“If you are to do important work then you must work on the right problem at the right time and in the right way. Without any one of the three, you may do a good work but you will almost certainly miss real greatness…”
The research directions in the Multi-scale Product & Process Systems Engineering Laboratory center on a fundamental understanding, theoretical development, and practical application of key issues in the areas of (i) Data-driven modeling and optimization under uncertainty, (ii) Advanced process manufacturing, and (iii) Integrated product/material-process design, synthesis and discovery. Therefore, we strategically integrate different technologies across broad temporal and spatial scales, from the molecular/atomic level to the mesoscopic/ macroscopic levels.
· Data-driven modeling and optimization under uncertainty
Within this direction, our research activities integrate the (dynamic) data reconciliation and (dynamic) data-driven modeling technologies to describe the (dynamic) chemical processes and energy systems. We firstly develop the controlled-accuracy technologies and on-line updating strategies for building data-driven models. We then address the uncertainty quantification associated with the data-driven models. The relevant applications involve the plant-wide planning, scheduling of chemical processes and novel energy conversion systems; the process design and optimization under uncertainty; and the product/material design and discovery.
· Smart process manufacturing
Regarding the smart process manufacturing, we aim at the improvement of agility for refinery- petrochemical plants in face of the endogenous and exogenous uncertainties. Based on the developed data-driven modeling technologies and mixed integer optimization, one of our objectives is to formulate the mathematical model for the plant-wide planning/scheduling. We also focus on the strategic integration of scheduling, real-time optimization, and reliable control strategies. In order to effectively deal with various uncertainties, we combine the stochastic programming/chance-constrained programming with robust optimization to formulate a new framework for optimization under uncertainty.
· Integrated product/material-process design, synthesis and discovery
Materials (adsorbents/membranes for CO2 capture, solvents/catalysts for chemical reactions, solar cells for energy storage, photonic crystals, to name a few), which traditionally demonstrate certain superior properties while other properties are extremely poor, may not be suitable for large-scale deployment. Hence, we propose a computation-experiment-system evaluation framework for reliable material design, synthesis and discovery. We purpose, promoted by the Quantitative Structure–Activity/Property Relationships and surrogate model driven multi-scale technologies, the novel framework for chemical products (clean liquid fuels, high-end polymers, and high-performance rubbers/additives) development and discovery.