(4gd) Optimization, Learning, and Control for Smart Biomanufacturing

Biotechnology holds great promise as a transformative technology to address pressing sustainability and healthcare challenges by enabling the production of fuels, chemicals, materials, and biopharmaceuticals using living organisms. However, the high production costs of bulk chemicals and the lengthy, inefficient development processes for biologics limit the accessibility and affordability of biologics. On the other hand, smart manufacturing offers substantial improvements in efficiency, productivity, quality, flexibility, and sustainability. Therefore, integrating smart manufacturing with biotechnology—smart biomanufacturing—can significantly enhance the potential of biotechnology in tackling sustainability and healthcare issues. Despite this potential, there is a critical need for robust and efficient learning, optimization and control theories, algorithms and computational tools to advance smart biomanufacturing processes. Specially, my group will focus on the following research directions: (1) Online model-based design of experiments (MBDoE) for bioprocess model discrimination and parameter estimation; (2) Stochastic nonlinear model predictive control (SNMPC) for biomanufacturing; (3) Optimization theory and algorithms for the robust and efficient solution of large-scale optimization problems arising in MBDoE and SNMPC.

Teaching interests

I have served as a teaching assistant (TA) for the courses Chemical Engineering Optimization and Computer-Aided Process Design several times at The University of Manchester. In this role, I guided students in formulating and solving optimization problems related to unit operation design, process design, and process synthesis. I thoroughly enjoy the experience of sharing knowledge with students.

Drawing upon my undergraduate and graduate background in chemical engineering and my teaching experience, I am well-prepared to teach any core chemical engineering course. In particular, I am eager to teach the courses on Process Systems Engineering and Python Programming for Chemical Engineers. For all the courses, I will utilize tailor-made chemical engineering problems as examples and coursework. I believe my research experience in Process Systems Engineering (PSE) will contribute to the success of these courses.