MechE Seminar: Computing the Energy Transition: From Discrete Nonlinear Optimization to Hybrid Quantum Algorithms
Event details
| Date | 11.02.2026 |
| Hour | 15:30 › 16:30 |
| Speaker | Prof. David E. Bernal Neira, Davidson School of Chemical Engineering, Purdue University |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract: The transition to sustainable energy and infrastructure requires designing and operating systems where complex physical behaviors, such as nonlinear dynamics and thermodynamic constraints, interact with discrete decision-making at scale. As energy systems grow in complexity, the computational burden of optimizing their design and operation is reaching the limits of classical solvers. To enable the energy transition, Systems Engineering must evolve to integrate advanced mathematical models with innovative algorithms and emerging computing paradigms. This seminar presents a research trajectory focused on making intractable energy and infrastructure problems solvable.
The talk will cover the development of decomposition algorithms for Mixed-Integer Nonlinear Programming (MINLP), applied to water desalination plant synthesis, process maintenance, centralized vs distributed biofuel manufacturing, large-scale challenges in refinery planning, and energy storage integration. I will then pivot to the frontier of computational engineering, presenting results on benchmarking quantum and analog computing devices for real-world optimization tasks. I will show how decomposing problems based on their physical and mathematical structures would enable us to use (or not!) quantum hardware effectively within a hybrid workflow. The presentation will highlight how this fusion of rigorous optimization theory and advanced computing provides a pathway toward real-time, resilient decision support for multi-scale, complex systems. The talk will conclude with a brief outlook on how these rigorous computational methods can serve as the foundation for next-generation digital twins and lifecycle management tools for the energy transition.
Biography: David E. Bernal Neira is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University, where he leads the SECQUOIA group (Systems Engineering via Classical and Quantum Optimization for Industrial Applications). Holding undergraduate degrees in Physics and Chemical Engineering and a Ph.D. from Carnegie Mellon University, he bridges first-principles modeling with rigorous mathematical optimization.
His work focuses on Systems Engineering, specifically the co-design of models, algorithms, and control strategies for sustainable energy and critical infrastructure. He is a core developer of widely used open-source optimization software (including Pyomo and MindtPy). In addition to his classical optimization work, he serves as the Co-Chair of the INFORMS Quantum Computing Committee and previously worked as a Research Scientist at NASA’s Quantum AI Laboratory, where he benchmarked emerging hardware for engineering applications. He collaborates broadly with national laboratories and industry to translate theoretical advances into deployable decision-support tools.
The talk will cover the development of decomposition algorithms for Mixed-Integer Nonlinear Programming (MINLP), applied to water desalination plant synthesis, process maintenance, centralized vs distributed biofuel manufacturing, large-scale challenges in refinery planning, and energy storage integration. I will then pivot to the frontier of computational engineering, presenting results on benchmarking quantum and analog computing devices for real-world optimization tasks. I will show how decomposing problems based on their physical and mathematical structures would enable us to use (or not!) quantum hardware effectively within a hybrid workflow. The presentation will highlight how this fusion of rigorous optimization theory and advanced computing provides a pathway toward real-time, resilient decision support for multi-scale, complex systems. The talk will conclude with a brief outlook on how these rigorous computational methods can serve as the foundation for next-generation digital twins and lifecycle management tools for the energy transition.
Biography: David E. Bernal Neira is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University, where he leads the SECQUOIA group (Systems Engineering via Classical and Quantum Optimization for Industrial Applications). Holding undergraduate degrees in Physics and Chemical Engineering and a Ph.D. from Carnegie Mellon University, he bridges first-principles modeling with rigorous mathematical optimization.
His work focuses on Systems Engineering, specifically the co-design of models, algorithms, and control strategies for sustainable energy and critical infrastructure. He is a core developer of widely used open-source optimization software (including Pyomo and MindtPy). In addition to his classical optimization work, he serves as the Co-Chair of the INFORMS Quantum Computing Committee and previously worked as a Research Scientist at NASA’s Quantum AI Laboratory, where he benchmarked emerging hardware for engineering applications. He collaborates broadly with national laboratories and industry to translate theoretical advances into deployable decision-support tools.
Practical information
- General public
- Free