Stadtherr, Mark - University of Notre Dame - Chemical and Biomolecular Engineering

个人简介

Education B.Ch.E., University of Minnesota, Minneapolis (1972) Ph.D., Chemical Engineering, University of Wisconsin, Madison (1976) Biography Assistant Professor of Chemical Engineering, University of Illinois, Urbana, Illinois, 1976-1982. Associate Professor of Chemical Engineering, University of Illinois, Urbana, Illinois, 1982-1996. Visiting Professor, Centre for Process Systems Engineering, Imperial College, London, UK, January-May 2001. Professor of Chemical Engineering, University of Notre Dame, Notre Dame, Indiana, 1996-2009. Bernard Keating-Crawford Professor of Engineering, University of Notre Dame, Notre Dame, Indiana, 2009-present. Concurrent Professor of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, 2010-present.

研究领域

Modeling and computation Energy and sustainability Global optimization Ionic liquids

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

D. A. M?ce? and M. A. Stadtherr, “Computing Fuzzy Trajectories for Nonlinear Dynamic Systems,” Computers & Chemical Engineering, 52: 10-25, 2013. Y. Zhao and M. A. Stadtherr, “Global Solution of Min-Max Optimization Problems for Nonlinear Dynamic Systems,” Computer-Aided Chemical Engineering, 30: 1287-1291, 2012. Y. Zhao, and M. A. Stadtherr, “Rigorous Global Optimization for Dynamic Systems Subject to Inequality Path Constraints,” Industrial & Engineering Chemistry Research, 48:7246-7256, 2011. J. A. Enszer, Y. Lin, S. Ferson, G. F. Corliss and M. A. Stadtherr, “Probability Bounds Analysis for Nonlinear Dynamic Process Models,” AIChE Journal, 57:404-422, 2011. L. D. Simoni, A. Chapeaux. J.F. Brennecke and M.S. Stadtherr, “Extraction of Biofuels and Biofeedstocks from Aqueous Solutions Using Ionic Liquids,” Computers & Chemical Engineering, 34:3893-3901, 2010. Y. Lin and M. A. Stadtherr, “Rigorous Model-Based Safety Analysis for Nonlinear Continuous-Time Systems,” Computers & Chemical Engineering, 33:493-502, 2009. A method is presented for the quantitative, model-based safety analysis of nonlinear continuous-time hybrid systems. This method uses the region-transition-model (RTM) framework of [Huang, H., Adjiman, C. S., & Shah, N. (2002). Quantitative framework for reliable safety analysis. AIChE Journal, 48, 78–96], together with a recently developed technique [Lin, Y., & Stadtherr, M. A. (2007). Validated solutions of initial value problems for parametric ODEs. Applied Numerical Mathematics, 57, 1145–1162] for the rigorous global analysis of nonlinear, continuous-time systems with uncertain initial conditions and/or parameters. Given an operating region described by bounds on possible initial conditions, inputs and model parameters, and a finite time horizon, the method can determine which operating subregions lead to safe operation. Numerical examples are presented that demonstrate the effectiveness of the method. This approach can supplement and complement the more qualitative techniques that are widely used for hazard identification and safety analysis. Y. Lin and M. A. Stadtherr, “Fault Detection in Nonlinear Continuous-Time Systems with Uncertain Parameters,” AIChE Journal, 54:2335-2345, 2008. In model-based fault diagnosis for dynamic systems with uncertain parameters, an envelope of all fault-free behaviors can be determined from the model and used as a reference for detecting faults. We demonstrate here a method for generating an envelope that is rigorously guaranteed to be complete, but without significant overestimation. The method is based on an interval approach, but uses Taylor models to reduce the overestimation often associated with interval methods. To speed fault detection, a method that uses bounded-error measurement data and a constraint propagation procedure is proposed for shrinking the envelope. Several fault detection scenarios involving nonlinear, continuous-time systems are used to evaluate this approach. L. D. Simoni, A. Chapeaux, J. F. Brennecke and M. A. Stadtherr, “Asymmetric Framework for Predicting Liquid?Liquid Equilibrium of Ionic Liquid?Mixed-Solvent Systems,” Industrial & Engineering Chemistry Research, 48:7246-7265, 2009.