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Research Themes
Process Optimization
Optimization of process plant operation and design is of critical importance for industries to remain competitive in an environment of increased globalization, narrowing profit margins, more stringent product quality requirements and tightening environmental constraints. We are involved in the development of optimization algorithms and tools, as well as a variety of applications including nonlinear predictive control, real-time optimization, supply chain optimization, planning and scheduling, integrated plant and control system design, abnormal situation response, and batch process operation and control. Key areas of research are the following:

Dynamic optimization is a key thread that runs through much of our work. It involves solution of an optimization problem that includes a differential or differential-algebraic equation system as constraints. Applications that we consider include optimization of plant operation in response to demand and electricity price fluctuations, optimization of industrial batch process operations, and optimal response under plant failure conditions, examples of which are given below.

  • Optimization of Air Separation Unit (ASU) Operation
    Cryogenic air separation plants separate air into oxygen, nitrogen and argon products by distillation. They are huge consumers of electricity, primarily due to the compression required to liquify the air feed, and would consequently benefit from demand response operation, where the production levels are adjusted in accordance with electricity price fluctuations. MACC researchers have been involved in the development of first-principles dynamic...

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Dr. Chris L. E. Swartz
Professor and Director, MACC
Daniela Dering
M.A.Sc. Candidate
Anthony Quarshie
Ph.D. Candidate


A supply chain (SC) is a network of facilities that performs the functions of raw material procurement, raw material transformation into intermediate and finished products, and distribution of products to customers. A supply chain typically covers a large geographical region, and its operation has a significant impact on the economic performance of an enterprise. Within our group, we consider strategies for optimal supply chain operation and design, as well as the development of computational tools for supply chain performance analysis. Work in this area includes (i) a novel robust model predictive control formulation for application to process supply chain systems, (ii) a supply chain formulation that includes time-limited transportation contracts within an optimal supply chain design, and (iii) development of a systematic framework for supply chain operability analysis, motivated by Canadian forest products industry transformation from commodity production to integrated... [read more]

Dr. Chris L. E. Swartz
Professor and Director, MACC
Jing Wang
Ph.D. Candidate


Activities of MACC in planning and scheduling are strongly oriented toward industrial applications, through which we have made advances in novel formulations and computational strategies. This includes optimal scheduling of furnace and converter operation in a nickel smelting plant, optimal production scheduling in a food manufacturing operation, and optimal scheduling of hydropower generation systems. The hydropower scheduling application includes consideration of uncertainty in electricity prices, inflows and plant parameters, for which stochastic programming and model-based feedback control strategies have been developed. Applications of planning under include optimal raw material purchase planning under uncertainty in primary steelmaking, and multiperiod refinery planning. The above studies have involved collaboration with five industrial partners, leading in several cases to in-house adoption of the approaches within the company. [read more]

Dr. Chris L. E. Swartz
Professor and Director, MACC
Dr. Thomas E. Marlin
Professor Emeritus
Ariel Boucheikhchoukh
M.A.Sc. Candidate
Pedro Castillo
Ph.D. Candidate
Mahir Jalanko
Ph.D. Candidate
Pulkit Mathur
Ph.D. Candidate


An effective way of incorporating uncertainty in optimization formulations is to use a two-stage stochastic programming approach, in which multiple scenarios corresponding to uncertainty realizations are embedded within a single optimization formulation. However, the already large- scale dynamic optimization problems typical of complex industrial systems becomes significantly amplified with an increasing number of scenarios. We developed within MACC a novel parallel computing approach for solving large-scale dynamic optimization problems problems of this type. It utilizes a multiple-shooting method for integration of the differential- algebraic equation (DAE) system, in which the time horizon is partitioned into a number of intervals, with the initial states in each interval treated as optimization decision variables. The integration over the intervals for the various scenarios can thus be treated as independent integration tasks, suitable for distribution to multiple... [read more]

Dr. Chris L. E. Swartz
Professor and Director, MACC
Dr. Thomas A. Adams II
Associate Professor
Dr. Kamil A. Khan
Assistant Professor
Huiyi Cao
Ph.D. Candidate
Madison Glover
M.A.Sc. Candidate
Chiral Mehta
M.A.Sc. Candidate
Yingkai Song
Ph.D. Candidate
Yingwei Yuan
M.A.Sc. Candidate