce the wallclock time required to solve these linear equation systems using parallel/vector(More)
A new approach is described for the rigorous global optimization of dynamic systems subject to inequality path constraints (IPCs). This method employs the sequential (control parameterization) approach and is based on techniques developed for the verified solution of parametric systems of ordinary differential equations. These techniques provide rigorous(More)