Students on Summer Project Team:
Aziz Alsalem '22, Chemical Engineering
Robby Griswold '21, Chemical Engineering
Nick Kosir '20, IDEAS
Jingming Shi '23, Chemical Engineering

Faculty Mentor(s): Mayuresh Kothare, Professor and Chair, Chemical & Biomolecular Engineering; Srinivas Rangarajan, Assistant Professor, Chemical & Biomolecular Engineering

Project Video: Click here to view

Project description:
Full project title: Data to Control--Towards Data-Driven Model Predictive Control for Chemical Process Automation

Most chemical and biological processes are dynamical systems. This means that their state variables (i.e. variables that characterize what state the system is in) are continuously changing, often underlined by highly nonlinear correlated behavior that many not be easily captured by physics-based models. Modern plants in the energy and chemical industry have advanced data acquisition technologies, enabled in many cases by solutions offered by OSISoft LLC, the industrial partners on this project. These technologies allow for collecting, storing, and analyzing data from thousands of sensors every second (or faster). Our ultimate goal is to leverage this data to design, optimize, and control new energy and chemical systems. We began addressing this larger goal by developing algorithms that allowed us to extract the underlying ordinary differential equations from time-varying data. This algorithm then allowed us to take time-varying plant data and build data-driven dynamic equations that accurately captures the overall process. We specifically built on the state-of-the-art algorithms from the applied mathematics community on inferring equations from data that have been successfully applied in the fluid mechanics domain by incorporating a number of new features including the concept of infusing chemical engineering domain knowledge as constraints while training the data-driven model.