Our program focuses on maintaining a balance between fundamentals and practice in the following areas:

• Optimization Theory and Algorithms: modeling and algorithms applied to the design and control of chemical process systems.

• Model Predictive Control (MPC) and Moving Horizon Estimation (MHE): large-scale MPC and MHE, model-based control, constrained control, adaptive control, multivariable control.

• Data Science and Machine Learning: modeling and algorithms that combine physics and data-driven approaches.

• Sustainability, Life-Cycle Assessment, and Circularity: Modeling and optimization applied to the analysis and design of chemical process systems (including manufacturing, healthcare, materials, energy systems, agriculture, and supply chains). 

• Applications: The work carried out is fundamental and has broad applications to (among others): 

• Manufacturing, Energy, Agriculture, Materials

• Data-Driven and Learning-Based Control and Estimation

• Sensor Design and Analysis.

• Analysis of High-Dimensional Data

• Control System Monitoring and Diagnosis

• Dynamic Modeling of Chemical Processes 

• Production Planning and Scheduling

• Dynamic System Identification

• Statistical Process Monitoring and Fault Diagnosis

The combined process modeling, control and optimization programs of the consortium have more than 25 full-time graduate students and postdoctoral researchers in addition to the six faculty members supervising the ongoing research.