Faculty: Dr. C. L. E. Swartz

Dr. Swartz

 

Professor
Associate Chair (Graduate)
Department of Chemical Engineering

Dofasco Chair in Process Automation and
Information Technology

Director:
MACC (McMaster Advanced Control Consortium)

Member:
McMaster Steel Research Centre

McMaster University
1280 Main Street West, Hamilton
Ontario, Canada  L8S 4L7

office: JHE/360
email:  swartzc@mcmaster.ca
voice:  (905) 525-9140 ext. 27945
fax:     (905) 521-1350

BSc(Eng) Cape Town PhD Wisconsin MAIChE

Research Interests

My research interests are in the general area of process systems engineering, with emphasis on the integration of design and control, model-based control, optimization and numerical computation.  Our primary research objective is to develop computational strategies, algorithms and software for enhanced operation and design of process plants.

The design of a plant can have a significant impact on its ability to be satisfactorily controlled.  A plant design which is optimal on the basis of steady-state considerations, but which has poor dynamic characteristics, may not be able to achieve the expected economic performance in practice.  Moreover, the unfavorable dynamic characteristics could seriously affect the plant’s ability to handle safety and environmental constraints effectively.  Our research group has been involved in the development of optimization-based computational strategies, both for assessing plant operability and for incorporating operability requirements into optimal design calculations.  Our initial work in this area focused primarily on the use of parametrization of all stabilizing linear controllers within an optimization framework to provide a performance limit for linear control, which could be used to assess achievable control performance for alternative process plant configurations.  We subsequently developed formulation strategies for rigorous incorporation of actuator saturation effects; failure to do so typically results in overly conservative designs.  More recently, we proposed a strategy for accommodating constrained predictive control within an integrated design and control framework by formulating the problem as a mathematical program with complementarity constraints (MPCC).  A current application involves the design of air separation plants for rapid response to electricity price and customer demand fluctuations. 

Dynamic optimization is a key thread that runs through much of our work.  The formulation and solution of dynamic optimization problems play a prominent role in the following application areas we have recently pursued:

  • Modeling and Optimization of Electric Arc Furnace Operation
    Electric arc furnaces (EAFs) are widely used in the steel industry for melting scrap.  The highly energy intensive nature of these operations, coupled with their complexity, make them prime candidates for optimization.  A first-principles based dynamic model of the EAF was developed and calibrated to an industrial operation by estimating model parameters using plant data.   Optimization of input trajectories for a number of constraint and objective function scenarios demonstrated significant potential savings.  A further study is under way, focusing on model reduction, alternative numerical solution approaches, feedback mechanisms and plant trials.  
  • Dynamic Optimization of Integrated Process Units Under Partial Shutdown Conditions
    S    hutdowns in chemical processing plants are detrimental both to plant economics and critical product characteristics.  These situations can be due to routine maintenance, or due to the more extreme case of equipment failure.  In a recent study, we developed a formulation and computational strategy for determining optimal operating policies in the face of shutdowns in multi-unit operations, with application to a Kraft pulp mill.  Extensions have included the optimal design of additional buffer capacities in accordance with probability distributions of failure type and duration; and consideration of model uncertainty via multi-period optimization and feedback strategies.  Current work extends the approach to include model discontinuities, and optimal relaxation of specifications under abnormal operating conditions.

Focus areas in model-based control have been supervisory control strategies and the interaction between model predictive control (MPC) and a higher-level optimization layer.  Work in these areas include the development of a sequential optimization procedure for handling prioritized control objectives and, more recently, analysis of the performance of LP-MPC cascade control systems - a common configuration in commercial MPC implementations.   Despite the relatively wide application of LP-MPC systems, instances of poor performance have been reported in which the variation in set-points computed by the linear program (LP) exceeds that of the corresponding controlled variables.  Through an LP sensitivity analysis, we identified conditions under which measurement noise may be amplified by the LP.  Continuing work will consider the development of appropriate design strategies for such systems. 

Finally, we also have industrial collaborations in scheduling and supply chain optimization.  Key drivers in the process industry toward an increased focus on supply chain technologies are increasing pressure to reduce costs and inventories due to market competition, a shift from commodity products toward low-volume, demand-driven specialty products, globalization of operations, and more rapidly fluctuating demands.  Key challenges include the high dimension of the optimization formulations, mechanisms to handle process and demand uncertainty, and integration across the levels of the process automation hierarchy. 

I collaborate with several industries through the McMaster Advanced Control Consortium (MACC), and the McMaster Steel Research Centre.  MACC fosters industrially relevant research in process systems engineering and provides a community of academic researchers and industrial practitioners who share knowledge and experiences. Further information about MACC is available at the following website:

McMaster Advanced Control Consortium

Selected Publications

Chong, Z. and Swartz, C.L.E. (2011).  Discontinuous modeling formulations for the optimal control of partial shutdowns.  Proc. 18th IFAC World Congress, Milan.

Cao, Y., Swartz, C.LE. and Baldea, M. (2011).  Design for dynamic performance: Application to an air separation unit.  Proc. American Control Conference, San Francisco.

Mastragostino, R. and Swartz, C.L.E. (2011).  Operability considerations in process supply chain design for forest industry transformations.  Paper 614e, AIChE Annual Meeting, Minneapolis.

Hazaras, M., Swartz, C.L.E.  and Marlin, T.E. (2010).  Optimal scheduling of an industrial food manufacturing facility.  Paper 356e, AIChE Annual Meeting, Salt Lake City.

Chong, Z. and Swartz, C.L.E. (2009).  Model-based control of multi-unit systems under partial shutdown conditions.  American Control Conference, St. Louis, Missouri. 

Nikandrov, A. and Swartz, C.L.E. (2009).  Sensitivity analysis of LP-MPC cascade control systems”, Journal of Process Control, 19, 16-24.

Baker, R. and Swartz, C.L.E. (2008).  Interior point solution of multilevel quadratic programming problems in constrained model predictive control applications.  Ind. Eng. Chem. Res., 47(1), 81-91. 

Lam, D.K., Baker, R. and Swartz, C.L.E. (2007).  Reference trajectory optimization under constrained predictive control, Can. J Chem. Eng., 85 (4), 454-464.

MacRosty, R.D.M. and Swartz, C.L.E. (2007).  Dynamic optimization of electric arc furnace operation.  AIChE J., 53(3), 640-653. 

MacRosty, R.D.M. and Swartz, C.L.E. (2005).  Dynamic modeling of an industrial electric arc furnace.  Ind. Eng. Chem. Res., 44, 8067-8083. 

Swartz, C.L.E. (2004). The use of controller parametrization in the integration of design and control.  In The Integration of Process Design and Control, P. Seferlis and M.C. Georgiadis, eds., Elsevier, pp 239-263. 

Baker, R. and Swartz, C.L.E. (2004).  Simultaneous solution strategies for inclusion of input saturation in the optimal design of dynamically operable plants.  Optimization and Engineering, 5, 5-24.