B. Erik Ydstie-Chemical Engineering - Carnegie Mellon University

B. Erik Ydstie

Professor of Chemical Engineering, Professor of Electrical Engineering at Carnegie Mellon (Courtesy), Professor II of Electrical Engineering at NTNU, Trondheim, Norway

Office: Doherty Hall 4210A
Phone: 412-268-2235
Fax: 412-268-7139


Dr. B. Erik Ydstie received his BS in Chemistry from the University of Trondheim in 1977 after which he entered Imperial College of Science and Technology in London. He received his PhD in Chemical Engineering and the Diploma of Imperial College 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 in Australia, Ecole des Mines de Paris, and Imperial College in London. He served 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 (1999-2000) and as Board Member and Chairman of Solar Silicon LLC. Prof. Ydstie founded Industrial Learning Systems to take advantage of his advances in adaptive control; he is the current CTO/CEO of that company. Professor Ydstie has consulted with many major chemical companies, including PPG, Alcoa, Elekem, Emerson Process Management, Heller-Ehrman, REC Silicon and Hydro Solar.


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


Prof. Ydstie had a number of graduate students working on diverse research projects related to adaptive control, process modeling, complex process networks, carbothermic aluminum production, fluid bed reactor modeling design and control, and silicon wafer manufacturing. A brief review of current and recent students research interests and affiliations where appropriate is listed her.  Yuan Xu (OSisoft), Mohit Aggarwal (AirProducts) and Max Fahrenkopf (ExxonMobil) developed a new theory for stability analysis and control design for single and multistage distillation. Dimitrios Gerogiorgis (Univ. of  Edinburgh) and Vianey Osorio (Chevron) applied this theory to the carbothermic reduction to make aluminum. We are currently extending the theory to reactive and heterogeneous distillation. PhD students Duncan Coffey (DuPont), Kendell Jillson (OSIsoft) and Michael Wartmann (Shell) developed control strategies for complex networks using passivity theory. This theory was applied to the integrated gasification, combined cycle and chemical looping combustion power generation systems, bio-mass gasification and enhanced oil recovery systems. Christy White Juan Du (PostDoc UT Austin) worked nonlinear control and stability analysis of particulate systems with application to high purity silicon production. Tim Mc Farland is using the theory to study control system design for batch chemical processes. Juhua Liu (ABB) applied the theory frequency stabilization on the interconnected grid with wind and solar power. Blake Rawlings is currently working on control system verification and the e failsafe chemical plants, Juan Morinelly is working on enhanced oil recovery and Jianying Ke is continuing German Oliveros (Dow Chemical) work on silicon wafers for solar cells. Rocco Panella (Intel) worked on dye sensitized solar cells. Finally Edoardo Dozal (Shell) worked on improving stability of adaptive predictive control systems. His research results form the basis for algorithms used by Industrial Learning Systems for adaptive control of power plants and refineries.
Design and control of Solar Cell production processes

In this program (funded by REC Silicon, BP Solar, National Science Foundation, Hemlock Semiconductor) we develop models and control strategies for silicon based solar cells. In one project we look at the development of fluid bed reactors for solar grade silicon, in another we develop a new continuous process for making silicon wafers. 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 faced a critical shortage of solar-grade silicon, and manufacturers worldwide developed 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. Along this research we also have developed technology for continuous production of silicon wafers. In this new process we float a solid sheet of silicon on a liquid bath and continually withdraw the crystal as it freezes in from behind. We are currently developing mathematical control models and commissioning a large experimental system to verify the process concept.

Dynamics and Control of Complex Process Networks

We have introduced a new framework for studying dynamics, distributed control, and optimization of complex networks. The theory can be applied to batch and continuous processes; it is funded by Teknova, Petroleum Research Fund, Dow Chemicals and NSF. The networks we study 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 distillation control, silicon production processes and enhanced oil recovery systems and control of organic Rankin Cycle heat recovery systems in combined cycle gas turbine systems. The main application is on oil platforms and the case study is developed by Shell for the Draugen Platform in the North Sea.

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. The research, which had been funded by Alcoa and NSF, uses the carbothermic reduction of alumina 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.

Research Websites

Center for Advanced Process Decision-making
Energy Science and Engineering
Process Systems Engineering
Research Group Site


  1. Short Courses: Professor Ydstie has developed a 5-day short course in adaptive control that has been offered many times at NTNU, at ETH and Univ. of Vigo. He has also developed a short course in advanced process modeling and control.
  2. Textbook: He has developed a course and a preliminary version of an undergraduate textbook in process control. It does not use the Laplace transform.
  3. Industrial Learning Systems: We have developed software for process identification, real time optimization and adaptive control for power plants, combine cycle systems and refineries.  ILS has five full time and four part time employees.

Awards and Honors

  1. Plenary Lecture Japan/Norway Technology Forum, 2001
  2. Distinguished Lecturer,    University of Alberta, 2005
  3. AIChE CAST Division Plenary Lecture, Annual Meeting, 2005
  4. 13th Roger W. Sargent Lecture, Imperial College, 2006
  5. Computing and Systems Technology Award, CAST Division of AIChE , 2007
  6. Keenan Symposium Lecture, MIT, 2008
  7. Kun Li Award for Excellence in Education, CMU, 2008, 2011
  8. AIChE CAST Division Plenary Lecture, Annual Meeting, 2011
  9. DOWD Fellow, Carnegie Institute of Technology, 2012
  10. Best Paper Computers & Chemical Engineering, 2012


Recent Publications

Selected Publications

Patent Activity

Full Publications

Recent Publications

  1. RANJAN S, B BALAJI, R PANELLA and BE YDSTIE, Silicon Solar Cell Production- Review, Computers and Chemical Engineering, Vol. 35 (2011) 1439– 1453. Best paper prize 2011.
  2. YDSTIE, BE and J. DU, Producing Poly-Silicon from Silane in a Fluid Bed Reactor, Solar Cells - Silicon Wafer-Based Technologies, Leonid Kosyachenko   (Ed.), ISBN: 979-953-307-192-2, InTech
  3. DU, J and B.E. YDSTIE, Modeling and control of particulate processes and application to poly-silicon production, Chemical Engineering Science, Vol 67, Issue 1, January 2012, Pages 120-130
  4. BALDEA, M., N. EL-FARRA and B E YDSTIE, Dynamics and Control of Chemical Process Networks: Integrating Physics, Communication and Computation, Computers & Chemical Engineering, Vol. 51, No 5 April 2013, Pages 42–54.
  5. HIOE, D., J.BAO and B E YDSTIE, Dissipativity Analysis for Networks of Process Systems, Computers & Chemical Engineering, Vol 50, N0 5, pp. 207-219, March
  6. B E YDSTIE, Z. LIN and M. AGGARWAL, "Note on flash and distillation systems." AIChE Journal 59.9 (2013): 3322-3332
  7. OLIVEROS, R. LIU, S. SEETHARAMAN and B E YDSTIE, Silicon Wafers for Solar Cells by Horizontal Ribbon Growth, Ind. Eng. Chem. Res, 52 (9), pp. 3239-3246. DOI: 10.1021/ie301857
  8. GJERSTAD, AK, R. TIME, BE YDSTIE, K. BJØRKVOLL, "An Explicit and Continuously Differentiable Flow Equation for Non-Newtonian Fluids in Pipes." SPE Journal Preprint (2013)

Selected Publications

  1. HANGOS, K.M., A.A. ALONSO, J.D. PERKINS and B.E. YDSTIE, “Thermodynamic Approach to the Structural Stability of Process Plants,” AIChE Journal, Vol 45, (1999), pp 802-816.
  2. ALONSO, A. A. and YDSTIE, B. E. ``Stabilization of Distributed Systems using Irreversible Thermodynamics", Automatica., Vol. 37, (2001) pp. 1739-1755.
  3. YDSTIE, B.ERIK, (2002), New Vistas for Process Control: Integrating Physics and Communication Networks, AIChE J. Vol. 48, No 3, pp. 422-426 (front page article). Also in CEP, March 2002, P18.
  4. YDSTIE, B.ERIK, “Distributed Decision Making in Complex Organizations: The Adaptive Enterprise,” Comp. Chem Eng., Vol 29, (2004) pp. 11-27.
  5. WHITE CHRISTY M. and B. ERIK YDSTIE, “Size distribution modeling for fluidized bed solar-grade silicon production, Powder Technology 163 (2006) 51-58.
  6. YDSTIE, B.E. and Y. JIAO, “Passivity Based Control of the Float Glass Process: Multi-scale decomposition and real-time optimization of complex flows” IEEE Control Systems Magazine, Dec 2006, Vol 26, No 6, pp64-72. (front page article)
  7. DOZAL ME, K. R JILLSON and BE. YDSTIE Supply Chains as Dynamical Systems, S. Pistikopolis Eds. Wiley Inter Science, (L. Papageorgiou and S. Pistikopolis eds), New York, 2007
  8. WEN, C., X. MA and B. ERIK YDSTIE, Analytical Expression of Explicit MPC Solution via Lattice Piecewise-Affine Function, Automatica, Volume 45, (4), 2009, pp. 910-917
  9. WEN, C., X., B. ERIK YDSTIE  and X. Ma "Stochastic mixed integer nonlinear programming using rank filter and ordinal optimization." AIChE Journal 55.11 (2009): 2873-2882.
  10. BALAJI, S., J. ILIC, B. E. YDSTIE, and B. H. KROGH,  Control-Based Modeling and Simulation of the Chemical-Looping Combustion Process, Ind. Eng. Chem. Res. 2010, 49, 4566–4575
  11. LI K., K. H. CHAN, B. E. YDSTIE, R. BINDLISH, Passivity-based adaptive inventory control Journal of Process Control, Vol. 20 (2010) pp. 1126–1132
  12. AGGARWAL M, S. BALAJI, BE YDSTIE, Invariant Based Modeling and Control of Multi-Phase Reactor Systems. Journal of Process Control, Vol. 21, Issue 10, pp. 1390-1406

Patent activity

  1. B. E. Ydstie, Apparatuses, Systems, and Methods Utilizing Adaptive Control, US 2009/0132064 A1. 2005, Pub. Date May 21, 2009
  2. B. E. Ydstie, S Ranjan, B. Sukumar and S. Seetharaman, A Method for Making mono and multi-crystalline Silicon Sheets.  PCT/US2009/006114. Pub. Date, Nov 17, 2011
  3. Cheng X, C. Wen, R. Kephart, B. E. Ydstie, Improved Decentralized Industrial Process Simulation System, Patent App., June, 2010.

Full Publications