We are interested in the optimal design and operation of sustainable process and energy systems. Major challenges in sustainable engineering include the quantification of environmental (and social) impacts, the drawing of appropriate system boundaries, and decision making across multiple time and length scales. Especially multiscale optimization is becoming increasingly important as the design of systems involving intermittent renewable energy sources has to simultaneously account for operational considerations. As an example, the figure below shows the design of a renewables-based fuels and power production network and the resulting power generation schedule. In our research, we develop efficient multiscale optimization approaches based on judicious surrogate modeling and tailored solution algorithms that take advantage of parallel computing infrastructures.
A key lesson in sustainable engineering over the last few decades has been the importance of systems-level analyses that help avoid processes that are seemingly sustainable but have negative impacts further up- or downstream. To this end, we assess new technologies from a systems perspective, considering design, operations, life cycle, and supply chain aspects in our analysis.
Of particular interest to us are systems that integrate agricultural, chemical, energy, and natural processes. We seek to understand the potential synergies within such integrated systems as we believe that only the optimal interplay between various processes can lead to truly sustainable solutions.
Zhang, Q., Martín, M., & Grossmann, I. E. (in press). Integrated design and operation of renewables-based fuels and power production networks. Computers & Chemical Engineering.
Zhang, Q., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). Data-driven construction of Convex Region Surrogate models. Optimization & Engineering, 17(2), 289-332.
Zhang, Q., Shah, N., Wassick, J., Helling, R., & Van Egerschot, P. (2014). Sustainable supply chain optimisation: An industrial case study. Computers & Industrial Engineering, 74, 68-83.