Unlike the rest of this year’s Process Automation Hall of Fame class, who pursued new career opportunities across a variety of industrial organizations and academic institutions, new opportunities to innovate instead came straight to Lorenz (Larry) Biegler’s doorstep.
In what seems a rarity in this day and age, Biegler has strung together 40 years—and counting—in Carnegie Mellon University’s chemical engineering department, where he is now the Covestro University Professor, exploring four generations of algorithms for solving large-scale, nonlinear process optimization problems. He first caught the optimization bug during graduate studies at the University of Wisconsin-Madision, where he rubbed elbows with the likes of fellow Process Automation Hall of Famers Harmon Ray, Jim Rawlings and Babatunde Ogunnaike.
This year, we welcome five new members to the Control Process Automation Hall of Fame.
- Thomas A. Badgwell, Chief Technology Officer, Collaborative Systems Integration
- Lorenz (Larry) Biegler, Covestro University Professor, Dept. of Chemical Engineering, Carnegie Mellon University
- Andy Chatha, President & CEO, ARC Advisory Group
- Thomas E. Marlin, Professor Emeritus, Dept. of Chemical Engineering, McMaster University
- Brian L. Ramaker, Shell Oil Co. (retired)
“My work has advanced from steady-state to dynamic processes, and from optimal process design to optimal recipes for dynamic operations,” Biegler explains. This progression has involved increasingly capable algorithms enabled by exponential increases in available computing power—and the acronym alphabet soup has evolved as well, from real-time optimization (RTO) and multivariable predictive control (MPC) to nonlinear model predictive control (NMPC), economic nonlinear model predictive control (eNMPC) and dynamic real-time optimization (D-RTO).
Among the pioneering work of which he’s most proud is his work on the paradigm of simultaneous strategies for simulation and optimization. “You don't search for the optimum with repeated simulations—instead, you solve for it directly,” he says. The algorithms are super-fast, and because the simulation is effectively inside the optimization, it can stay bounded and won’t blow up. This approach has already proven important in reactor design, where researchers have been able to confidently push the envelope on potentially runaway reactions—improving yields while still ensuring safe operations.
Even 40 years on, Biegler still gets excited when talk turns to time-dependent differential equations. He’s clearly passionate about his subject matter, and cites working with students, learning new applications and witnessing the impact of advanced optimization strategies as the most satisfying aspects of his career. For those who would also seek to follow his footsteps in the successful practice of the process automation, he recommends first mastering the fundamentals of math and computer science. “Don’t focus too much on the ‘technology of the moment’ that will fade over a career,” he recommends. “Instead, stay focused on the persistent problems that underlie process automation, control and decision-making.”
