Lorenz Biegler

About

Professor Lorenz T. Biegler is the Covestro University Professor of Chemical Engineering at Carnegie Mellon University. His research focuses on the development and application of optimization and numerical methods for process design, analysis, operations, and control in chemical engineering. His work includes advanced algorithms for flowsheet simulation, optimization, and sensitivity analysis, with an emphasis on numerical methods for challenging process applications. He has developed interior point and active set algorithms for nonlinear programming and nonlinear complementarity problems, as well as efficient decomposition strategies that serve as core solvers in process design, control, and operations. Professor Biegler's research prioritizes efficient methods for large-scale optimization problems arising in chemical engineering and energy systems. He also works on large-scale nonlinear programming methods for systems of differential and algebraic equations (DAEs), addressing stability, accuracy, optimal control problems, and large-scale nonlinear programming formulations applicable to batch processes, fuel cells, dynamic separations, biological fermentations, and polymerization reactors. Additionally, his research includes real-time algorithms for nonlinear estimation, control, and optimization, applying maximum likelihood and optimization concepts for parameter estimation, data reconciliation, and gross error detection in steady and dynamic problems. These methods are…

Research topics

• Computer Science
• Engineering
• Mathematical optimization
• Mathematics
• Distributed computing
• Software engineering
• Operating system
• Physics
• Systems engineering
• Mathematical analysis

Selected publications

• The <scp>IDAES</scp> process modeling framework and model library—Flexibility for process simulation and optimization

  Journal of Advanced Manufacturing and Processing · 2021 · 109 citations

  Abstract Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state‐of‐the‐art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform…

  DOI

• Heat exchanger network synthesis with detailed exchanger designs—2. Hybrid optimization strategy for synthesis of heat exchanger networks

  AIChE Journal · 2020 · 30 citations

  Senior authorCorresponding

  Abstract We propose a new strategy to synthesize heat exchanger networks with detailed designs of individual heat exchangers. The proposed strategy uses a multistep approach by first obtaining a heat exchanger network topology through solving a modified version of the mixed integer nonlinear programming (MINLP) stage‐wise superstructure of Yee and Grossmann, which includes a smoothed LMTD approximation and pressure drops. In a second nonlinear programming (NLP) suboptimization step, we allow for…

  DOI

• Dynamic optimization with complementarity constraints: Smoothing for direct shooting

  Computers & Chemical Engineering · 2020 · 22 citations

  DOI

• Optimization of direct air capture processes using reactive transport models of adsorption-desorption cycles

  Computers & Chemical Engineering · 2025-08-28 · 3 citations

  articleOpen access

  In this study, we develop and implement a reactive transport model in COMSOL Multiphysics® to address the challenges of direct air carbon capture. The model is validated against experimental data and used to simulate the cyclic steady state of the adsorption-desorption process. The optimization of this model is achieved through advanced trust-region methods integrated with Gaussian Processes. Key decision variables, including adsorption and desorption times, desorption temperature and pressure,…

  PublisherDOI

• Surrogate model optimization: a comparison case study with pooling problems of CO2 point sources

  Computers & Chemical Engineering · 2025-05-18 · 2 citations

  articleOpen accessSenior author

  In this work, we present a benchmark study to leverage the implementation of surrogate models (SMs) within mathematical optimization problems for the integration of carbon capture technologies within an industrial complex, focusing on the pooling of CO₂ streams to enhance efficiency and reduce capture costs. The SMs are built using data from rigorous process simulations in Aspen Plus, with each data point generated by solving equation-oriented optimization problems. We evaluate five different SM…

  PublisherDOI

Recent grants

• Algorithmic Advances for Large-Scale Dynamic Process Optimization

  NSF · $304k · 2003–2007

• Collaborative Proposal: Large-Scale Optimization Strategies for Design under Uncertainty

  NSF · $251k · 2005–2009

• GOALI: Fast Nonlinear Model Predictive Control for Dynamic Real-time Optimization

  NSF · $332k · 2012–2016

Frequent coauthors

• Stephen E. Zitney

  National Energy Technology Laboratory

  66 shared

• Zhijiang Shao

  Zhejiang University

  44 shared

• Sachin C. Patwardhan

  42 shared

• Yidong Lang

  Carnegie Mellon University

  40 shared

• Myung S. Jhon

  Carnegie Mellon University

  36 shared

• Anshul Agarwal

  33 shared

• Rui Huang

  32 shared

• Carl D. Laird

  31 shared

Labs

• Biegler GroupPI

  Develops and applies optimization and numerical methods for process design, analysis, operations, and control.

Education

• Ph.D., Chemical Engineering

  University of Wisconsin, Madison

  1981

• M.S., Chemical Engineering

  University of Wisconsin, Madison

  1979

• B.S., Chemical Engineering

  Illinois Institute of Technology

  1977

Awards & honors

• Sargent Medal (2022) by IChemE
• Control Magazine’s Hall of Fame (2022)
• Long-Term Achievements Award in Computer-Aided Process Engin…
• Honorary doctorate in engineering sciences from the Technica…
• Fellow of AIChE

Similar researchers at Carnegie Mellon University

• Ignacio Grossmannh-117
• Neil Donahueh-116
• Robert Tiltonh-59