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MACC Researchers
Dr. Vladimir Mahalec
Professor
Dep. of Chem. Eng,
Associate Director for Grad Studies, WBooth School of Eng. Practice & Tech.

McMaster University
1280 Main Street West
Hamilton, ON L8S 4L7

Office: ETB-505
Voice: (905) 525-9140 x 26386
Email: mahalec@mcmaster.ca

Dipl. Ing., U. of Zagreb, Croatia
Ph.D. Chemical Engineering, University of Houston

Topics
Optimal Planning and Scheduling
Supply Chain Optimization

Research Interests

Software engineering and application design, development, and implementation (advanced process control, process engineering and design, planning, scheduling, plant optimization, feedstock selection, trading and logistics). Expertise in value chain optimization in petroleum, petrochemicals, and other process industries.


Employment History

1991-2006 Aspen Technology Inc. Cambridge Massachusetts, Senior Vice President
for Technology Management

1978-1991 Esso Petroleum, Manager, Process control Group

1977-1978 MIT Cambridge Massachusetts, Research Associate


Research Interests

  • Civic and industrial energy systems have traditionally been designed and operated as separate, stand alone entities. Individually, they both offer ample opportunities to improve their performance and part of our research is devoted to improving designs of CCHP systems. Additional, possibly even greater opportunities lie in integrated design and operation of civic and industrial energy systems.
  • Design and operation of process plants enables improvements in plant performance via designs which minimize energy usage and optimal operation. Besides working on more efficient plant designs, our research includes globally optimal nonlinear control and dynamic real-time optimization, process scheduling, and production planning. We have introduced inventory-pinch concept as a basis for aggregation and decomposition of large scale nonlinear planning and scheduling problems and are currently exploring its use in planning under uncertainty.
  • Structure design and operation of supply chains in real life is carried out in a competitive environment. Part of our research is to define game-theoretic planning framework which accounts for actions of various players in the market and enables best decision making in light of the competitive decisions by other players.
  • Process modelling efforts in our team focus on combining models derived from actual plant data with the first principles (hybrid models) in order to develop modeling methodology leading to models which are very accurate, small in size, and mostly linear. This enables the same process unit model to be used in scheduling, in planning, and in real-time optimization.
  • While we do not focus on inventing new algorithms, in case that the existing methods are not adequate for solving problems at hand, we do not shy away from pursuing development of algorithms as needed to solve problems at hand.

MACC Publications

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