Skip to main content
Fernando V. Lima
Associate Professor, Chemical and Biomedical Engineering


Fernando V. Lima joined the faculty as an Assistant professor of Chemical Engineering at West Virginia University (WVU) in January 2013. He is now Associate Professor of Chemical Engineering since August 2019. Dr. Lima is also currently Adjunct Faculty in the Department of Chemical Engineering at Carnegie Mellon University (CMU). His research group at WVU focuses on the development and implementation of process systems engineering methods for process design and intensification, advanced control and state estimation, modular energy systems and sustainability. He received his B.S. degree from the University of São Paulo in 2003 and his Ph.D. from Tufts University in 2007, both in Chemical Engineering. Upon completion of his Ph.D., he was a research associate at the University of Wisconsin-Madison and a postdoctoral associate at the University of Minnesota. See Dr. Lima's Google Scholar and LinkedIn profiles here.

His research awards include the American Chemical Society Petroleum Research Fund (ACS-PRF) Doctoral New Investigator (DNI) Award, the Faculty Early Career Development Program Award from National Science Foundation (NSF-CAREER), and the WVU Statler College of Engineering Excellence in Research Awards (2). Dr. Lima was the Area Chair for the Next-Gen Manufacturing Sessions of AIChE 2021 and the 2022 Program Coordinator for CAST 10B Area of AIChE. He is currently the CAST 2023-2025 Director. Dr. Lima has been a guest editor for Special Issues of Industrial and Engineering Chemistry Research, Processes, and Frontiers journals and is a member of the Editorial Board of Journal of Process Control.

Control, Optimization and Design for Energy and Sustainability (CODES) Group

CODES Block Flow Diagram of Integrated Process Control and State Estimation Framework

Research Areas

  • Process Operability for Design and Intensification
  • Model-based Advanced Control and Optimization
  • Interfacing Process Systems Engineering with Machine Learning
  • Industry 4.0, Digital Twins and State Estimation
  • Sustainability Assessment and Pollution Control
  • Energy and Power Systems Applications
  • Membrane Reactor and Modular Systems Analysis

Software Development