Laird Research Group

Dr. Carl D. Laird
Assistant Professor and
William & Ruth Neely Faculty Fellow
Artie McFerrin Department of Chemical Engineering

Our expertise includes large-scale modeling, design and operations under uncertainty, parameter estimation, and inversion. Research applications include traditional chemical engineering processes, in addition to homeland security applications, infectious disease modeling and estimation, and network problems.

Pyomo - Optimization Modeling in Python (Springer Optimization and Its Applications)

by William E. Hart, Carl Laird, Jean-Paul Watson and
David L. Woodruff

Hardcover Release Date: February 29, 2012

For more information, or to order from Springer, click here!
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Today, vaccines play an important role in human lives. To address dynamic national requirements for various pharmaceuticals, there is a need for reconfigurable therapeutics manufacturing facilities that are capable of rapid product changeover. Limitations of the existing manufacturing infrastructure are made painfully clear by recurring drug shortages. This research focuses on developing a flexible modeling and nonlinear programming framework for optimizing virus-based production of therapeutics in a reconfigurable facility.

Large-scale, nonlinear dynamic models frequently arise when describing the physics of important engineering and biological problems. In this research, we are developing new algorithms that exploit concurrent computing architectures to provide efficient parallel solution of large-scale parameter estimation problems, focusing on those with both temporal and spatial structure that can be exploited.

Continued gratitude goes to the National Science Foundation Cyber-Enabled Discovery and Innovation (CDI) for their financial support.

Because of ever rising global energy demands, there is an increase in the number of liquefied natural gas production facilities and terminals worldwide. To properly and safely design these facilities, accurate and effective modeling of vapor clouds resulting from spills is a necessity. This research will use data from LNG dispersion tests conducted by the Mary K. O'Connor Process Safety Center at Texas A&M University to perform parameter estimation in an effort to improve simplified models currently accepted for use by the EPA.

Continued gratitude goes to the Mary K. O'Connor Process Safety Center for their financial support.

As the size of nonlinear optimization problems continues to increase, general purpose tools, coupled with standard desktop computing hardware, are not able to provide efficient solution. Furthermore, computer chip manufacturers are no longer focusing on increasing clock speed, but rather on hyper-threading and multicore architectures. This research focuses on the development of numerical solution strategies that can efficiently utilize emerging computing architectures like the GPU.

Continued gratitude goes to the National Science Foundation Faculty Early Career Development (CAREER) Award for their financial support.

This research focuses on the development of real-time algorithms for performing source inversion and response planning in the event of an accidental or intentional contamination in a water distribution system. Since these systems would work in a real-time setting, these algorithms must be able to solve efficiently and effectively for very large networks when given only limited measurement information due to limiting resources. To help meet these goals we have been developing efficient water quality models that can be used in an inversion context, as well as mixed-integer linear and quadratic programming formulations for source location identification and optimal sampling. These approaches have been shown to effectively find the source and extent of contamination given a real water network model.

Continued gratitude goes to Sandia National Laboratories and PUB Singapore for their financial support.

Reliable, mechanistic models for the spread of infectious disease are valuable for improving our understanding of important factors affecting disease dynamics and for helping guide public health decision making. Our work has focused on developing deterministic and stochastic discrete-time and continuous time models of childhood infectious diseases. We bring large-scale nonlinear optimization expertise to provide a framework for efficient estimation of long-term dynamic models. These estimation problems are similar to classic ill-posed inverse formulations, and effective regularization is required to obtain reliable seasonal estimates. Current research includes further refinement of model structure to improve their ability to extrapolate.

Continued gratitude goes to Sandia National Laboratories, and the ASCR program in the Office of Science of the Department of Energy for their financial support.