Control Room--> Bioreactor Process Oil Refinery

Faculty

Dr. B. Erik Ydstie

  • Professor, Chemical Engineering and Electrical Engineering, Carnegie Mellon University
  • Professor II, Electrical Engineering at NUST, Trondheim, Norway

Research Profile: Dynamics and Control of Complex Process Networks; Real Time Adaptive Control and Optimization; Design and Control of Multi-Phase Reactor Systems; Design and Control of Particulate Processes

Education:

  • Ph.D., 1982, Chemical Engineering, Imperial College, London, UK
  • B.S., 1977, Chemistry University of Trondheim, Norway

Honors and Awards:

  • CAST award of the AICHE, 2007
  • 13th RWH Sargent Lecture Imperial College, London, 2006
  • NTNF (Norwegian Science Research Foundation) Fellowship, 1981-1982
  • British Petroleum Fellowship, 1977-1980

Biography

Dr. B. Erik Ydstie is currently Professor of Chemical Engineering. He received his BS in Chemistry from the University of Trondheim in 1977 after which he entered Imperial College, receiving his PhD in 1982. Prof. Ydstie entered academics at the University of Massachusetts in 1982 where he taught and did research until 1992 when he joined the Department of Chemical Engineering at Carnegie Mellon. Prof. Ydstie also has held or holds appointments in the Departments of Electrical Engineering and in Materials Science at the Norwegian University of Science and Technology in Trondheim. He held academic visiting positions at the University of Newcastle, Ecole des Mines de Paris, and Imperial College. He serves on the Advisory Boards of the ACS Petroleum Research Fund and Worchester Polytechnic Institute. Prof. Ydstie has had industrial appointments as R&D Director of ELKEM ASA and as Board Member and Chairman of Solar Silicon. Prof. Ydstie founded Industrial Learning Systems to take advantage of his advances in adaptive control; he is the current CTO/CEO of that company.

Research Interests

Prof. Ydstie had 5 MS, 6 PhD and 4 PostDoc students working on projects related to adaptive control, process modeling, complex process networks and solar cells. Yuan Xu developed a new theory for representing phase equilibrium problems based the idea of a convex envelope. The result showed that prior assumptions we made about the geometry of the entropy were not correct. Kendell Jillson developed control strategies for IGCC processes with CO2- recycle. Mohit Aggarwal spent the summer at Air Products developing models for bio-mass gasification. He obtained a startling new result for stability of process systems using variational calculus. Michael Wartmann spent the fall in Delft working on enhanced oil recovery with the Royal Dutch Shell research group. He developed some very interesting results on distributed dynamic optimization with application to irreversible thermodynamics. Juan Du (new student) started her work on nonlinear control and stability analysis of hybrid system. The main application domain is expected to be failsafe chemical plants and solar grade silicon processes. Rocco Panella started work on dye sensitized solar cells.

Design and control of particulate processes with application to Solar Cell production
In this project we develop models and control strategies for solar grade Silicon production. Solar grade silicon is in high demand due to the very rapidly growing interest in using solar cells to generate electricity for domestic uses, telecommunications and distributed power generation in the third world. The photovoltaic industry is facing a critical shortage of solar-grade silicon, and a large number of manufacturers worldwide are trying to develop new production technology to meet the demand in a market that continues to grow at a rate of 30-35% annually. Our investigation is aimed towards design and control of fluidized bed reactors for decomposition of Silane. The project is carried out in cooperation with SGS LLC in Moses Lake WA where the pilot plant experiments are carried out.

Dynamics and Control of Complex Process Networks
We have introduced a framework for studying dynamics, distributed control, and optimization of complex networks. These networks represent self-organizing structures so that stability and optimality follow as a consequence of how the networks are put together and how they are connected with other networks. The class is sufficiently broad to cover process networks, bio-chemical networks, reaction networks, and supply chains. The study has led to a decomposition of the business decision making processes, optimal behaviors and decentralized decision making. We use the formalism of irreversible thermodynamics and the passivity theory of nonlinear control as a basis for this theory. We are currently investigating the application of the theory to bio-chemical networks, specifically in the optimization and control of metabolic pathways.

Real Time Adaptive Control and Optimization
We are developing online optimization techniques for constrained and unconstrained optimization using input output data. The aim is to develop stand alone optimization modules that gather information by simulation and/or experimentation and adaptively controls the process so that over time the optimizer converges to the optimal decision maker. The optimizer we developed is based a method referred to as Q learning which was developed in the area of computer science for optimal control of discrete and continuous Markov decision problems. We have adapted the method for real time process control and we have developed a theoretical foundation for on-line decision making using adaptive control theory. The advantages of the adaptive methods over other traditional optimization approaches are that it uses current process models to develop policies and that they self-learning in the sense that they adapt as process conditions change.

Design and Control of Multi-Phase Reactor Systems
The focus of this research is on passivity-based control of multiphase reaction systems. To study this type of system, a metallurgic process has been chosen involving the carbothermic reduction of aluminum. This process takes carbon and aluminum oxide feed to produce aluminum in a complex sequence of reaction steps. The fluid flow dynamics and the heat transfer properties must be managed carefully since the reactions occur at elevated temperatures (above 2000 degrees C). Pilot plant studies of the reaction are carried out by ALCOA, the worlds leading producer of Aluminum. AT CMU we develop dynamic models for conceptual design, process optimization and process control.

More Information

Current Information and Publications

SRI Director
Dr. Christos Georgakis
Professor, Department of Chemical and Biological Engineering, Gordon Senior Faculty Fellow of Systems Engineering
Tufts University
Affiliated Faculty at Tufts
Dr. Kyongbum Lee
Associate Professor and Chair, Department of Chemical and Biological Engineering
Tufts University
Dr. Nikhil Nair
Asistant Professor, Department of Chemical and Biological Engineering
Tufts University
Affiliated Faculty Outside Tufts
Dr. Bhavik Bakshi
Professor , Departmants of Chemical & Biomolecular Engineering, Ohio State University
Dr. Dominique Bonvin
Professor, Automatic Control Laboratory École Polytechnique Fédéral de Lausanne (EPFL) Switzerland
Dr. B. Erik Ydstie
Professor, Departmant of Chemical Engineering, Carnegie Mellon University, Professor of Electrical Engineering by Courtesy, CMU , and Professor II of Electrical Engineering at NUST, Trondheim, Norway
Contact SRI
Tufts University
Science and Technology Center
4 Colby Strett, Room 273
Medford, MA 02155
(617) 627-2573
(617) 627-3991 [FAX]
Christos.Georgakis@tufts.edu